From 2241453252147bb7362a286977ee9f9a92130062 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Wed, 6 Aug 2025 14:12:42 +0800
Subject: [PATCH 001/287] CANN: add support for ACL Graph (#15065)

* feat(cann): add optional support for ACL Graph execution

This commit adds support for executing ggml computational graphs using
Huawei's ACL graph mode via the USE_CANN_GRAPH flag. The support can be
enabled at compile time using the CMake option:

    -DUSE_CANN_GRAPH=ON

By default, ACL graph execution is **disabled**, and the fallback path
uses node-by-node execution.

Key additions:
- CMake option  to toggle graph mode
- Graph capture and execution logic using
- Tensor property matching to determine whether graph update is required
- Safe fallback and logging if the environment variable LLAMA_SET_ROWS
  is unset or invalid

This prepares the backend for performance improvements in repetitive graph
execution scenarios on Ascend devices.

Signed-off-by: noemotiovon <757486878@qq.com>

* Fix review comments

Signed-off-by: noemotiovon <757486878@qq.com>

* remane USE_CANN_GRAPH to USE_ACL_GRAPH

Signed-off-by: noemotiovon <757486878@qq.com>

* fix typo

Signed-off-by: noemotiovon <757486878@qq.com>

---------

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/CMakeLists.txt |  14 +++
 ggml/src/ggml-cann/common.h       |  36 ++++++
 ggml/src/ggml-cann/ggml-cann.cpp  | 203 ++++++++++++++++++++++++++----
 3 files changed, 231 insertions(+), 22 deletions(-)

diff --git a/ggml/src/ggml-cann/CMakeLists.txt b/ggml/src/ggml-cann/CMakeLists.txt
index 7742b39153..aee5e7b06e 100755
--- a/ggml/src/ggml-cann/CMakeLists.txt
+++ b/ggml/src/ggml-cann/CMakeLists.txt
@@ -31,6 +31,13 @@ string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
 message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")
+option(USE_ACL_GRAPH "Enable CANN graph execution (ACL graph mode)" OFF)
+
+if(USE_ACL_GRAPH AND (SOC_TYPE_MAJOR_SN STREQUAL "310P" OR SOC_TYPE_COMPILE_OPTION STREQUAL "ASCEND_310P"))
+    message(FATAL_ERROR
+        "CANN Graph (ACL graph mode) is not supported on 310P devices. "
+        "Please build with -DUSE_ACL_GRAPH=OFF or use a supported SOC.")
+endif()
 
 if (CANN_INSTALL_DIR)
     # Only Support Linux.
@@ -68,6 +75,13 @@ if (CANN_INSTALL_DIR)
 
     target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
 
+    if (USE_ACL_GRAPH)
+        target_compile_definitions(ggml-cann PRIVATE USE_ACL_GRAPH)
+        message(STATUS "CANN: USE_ACL_GRAPH is enabled.")
+    else()
+        message(STATUS "CANN: USE_ACL_GRAPH is disabled.")
+    endif()
+
     message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
     message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 else()
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 8dfe3b061c..9d294f72b6 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -337,6 +337,29 @@ private:
     int32_t device_;
 };
 
+#ifdef USE_ACL_GRAPH
+struct ggml_graph_node_properties {
+    void * node_address;
+    ggml_op node_op;
+    int64_t ne[GGML_MAX_DIMS];
+    size_t nb[GGML_MAX_DIMS];
+    void * src_address[GGML_MAX_SRC];
+    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
+};
+
+struct ggml_cann_graph {
+    ~ggml_cann_graph() {
+        if (graph != nullptr) {
+            aclmdlRIDestroy(graph);
+        }
+    }
+
+    aclmdlRI graph = nullptr;
+
+    std::vector<ggml_graph_node_properties> ggml_graph_properties;
+};
+#endif  // USE_ACL_GRAPH
+
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -345,8 +368,13 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+#ifdef USE_ACL_GRAPH
+    /// Cached CANN ACL graph used for executing the current ggml computation graph.
+    std::unique_ptr<ggml_cann_graph> cann_graph;
+#endif
     cann_task_queue task_queue;
     bool async_mode;
+    bool support_set_rows;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -362,6 +390,14 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
+
+        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
+        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
+
+        if (!support_set_rows) {
+            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
+                    "Falling back to eager mode.\n", __func__);
+        }
     }
 
     /**
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 551c1f7673..cf575b3675 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2075,6 +2075,160 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
     ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
 }
 
+#ifdef USE_ACL_GRAPH
+/**
+ * @brief Populate the internal CANN graph node properties from the ggml computation graph.
+ *
+ * This function copies all node attributes (operation type, dimensions, strides, input sources,
+ * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The ggml computational graph.
+ */
+static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
+        ggml_tensor * node = cgraph->nodes[node_idx];
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
+
+        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
+        }
+        for (int src = 0; src < GGML_MAX_SRC; src++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
+                node->src[src] ? node->src[src]->data : nullptr;
+        }
+        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
+    }
+}
+
+/**
+ * @brief Check if a ggml tensor node matches a previously captured CANN graph node.
+ *
+ * This function compares all relevant fields (address, op type, shape, source inputs, op params)
+ * to determine whether the current node matches a previously recorded version.
+ *
+ * @param node                  The current ggml tensor node.
+ * @param graph_node_properties The stored properties of a CANN graph node.
+ * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise.
+ */
+static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
+    if (node->data != graph_node_properties->node_address &&
+           node->op != GGML_OP_VIEW) {
+        return false;
+    }
+    if (node->op != graph_node_properties->node_op) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (node->ne[i] != graph_node_properties->ne[i]) {
+            return false;
+        }
+        if (node->nb[i] != graph_node_properties->nb[i]) {
+            return false;
+        }
+    }
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (node->src[i] &&
+            node->src[i]->data != graph_node_properties->src_address[i] &&
+            node->op != GGML_OP_VIEW
+        ) {
+            return false;
+        }
+    }
+    if (node->op == GGML_OP_SCALE &&
+        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
+        return false;
+    }
+    return true;
+}
+
+/**
+ * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
+ *
+ * This checks whether the number or properties of ggml graph nodes have changed
+ * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The current ggml computation graph.
+ * @return true if an update is required; false otherwise.
+ */
+static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    // The number of nodes is different, so the graph needs to be reconstructed.
+    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+        return true;
+    }
+
+    // The number of nodes is the same; iterate over each node to check whether they match.
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        bool has_matching_properties = ggml_graph_node_has_matching_properties(
+            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
+        if(!has_matching_properties) {
+            return true;
+        }
+    }
+    return false;
+}
+#endif  // USE_ACL_GRAPH
+
+/**
+ * @brief Evaluate the computation graph and optionally capture or execute it using CANN graph API.
+ *
+ * If CANN graph execution is enabled and graph capture is required, this function begins
+ * graph capture, runs the graph, ends capture, and stores the captured graph.
+ *
+ * Otherwise, it falls back to op-by-op execution using the CANN compute kernel dispatcher.
+ *
+ * @param cann_ctx                 The CANN backend context.
+ * @param cgraph                   The ggml computation graph.
+ * @param use_cann_graph           Whether to use CANN graph execution.
+ * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
+ */
+static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
+    bool & use_cann_graph, bool & cann_graph_update_required) {
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) {
+        if (cann_ctx->cann_graph->graph != nullptr) {
+            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
+            cann_ctx->cann_graph->graph = nullptr;
+        }
+        ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
+    }
+#endif // USE_ACL_GRAPH
+
+    // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
+    // With the use of CANN graphs, the execution will be performed by the graph launch.
+    if (!use_cann_graph || cann_graph_update_required) {
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            ggml_tensor * node = cgraph->nodes[i];
+
+            if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+                continue;
+            }
+
+            bool ok = ggml_cann_compute_forward(*cann_ctx, node);
+            if (!ok) {
+                GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+            }
+            GGML_ASSERT(ok);
+        }
+    }
+
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
+        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
+    }
+
+    if (use_cann_graph) {
+        // Execute graph
+        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
+    }
+#endif // USE_ACL_GRAPH
+}
+
+
 /**
  * @brief Computes a computational graph using a CANN backend.
  *
@@ -2091,27 +2245,38 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_t backend, ggml_cgraph* cgraph) {
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
-
     ggml_cann_set_device(cann_ctx->device);
-    //release temp buffer create by set tensor.
     release_nz_workspace();
+#ifdef USE_ACL_GRAPH
+    bool use_cann_graph = true;
+    bool cann_graph_update_required = false;
 
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor* node = cgraph->nodes[i];
-
-        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
-            continue;
-        }
-
-        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
-
-        if (!ok) {
-            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
-                    node->name, ggml_op_name(node->op));
-        }
-        GGML_ASSERT(ok);
+    // check environment LLAMA_SET_ROWS
+    if (!cann_ctx->support_set_rows) {
+        use_cann_graph = false;
     }
 
+    if (use_cann_graph) {
+        if (cann_ctx->cann_graph == nullptr) {
+            cann_ctx->cann_graph.reset(new ggml_cann_graph());
+            cann_graph_update_required = true;
+        }
+
+        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
+        set_ggml_graph_node_properties(cann_ctx, cgraph);
+    }
+#else
+    bool use_cann_graph = false;
+    bool cann_graph_update_required = false;
+#endif  // USE_ACL_GRAPH
+
+    evaluate_and_capture_cann_graph(
+        cann_ctx,
+        cgraph,
+        use_cann_graph,
+        cann_graph_update_required
+    );
+
     return GGML_STATUS_SUCCESS;
 }
 
@@ -2226,12 +2391,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
-
-            if (!ggml_are_same_shape(op, src) && !ggml_is_contiguous(op)) {
-                // unsupport dst is not contiguous.
-                return false;
-            }
-
             return true;
         } break;
         case GGML_OP_CONT: {

From 25726898e855ec6dffba227f2233a63c57184036 Mon Sep 17 00:00:00 2001
From: stevenkuang <stevenkuang@tencent.com>
Date: Wed, 6 Aug 2025 17:48:30 +0800
Subject: [PATCH 002/287] chat : fix hunyuan auto-detection (#15114)

Signed-off-by: stevenkuang <stevenkuang@tencent.com>
---
 src/llama-chat.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 1baf7350a6..da814f00a8 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -193,11 +193,11 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_LLAMA4;
     } else if (tmpl_contains("<|endofuserprompt|>")) {
         return LLM_CHAT_TEMPLATE_DOTS1;
-    } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
+    } else if (tmpl_contains("<|extra_0|>") && tmpl_contains("<|extra_4|>")) {
         return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
     } else if (tmpl_contains("<|start|>") && tmpl_contains("<|channel|>")) {
         return LLM_CHAT_TEMPLATE_OPENAI_MOE;
-    } else if (tmpl_contains("<｜hy_place▁holder▁no▁2｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
+    } else if (tmpl_contains("<｜hy_Assistant｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
         return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
     } else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
         return LLM_CHAT_TEMPLATE_KIMI_K2;

From 65c797c4fad4d9966695ac4b4a1560be44109267 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 6 Aug 2025 13:26:49 +0200
Subject: [PATCH 003/287] chat : fix yandex chat template (#15116)

---
 src/llama-chat.cpp           | 2 --
 tests/test-chat-template.cpp | 4 ++--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index da814f00a8..0a96a9a579 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -625,8 +625,6 @@ int32_t llm_chat_apply_template(
     } else if (tmpl == LLM_CHAT_TEMPLATE_YANDEX) {
         // Yandex template ("\n\n" is defined as EOT token)
 
-        ss << "<s>";
-
         for (size_t i = 0; i < chat.size(); i++) {
             std::string role(chat[i]->role);
             if (role == "user") {
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 321ae73062..edfac3b08b 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -277,9 +277,9 @@ int main(void) {
         {
             /* .name= */ "yandex/YandexGPT-5-Lite-8B-instruct",
             /* .template_str= */ "<s>{%- set names = {'assistant': ' Ассистент:', 'user': ' Пользователь:'} %}\n{%- set tools_prefix = 'Тебе доступны следующие функции:' %}\n{%- macro __render_tool(tool) %}\n    {%- set name = tool.function.name %}\n    {%- set description = tool.function.description|default('') %}\n    {%- set parameters = tool.function.parameters|tojson %}\n    {{- '\\n' }}function {{ '{' }}'name':'{{ name }}',\n    {%- if tool.function.description %}'description':'{{ description }}',{% endif %}\n'parameters':{{ parameters }}\n    {{- '}' }}\n{%- endmacro %}\n{%- macro __render_tools(tools) %}\n    {{- tools_prefix }}\n    {%- for tool in tools %}\n        {{- __render_tool(tool) }}\n    {%- endfor %}\n    {{- '\\n\\n' }}\n{%- endmacro %}\n{%- macro __render_tool_message(message) %}\n    {{- '\\n\\nРезультат вызова' }} {{ message.name }}: {{ message.content }} {{ '\\n\\n' }}\n{%- endmacro %}\n{%- if tools -%}\n    {{- __render_tools(tools) }}\n{%- endif -%}\n{%- macro __render_user_message(message) %}\n{{ names.user }} {{ message.content + '\\n\\n' }}\n{%- endmacro %}\n{%- macro __render_assistant_message(message) %}\n    {{- names.assistant }}\n    {%- set call = message['function_call'] %}\n    {%- if call %}\n        {{- '\\n[TOOL_CALL_START]' }}{{ call.name }}{{ '\\n' }}{{ call.arguments|tojson }}\n    {%- else %}\n        {{- ' ' + message.content + '\\n\\n' }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not add_generation_prompt is defined %}\n{%- set add_generation_prompt = false %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'user' %}\n        {{- __render_user_message(message) }}\n    {%- endif %}\n    {%- if message.role == 'assistant' and not loop.last %}\n        {{- __render_assistant_message(message) }}\n    {%- endif %}\n    {%- if message.role == 'tool' %}\n        {{- __render_tool_message(message) }}\n    {%- endif %}\n    {%- if loop.last %}\n        {{- ' Ассистент:[SEP]' }}\n    {%- endif %}\n{%- endfor %}\n",
-            /* .expected_output= */ "<s> Пользователь: Hello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент:    I am an assistant   \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
+            /* .expected_output= */ " Пользователь: Hello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент:    I am an assistant   \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
             /* .expected_output_jinja= */ "<s> Пользователь: You are a helpful assistant\nHello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент:    I am an assistant   \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
-            /* .bos_token= */ "",
+            /* .bos_token= */ "<s>",
             /* .eos_token= */ "",
         },
         {

From 0d8831543cdc368fb248bae6f1b4aa5516684edc Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 6 Aug 2025 05:37:35 -0700
Subject: [PATCH 004/287] ggml : fix fallback to CPU for ununsupported ops
 (#15118)

---
 ggml/src/ggml-backend.cpp      |  9 +++++++--
 ggml/src/ggml-cpu/ggml-cpu.cpp | 37 ++++++++++++++++------------------
 ggml/src/ggml-cpu/traits.cpp   |  4 ++--
 ggml/src/ggml-cpu/traits.h     |  2 +-
 4 files changed, 27 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index eaf41e5a6c..1b9d29e911 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1071,6 +1071,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 }
             }
         }
+        // if the node is still unassigned, assign it to the first backend that supports it
+        for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
+            ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
+        }
+        GGML_ASSERT(*cur_backend_id != -1);
     }
 
     // pass 5: split graph, find tensors that need to be copied
@@ -1098,7 +1103,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
+            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;
@@ -1156,7 +1161,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
                 size_t src_id = hash_id(src);
                 const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                assert(src_backend_id != -1); // all inputs should be assigned by now
+                GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now
 
                 if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                     if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index c9daa4c39e..e16cdc9d49 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -35,7 +35,7 @@
 
 // ggml-backend interface
 
-std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
     static std::vector<ggml_backend_buffer_type_t> bufts = []() {
         std::vector<ggml_backend_buffer_type_t> bufts;
 
@@ -57,8 +57,6 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
         }
 #endif
 
-        bufts.push_back(NULL);
-
         return bufts;
     }();
 
@@ -66,14 +64,20 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
 }
 
 static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
-    return ggml_backend_cpu_get_extra_buffers_type().data();
+    static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
+        std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
+        bufts.push_back(nullptr);
+        return bufts;
+    }();
+
+    return extra_bufts.data();
 
     GGML_UNUSED(device);
 }
 
 static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
-    for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra && extra == buft) {
+    for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
+        if (extra == buft) {
             return true;
         }
     }
@@ -397,20 +401,13 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         return true;
     }
 
-    // extra_buffer_op?
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra) {
-            auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
-            if (buf_extra && buf_extra->supports_op(dev, op)) {
-                return true;
-            }
-        }
-    }
-
-    // the other case need host buffer.
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
-            return false;
+    // check extra buffer types
+    // note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
+    for (int i = 0; i < 4; i++) {
+        if (op->src[i] && op->src[i]->buffer &&
+            ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
+            auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
+            return buf_extra->supports_op(dev, op);
         }
     }
 
diff --git a/ggml/src/ggml-cpu/traits.cpp b/ggml/src/ggml-cpu/traits.cpp
index 139fa59641..4f32f10255 100644
--- a/ggml/src/ggml-cpu/traits.cpp
+++ b/ggml/src/ggml-cpu/traits.cpp
@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
 }  // namespace ggml::cpu
 
 bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
 }
 
 bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);
diff --git a/ggml/src/ggml-cpu/traits.h b/ggml/src/ggml-cpu/traits.h
index 99a6186b1d..f4e0990ddf 100644
--- a/ggml/src/ggml-cpu/traits.h
+++ b/ggml/src/ggml-cpu/traits.h
@@ -33,6 +33,6 @@ class extra_buffer_type {
 }  // namespace ggml::cpu
 
 // implemented in ggml-cpu.cpp.
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();
 
 #endif

From 476aa3fd5779b32d06cd84338f777da82341195c Mon Sep 17 00:00:00 2001
From: Juk Armstrong <69222624+jukofyork@users.noreply.github.com>
Date: Wed, 6 Aug 2025 17:28:48 +0100
Subject: [PATCH 005/287] Fixed name `-override-tensors` to `-override-tensor`
 (#15129)

---
 tools/llama-bench/llama-bench.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index ba0699c432..10b48c5568 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -374,7 +374,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -embd, --embeddings <0|1>                 (default: %s)\n",
            join(cmd_params_defaults.embeddings, ",").c_str());
     printf("  -ts, --tensor-split <ts0/ts1/..>          (default: 0)\n");
-    printf("  -ot --override-tensors <tensor name pattern>=<buffer type>;...\n");
+    printf("  -ot --override-tensor <tensor name pattern>=<buffer type>;...\n");
     printf("                                            (default: disabled)\n");
     printf("  -nopo, --no-op-offload <0|1>              (default: 0)\n");
     printf("\n");

From 3db4da56a5a00c4b47bbb3b18f85fb4473662adb Mon Sep 17 00:00:00 2001
From: Sachin Desai <smdesai@gmail.com>
Date: Wed, 6 Aug 2025 11:27:30 -0700
Subject: [PATCH 006/287] chat : support Granite model reasoning and tool call
 (#14864)

---
 common/chat-parser.cpp                        |  10 +-
 common/chat.cpp                               | 129 ++++++++++++++++++
 common/chat.h                                 |   1 +
 common/common.h                               |   1 +
 .../ibm-granite-granite-3.3-2B-Instruct.jinja |  59 ++++++++
 tests/test-chat.cpp                           |  53 +++++++
 6 files changed, 252 insertions(+), 1 deletion(-)
 create mode 100644 models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja

diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp
index 18a30e49aa..96ba8f533e 100644
--- a/common/chat-parser.cpp
+++ b/common/chat-parser.cpp
@@ -55,7 +55,15 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
     std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
     std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
+    std::string arguments = "";
+    if (tool_call.contains("arguments")) {
+        if (tool_call.at("arguments").is_object()) {
+            arguments = tool_call.at("arguments").dump();
+        } else {
+            arguments = tool_call.at("arguments");
+        }
+    }
+
     return add_tool_call(name, id, arguments);
 }
 
diff --git a/common/chat.cpp b/common/chat.cpp
index 60805ab3b5..316bd24170 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -606,6 +606,7 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
+        case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
         case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
         default:
             throw std::runtime_error("Unknown chat format");
@@ -618,6 +619,7 @@ const char * common_reasoning_format_name(common_reasoning_format format) {
         case COMMON_REASONING_FORMAT_AUTO:     return "auto";
         case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
         case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
+        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
         default:
             throw std::runtime_error("Unknown reasoning format");
     }
@@ -1734,6 +1736,124 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
     builder.add_content(builder.consume_rest());
 }
 
+static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    // Pass thinking context for Granite template
+    json additional_context = {
+        {"thinking", inputs.enable_thinking},
+    };
+
+    data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
+    data.format = COMMON_CHAT_FORMAT_GRANITE;
+
+    if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    if (!inputs.tools.is_null()) {
+        // Granite uses <|tool_call|> followed by JSON list
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+                tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
+"-args", {
+                    {"type", "object"},
+                    {"properties", {
+                        {"name", {{"const", name}}},
+                        {"arguments", parameters},
+                    }},
+                    {"required", json::array({"name", "arguments"})},
+                })));
+            });
+
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
+            auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
+
+            if (data.thinking_forced_open) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
+            } else {
+                builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
+            }
+
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
+                "<|tool_call|>"
+            });
+
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+                "<|tool_call|>",
+            };
+        });
+    } else {
+        // Handle thinking tags for non-tool responses
+        if (data.thinking_forced_open && inputs.enable_thinking) {
+            data.grammar_lazy = false;
+            data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
+            });
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+            };
+        }
+    }
+
+    return data;
+}
+
+static void common_chat_parse_granite(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<think>", "</think>");
+
+    // Parse response tags using regex
+    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
+    if (auto res = builder.try_find_regex(response_regex)) {
+        // Extract the content between the tags (capture group 1)
+        auto content = builder.str(res->groups[1]);
+        builder.add_content(content);
+        builder.move_to(res->groups[0].end);
+    }
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        // Expect JSON array of tool calls
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.add_tool_calls(tool_calls_data.json)) {
+                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
+            }
+        } else {
+            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
+        }
+    } else {
+        builder.add_content(builder.consume_rest());
+    }
+}
+
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -1805,6 +1925,11 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_command_r7b(tmpl, params);
     }
 
+    // Granite (IBM) - detects thinking / tools support
+    if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) {
+        return common_chat_params_init_granite(tmpl, params);
+    }
+
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
@@ -1865,6 +1990,7 @@ static common_chat_params common_chat_templates_apply_legacy(
     int alloc_size = 0;
     std::vector<llama_chat_message> chat;
     std::vector<std::string> contents;
+
     for (const auto & msg : inputs.messages) {
         auto content = msg.content;
         for (const auto & part : msg.content_parts) {
@@ -1966,6 +2092,9 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_COMMAND_R7B:
             common_chat_parse_command_r7b(builder);
             break;
+        case COMMON_CHAT_FORMAT_GRANITE:
+            common_chat_parse_granite(builder);
+            break;
         case COMMON_CHAT_FORMAT_GPT_OSS:
             common_chat_parse_gpt_oss(builder);
             break;
diff --git a/common/chat.h b/common/chat.h
index b014f9f0aa..eb628d8bc2 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -109,6 +109,7 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
+    COMMON_CHAT_FORMAT_GRANITE,
     COMMON_CHAT_FORMAT_GPT_OSS,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
diff --git a/common/common.h b/common/common.h
index 6c1c7ee237..5eab199af5 100644
--- a/common/common.h
+++ b/common/common.h
@@ -239,6 +239,7 @@ enum common_reasoning_format {
     COMMON_REASONING_FORMAT_AUTO,
     COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
     COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
+    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };
 
 struct common_params {
diff --git a/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja b/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja
new file mode 100644
index 0000000000..f506536096
--- /dev/null
+++ b/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja
@@ -0,0 +1,59 @@
+{# Alias tools -> available_tools #}
+{%- if tools and not available_tools -%}
+    {%- set available_tools = tools -%}
+{%- endif -%}
+{%- if messages[0]['role'] == 'system' %}
+     {%- set system_message = messages[0]['content'] %}
+     {%- set loop_messages = messages[1:] %}
+ {%- else %}
+     {%- set system_message = "Knowledge Cutoff Date: April 2024. Today's Date: " + strftime_now('%B %d, %Y') + ". You are Granite, developed by IBM." %}
+     {%- if available_tools and documents %}
+         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+     {%- elif available_tools %}
+         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
+     {%- elif documents %}
+         {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+    {%- elif thinking %}
+    {%- set system_message = system_message + " You are a helpful AI assistant.
+Respond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between <think></think> and write your response between <response></response> for each user query." %}
+     {%- else %}
+         {%- set system_message = system_message + " You are a helpful AI assistant." %}
+     {%- endif %}
+     {%- if 'citations' in controls and documents %}
+         {%- set system_message = system_message + '
+Use the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
+     {%- endif %}
+     {%- if 'hallucinations' in controls and documents %}
+         {%- set system_message = system_message + '
+Finally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}
+     {%- endif %}
+     {%- set loop_messages = messages %}
+ {%- endif %}
+ {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>
+' }}
+ {%- if available_tools %}
+     {{- '<|start_of_role|>available_tools<|end_of_role|>' }}
+     {{- available_tools | tojson(indent=4) }}
+     {{- '<|end_of_text|>
+' }}
+ {%- endif %}
+ {%- if documents %}
+     {%- for document in documents %}
+         {{- '<|start_of_role|>document {"document_id": "' + document['doc_id'] | string + '"}<|end_of_role|>
+' }}
+         {{- document['text'] }}
+         {{- '<|end_of_text|>
+' }}
+              {%- endfor %}
+ {%- endif %}
+ {%- for message in loop_messages %}
+     {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>
+' }}
+     {%- if loop.last and add_generation_prompt %}
+         {{- '<|start_of_role|>assistant' }}
+             {%- if controls %}
+                 {{- ' ' + controls | tojson()}}
+             {%- endif %}
+         {{- '<|end_of_role|>' }}
+     {%- endif %}
+ {%- endfor %}
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 73c98bfa20..99b4b4d5ba 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -1386,6 +1386,59 @@ static void test_template_output_parsers() {
                 "{\"arg1\": 1}\n"
                 "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>");
     }
+    {
+        auto tmpls = read_templates("models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja");
+        std::vector<std::string> end_tokens{ "<|end_of_text|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
+
+        assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
+
+        // Test parsing regular content
+        assert_msg_equals(message_assist,
+            common_chat_parse(
+                "Hello, world!\nWhat's up?",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_GRANITE}));
+
+        // Test parsing content with thinking
+        assert_msg_equals(message_assist_thoughts,
+            common_chat_parse(
+                "<think>I'm\nthinking</think>Hello, world!\nWhat's up?",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GRANITE,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_GRANITE,
+                }));
+
+        // Test parsing tool calls
+        assert_msg_equals(message_assist_call,
+            common_chat_parse(
+                "<|tool_call|>[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_GRANITE}));
+
+        // Test template generation for regular content
+        test_templates(tmpls.get(), end_tokens, message_assist, tools,
+                      "Hello, world!\nWhat's up?",
+                      /* expect_grammar_triggered= */ false);
+
+        // Test template generation for tool calls
+        test_templates(tmpls.get(), end_tokens, message_assist_call_id, tools,
+                      "{\n"
+                      "  \"tool_calls\": [\n"
+                      "    {\n"
+                      "      \"name\": \"special_function\",\n"
+                      "      \"arguments\": {\n"
+                      "        \"arg1\": 1\n"
+                      "      },\n"
+                      "      \"id\": \"123456789\"\n"
+                      "    }\n"
+                      "  ]\n"
+                      "}",
+                      /* expect_grammar_triggered= */ false
+        );
+    }
 }
 
 static void test_msg_diffs_compute() {

From e725a1a982ca870404a9c4935df52466327bbd02 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Thu, 7 Aug 2025 04:12:17 +0900
Subject: [PATCH 007/287] opencl: add `swiglu_oai` and  `add_id` (#15121)

* opencl: add `swiglu-oai`

* opencl: add `add_id`

* opencl: add missing `add_id.cl`
---
 ggml/src/ggml-opencl/CMakeLists.txt    |   1 +
 ggml/src/ggml-opencl/ggml-opencl.cpp   | 112 ++++++++++++++++++++++++-
 ggml/src/ggml-opencl/kernels/add_id.cl |  42 ++++++++++
 ggml/src/ggml-opencl/kernels/glu.cl    |  41 +++++++++
 4 files changed, 194 insertions(+), 2 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/kernels/add_id.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 3adea83615..d8290faa46 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -55,6 +55,7 @@ endfunction()
 
 set(GGML_OPENCL_KERNELS
     add
+    add_id
     argsort
     clamp
     cpy
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index bb8b310b98..eea6ad6cab 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -345,6 +345,7 @@ struct ggml_backend_opencl_context {
     cl_command_queue queue;
 
     cl_program program_add;
+    cl_program program_add_id;
     cl_program program_clamp;
     cl_program program_cpy;
     cl_program program_cvt;
@@ -404,6 +405,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16;
     cl_kernel kernel_div, kernel_div_row, kernel_div_f16, kernel_div_row_f16;
     cl_kernel kernel_sub, kernel_sub_row, kernel_sub_f16, kernel_sub_row_f16;
+    cl_kernel kernel_add_id;
     cl_kernel kernel_scale;
     cl_kernel kernel_silu, kernel_silu_4;
     cl_kernel kernel_gelu, kernel_gelu_4;
@@ -412,7 +414,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_relu;
     cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
     cl_kernel kernel_clamp;
-    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_geglu_erf, kernel_geglu_quick,
+    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_swiglu_oai, kernel_geglu_erf, kernel_geglu_quick,
               kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
     cl_kernel kernel_norm;
     cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
@@ -681,6 +683,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // add_id
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "add_id.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("add_id.cl");
+#endif
+        backend_ctx->program_add_id =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // clamp
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -787,6 +805,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_geglu           = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err));
         CL_CHECK((backend_ctx->kernel_reglu           = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err));
         CL_CHECK((backend_ctx->kernel_swiglu          = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu", &err), err));
+        CL_CHECK((backend_ctx->kernel_swiglu_oai      = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu_oai", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_erf       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_erf", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_quick     = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_quick", &err), err));
         CL_CHECK((backend_ctx->kernel_geglu_f16       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_f16", &err), err));
@@ -2467,6 +2486,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return (op->src[0]->type == op->src[1]->type) &&
                    (op->src[0]->type == op->type) &&
                    (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
+        case GGML_OP_ADD_ID:
+            return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_GELU:
@@ -2488,6 +2509,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
@@ -3824,6 +3846,75 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
     }
 }
 
+static void ggml_cl_add_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const ggml_tensor * src2 = dst->src[2];
+    GGML_ASSERT(src2);
+    GGML_ASSERT(src2->extra);
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous_rows(src0));
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+
+    const cl_ulong nb11 = src1->nb[1];
+
+    const cl_ulong nb21 = src2->nb[1];
+
+    const int ne0 = dst->ne[0];
+    const int ne1 = dst->ne[1];
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offset2 = extra2->offset + src2->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel = backend_ctx->kernel_add_id;
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb21));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne0));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne1));
+
+    int nth = MIN(ne00, (int) backend_ctx->get_kernel_workgroup_size(kernel));
+    size_t global_work_size[] = { (size_t)ne01*nth, (size_t)ne02, 1 };
+    size_t local_work_size[] = { (size_t)nth, 1, 1 };
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+}
+
 static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -7005,6 +7096,9 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
                 kernel = backend_ctx->kernel_swiglu_f16;
             }
             break;
+        case GGML_GLU_OP_SWIGLU_OAI:
+            kernel = backend_ctx->kernel_swiglu_oai;
+            break;
         case GGML_GLU_OP_GEGLU_ERF:
             if (dst->type == GGML_TYPE_F32) {
                 kernel = backend_ctx->kernel_geglu_erf;
@@ -7040,7 +7134,10 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
 
     const cl_ulong nb1  = dst->nb[1];
 
-    const int swp = ((const int32_t *) dst->op_params)[1];
+    const int   swp   = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
     const int ne00_off = src1 ? 0 : (swp ? ne0 : 0);
     const int ne10_off = src1 ? 0 : (swp ? 0 : ne0);
 
@@ -7057,6 +7154,11 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
     CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne00_off));
     CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10_off));
 
+    if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU_OAI) {
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float), &limit));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float), &alpha));
+    }
+
     const size_t nrows = ggml_nrows(src0);
     size_t nth = 512;
     size_t global_work_size[] = {nrows*nth, 1, 1};
@@ -7113,6 +7215,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             }
             func = ggml_cl_add;
             break;
+        case GGML_OP_ADD_ID:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_add_id;
+            break;
         case GGML_OP_MUL:
             if (!any_on_device) {
                 return false;
diff --git a/ggml/src/ggml-opencl/kernels/add_id.cl b/ggml/src/ggml-opencl/kernels/add_id.cl
new file mode 100644
index 0000000000..e9c6d55e6a
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/add_id.cl
@@ -0,0 +1,42 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// add_id
+//------------------------------------------------------------------------------
+kernel void kernel_add_id(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * src2,
+    ulong         offset2,
+    global char * dst,
+    ulong         offsetd,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb11,
+    ulong         nb21,
+    int           ne0,
+    int           ne1
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    src2 = (global char*)((global char*)src2 + offset2);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    int i1 = get_group_id(0);
+    int i2 = get_group_id(1);
+
+    const int i11 = *((global const int *) (src2 + i1*sizeof(int) + i2*nb21));
+
+    const size_t nb1 = ne0 * sizeof(float);
+    const size_t nb2 = ne1 * nb1;
+
+    global float * dst_row  = (global float *)((global char *)dst  + i1*nb1 + i2*nb2);
+    global float * src0_row = (global float *)((global char *)src0 + i1*nb01 + i2*nb02);
+    global float * src1_row = (global float *)((global char *)src1 + i11*nb11);
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        dst_row[i0] = src0_row[i0] + src1_row[i0];
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/glu.cl b/ggml/src/ggml-opencl/kernels/glu.cl
index 7cca16e6a9..059a4bbf1b 100644
--- a/ggml/src/ggml-opencl/kernels/glu.cl
+++ b/ggml/src/ggml-opencl/kernels/glu.cl
@@ -202,6 +202,47 @@ kernel void kernel_swiglu_f16(
     }
 }
 
+//------------------------------------------------------------------------------
+// swiglu_oai
+//------------------------------------------------------------------------------
+kernel void kernel_swiglu_oai(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * dst,
+    ulong         offsetd,
+    ulong         nb01,
+    ulong         nb11,
+    int           ne0,
+    ulong         nb1,
+    int           ne00_off,
+    int           ne10_off,
+    float         limit,
+    float         alpha
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    global float * src0_row = (global float *) ((global char *) src0 + get_group_id(0)*nb01) + ne00_off;
+    global float * src1_row = (global float *) ((global char *) src1 + get_group_id(0)*nb11) + ne10_off;
+    global float * dst_row  = (global float *) ((global char *) dst  + get_group_id(0)*nb1);
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        float x0 = src0_row[i0];
+        float x1 = src1_row[i0];
+
+        x0 = min(x0, limit);
+        x1 = max(min(x1, limit), -limit);
+
+        float out_glu = x0 / (1.0f + exp(-x0 * alpha));
+        out_glu = out_glu * (1.0f + x1);
+
+        dst_row[i0] = out_glu;
+    }
+}
+
 //------------------------------------------------------------------------------
 // geglu_erf
 //------------------------------------------------------------------------------

From 756cfea82608911bbfcbf45164b8fdaddbafaa31 Mon Sep 17 00:00:00 2001
From: rmatif <kingrealriadh@gmail.com>
Date: Wed, 6 Aug 2025 23:17:51 +0200
Subject: [PATCH 008/287] fix profiling crash (#15072)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index eea6ad6cab..4f765ab530 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -602,6 +602,7 @@ struct ggml_backend_opencl_context {
         if (ref_count == 0) {
 #ifdef GGML_OPENCL_PROFILING
             write_profiling_info();
+            profiling_info.clear();
 #endif
         }
     }

From 5fd160bbd9d70b94b5b11b0001fd7f477005e4a0 Mon Sep 17 00:00:00 2001
From: Reese Levine <reeselevine1@gmail.com>
Date: Wed, 6 Aug 2025 15:14:40 -0700
Subject: [PATCH 009/287] ggml: Add basic SET_ROWS support in WebGPU (#15137)

* Begin work on set_rows

* Work on set rows

* Add error buffers for reporting unsupported SET_ROWS indices

* Remove extra comments
---
 .github/workflows/build.yml                   |   2 -
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          | 237 +++++++++++++++---
 .../ggml-webgpu/wgsl-shaders/set_rows.wgsl    |  82 ++++++
 3 files changed, 286 insertions(+), 35 deletions(-)
 create mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 63e40c3586..3d4f837e24 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -179,7 +179,6 @@ jobs:
       - name: Test
         id: cmake_test
         run: |
-          export LLAMA_SET_ROWS=0
           cd build
           ctest -L main --verbose --timeout 900
 
@@ -438,7 +437,6 @@ jobs:
       - name: Test
         id: cmake_test
         run: |
-          export LLAMA_SET_ROWS=0
           cd build
           # This is using llvmpipe and runs slower than other backends
           ctest -L main --verbose --timeout 3600
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index 5009e26a20..ba1addc8d9 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -19,18 +19,21 @@
 #include <vector>
 
 #ifdef GGML_WEBGPU_DEBUG
-#    define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl
+#    define WEBGPU_LOG_DEBUG(msg)  std::cout << msg << std::endl
+#    define WEBGPU_DEBUG_BUF_ELEMS 32
 #else
 #    define WEBGPU_LOG_DEBUG(msg) ((void) 0)
 #endif  // GGML_WEBGPU_DEBUG
 
 /* Constants */
 
-#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 16
-#define WEBGPU_MUL_MAT_WG_SIZE           64
-#define WEBGPU_NUM_PARAM_BUFS            100
-#define WEBGPU_PARAMS_BUF_SIZE_BYTES     256
-#define WEBGPU_STORAGE_BUF_BINDING_MULT  4  // a storage buffer binding size must be a multiple of 4
+#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE     16
+#define WEBGPU_MUL_MAT_WG_SIZE               64
+#define WEBGPU_NUM_PARAM_BUFS                100
+#define WEBGPU_PARAMS_BUF_SIZE_BYTES         128  // enough for 32 parameters
+#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS       32
+#define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4
+#define WEBGPU_STORAGE_BUF_BINDING_MULT      4  // a storage buffer binding size must be a multiple of 4
 
 /* End Constants */
 
@@ -54,46 +57,42 @@ static void ggml_webgpu_create_buffer(wgpu::Device &    device,
                                       wgpu::BufferUsage usage,
                                       const char *      label);
 
-struct webgpu_param_bufs {
+struct webgpu_pool_bufs {
     wgpu::Buffer host_buf;
     wgpu::Buffer dev_buf;
 };
 
 // Holds a pool of parameter buffers for WebGPU operations
-struct webgpu_param_buf_pool {
-    std::vector<webgpu_param_bufs> free;
+struct webgpu_buf_pool {
+    std::vector<webgpu_pool_bufs> free;
 
     std::mutex mutex;
 
     std::condition_variable cv;
 
-    void init(wgpu::Device device) {
-        for (int i = 0; i < WEBGPU_NUM_PARAM_BUFS; i++) {
+    void init(wgpu::Device      device,
+              int               num_bufs,
+              size_t            buf_size,
+              wgpu::BufferUsage dev_buf_usage,
+              wgpu::BufferUsage host_buf_usage) {
+        for (int i = 0; i < num_bufs; i++) {
             wgpu::Buffer host_buf;
             wgpu::Buffer dev_buf;
-            ggml_webgpu_create_buffer(device,
-                                      host_buf,
-                                      WEBGPU_PARAMS_BUF_SIZE_BYTES,
-                                      wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite,
-                                      "ggml_webgpu_host_params_buf");
-            ggml_webgpu_create_buffer(device,
-                                      dev_buf,
-                                      WEBGPU_PARAMS_BUF_SIZE_BYTES,
-                                      wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
-                                      "ggml_webgpu_dev_params_buf");
+            ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_pool_buf");
+            ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");
             free.push_back({ host_buf, dev_buf });
         }
     }
 
-    webgpu_param_bufs alloc_bufs() {
+    webgpu_pool_bufs alloc_bufs() {
         std::unique_lock<std::mutex> lock(mutex);
         cv.wait(lock, [this] { return !free.empty(); });
-        webgpu_param_bufs bufs = free.back();
+        webgpu_pool_bufs bufs = free.back();
         free.pop_back();
         return bufs;
     }
 
-    void free_bufs(std::vector<webgpu_param_bufs> bufs) {
+    void free_bufs(std::vector<webgpu_pool_bufs> bufs) {
         std::lock_guard<std::mutex> lock(mutex);
         free.insert(free.end(), bufs.begin(), bufs.end());
         cv.notify_all();
@@ -121,10 +120,12 @@ struct webgpu_context_struct {
 
     bool device_init = false;
 
-    webgpu_param_buf_pool param_buf_pool;
+    webgpu_buf_pool param_buf_pool;
+    webgpu_buf_pool set_rows_error_buf_pool;
 
     wgpu::ComputePipeline memset_pipeline;
     wgpu::ComputePipeline mul_mat_pipeline;
+    wgpu::ComputePipeline set_rows_pipeline;
     wgpu::ComputePipeline cpy_pipeline;
 
     size_t memset_bytes_per_thread;
@@ -136,9 +137,16 @@ struct webgpu_context_struct {
     std::vector<wgpu::CommandBuffer> staged_command_bufs;
 
     // Parameter buffers associated with the staged command buffers
-    std::vector<webgpu_param_bufs> staged_param_bufs;
+    std::vector<webgpu_pool_bufs> staged_param_bufs;
+    // Buffers associated with set_rows operations, used to store potential errors
+    std::vector<webgpu_pool_bufs> staged_set_row_error_bufs;
 
     std::vector<wgpu::FutureWaitInfo> callback_futures;
+
+#ifdef GGML_WEBGPU_DEBUG
+    wgpu::Buffer debug_host_buf;
+    wgpu::Buffer debug_dev_buf;
+#endif
 };
 
 typedef std::shared_ptr<webgpu_context_struct> webgpu_context;
@@ -249,20 +257,55 @@ static void ggml_backend_webgpu_submit_queue(webgpu_context & ctx) {
         return;
     }
     ctx->queue.Submit(ctx->staged_command_bufs.size(), ctx->staged_command_bufs.data());
+
+    // If there are SET_ROWS operations in this submission, copy their error buffers to the host.
+    if (ctx->staged_set_row_error_bufs.size() > 0) {
+        wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
+        for (auto & error_bufs : ctx->staged_set_row_error_bufs) {
+            // Copy the error buffer to the host buffer
+            encoder.CopyBufferToBuffer(error_bufs.dev_buf, 0, error_bufs.host_buf, 0, error_bufs.host_buf.GetSize());
+        }
+        wgpu::CommandBuffer commands = encoder.Finish();
+        ctx->queue.Submit(1, &commands);
+    }
+
     ctx->staged_command_bufs.clear();
-    std::vector<webgpu_param_bufs> staged_param_bufs = std::move(ctx->staged_param_bufs);
+    std::vector<webgpu_pool_bufs> staged_param_bufs         = std::move(ctx->staged_param_bufs);
+    std::vector<webgpu_pool_bufs> staged_set_row_error_bufs = std::move(ctx->staged_set_row_error_bufs);
 
     // Free the staged parameter buffers once the submission completes
-    wgpu::Future f = ctx->queue.OnSubmittedWorkDone(
+    wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone(
         wgpu::CallbackMode::AllowSpontaneous,
         [ctx, staged_param_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
             if (status != wgpu::QueueWorkDoneStatus::Success) {
                 GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
             }
-            // Free the staged parameter buffers
+            // Free the staged buffers
             ctx->param_buf_pool.free_bufs(staged_param_bufs);
         });
-    ctx->callback_futures.push_back({ f });
+    ctx->callback_futures.push_back({ p_f });
+
+    // Check for errrors in SET_ROWS operations
+    for (auto & error_bufs : staged_set_row_error_bufs) {
+        wgpu::Future f = error_bufs.host_buf.MapAsync(
+            wgpu::MapMode::Read,
+            0,
+            error_bufs.host_buf.GetSize(),
+            wgpu::CallbackMode::AllowSpontaneous,
+            [ctx, error_bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) {
+                if (status != wgpu::MapAsyncStatus::Success) {
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", message.data);
+                } else {
+                    const uint32_t * error_data = (const uint32_t *) error_bufs.host_buf.GetConstMappedRange();
+                    if (*error_data) {
+                        GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
+                    }
+                    // We can't unmap in here due to WebGPU reentrancy limitations.
+                    ctx->set_rows_error_buf_pool.free_bufs({ error_bufs });
+                }
+            });
+        ctx->callback_futures.push_back({ f });
+    }
 }
 
 static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
@@ -283,13 +326,34 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
                           UINT64_MAX);
 }
 
+#ifdef GGML_WEBGPU_DEBUG
+// This function adds debugging information to shaders, as WebGPU does not support printing directly.
+// To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and
+// debug statements in the shader, and then call this function after encoding the commands and submitting them.
+static void ggml_backend_webgpu_debug(webgpu_context & ctx) {
+    wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
+    encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize());
+    wgpu::CommandBuffer commands = encoder.Finish();
+    ctx->queue.Submit(1, &commands);
+
+    ggml_backend_webgpu_map_buffer(ctx, ctx->debug_host_buf, wgpu::MapMode::Read, 0, ctx->debug_host_buf.GetSize());
+    const uint32_t * debug_data = (const uint32_t *) ctx->debug_host_buf.GetConstMappedRange();
+    std::cout << "debug data:";
+    for (size_t i = 0; i < WEBGPU_DEBUG_BUF_ELEMS; i++) {
+        std::cout << "  " << i << ": " << debug_data[i];
+    }
+    std::cout << "\n";
+    ctx->debug_host_buf.Unmap();
+}
+#endif
+
 static void ggml_backend_webgpu_build_and_enqueue(webgpu_context &                  ctx,
                                                   wgpu::ComputePipeline &           pipeline,
                                                   std::vector<uint32_t>             params,
                                                   std::vector<wgpu::BindGroupEntry> bind_group_entries,
                                                   uint32_t                          wg_x,
                                                   bool                              submit_and_wait = false) {
-    webgpu_param_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
+    webgpu_pool_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
 
     ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, params_bufs.host_buf.GetSize());
     uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange();
@@ -429,6 +493,76 @@ static void ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor
     ggml_backend_webgpu_build_and_enqueue(ctx, ctx->cpy_pipeline, params, entries, wg_x);
 }
 
+static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * idx, ggml_tensor * dst) {
+    // For set rows specifically, we need to check if src and idx are empty tensors.
+    if (ggml_is_empty(src) || ggml_is_empty(idx)) {
+        return;
+    }
+
+    webgpu_pool_bufs error_bufs = ctx->set_rows_error_buf_pool.alloc_bufs();
+    if (error_bufs.host_buf.GetMapState() == wgpu::BufferMapState::Mapped) {
+        error_bufs.host_buf.Unmap();
+    }
+
+    size_t src_offset       = ggml_backend_webgpu_tensor_offset(src);
+    // assumes power of 2 offset alignment
+    size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+    // align to minimum offset alignment
+    src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+    size_t idx_offset       = ggml_backend_webgpu_tensor_offset(idx);
+    size_t idx_misalignment = idx_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+    idx_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+    size_t dst_offset       = ggml_backend_webgpu_tensor_offset(dst);
+    size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+    dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+
+    std::vector<uint32_t> params = { (uint32_t) (src_misalignment / ggml_type_size(src->type)),
+                                     (uint32_t) (idx_misalignment / ggml_type_size(idx->type)),
+                                     (uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
+                                     // Convert byte-strides to element-strides
+                                     (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+                                     (uint32_t) (idx->nb[0] / ggml_type_size(idx->type)),
+                                     (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)),
+                                     (uint32_t) (idx->nb[2] / ggml_type_size(idx->type)),
+                                     (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+                                     (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+                                     (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+                                     // Shape of src
+                                     (uint32_t) src->ne[0],
+                                     (uint32_t) src->ne[1],
+                                     (uint32_t) src->ne[2],
+                                     (uint32_t) src->ne[3],
+                                     // Shape of idx
+                                     (uint32_t) (idx->ne[1]),
+                                     (uint32_t) (idx->ne[2]) };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        { .binding = 0,
+         .buffer  = ggml_backend_webgpu_tensor_buf(src),
+         .offset  = ggml_backend_webgpu_tensor_offset(src),
+         .size    = ggml_nbytes(src)                                                                       },
+        { .binding = 1,
+         .buffer  = ggml_backend_webgpu_tensor_buf(idx),
+         .offset  = ggml_backend_webgpu_tensor_offset(idx),
+         .size    = ggml_nbytes(idx)                                                                       },
+        { .binding = 2,
+         .buffer  = ggml_backend_webgpu_tensor_buf(dst),
+         .offset  = ggml_backend_webgpu_tensor_offset(dst),
+         .size    = ggml_nbytes(dst)                                                                       },
+        { .binding = 3, .buffer = error_bufs.dev_buf,    .offset = 0, .size = error_bufs.dev_buf.GetSize() }
+    };
+
+    size_t   max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
+    uint32_t wg_x        = (src->ne[1] * src->ne[2] * src->ne[3] + max_wg_size - 1) / max_wg_size;
+
+    std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
+    ctx->staged_set_row_error_bufs.push_back(error_bufs);
+
+    ggml_backend_webgpu_build_and_enqueue(ctx, ctx->set_rows_pipeline, params, entries, wg_x);
+}
+
 static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
     std::vector<uint32_t> params = {
         (uint32_t) dst->ne[1],                                  // number of rows in result (M)
@@ -487,6 +621,11 @@ static bool ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
                 ggml_webgpu_cpy(ctx, src0, node);
                 break;
             }
+        case GGML_OP_SET_ROWS:
+            {
+                ggml_webgpu_set_rows(ctx, src0, src1, node);
+                break;
+            }
         case GGML_OP_MUL_MAT:
             {
                 ggml_webgpu_mul_mat(ctx, src0, src1, node);
@@ -771,6 +910,14 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
     ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline, wgsl_mul_mat, "mul_mat");
 }
 
+static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
+    std::vector<wgpu::ConstantEntry> constants(1);
+    constants[0].key   = "wg_size";
+    constants[0].value = webgpu_ctx->limits.maxComputeWorkgroupSizeX;
+    ggml_webgpu_create_pipeline(
+        webgpu_ctx->device, webgpu_ctx->set_rows_pipeline, wgsl_set_rows, "set_rows", constants);
+}
+
 static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
     std::vector<wgpu::ConstantEntry> constants(1);
     constants[0].key   = "wg_size";
@@ -827,11 +974,35 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
         webgpu_ctx->queue = webgpu_ctx->device.GetQueue();
 
         // Create buffer pool for shader parameters
-        webgpu_ctx->param_buf_pool.init(webgpu_ctx->device);
+        webgpu_ctx->param_buf_pool.init(webgpu_ctx->device,
+                                        WEBGPU_NUM_PARAM_BUFS,
+                                        WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                                        wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
+                                        wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
+        webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->device,
+                                                 WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
+                                                 WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
+                                                 wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
+                                                 wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
 
         ggml_webgpu_init_memset_pipeline(webgpu_ctx);
         ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
+        ggml_webgpu_init_set_rows_pipeline(webgpu_ctx);
         ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
+
+#ifdef GGML_WEBGPU_DEBUG
+        // Initialize debug buffers
+        ggml_webgpu_create_buffer(webgpu_ctx->device,
+                                  webgpu_ctx->debug_host_buf,
+                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                                  wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead,
+                                  "debug_host_buf");
+        ggml_webgpu_create_buffer(webgpu_ctx->device,
+                                  webgpu_ctx->debug_dev_buf,
+                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                                  wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc,
+                                  "debug_dev_buf");
+#endif
         webgpu_ctx->device_init = true;
     }
 
@@ -882,7 +1053,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
             return true;
-        case GGML_OP_CPY:
+        case GGML_OP_CPY | GGML_OP_SET_ROWS:
             return op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_MUL_MAT:
             return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl
new file mode 100644
index 0000000000..4bd6f94a23
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl
@@ -0,0 +1,82 @@
+enable f16;
+
+@group(0) @binding(0)
+var<storage, read_write> src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> idx: array<u32>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<f16>;
+
+@group(0) @binding(3)
+var<storage, read_write> error: atomic<u32>;
+
+struct Params {
+    offset_src: u32, // in elements
+    offset_idx: u32, // in elements
+    offset_dst: u32, // in elements
+
+    // Strides (in elements)
+    stride_src1: u32,
+    stride_src2: u32,
+    stride_src3: u32,
+
+    stride_idx0: u32,
+    stride_idx1: u32,
+    stride_idx2: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    // Shape of src
+    ne0: u32,
+    n_rows: u32,
+    ne2: u32,
+    ne3: u32,
+
+    // Shape of idx
+    idx1: u32,
+    idx2: u32,
+};
+
+@group(0) @binding(4)
+var<uniform> params: Params;
+
+override wg_size: u32;
+@compute @workgroup_size(wg_size)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x >= params.n_rows * params.ne2 * params.ne3) {
+        return;
+    }
+    var i = gid.x;
+    let i_src3 = i / (params.ne2 * params.n_rows);
+    let i_dst3 = i / (params.ne2 * 3);
+
+    i = i % (params.ne2 * params.n_rows);
+    let i_src2 = i / params.n_rows;
+    let i_src1 = i % params.n_rows;
+
+    let i_idx2 = i_src3 % params.idx2;
+    let i_idx1 = i_src2 % params.idx1;
+    let i_idx0 = i_src1;
+
+    let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2;
+
+    let idx_high_val = idx[idx_high];
+    let idx_low_val = idx[idx_high + 1];
+
+    if (idx_low_val != 0) {
+        // Upper bits of index are not zero, output will be incorrect
+        atomicStore(&error, 1);
+        return;
+    }
+
+    let i_dst_row = params.offset_dst + idx_high_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3;
+    let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3;
+
+    for (var i: u32 = 0; i < params.ne0; i++) {
+      dst[i_dst_row + i] = f16(src[i_src_row + i]);
+    }
+}

From 36d3f00e142696f708ab297b9f8f1c825594712d Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 7 Aug 2025 05:31:48 +0200
Subject: [PATCH 010/287] requirements : fix PyTorch uint64 compatibility
 (#15134)

This commit addresses an issue with the convert_hf_to_gguf script
which is currently failing with:
```console
AttributeError: module 'torch' has no attribute 'uint64'
```

This occurred because safetensors expects torch.uint64 to be available
in the public API, but PyTorch 2.2.x only provides limited support for
unsigned types beyond uint8 it seems. The torch.uint64 dtype exists but
is not exposed in the standard torch namespace
(see pytorch/pytorch#58734).

PyTorch 2.4.0 properly exposes torch.uint64 in the public API, resolving
the compatibility issue with safetensors. This also required torchvision
to updated to =0.19.0 for compatibility.

Refs: https://huggingface.co/spaces/ggml-org/gguf-my-repo/discussions/186#68938de803e47d990aa087fb
Refs: https://github.com/pytorch/pytorch/issues/58734
---
 requirements/requirements-convert_hf_to_gguf.txt | 2 +-
 tools/mtmd/requirements.txt                      | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/requirements/requirements-convert_hf_to_gguf.txt b/requirements/requirements-convert_hf_to_gguf.txt
index fd21ec4795..766745f42f 100644
--- a/requirements/requirements-convert_hf_to_gguf.txt
+++ b/requirements/requirements-convert_hf_to_gguf.txt
@@ -2,7 +2,7 @@ mistral-common>=1.8.3
 
 -r ./requirements-convert_legacy_llama.txt
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch~=2.2.1; platform_machine != "s390x"
+torch~=2.4.0; platform_machine != "s390x"
 
 # torch s390x packages can only be found from nightly builds
 --extra-index-url https://download.pytorch.org/whl/nightly
diff --git a/tools/mtmd/requirements.txt b/tools/mtmd/requirements.txt
index ad069f7745..a9d788f265 100644
--- a/tools/mtmd/requirements.txt
+++ b/tools/mtmd/requirements.txt
@@ -1,5 +1,5 @@
 -r ../../requirements/requirements-convert_legacy_llama.txt
 --extra-index-url https://download.pytorch.org/whl/cpu
 pillow~=11.3.0
-torch~=2.2.1
-torchvision~=0.17.1
+torch~=2.4.0
+torchvision~=0.19.1

From 20638e4f16fcc21f836c7556e83bbf532bb5a0f0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 7 Aug 2025 08:50:30 +0200
Subject: [PATCH 011/287] scripts: fix crash when --tool is not set (#15133)

---
 scripts/compare-llama-bench.py | 45 +++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 22 deletions(-)

diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index c974d83b57..8366f89a08 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -315,28 +315,29 @@ class LlamaBenchData:
 
 
 class LlamaBenchDataSQLite3(LlamaBenchData):
-    connection: sqlite3.Connection
+    connection: Optional[sqlite3.Connection] = None
     cursor: sqlite3.Cursor
     table_name: str
 
     def __init__(self, tool: str = "llama-bench"):
         super().__init__(tool)
-        self.connection = sqlite3.connect(":memory:")
-        self.cursor = self.connection.cursor()
+        if self.connection is None:
+            self.connection = sqlite3.connect(":memory:")
+            self.cursor = self.connection.cursor()
 
-        # Set table name and schema based on tool
-        if self.tool == "llama-bench":
-            self.table_name = "llama_bench"
-            db_fields = LLAMA_BENCH_DB_FIELDS
-            db_types = LLAMA_BENCH_DB_TYPES
-        elif self.tool == "test-backend-ops":
-            self.table_name = "test_backend_ops"
-            db_fields = TEST_BACKEND_OPS_DB_FIELDS
-            db_types = TEST_BACKEND_OPS_DB_TYPES
-        else:
-            assert False
+            # Set table name and schema based on tool
+            if self.tool == "llama-bench":
+                self.table_name = "llama_bench"
+                db_fields = LLAMA_BENCH_DB_FIELDS
+                db_types = LLAMA_BENCH_DB_TYPES
+            elif self.tool == "test-backend-ops":
+                self.table_name = "test_backend_ops"
+                db_fields = TEST_BACKEND_OPS_DB_FIELDS
+                db_types = TEST_BACKEND_OPS_DB_TYPES
+            else:
+                assert False
 
-        self.cursor.execute(f"CREATE TABLE {self.table_name}({', '.join(' '.join(x) for x in zip(db_fields, db_types))});")
+            self.cursor.execute(f"CREATE TABLE {self.table_name}({', '.join(' '.join(x) for x in zip(db_fields, db_types))});")
 
     def _builds_init(self):
         if self.connection:
@@ -397,9 +398,6 @@ class LlamaBenchDataSQLite3(LlamaBenchData):
 
 class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
     def __init__(self, data_file: str, tool: Any):
-        super().__init__(tool)
-
-        self.connection.close()
         self.connection = sqlite3.connect(data_file)
         self.cursor = self.connection.cursor()
 
@@ -411,27 +409,28 @@ class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
         if tool is None:
             if "llama_bench" in table_names:
                 self.table_name = "llama_bench"
-                self.tool = "llama-bench"
+                tool = "llama-bench"
             elif "test_backend_ops" in table_names:
                 self.table_name = "test_backend_ops"
-                self.tool = "test-backend-ops"
+                tool = "test-backend-ops"
             else:
                 raise RuntimeError(f"No suitable table found in database. Available tables: {table_names}")
         elif tool == "llama-bench":
             if "llama_bench" in table_names:
                 self.table_name = "llama_bench"
-                self.tool = "llama-bench"
+                tool = "llama-bench"
             else:
                 raise RuntimeError(f"Table 'test' not found for tool 'llama-bench'. Available tables: {table_names}")
         elif tool == "test-backend-ops":
             if "test_backend_ops" in table_names:
                 self.table_name = "test_backend_ops"
-                self.tool = "test-backend-ops"
+                tool = "test-backend-ops"
             else:
                 raise RuntimeError(f"Table 'test_backend_ops' not found for tool 'test-backend-ops'. Available tables: {table_names}")
         else:
             raise RuntimeError(f"Unknown tool: {tool}")
 
+        super().__init__(tool)
         self._builds_init()
 
     @staticmethod
@@ -653,6 +652,8 @@ if not bench_data:
 if not bench_data.builds:
     raise RuntimeError(f"{input_file} does not contain any builds.")
 
+tool = bench_data.tool  # May have chosen a default if tool was None.
+
 
 hexsha8_baseline = name_baseline = None
 

From 1d72c841888b9450916bdd5a9b3274da380f5b36 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 7 Aug 2025 10:53:21 +0200
Subject: [PATCH 012/287] CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16 (#15131)

* CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16
---
 ggml/src/ggml-cuda/common.cuh            |  12 +-
 ggml/src/ggml-cuda/fattn-mma-f16.cuh     |  12 +-
 ggml/src/ggml-cuda/fattn.cu              |   4 +-
 ggml/src/ggml-cuda/ggml-cuda.cu          |  31 +-
 ggml/src/ggml-cuda/mma.cuh               | 110 ++++--
 ggml/src/ggml-cuda/mmf.cu                | 431 +++++++++++++++++++++++
 ggml/src/ggml-cuda/mmf.cuh               |   5 +
 ggml/src/ggml-cuda/mmq.cu                |   2 +-
 ggml/src/ggml-cuda/mmq.cuh               | 264 +++++++-------
 ggml/src/ggml-cuda/{mmv.cu => mmvf.cu}   |  94 ++---
 ggml/src/ggml-cuda/{mmv.cuh => mmvf.cuh} |   6 +-
 ggml/src/ggml-cuda/vendors/hip.h         |   1 +
 ggml/src/ggml-cuda/vendors/musa.h        |   3 +-
 13 files changed, 750 insertions(+), 225 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/mmf.cu
 create mode 100644 ggml/src/ggml-cuda/mmf.cuh
 rename ggml/src/ggml-cuda/{mmv.cu => mmvf.cu} (86%)
 rename ggml/src/ggml-cuda/{mmv.cuh => mmvf.cuh} (55%)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 8f27255476..2e5d48797f 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -233,9 +233,13 @@ typedef float2 dfloat2;
 #endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
 
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define NEW_MMA_AVAILABLE
+#define TURING_MMA_AVAILABLE
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#define AMPERE_MMA_AVAILABLE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #define CP_ASYNC_AVAILABLE
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
@@ -303,10 +307,14 @@ static bool amd_mfma_available(const int cc) {
 }
 
 // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
-static bool new_mma_available(const int cc) {
+static bool turing_mma_available(const int cc) {
     return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
 
+static bool ampere_mma_available(const int cc) {
+    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
+}
+
 static bool cp_async_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index e7570f9d3b..3712538441 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -418,7 +418,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         float        * const __restrict__ KQ_max,
         float        * const __restrict__ KQ_rowsum,
         const int kb0) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
     typedef fattn_mma_f16_config<DKQ, DV> c;
 
 #ifdef CP_ASYNC_AVAILABLE
@@ -776,7 +776,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
     GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
     NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup>
@@ -800,7 +800,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jt,
         const int kb0_start,
         const int kb0_stop) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     typedef fattn_mma_f16_config<DKQ, DV> c;
@@ -1196,7 +1196,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     GGML_UNUSED(stride_Q2); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
     GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
     NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla>
@@ -1223,7 +1223,7 @@ static __global__ void flash_attn_ext_f16(
                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
                             const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-#if defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
+#if defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1354,7 +1354,7 @@ static __global__ void flash_attn_ext_f16(
     GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
     GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
+#endif // defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index 656e04a473..8ddd0415b7 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -327,7 +327,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
     const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
     const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
-    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
+    const bool mma_faster_for_bs1 = turing_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
         (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
     const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
     if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
@@ -340,7 +340,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     }
 
     // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
-    if (fp16_mma_available(cc) && !new_mma_available(cc)) {
+    if (fp16_mma_available(cc) && !turing_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
         return;
     }
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 60e481b95a..ec7ab25518 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -22,8 +22,9 @@
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
 #include "ggml-cuda/im2col.cuh"
+#include "ggml-cuda/mmf.cuh"
 #include "ggml-cuda/mmq.cuh"
-#include "ggml-cuda/mmv.cuh"
+#include "ggml-cuda/mmvf.cuh"
 #include "ggml-cuda/mmvq.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
@@ -2008,7 +2009,9 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     const bool bad_padding_clear = ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE
         && ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src;
 
-    bool use_mul_mat_vec   = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
+    bool use_mul_mat_vec_f = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+    bool use_mul_mat_f     = !ggml_is_quantized(src0->type)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
     bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
@@ -2028,14 +2031,18 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
             }
 
             const int cc            = ggml_cuda_info().devices[id].cc;
+            const int warp_size     = ggml_cuda_info().devices[id].warp_size;
             use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-            use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
+            use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
+            use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
             any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
         }
     } else {
         const int cc            = ggml_cuda_info().devices[ctx.device].cc;
+        const int warp_size     = ggml_cuda_info().devices[ctx.device].warp_size;
         use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-        use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
+        use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
+        use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
         any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
     }
 
@@ -2048,15 +2055,17 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
     //TODO update for generic tensor parallelism
-    const int cc                     = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const int cc                 = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     bool use_batched_cublas_f16  = src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16);
     bool use_batched_cublas_bf16 = src0->type == GGML_TYPE_BF16 && bf16_mma_hardware_available(cc);
     bool use_batched_cublas_f32  = src0->type == GGML_TYPE_F32;
 
-    if (!split && use_mul_mat_vec) {
+    if (!split && use_mul_mat_vec_f) {
         // the custom F16 vector kernel can be used over batched cuBLAS GEMM
         // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
-        ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst);
+        ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst);
+    } else if (!split && use_mul_mat_f) {
+        ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_vec_q) {
         ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_q) {
@@ -2065,8 +2074,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
         && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // general KQ + KQV multi-batch without FlashAttention
         ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
-    } else if (use_mul_mat_vec) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec, nullptr);
+    } else if (use_mul_mat_vec_f) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_f, nullptr);
     } else if (use_mul_mat_vec_q) {
         ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda);
     } else if (use_mul_mat_q) {
@@ -2094,7 +2103,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
             if (ggml_is_quantized(src0->type)) {
                 ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
             } else {
-                ggml_cuda_mul_mat_vec(ctx, src0, src1, ids, dst);
+                ggml_cuda_mul_mat_vec_f(ctx, src0, src1, ids, dst);
             }
             return;
         }
@@ -3516,7 +3525,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
 #endif // FLASH_ATTN_AVAILABLE
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
-                if (!new_mma_available(cc)) {
+                if (!turing_mma_available(cc)) {
                     return false;
                 }
                 const int gqa_ratio = op->src[0]->ne[2] / op->src[1]->ne[2];
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index a86365c6a0..83ee16b27d 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -23,13 +23,13 @@
 static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
     int ret = 0;
 
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
     asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
         : "=r"(ret) : "r"(x));
 #else
     GGML_UNUSED(x);
     NO_DEVICE_CODE;
-#endif // defined(NEW_MMA_AVAILABLE)
+#endif // defined(TURING_MMA_AVAILABLE)
     return ret;
 }
 
@@ -167,6 +167,38 @@ namespace ggml_cuda_mma {
         }
     };
 
+    template <int I_, int J_>
+    struct tile<I_, J_, nv_bfloat162> {
+        static constexpr int I  = I_;
+        static constexpr int J  = J_;
+        static constexpr int ne = I * J / WARP_SIZE;
+        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
+
+        static __device__ __forceinline__ int get_i(const int l) {
+            if constexpr (I == 8 && J == 8) {
+                return threadIdx.x / 4;
+            } else if constexpr (I == 16 && J == 4) {
+                return l * 8 + threadIdx.x / 4;
+            } else if constexpr (I == 16 && J == 8) {
+                return (l % 2) * 8 + threadIdx.x / 4;
+            } else {
+                static_assert(I == -1 && J == -1, "template specialization not implemented");
+            }
+        }
+
+        static __device__ __forceinline__ int get_j(const int l) {
+            if constexpr (I == 8 && J == 8) {
+                return l * 4 + threadIdx.x % 4;
+            } else if constexpr (I == 16 && J == 4) {
+                return threadIdx.x % 4;
+            } else if constexpr (I == 16 && J == 8) {
+                return (l / 2) * 4 + threadIdx.x % 4;
+            } else {
+                static_assert(I == -1 && J == -1, "template specialization not implemented");
+            }
+        }
+    };
+
     template <int I, int J>
     static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
         tile<I, J/2, half2> ret;
@@ -209,7 +241,7 @@ namespace ggml_cuda_mma {
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<8, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + ((threadIdx.x / t.I) * (t.J / 2)) % t.J;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -217,13 +249,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 4, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -232,13 +264,13 @@ namespace ggml_cuda_mma {
 #else
         load_generic(xs0, stride);
         GGML_UNUSED(t);
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#if defined(NEW_MMA_AVAILABLE)
+#if defined(TURING_MMA_AVAILABLE)
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
@@ -246,13 +278,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix_trans(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
@@ -263,12 +295,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(xs0);
         GGML_UNUSED(stride);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 4, int> & A, const tile<8, 4, int> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -287,12 +319,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 8, int> & A, const tile<8, 8, int> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -317,12 +349,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 4, half2> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -344,12 +376,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, half2> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -380,12 +412,29 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
+    }
+
+    static __device__ __forceinline__ void mma(
+            tile<16, 8, float> & D, const tile<16, 8, float> & A, const tile<8, 8, float> & B) {
+#ifdef AMPERE_MMA_AVAILABLE
+        const int * Axi = (const int *) A.x;
+        const int * Bxi = (const int *) B.x;
+        int       * Dxi = (int       *) D.x;
+        asm("mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+        GGML_UNUSED(D);
+        GGML_UNUSED(A);
+        GGML_UNUSED(B);
+        NO_DEVICE_CODE;
+#endif // AMPERE_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, float> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -407,12 +456,29 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
+    }
+
+    static __device__ __forceinline__ void mma(
+            tile<16, 8, float> & D, const tile<16, 8, nv_bfloat162> & A, const tile<8, 8, nv_bfloat162> & B) {
+#ifdef AMPERE_MMA_AVAILABLE
+        const int * Axi = (const int *) A.x;
+        const int * Bxi = (const int *) B.x;
+        int       * Dxi = (int       *) D.x;
+        asm("mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+        GGML_UNUSED(D);
+        GGML_UNUSED(A);
+        GGML_UNUSED(B);
+        NO_DEVICE_CODE;
+#endif // AMPERE_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 16, float> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef NEW_MMA_AVAILABLE
+#ifdef TURING_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -443,7 +509,7 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // NEW_MMA_AVAILABLE
+#endif // TURING_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
new file mode 100644
index 0000000000..1437367e87
--- /dev/null
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -0,0 +1,431 @@
+#include "ggml.h"
+#include "common.cuh"
+#include "mma.cuh"
+#include "mmf.cuh"
+
+using namespace ggml_cuda_mma;
+
+#define MMF_ROWS_PER_BLOCK 32
+
+template <typename T, int rows_per_block, int cols_per_block, int nwarps>
+__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)
+static __global__ void mul_mat_f(
+        const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
+        const int ncols, const int nchannels_y, const int stride_row, const int stride_col_y, const int stride_col_dst,
+        const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
+        const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    typedef tile<16, 8, T>     tile_A;
+    typedef tile< 8, 8, T>     tile_B;
+    typedef tile<16, 8, float> tile_C;
+
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+    constexpr int tile_k_padded = warp_size + 4;
+    constexpr int ntA = rows_per_block / tile_A::I;
+    constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I;
+
+    const int row0        = blockIdx.x * rows_per_block;
+    const int channel_dst = blockIdx.y;
+    const int channel_x   = channel_dst / channel_ratio;
+    const int channel_y   = channel_dst;
+    const int sample_dst  = blockIdx.z;
+    const int sample_x    = sample_dst / sample_ratio;
+    const int sample_y    = sample_dst;
+
+    x   += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row0*stride_row ;
+    y   += int64_t(sample_y)  *stride_sample_y   + channel_y  *stride_channel_y;
+    dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst;
+
+    const float2 * y2 = (const float2 *) y;
+
+    extern __shared__ char data_mmv[];
+
+    tile_C C[ntA][ntB];
+
+    T * tile_xy = (T *) data_mmv + threadIdx.y*(tile_A::I * tile_k_padded);
+
+    for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) {
+        tile_A A[ntA][warp_size / tile_A::J];
+#pragma unroll
+        for (int itA = 0; itA < ntA; ++itA) {
+#pragma unroll
+            for (int i = 0; i < tile_A::I; ++i) {
+                tile_xy[i*tile_k_padded + threadIdx.x] = x[(itA*tile_A::I + i)*stride_row  + col];
+            }
+#pragma unroll
+            for (int k0 = 0; k0 < warp_size; k0 += tile_A::J) {
+                load_ldmatrix(A[itA][k0/tile_A::J], tile_xy + k0, tile_k_padded);
+            }
+        }
+
+#pragma unroll
+        for (int itB = 0; itB < ntB; ++itB) {
+            if constexpr (std::is_same_v<T, float>) {
+#pragma unroll
+                for (int j0 = 0; j0 < tile_B::I; ++j0) {
+                    const int j = j0 + itB*tile_B::I;
+
+                    tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[j*stride_col_y + col] : 0.0f;
+                }
+            } else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
+#pragma unroll
+                for (int j0 = 0; j0 < tile_B::I; ++j0) {
+                    const int j = j0 + itB*tile_B::I;
+
+                    const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f);
+                    tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
+                }
+            } else {
+                static_assert(std::is_same_v<T, void>, "unsupported type");
+            }
+#pragma unroll
+            for (int k0 = 0; k0 < warp_size; k0 += tile_B::J) {
+                tile_B B;
+                load_ldmatrix(B, tile_xy + k0, tile_k_padded);
+#pragma unroll
+                for (int itA = 0; itA < ntA; ++itA) {
+                    mma(C[itA][itB], A[itA][k0/tile_B::J], B);
+                }
+            }
+        }
+    }
+
+    float * buf_iw = (float *) data_mmv;
+    constexpr int kiw = nwarps*rows_per_block + 4;
+
+    if (nwarps > 1) {
+        __syncthreads();
+    }
+#pragma unroll
+    for (int itB = 0; itB < ntB; ++itB) {
+#pragma unroll
+        for (int itA = 0; itA < ntA; ++itA) {
+#pragma unroll
+            for (int l = 0; l < tile_C::ne; ++l) {
+                const int i = threadIdx.y*rows_per_block + itA*tile_C::I + tile_C::get_i(l);
+                const int j = itB*tile_C::J + tile_C::get_j(l);
+                buf_iw[j*kiw + i] = C[itA][itB].x[l];
+            }
+        }
+    }
+
+    if (nwarps > 1) {
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+
+        if (j0 + nwarps > cols_per_block && j >= cols_per_block) {
+            return;
+        }
+
+        float sum = 0.0f;
+        static_assert(rows_per_block == warp_size, "need loop/check");
+#pragma unroll
+        for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) {
+            const int i = i0 + threadIdx.x;
+
+            sum += buf_iw[j*kiw + i];
+        }
+        dst[j*stride_col_dst + row0 + threadIdx.x] = sum;
+    }
+#else
+    NO_DEVICE_CODE;
+    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(ids); GGML_UNUSED(dst);
+    GGML_UNUSED(ncols); GGML_UNUSED(nchannels_y); GGML_UNUSED(stride_row); GGML_UNUSED(stride_col_y); GGML_UNUSED(stride_col_dst);
+    GGML_UNUSED(channel_ratio); GGML_UNUSED(stride_channel_x); GGML_UNUSED(stride_channel_y); GGML_UNUSED(stride_channel_dst);
+    GGML_UNUSED(sample_ratio); GGML_UNUSED(stride_sample_x); GGML_UNUSED(stride_sample_y); GGML_UNUSED(stride_sample_dst);
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+}
+
+template <typename T, int cols_per_block>
+static void mul_mat_f_cuda(
+        const T * x, const float * y, const int32_t * ids, float * dst,
+        const int64_t ncols_x, const int64_t nrows_x,
+        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
+        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
+        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
+        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
+        cudaStream_t stream) {
+    typedef tile<16, 8, T>     tile_A;
+    typedef tile< 8, 8, T>     tile_B;
+    typedef tile<16, 8, float> tile_C;
+
+    GGML_ASSERT(!ids && "mul_mat_id not implemented");
+
+    GGML_ASSERT(ncols_x      % 2 == 0);
+    GGML_ASSERT(stride_row   % 2 == 0);
+    GGML_ASSERT(stride_col_y % 2 == 0);
+    GGML_ASSERT(ids || nchannels_dst % nchannels_x == 0);
+    GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
+    const int64_t channel_ratio = nchannels_dst / nchannels_x;
+    const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
+
+    const int device = ggml_cuda_get_device();
+    const int warp_size = ggml_cuda_info().devices[device].warp_size;
+
+    int64_t nwarps_best     = 1;
+    int64_t niter_best      = (ncols_x + warp_size*2 - 1) / (warp_size*2);
+    int64_t max_block_size  = 256;
+    for (int64_t nwarps = 2; nwarps <= max_block_size/warp_size; nwarps++) {
+        const int64_t niter = (ncols_x + nwarps*warp_size*2 - 1) / (nwarps*warp_size*2);
+        if (niter < niter_best) {
+            niter_best  = niter;
+            nwarps_best = nwarps;
+        }
+    }
+
+    constexpr int rows_per_block = MMF_ROWS_PER_BLOCK;
+    const int nbytes_shared_iter = nwarps_best * tile_A::I * (warp_size + 4) * 4;
+    const int nbytes_shared_combine = GGML_PAD(cols_per_block, tile_B::I) * (nwarps_best*rows_per_block + 4) * 4;
+    const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine);
+    const dim3 block_nums(nrows_x/rows_per_block, nchannels_dst, nsamples_dst);
+    const dim3 block_dims(warp_size, nwarps_best, 1);
+    switch (nwarps_best) {
+        case 1: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 1><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 2: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 2><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 3: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 3><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 4: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 4><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 5: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 5><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 6: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 6><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 7: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 7><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        case 8: {
+            mul_mat_f<T, rows_per_block, cols_per_block, 8><<<block_nums, block_dims, nbytes_shared, stream>>>
+                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+        } break;
+        default: {
+            GGML_ABORT("fatal error");
+        } break;
+    }
+}
+
+template <typename T>
+static void mul_mat_f_switch_cols_per_block(
+        const T * x, const float * y, const int32_t * ids, float * dst,
+        const int64_t ncols_x, const int64_t nrows_x, const int64_t ncols_dst,
+        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
+        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
+        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
+        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
+        cudaStream_t stream) {
+    switch (ncols_dst) {
+        case  1: {
+            mul_mat_f_cuda<T,  1>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  2: {
+            mul_mat_f_cuda<T,  2>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  3: {
+            mul_mat_f_cuda<T,  3>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  4: {
+            mul_mat_f_cuda<T,  4>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  5: {
+            mul_mat_f_cuda<T,  5>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  6: {
+            mul_mat_f_cuda<T,  6>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  7: {
+            mul_mat_f_cuda<T,  7>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  8: {
+            mul_mat_f_cuda<T,  8>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case  9: {
+            mul_mat_f_cuda<T,  9>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 10: {
+            mul_mat_f_cuda<T, 10>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 11: {
+            mul_mat_f_cuda<T, 11>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 12: {
+            mul_mat_f_cuda<T, 12>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 13: {
+            mul_mat_f_cuda<T, 13>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 14: {
+            mul_mat_f_cuda<T, 14>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 15: {
+            mul_mat_f_cuda<T, 15>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        case 16: {
+            mul_mat_f_cuda<T, 16>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
+                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
+        } break;
+        default: {
+            GGML_ABORT("fatal error");
+        } break;
+    }
+}
+
+void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+    GGML_ASSERT(        src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
+    GGML_ASSERT(         dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const size_t ts_src0 = ggml_type_size(src0->type);
+    const size_t ts_src1 = ggml_type_size(src1->type);
+    const size_t ts_dst  = ggml_type_size(dst->type);
+
+    GGML_ASSERT(ne13 == ne3);
+
+    GGML_ASSERT(        nb00       == ts_src0);
+    GGML_ASSERT(        nb10       == ts_src1);
+    GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
+    GGML_ASSERT(        nb0        == ts_dst);
+
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
+
+    const float   * src1_d =       (const float   *) src1->data;
+    const int32_t *  ids_d = ids ? (const int32_t *)  ids->data : nullptr;
+    float         *  dst_d =       (float         *)  dst->data;
+
+    const int64_t s01 = src0->nb[1] / ts_src0;
+    const int64_t s11 = src1->nb[1] / ts_src1;
+    const int64_t s1  =  dst->nb[1] / ts_dst;
+    const int64_t s02 = src0->nb[2] / ts_src0;
+    const int64_t s12 = src1->nb[2] / ts_src1;
+    const int64_t s2  =  dst->nb[2] / ts_dst;
+    const int64_t s03 = src0->nb[3] / ts_src0;
+    const int64_t s13 = src1->nb[3] / ts_src1;
+    const int64_t s3  =  dst->nb[3] / ts_dst;
+
+    // For MUL_MAT_ID the memory layout is different than for MUL_MAT:
+    const int64_t ncols_dst          = ids ? ne2  : ne1;
+    const int64_t nchannels_y        = ids ? ne11 : ne12;
+    const int64_t nchannels_dst      = ids ? ne1  : ne2;
+    const int64_t stride_channel_dst = ids ? s1   : s2;
+    const int64_t stride_channel_y   = ids ? s11  : s12;
+
+    GGML_ASSERT(!ids || ncols_dst == 1);
+
+    switch (src0->type) {
+        case GGML_TYPE_F32: {
+            const float * src0_d = (const float *) src0->data;
+            constexpr int vals_per_T = 1;
+            mul_mat_f_switch_cols_per_block(
+                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
+                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
+                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
+        } break;
+        case GGML_TYPE_F16: {
+            const half2 * src0_d = (const half2 *) src0->data;
+            constexpr int vals_per_T = 2;
+            mul_mat_f_switch_cols_per_block(
+                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
+                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
+                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
+        } break;
+        case GGML_TYPE_BF16: {
+            const nv_bfloat162 * src0_d = (const nv_bfloat162 *) src0->data;
+            constexpr int vals_per_T = 2;
+            mul_mat_f_switch_cols_per_block(
+                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
+                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
+                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
+        } break;
+        default:
+            GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
+    }
+}
+
+bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, int64_t ne11) {
+    if (src0_ne[0] % (warp_size * (4/ggml_type_size(type))) != 0) {
+        return false;
+    }
+    if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) {
+        return false;
+    }
+    if (ne11 > 16) {
+        return false;
+    }
+    switch (type) {
+        case GGML_TYPE_F32:
+            return ampere_mma_available(cc);
+        case GGML_TYPE_F16:
+            return turing_mma_available(cc);
+        case GGML_TYPE_BF16:
+            return ampere_mma_available(cc);
+        default:
+            return false;
+    }
+}
diff --git a/ggml/src/ggml-cuda/mmf.cuh b/ggml/src/ggml-cuda/mmf.cuh
new file mode 100644
index 0000000000..785f9f211c
--- /dev/null
+++ b/ggml/src/ggml-cuda/mmf.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+
+bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, int64_t ne11);
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 8954a38310..384ee7615f 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -310,7 +310,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return false;
     }
 
-    if (new_mma_available(cc)) {
+    if (turing_mma_available(cc)) {
         return true;
     }
 
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 1634725c20..96129bd831 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -92,7 +92,7 @@ struct tile_x_sizes {
 };
 
 static int get_mmq_x_max_host(const int cc) {
-    return (amd_mfma_available(cc) || new_mma_available(cc)) ? 128 :
+    return (amd_mfma_available(cc) || turing_mma_available(cc)) ? 128 :
         GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA ?
 #ifdef GGML_CUDA_FORCE_MMQ
             128                     : 64;
@@ -102,9 +102,9 @@ static int get_mmq_x_max_host(const int cc) {
 }
 
 static constexpr __device__ int get_mmq_x_max_device() {
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     return 128;
-#else // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#else // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
 #if defined(GGML_USE_HIP)
     return 64;
@@ -121,7 +121,7 @@ static constexpr __device__ int get_mmq_x_max_device() {
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
 
 #endif // defined(GGML_USE_HIP)
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 }
 
 static int get_mmq_y_host(const int cc) {
@@ -233,7 +233,7 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
 static int mmq_get_granularity_host(const int mmq_x, const int cc) {
     if (amd_mfma_available(cc)) {
         return mmq_x >= 128 ? 32 : 16;
-    } else if (new_mma_available(cc) && mmq_x >= 48) {
+    } else if (turing_mma_available(cc) && mmq_x >= 48) {
         return 16;
     } else {
         return 8;
@@ -244,7 +244,7 @@ static int mmq_get_granularity_host(const int mmq_x, const int cc) {
 static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
     return mmq_x >= 128 ? 32 : 16;
 }
-#elif defined(NEW_MMA_AVAILABLE)
+#elif defined(TURING_MMA_AVAILABLE)
 static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
     return mmq_x >= 48 ? 16 : 8;
 }
@@ -279,14 +279,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_0);
     constexpr int nrows = warp_size / threads_per_row;
@@ -305,12 +305,12 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
         const int qs0 = get_int_b2(bxi->qs, kqsx);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0]     = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808);
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808);
 #else
         x_qs[i*(MMQ_TILE_NE_K + 1) + txi] = qs0;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI4_0;
@@ -327,11 +327,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0           + kbxd] = bxi->d;
 #else
         x_df[i*(MMQ_TILE_NE_K/QI4_0) + i/QI4_0 + kbxd] = bxi->d;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -382,14 +382,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_1);
     constexpr int nrows = warp_size / threads_per_row;
@@ -408,12 +408,12 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
         const int qs0 = get_int_b4(bxi->qs, kqsx);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0]     = (qs0 >> 0) & 0x0F0F0F0F;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
         x_qs[i*(MMQ_TILE_NE_K + 1) + txi] = qs0;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI4_1;
@@ -430,11 +430,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_dm[i*MMQ_MMA_TILE_X_K_Q8_1           + kbxd] = bxi->dm;
 #else
         x_dm[i*(MMQ_TILE_NE_K/QI4_1) + i/QI4_1 + kbxd] = bxi->dm;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -485,14 +485,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR5_0);
     constexpr int nrows = warp_size / threads_per_row;
@@ -527,13 +527,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
         qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI5_0;
@@ -550,11 +550,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0           + kbxd] = bxi->d;
 #else
         x_df[i*(MMQ_TILE_NE_K/QI5_0) + i/QI5_0 + kbxd] = bxi->d;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -563,14 +563,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR5_1);
     constexpr int nrows = warp_size / threads_per_row;
@@ -603,13 +603,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
         qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI5_1;
@@ -626,11 +626,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_dm[i*MMQ_MMA_TILE_X_K_Q8_1           + kbxd] = bxi->dm;
 #else
         x_dm[i*(MMQ_TILE_NE_K/QI5_1) + i/QI5_1 + kbxd] = bxi->dm;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -639,14 +639,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_tile + 2*MMQ_TILE_NE_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     // MMQ_ITER_K / (4 * QR8_0) == 64 required. but NV has only 32 threads per warp
     constexpr int threads_per_row = 32;
@@ -665,13 +665,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0             + txi] = get_int_b2(bxi[0].qs,                   kqsx);
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + MMQ_TILE_NE_K + txi] = get_int_b2(bxi[MMQ_TILE_NE_K/QI8_0].qs, kqsx);
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + 0             + txi] = get_int_b2(bxi[0].qs,                   kqsx);
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + MMQ_TILE_NE_K + txi] = get_int_b2(bxi[MMQ_TILE_NE_K/QI8_0].qs, kqsx);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = 2*MMQ_TILE_NE_K / QI8_0;
@@ -688,11 +688,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0                 + kbxd] = bxi->d;
 #else
         x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -701,14 +701,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_MXFP4, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR_MXFP4);
     constexpr int nrows = warp_size / threads_per_row;
@@ -730,13 +730,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
         const int k0 = kbx * (2 * QI_MXFP4) + kqsx;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + 0]        = v.x;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + QI_MXFP4] = v.y;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0]        = v.x;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + QI_MXFP4] = v.y;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI_MXFP4;
@@ -753,11 +753,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_mxfp4 * bxi = (const block_mxfp4 *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_1                 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f;
 #else
         x_df[i*(MMQ_TILE_NE_K/QI_MXFP4) + i/QI_MXFP4 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -1178,7 +1178,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
             }
         }
     }
-#elif defined(NEW_MMA_AVAILABLE)
+#elif defined(TURING_MMA_AVAILABLE)
 
     typedef tile<16, 4, int> tile_A;
     typedef tile<16, 8, int> tile_A_8;
@@ -1264,14 +1264,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
     constexpr int nwarps = mmq_get_nwarps_device();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR2_K);
     constexpr int nrows = ggml_cuda_get_physical_warp_size() / threads_per_row;
@@ -1295,11 +1295,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
             const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + k] = x_qs_k;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int sc_m = bxi->scales[kqsx];
@@ -1310,11 +1310,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4));
 #endif // FAST_FP16_AVAILABLE
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik;
 #else
         x_dm[i*(MMQ_TILE_NE_K + 1)   + kqsx] = x_dm_ik;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -1452,7 +1452,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
             }
         }
     }
-#elif defined(NEW_MMA_AVAILABLE)
+#elif defined(TURING_MMA_AVAILABLE)
 
     typedef tile<16, 4, int> tile_A;
     typedef tile<16, 8, int> tile_A_8;
@@ -1582,7 +1582,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
@@ -1590,7 +1590,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_df + txs.dm);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR3_K);
     constexpr int nrows = warp_size / threads_per_row;
@@ -1618,11 +1618,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
             const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + k] = x_qs_k;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
     }
 
@@ -1649,7 +1649,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         const int8_t * sc8 = (const int8_t *) &sc;
         const float d = bxi->d;
 
@@ -1659,10 +1659,10 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         }
 #else
         x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = sc;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
-#if !(defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE))
+#if !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*warp_size) {
         int i = (i0 + threadIdx.y*warp_size + threadIdx.x) % mmq_y;
@@ -1675,7 +1675,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         x_df[i] = bxi->d;
     }
-#endif // !(defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE))
+#endif // !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE))
 }
 
 template <int mmq_x, int mmq_y>
@@ -1728,7 +1728,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K);
 #else
@@ -1736,7 +1736,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_dm + txs.dm);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_K);
     constexpr int nrows = warp_size / threads_per_row;
@@ -1753,15 +1753,15 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
         const int qs0 = get_int_b4(bxi->qs, txi);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(txi/8) + txi % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(txi/8) + txi % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
         x_qs[i*(MMQ_TILE_NE_K + 1) + txi] = qs0;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     constexpr int rows_per_warp = warp_size / 2;
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*rows_per_warp) {
@@ -1829,7 +1829,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = scales8;
     }
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 }
 
 template <int mmq_x, int mmq_y>
@@ -1872,7 +1872,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + MMQ_TILE_NE_K*2);
 #else
@@ -1880,7 +1880,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_dm + txs.dm);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR5_K);
     constexpr int nrows = warp_size / threads_per_row;
@@ -1908,16 +1908,16 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int kq0 = ky - ky % (QI5_K/2) + txi % (QI5_K/4) + 0;
         const int kq1 = ky - ky % (QI5_K/2) + txi % (QI5_K/4) + QI5_K/4;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq0] = ql0 | qh0;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq1] = ql1 | qh1;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     constexpr int rows_per_warp = warp_size / 2;
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*rows_per_warp) {
@@ -1986,7 +1986,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = scales8;
     }
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 }
 
 template <int mmq_x, int mmq_y>
@@ -2029,7 +2029,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
     int   * x_sc = (int   *) (x_df + MMQ_TILE_NE_K/QI6_K);
@@ -2038,7 +2038,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
     int   * x_sc = (int   *) (x_df + txs.dm);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR6_K);
     constexpr int nrows = warp_size / threads_per_row;
@@ -2065,13 +2065,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int kq0 = 2*txi - txi % (QI6_K/2) + 0;
         const int kq1 = 2*txi - txi % (QI6_K/2) + QI6_K/2;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
         x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
 #pragma unroll
@@ -2084,11 +2084,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q6_K]           = bxi->d;
 #else
         x_df[i*(MMQ_TILE_NE_K/QI6_K) + i/QI6_K] = bxi->d;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int rows_per_warp = warp_size / 4;
@@ -2102,11 +2102,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (MMQ_TILE_NE_K/8)) / 4;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x%4] = get_int_b2(bxi->scales, threadIdx.x % (MMQ_TILE_NE_K/8));
 #else
         x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + threadIdx.x%(MMQ_TILE_NE_K/8)] = get_int_b2(bxi->scales, threadIdx.x%(QI6_K/8));
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2199,7 +2199,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
             }
         }
     }
-#elif defined(NEW_MMA_AVAILABLE)
+#elif defined(TURING_MMA_AVAILABLE)
 
     typedef tile<16, 4, int> tile_A;
     typedef tile< 8, 4, int> tile_B;
@@ -2311,14 +2311,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_NL);
     constexpr int nrows = warp_size / threads_per_row;
@@ -2340,13 +2340,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
         const int k0 = kbx * (2 * QI4_NL) + kqsx;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0]      = v.x;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + QI4_NL] = v.y;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0]      = v.x;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + QI4_NL] = v.y;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI4_NL;
@@ -2363,11 +2363,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
 
         const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0             + kbxd] = __half2float(bxi->d);
 #else
         x_df[i*(MMQ_TILE_NE_K/QI4_NL) + i/QI4_NL + kbxd] = __half2float(bxi->d);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2376,14 +2376,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_XXS)) / 2;
     constexpr int nrows = warp_size / threads_per_row;
@@ -2414,22 +2414,22 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
             const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid0;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid1;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int ls = aux32 >> 28;
         const float d = bxi->d;
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0   + kqsx] = (ls*d + d/2)/4;
 #else
         x_df[i*(MMQ_TILE_NE_K/4) + i/4 + kqsx] = (ls*d + d/2)/4;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2438,14 +2438,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_XS)) / 2;
     constexpr int nrows = warp_size / threads_per_row;
@@ -2472,24 +2472,24 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
             const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int ls = bxi->scales[kqsx];
         const float d = bxi->d;
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K                   + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K                   + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
 #else
         x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2498,14 +2498,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_S)) / 2;
     constexpr int nrows = warp_size / threads_per_row;
@@ -2539,24 +2539,24 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
             const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int ls = bxi->scales[kqsx];
         const float d = bxi->d;
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K                   + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*MMQ_MMA_TILE_X_K_Q3_K                   + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
 #else
         x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
         x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2565,14 +2565,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR3_XXS)) / 2;
     constexpr int nrows = warp_size / threads_per_row;
@@ -2601,22 +2601,22 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
             const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int ls = aux32 >> 28;
         const float d = bxi->d;
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0     + kqsx] = (ls*d + d/2)/2;
 #else
         x_df[i*(MMQ_TILE_NE_K/4) + i/4   + kqsx] = (ls*d + d/2)/2;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2625,14 +2625,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR3_S)) / 2;
     constexpr int nrows = warp_size / threads_per_row;
@@ -2668,22 +2668,22 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
             const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+0)] = grid_l;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+1)] = grid_h;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F);
         const float d = bxi->d;
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0     + kqsx] = ls*d;
 #else
         x_df[i*(MMQ_TILE_NE_K/4) + i/4   + kqsx] = ls*d;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2692,14 +2692,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     half2 * x_ds = (half2 *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_ds = (half2 *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR1_S);
     constexpr int nrows = warp_size / threads_per_row;
@@ -2727,23 +2727,23 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
             const int grid0 = (grid >> 0) & 0x0F0F0F0F;
             const int grid1 = (grid >> 4) & 0x0F0F0F0F;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0;
             x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1;
 #else
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+0)] = grid0;
             x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+1)] = grid1;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         }
 
         const float  d1q   = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1);
         const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_ds[i*MMQ_MMA_TILE_X_K_Q8_1     + kqsx] = make_half2(d1q, d1q*delta);
 #else
         x_ds[i*(MMQ_TILE_NE_K/4) + i/4   + kqsx] = make_half2(d1q, d1q*delta);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2752,14 +2752,14 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
     constexpr int nwarps = mmq_get_nwarps_device();
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + txs.qs);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_XS);
     constexpr int nrows = warp_size / threads_per_row;
@@ -2779,13 +2779,13 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
         const int k0 = 8 * (kqsx / 4) + kqsx % 4;
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
         x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
 #else
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0] = v.x;
         x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 4] = v.y;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 
     constexpr int rows_per_warp = warp_size / 8;
@@ -2804,11 +2804,11 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
         const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F)
             | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
         x_df[i*MMQ_MMA_TILE_X_K_Q8_0   + threadIdx.x % 8] = d * (ls - 32);
 #else
         x_df[i*(MMQ_TILE_NE_K/4) + i/4 + threadIdx.x % 8] = d * (ls - 32);
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     }
 }
 
@@ -2859,9 +2859,9 @@ static __device__ __forceinline__ void mmq_write_back_mma(
     constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp.
 
     const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I);
-#if defined(NEW_MMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+#if defined(TURING_MMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
     static_assert(nwarps*tile_C::I == mmq_y, "nwarps*tile_C::I != mmq_y");
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*tile_C::J) {
@@ -3061,13 +3061,13 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
     int * tile_y = data_mul_mat_q + mmq_x;
     int * tile_x = tile_y + GGML_PAD(mmq_x*MMQ_TILE_Y_K, nwarps*warp_size);
 
-#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
     constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, need_check, type>::vec_dot_mma;
     constexpr mmq_write_back_t write_back = mmq_write_back_mma<type, mmq_x, mmq_y, need_check>;
 #else
     constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, need_check, type>::vec_dot_dp4a;
     constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, need_check>;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
     constexpr int blocks_per_iter = MMQ_ITER_K / qk;
 
@@ -3534,7 +3534,7 @@ static size_t mmq_get_nbytes_shared(const int mmq_x, const int mmq_y, const int
     const tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(type, mmq_y);
     const int mmq_tile_x_k = mmq_get_mma_tile_x_k(type);
     const size_t nbs_ids = mmq_x*sizeof(int);
-    const size_t nbs_x = (new_mma_available(cc) || amd_mfma_available(cc)) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
+    const size_t nbs_x = (turing_mma_available(cc) || amd_mfma_available(cc)) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
     const size_t nbs_y = mmq_x*sizeof(block_q8_1_mmq);
     return nbs_ids + nbs_x + GGML_PAD(nbs_y, nwarps*warp_size*sizeof(int));
 }
diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmvf.cu
similarity index 86%
rename from ggml/src/ggml-cuda/mmv.cu
rename to ggml/src/ggml-cuda/mmvf.cu
index e14c93516b..1ad4bc75ba 100644
--- a/ggml/src/ggml-cuda/mmv.cu
+++ b/ggml/src/ggml-cuda/mmvf.cu
@@ -1,9 +1,9 @@
 #include "ggml.h"
 #include "common.cuh"
-#include "mmv.cuh"
+#include "mmvf.cuh"
 
 template <typename T, typename type_acc, int ncols_dst, int block_size>
-static __global__ void mul_mat_vec(
+static __global__ void mul_mat_vec_f(
         const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
         const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
         const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
@@ -37,7 +37,7 @@ static __global__ void mul_mat_vec(
 
     float sumf[ncols_dst] = {0.0f};
 
-    if constexpr (std::is_same<T, float>::value) {
+    if constexpr (std::is_same_v<T, float>) {
         const float2 * x2 = (const float2 *) x;
 
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
@@ -50,10 +50,10 @@ static __global__ void mul_mat_vec(
                 sumf[j] += tmpx.y*tmpy.y;
             }
         }
-    } else if constexpr (std::is_same<T, half>::value) {
+    } else if constexpr (std::is_same_v<T, half>) {
         const half2 * x2 = (const half2 *) x;
 
-        if (std::is_same<type_acc, float>::value) {
+        if (std::is_same_v<type_acc, float>) {
             for (int col2 = tid; col2 < ncols2; col2 += block_size) {
                 const float2 tmpx = __half22float2(x2[col2]);
 
@@ -86,7 +86,7 @@ static __global__ void mul_mat_vec(
             NO_DEVICE_CODE;
 #endif // FP16_AVAILABLE
         }
-    } else if constexpr (std::is_same<T, nv_bfloat16>::value) {
+    } else if constexpr (std::is_same_v<T, nv_bfloat16>) {
         const int * x2 = (const int *) x;
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
             const int tmpx = x2[col2];
@@ -98,7 +98,7 @@ static __global__ void mul_mat_vec(
             }
         }
     } else {
-        static_assert(std::is_same<T, void>::value, "unsupported type");
+        static_assert(std::is_same_v<T, void>, "unsupported type");
     }
 
 #pragma unroll
@@ -126,7 +126,7 @@ static __global__ void mul_mat_vec(
 }
 
 template <typename T, typename type_acc, int ncols_dst>
-static void launch_mul_mat_vec_cuda(
+static void launch_mul_mat_vec_f_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -141,11 +141,9 @@ static void launch_mul_mat_vec_cuda(
     GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
     const int64_t channel_ratio = nchannels_dst / nchannels_x;
     const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
-    int device;
-    int warp_size;
 
-    CUDA_CHECK(cudaGetDevice(&device));
-    warp_size = ggml_cuda_info().devices[device].warp_size;
+    const int device = ggml_cuda_get_device();
+    const int warp_size = ggml_cuda_info().devices[device].warp_size;
 
     int64_t block_size_best = warp_size;
     int64_t niter_best      = (ncols + 2*warp_size - 1) / (2*warp_size);
@@ -161,54 +159,54 @@ static void launch_mul_mat_vec_cuda(
         }
     }
 
-    const int smem = warp_size*sizeof(float);
+    const int nbytes_shared = warp_size*sizeof(float);
     const dim3 block_nums(nrows, nchannels_dst, nsamples_dst);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
-            mul_mat_vec<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   64: {
-            mul_mat_vec<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   96: {
-            mul_mat_vec<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  128: {
-            mul_mat_vec<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  160: {
-            mul_mat_vec<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  192: {
-            mul_mat_vec<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  224: {
-            mul_mat_vec<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  256: {
-            mul_mat_vec<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, smem, stream>>>
+            mul_mat_vec_f<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, nbytes_shared, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
@@ -220,7 +218,7 @@ static void launch_mul_mat_vec_cuda(
 }
 
 template <typename T, typename type_acc>
-static void mul_mat_vec_cuda_switch_ncols_dst(
+static void mul_mat_vec_f_cuda_switch_ncols_dst(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -230,49 +228,49 @@ static void mul_mat_vec_cuda_switch_ncols_dst(
         cudaStream_t stream) {
     switch (ncols_dst) {
         case 1:
-            launch_mul_mat_vec_cuda<T, type_acc, 1>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 1>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 2:
-            launch_mul_mat_vec_cuda<T, type_acc, 2>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 2>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 3:
-            launch_mul_mat_vec_cuda<T, type_acc, 3>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 3>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 4:
-            launch_mul_mat_vec_cuda<T, type_acc, 4>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 4>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 5:
-            launch_mul_mat_vec_cuda<T, type_acc, 5>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 5>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 6:
-            launch_mul_mat_vec_cuda<T, type_acc, 6>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 6>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 7:
-            launch_mul_mat_vec_cuda<T, type_acc, 7>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 7>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 8:
-            launch_mul_mat_vec_cuda<T, type_acc, 8>
+            launch_mul_mat_vec_f_cuda<T, type_acc, 8>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
@@ -284,7 +282,7 @@ static void mul_mat_vec_cuda_switch_ncols_dst(
 }
 
 template<typename T>
-static void mul_mat_vec_cuda(
+static void mul_mat_vec_f_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst,
@@ -292,22 +290,22 @@ static void mul_mat_vec_cuda(
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
         const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         enum ggml_prec prec, cudaStream_t stream) {
-    if constexpr(std::is_same<T, half>::value) {
+    if constexpr(std::is_same_v<T, half>) {
         if (prec == GGML_PREC_DEFAULT) {
-            mul_mat_vec_cuda_switch_ncols_dst<T, half>
+            mul_mat_vec_f_cuda_switch_ncols_dst<T, half>
                 (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             return;
         }
     }
-    mul_mat_vec_cuda_switch_ncols_dst<T, float>
+    mul_mat_vec_f_cuda_switch_ncols_dst<T, float>
         (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
          nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
          stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
 }
 
-void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
     GGML_ASSERT(        src1->type == GGML_TYPE_F32);
     GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
     GGML_ASSERT(         dst->type == GGML_TYPE_F32);
@@ -355,19 +353,19 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0->data;
-            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0->data;
-            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
-            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
@@ -376,7 +374,7 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
     }
 }
 
-void ggml_cuda_op_mul_mat_vec(
+void ggml_cuda_op_mul_mat_vec_f(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
@@ -414,19 +412,19 @@ void ggml_cuda_op_mul_mat_vec(
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0_dd_i;
-            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0_dd_i;
-            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
-            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
         } break;
@@ -442,15 +440,15 @@ void ggml_cuda_op_mul_mat_vec(
     GGML_UNUSED(src1_padded_row_size);
 }
 
-bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
+bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
     if (src0_ne[0] % 2 != 0) {
         return false;
     }
     switch (type) {
         case GGML_TYPE_F32:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
-                if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
-                    return ne11 <= 8;
+                if (ampere_mma_available(cc)) {
+                    return ne11 <= 3;
                 }
                 if (cc >= GGML_CUDA_CC_TURING) {
                     return ne11 <= 4;
@@ -466,6 +464,9 @@ bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_
         case GGML_TYPE_F16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
+                if (ampere_mma_available(cc)) {
+                    return src0_small && ne11 == 1;
+                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }
@@ -486,6 +487,9 @@ bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_
         case GGML_TYPE_BF16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
+                if (ampere_mma_available(cc)) {
+                    return src0_small && ne11 == 1;
+                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }
diff --git a/ggml/src/ggml-cuda/mmv.cuh b/ggml/src/ggml-cuda/mmvf.cuh
similarity index 55%
rename from ggml/src/ggml-cuda/mmv.cuh
rename to ggml/src/ggml-cuda/mmvf.cuh
index 1330bcb6a8..1da460992e 100644
--- a/ggml/src/ggml-cuda/mmv.cuh
+++ b/ggml/src/ggml-cuda/mmvf.cuh
@@ -1,11 +1,11 @@
 #include "common.cuh"
 
-void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
 
-void ggml_cuda_op_mul_mat_vec(
+void ggml_cuda_op_mul_mat_vec_f(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream);
 
-bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);
+bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 8b172e60f4..c31f319232 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -200,6 +200,7 @@
 #endif
 
 typedef hip_bfloat16 nv_bfloat16;
+typedef short2 nv_bfloat162; // FIXME there is no 2x BF16 type being defined in bfloat16.h, ad-hoc compilation fix
 
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
index 1989632024..8c55a2e4e5 100644
--- a/ggml/src/ggml-cuda/vendors/musa.h
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -137,4 +137,5 @@
 #define cudaStreamEndCapture musaStreamEndCapture
 #define cudaOccupancyMaxActiveBlocksPerMultiprocessor musaOccupancyMaxActiveBlocksPerMultiprocessor
 
-typedef mt_bfloat16 nv_bfloat16;
+typedef __mt_bfloat16 nv_bfloat16;
+typedef __mt_bfloat162 nv_bfloat162;

From 9a96389544a08fd829fccda28142ce2066017fde Mon Sep 17 00:00:00 2001
From: Christian Kastner <ckk@kvr.at>
Date: Thu, 7 Aug 2025 13:45:41 +0200
Subject: [PATCH 013/287] ggml: Skip backend library linking code when
 GGML_BACKEND_DL=ON (#15094)

Any available libraries are found and loaded dynamically at runtime.
---
 ggml/cmake/ggml-config.cmake.in | 82 +++++++++++++++++----------------
 1 file changed, 42 insertions(+), 40 deletions(-)

diff --git a/ggml/cmake/ggml-config.cmake.in b/ggml/cmake/ggml-config.cmake.in
index 65a75a2f1c..91c9d5cd34 100644
--- a/ggml/cmake/ggml-config.cmake.in
+++ b/ggml/cmake/ggml-config.cmake.in
@@ -106,7 +106,7 @@ if(NOT TARGET ggml::ggml)
 
     find_library(GGML_LIBRARY ggml
         REQUIRED
-        HINTS ${GGML_LIB_DIR} ${GGML_BACKEND_DIR}
+        HINTS ${GGML_LIB_DIR}
         NO_CMAKE_FIND_ROOT_PATH)
 
     add_library(ggml::ggml UNKNOWN IMPORTED)
@@ -125,54 +125,56 @@ if(NOT TARGET ggml::ggml)
             IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
 
     set(_ggml_all_targets "")
-    foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
-        string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
-        string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+    if (NOT GGML_BACKEND_DL)
+        foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
+            string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
+            string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
 
-        find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
-            REQUIRED
-            HINTS ${GGML_LIB_DIR}
-            NO_CMAKE_FIND_ROOT_PATH)
+            find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+                REQUIRED
+                HINTS ${GGML_LIB_DIR}
+                NO_CMAKE_FIND_ROOT_PATH)
 
-        message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
+            message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
 
-        add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
-        set_target_properties(ggml::${_ggml_backend}
-            PROPERTIES
-                INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
-                IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-                IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
-                INTERFACE_COMPILE_FEATURES c_std_90
-                POSITION_INDEPENDENT_CODE ON)
-
-        string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
-        if(is_cpu_variant)
-            list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
-            set_target_properties(ggml::${_ggml_backend}
-            PROPERTIES
-                INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
-
-            if(GGML_CPU_INTERFACE_LINK_OPTIONS)
-                set_target_properties(ggml::${_ggml_backend}
-                    PROPERTIES
-                        INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
-            endif()
-
-        else()
-            list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+            add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
             set_target_properties(ggml::${_ggml_backend}
                 PROPERTIES
-                    INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
+                    INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
+                    IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+                    IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
+                    INTERFACE_COMPILE_FEATURES c_std_90
+                    POSITION_INDEPENDENT_CODE ON)
 
-            if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+            string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
+            if(is_cpu_variant)
+                list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+                set_target_properties(ggml::${_ggml_backend}
+                PROPERTIES
+                    INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
+
+                if(GGML_CPU_INTERFACE_LINK_OPTIONS)
+                    set_target_properties(ggml::${_ggml_backend}
+                        PROPERTIES
+                            INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
+                endif()
+
+            else()
+                list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
                 set_target_properties(ggml::${_ggml_backend}
                     PROPERTIES
-                        INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
-            endif()
-        endif()
+                        INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
 
-        list(APPEND _ggml_all_targets ggml::${_ggml_backend})
-    endforeach()
+                if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+                    set_target_properties(ggml::${_ggml_backend}
+                        PROPERTIES
+                            INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
+                endif()
+            endif()
+
+            list(APPEND _ggml_all_targets ggml::${_ggml_backend})
+        endforeach()
+    endif()
 
     list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}")
     set_target_properties(ggml::ggml

From 7ad67ba9fe2b909e271dd31b99c5fce3aba35899 Mon Sep 17 00:00:00 2001
From: uvos <carl@uvos.xyz>
Date: Thu, 7 Aug 2025 16:44:14 +0200
Subject: [PATCH 014/287] HIP: add cmake option to enable compiler output of
 kernel resource usage metrics (#15103)

---
 ggml/CMakeLists.txt              | 1 +
 ggml/src/ggml-hip/CMakeLists.txt | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 7c4666b7ed..1fb7abeaf0 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -176,6 +176,7 @@ option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
 option(GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12   "ggml: enable rocWMMA FlashAttention on GFX12"    OFF)
 option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)
+option(GGML_HIP_EXPORT_METRICS              "ggml: enable kernel perf metrics output"         OFF)
 option(GGML_MUSA_GRAPHS                     "ggml: use MUSA graph, experimental, unstable"    OFF)
 option(GGML_MUSA_MUDNN_COPY                 "ggml: enable muDNN for accelerated copy"         OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index e92ec7faa3..852de97346 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -121,6 +121,10 @@ if (GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 OR ${hip_VERSION} VERSION_GREATER_EQUAL 7
     add_compile_definitions(GGML_HIP_ROCWMMA_FATTN_GFX12)
 endif()
 
+if (GGML_HIP_EXPORT_METRICS)
+    set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Rpass-analysis=kernel-resource-usage --save-temps")
+endif()
+
 if (NOT GGML_CUDA_FA)
     add_compile_definitions(GGML_CUDA_NO_FA)
 endif()

From 99acbc9921b119aa7ed929eb5780a66a8f06e6d9 Mon Sep 17 00:00:00 2001
From: RunningLeon <mnsheng@yeah.net>
Date: Fri, 8 Aug 2025 00:20:40 +0800
Subject: [PATCH 015/287] llama : Support intern-s1 (#14875)

* support internvl

* support interns1

* resolve comments

* put interns1 in tensor mapping

* resolve comment

* move tokenizer changes to sub class
---
 convert_hf_to_gguf.py          | 109 ++++++++++++++++++++++++++++++++-
 gguf-py/gguf/tensor_mapping.py |  15 +++++
 2 files changed, 122 insertions(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 82b005e84a..8e8f09642c 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3328,7 +3328,13 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
 @ModelBase.register("InternVisionModel")
 class InternVisionModel(MmprojModel):
     def set_gguf_parameters(self):
+        assert self.hparams_vision is not None
+        if isinstance(self.hparams_vision['image_size'], list):
+            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
+        if isinstance(self.hparams_vision['patch_size'], list):
+            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
         super().set_gguf_parameters()
+
         hparams = self.hparams
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
         self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
@@ -3352,14 +3358,30 @@ class InternVisionModel(MmprojModel):
             return gguf.GGMLQuantizationType.F32
         return False
 
+    def _mapping_interns1_name(self, name):
+        names_map = {
+            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
+            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
+            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
+            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
+            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
+            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
+        }
+        if name in names_map:
+            name = names_map[name]
+        return name
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-        if name.startswith("vision_model") or name.startswith("mlp"):
+        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
+        # deal with intern-s1 special case
+        name = self._mapping_interns1_name(name)
+        if any([name.startswith(prefix) for prefix in vision_prefix]):
             # process visual tensors
             # correct name
             if name.startswith("vision_model"):
                 name = "vision_tower." + name
-            if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
+            if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
                 name += ".weight"
             # split QKV tensors if needed
             if ".qkv." in name:
@@ -3445,6 +3467,10 @@ class Qwen2MoeModel(TextModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # process the experts separately
+        name = name.replace("language_model.", "") # InternVL
+        if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
+            # skip visual tensors
+            return []
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -3498,6 +3524,85 @@ class Qwen3Model(Qwen2Model):
 class Qwen3MoeModel(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3MOE
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hparams = ModelBase.load_hparams(self.dir_model)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+    def set_vocab(self):
+        # deal with intern-s1
+        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
+            self._set_vocab_interns1()
+            return
+
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def _set_vocab_interns1(self):
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
+        vocab_size = self.hparams.get("vocab_size", len(vocab))
+        assert max(vocab.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
+        additional_special_tokens = []
+        if special_tokens_map_file.is_file():
+            with open(special_tokens_map_file, encoding = 'utf-8') as f:
+                additional_special_tokens = json.load(f).get('additional_special_tokens', [])
+        tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
+        if tokenizer_cfg_file.is_file():
+            with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
+                added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
+                token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
+                for token in additional_special_tokens:
+                    if token in token2ids_map:
+                        special_vocab._set_special_token(token, token2ids_map[token])
+        special_vocab._set_special_token('eos', 151645)
+        special_vocab._set_special_token("bos", 151643)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
 
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index a0f11563ac..dc7c03b464 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1110,11 +1110,13 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_CLS: (
             "vision_tower.vision_model.embeddings.class_embedding",
+            "model.vision_tower.embeddings.cls_token", # Intern-S1
             "vision_model.class_embedding", # llama 4
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_PATCH: (
             "vision_tower.vision_model.embeddings.patch_embedding",
+            "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
             "vpm.embeddings.patch_embedding",
             "model.vision_model.embeddings.patch_embedding", # SmolVLM
             "vision_tower.patch_conv", # pixtral
@@ -1124,6 +1126,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_POS: (
             "vision_tower.vision_model.embeddings.position_embedding",
+            "model.vision_tower.embeddings.position_embeddings", # Intern-S1
             "vpm.embeddings.position_embedding",
             "model.vision_model.embeddings.position_embedding", # SmolVLM
             "vision_model.positional_embedding_vlm", # llama 4
@@ -1131,6 +1134,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
+            "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.q_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4
@@ -1140,10 +1144,12 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_K: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
+            "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.k_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4
@@ -1153,10 +1159,12 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_V: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
+            "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4
@@ -1167,6 +1175,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
             "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm1",
             "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
             "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
@@ -1177,6 +1186,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_ATTN_O: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
             "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4
@@ -1187,6 +1197,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
             "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm2",
             "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
             "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4
@@ -1196,6 +1207,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_UP: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
+            "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc1",
             "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral
@@ -1211,6 +1223,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_DOWN: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
+            "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc2",
             "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
             "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral
@@ -1221,10 +1234,12 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_LAYER_SCALE_1: (
             "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1
         ),
 
         MODEL_TENSOR.V_LAYER_SCALE_2: (
             "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL
+            "model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1
         ),
 
         MODEL_TENSOR.V_PRE_NORM: (

From a0552c8beef74e843bb085c8ef0c63f9ed7a2b27 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 7 Aug 2025 15:07:11 -0500
Subject: [PATCH 016/287] vulkan: Add env var to disable host visible vidmem
 (#15109)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 165933a729..f1cb90e3b3 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -534,6 +534,7 @@ struct vk_device_struct {
     ggml_backend_buffer_type buffer_type;
 
     bool disable_fusion;
+    bool disable_host_visible_vidmem;
 
 #ifdef GGML_VULKAN_MEMORY_DEBUG
     std::unique_ptr<vk_memory_logger> memory_logger;
@@ -1804,6 +1805,8 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
         } else if (device->uma) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+        } else if (device->disable_host_visible_vidmem) {
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eDeviceLocal);
         } else {
             // use rebar if available, otherwise fallback to device only visible memory
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
@@ -3265,6 +3268,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
         const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY");
         device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr;
 
+        const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM");
+        device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr;
+
         bool fp16_storage = false;
         bool fp16_compute = false;
         bool maintenance4_support = false;

From c4f53563df4575196ea13f5ed669ea8ea659a6be Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 7 Aug 2025 15:44:20 -0500
Subject: [PATCH 017/287] vulkan: support fattn sinks (#15126)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 58 +++++++++++++------
 .../vulkan-shaders/flash_attn.comp            | 21 +++++++
 .../vulkan-shaders/flash_attn_base.comp       | 13 ++++-
 .../vulkan-shaders/flash_attn_cm1.comp        | 21 +++++++
 .../vulkan-shaders/flash_attn_cm2.comp        | 28 +++++++++
 .../flash_attn_split_k_reduce.comp            | 27 ++++++++-
 6 files changed, 148 insertions(+), 20 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index f1cb90e3b3..b1cbbc9866 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2286,14 +2286,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
     };
 
 #define CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, HSK, HSV, HEAD_SIZES) \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
 
 #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
         CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 64, 64, 64) \
@@ -2910,7 +2910,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 2, 4 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, 5 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_len, quantize_q8_1_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
 
     for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
@@ -6507,11 +6507,14 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
     return supported;
 }
 
-static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst, bool dryrun = false) {
     VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
     std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
     std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3];
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    if (sinks) {
+        std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3];
+    }
     std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
 
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
@@ -6710,10 +6713,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr;
-    size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0;
+    vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr, d_S = nullptr;
+    size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0, s_buf_offset = 0;
 
-    bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false;
+    bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false, S_uma = false;
 
     if (ctx->device->uma) {
         ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset);
@@ -6728,6 +6731,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
             ggml_vk_host_get(ctx->device, mask->data, d_M, m_buf_offset);
             M_uma = d_M != nullptr;
         }
+        if (sinks) {
+            ggml_vk_host_get(ctx->device, sinks->data, d_S, s_buf_offset);
+            S_uma = d_S != nullptr;
+        }
     }
 
 
@@ -6763,7 +6770,17 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         }
     }
 
-    uint32_t mask_n_head_log2 = ((mask != nullptr) << 16) | n_head_log2;
+    if (!S_uma) {
+        d_S = d_Q;
+        s_buf_offset = q_buf_offset;
+        if (sinks) {
+            ggml_backend_vk_buffer_context * s_buf_ctx = (ggml_backend_vk_buffer_context*)sinks->buffer->context;
+            d_S = s_buf_ctx->dev_buffer;
+            s_buf_offset = vk_tensor_offset(sinks) + sinks->view_offs;
+        }
+    }
+
+    uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2;
 
     const vk_flash_attn_push_constants pc = { N, KV,
                                               (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
@@ -6787,6 +6804,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                         vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
+                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE},
                                     },
                                     // We only use split_k when group query attention is enabled, which means
@@ -6796,10 +6814,11 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                     pc, { workgroups_x * pipeline->wg_denoms[0], workgroups_y, workgroups_z });
 
         ggml_vk_sync_buffers(subctx);
-        const std::array<uint32_t, 4> pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k };
+        const std::array<uint32_t, 5> pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k, (sinks != nullptr) };
         ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
                                     {
                                         vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE},
+                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
                                     },
                                     pc2, { (uint32_t)ne1, HSV, (uint32_t)ne3 });
@@ -6810,6 +6829,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                         vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
+                                        vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE},
                                         vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
                                     },
                                     pc, { workgroups_x, workgroups_y, workgroups_z });
@@ -9874,7 +9894,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         break;
 
     case GGML_OP_FLASH_ATTN_EXT:
-        ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
+        ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node->src[4], node, dryrun);
 
         break;
 
@@ -10951,8 +10971,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 if (head_sizes == FA_HEAD_SIZE_UNSUPPORTED) {
                     return false;
                 }
-                // TODO: support attention sinks [TAG_ATTN_SINKS]
-                if (op->src[4]) {
+                if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) {
                     return false;
                 }
                 if (op->src[0]->type != GGML_TYPE_F32) {
@@ -11547,6 +11566,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
     if (tensor->op == GGML_OP_FLASH_ATTN_EXT) {
         const float * params = (const float *)tensor->op_params;
         tensor_clone = ggml_flash_attn_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], params[0], params[1], params[2]);
+        if (src_clone[4]) {
+            ggml_flash_attn_ext_add_sinks(tensor_clone, src_clone[4]);
+        }
     } else if (tensor->op == GGML_OP_MUL_MAT) {
         tensor_clone = ggml_mul_mat(ggml_ctx, src_clone[0], src_clone[1]);
     } else if (tensor->op == GGML_OP_MUL_MAT_ID) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 45c6e7736a..d40848e15f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -305,6 +305,27 @@ void main() {
         return;
     }
 
+    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
+        [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
+            float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2);
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (sink > Mf[r]) {
+                ms = exp(Mf[r] - sink);
+
+                [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
+                    Of[r][d] *= ms;
+                }
+            } else {
+                vs = exp(sink - Mf[r]);
+            }
+
+            Lf[r] = Lf[r]*ms + vs;
+        }
+    }
+
     float Lfrcp[Br];
     [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
         Lfrcp[r] = 1.0 / Lf[r];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
index 7defe72b40..b57c9dcfc4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
@@ -50,10 +50,13 @@ layout (push_constant) uniform parameter {
     uint32_t k_num;
 } p;
 
+#define SINK_ENABLE_BIT (1<<24)
 #define MASK_ENABLE_BIT (1<<16)
 #define N_LOG2_MASK 0xFFFF
 
-layout (binding = 4) writeonly buffer O {D_TYPE data_o[];};
+layout (binding = 4) readonly buffer S {float data_s[];};
+
+layout (binding = 5) writeonly buffer O {D_TYPE data_o[];};
 
 #if defined(A_TYPE_PACKED16)
 #define BINDING_IDX_K 0
@@ -111,6 +114,14 @@ ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const i
     return ACC_TYPE(pow(base, ACC_TYPE(exph)));
 }
 
+// Load the sink value, indexed by Q's dimension 2.
+ACC_TYPE perElemOpGetSink(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2)
+{
+    const uint32_t h = iq2 + (r % p.gqa_ratio);
+
+    return ACC_TYPE(data_s[h]);
+}
+
 uint32_t i, N, KV, split_k_index, Tr, start_j, end_j,
          iq2, iq3, rk2, rk3, rv2, rv3, ik2, ik3, iv2, iv3,
          q_stride, k_stride, v_stride, m_stride;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 486735fe8b..230e815f22 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -329,6 +329,27 @@ void main() {
         return;
     }
 
+    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
+        [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
+            float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2);
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (sink > Mf[r]) {
+                ms = exp(Mf[r] - sink);
+
+                [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
+                    Of[r][d] *= ACC_TYPE(ms);
+                }
+            } else {
+                vs = exp(sink - Mf[r]);
+            }
+
+            Lf[r] = Lf[r]*ms + vs;
+        }
+    }
+
     float Lfrcp[rows_per_thread];
     [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
         Lfrcp[r] = 1.0 / Lf[r];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index 274f48fcab..b0564ca0bf 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -248,6 +248,34 @@ void main() {
     // resize L by using smear/reduce
     coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce);
 
+    if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> S;
+        coopMatPerElementNV(S, S, perElemOpGetSink, iq2);
+
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> Mr;
+
+        // resize M by using smear/reduce
+        coopMatReduceNV(Mr, M, gl_CooperativeMatrixReduceRowNV, smearReduce);
+
+        // O, Ldiag, Mr all have the same type so all element locations match
+        [[unroll]] for (uint32_t i = 0; i < Ldiag.length(); ++i) {
+            ACC_TYPE sink = S[i];
+
+            ACC_TYPE ms = ACC_TYPE(1.0f);
+            ACC_TYPE vs = ACC_TYPE(1.0f);
+
+            if (sink > Mr[i]) {
+                ms = exp(Mr[i] - sink);
+
+                O[i] *= ms;
+            } else {
+                vs = exp(sink - Mr[i]);
+            }
+
+            Ldiag[i] = Ldiag[i]*ms + vs;
+        }
+    }
+
     [[unroll]]
     for (int k = 0; k < Ldiag.length(); ++k) {
         Ldiag[k] = ACC_TYPE(1.0) / Ldiag[k];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
index 0a17a9df23..76ef4b6dfb 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
@@ -7,13 +7,15 @@ layout(constant_id = 0) const uint BLOCK_SIZE = 32;
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {float data_a[];};
-layout (binding = 1) writeonly buffer D {float data_d[];};
+layout (binding = 1) readonly buffer B {float data_s[];};
+layout (binding = 2) writeonly buffer D {float data_d[];};
 
 layout (push_constant) uniform parameter {
     uint D;
     uint N;
     uint ne3;
     uint k_num;
+    uint sinks;
 } p;
 
 shared float tmpsh[BLOCK_SIZE];
@@ -73,6 +75,22 @@ void main() {
     }
     L = tmpsh[0];
 
+    float sink;
+    if (p.sinks != 0) {
+        sink = data_s[n];
+
+        float ms = 1.0f;
+        float vs = 1.0f;
+
+        if (sink > m_max) {
+            ms = exp(m_max - sink);
+        } else {
+            vs = exp(sink - m_max);
+        }
+
+        L = L*ms + vs;
+    }
+
     L = 1.0 / L;
 
     // D dimension is split across workgroups in the y dimension
@@ -85,6 +103,13 @@ void main() {
             float m = data_a[m_offset + k * lm_stride];
             O += exp(m - m_max) * data_a[o_offset];
         }
+        if (p.sinks != 0) {
+            if (sink > m_max) {
+                float ms = 1.0f;
+                ms = exp(m_max - sink);
+                O *= ms;
+            }
+        }
         O *= L;
         data_d[iq3 * D * N + D * n + d] = O;
     }

From 50aa9389014bba2dd12234132aa6b8ca3601a17f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 7 Aug 2025 23:26:03 +0200
Subject: [PATCH 018/287] convert : support non-mxfp4 HF model (#15153)

* convert : support non-mxfp4 HF model

* rm redundant check

* disable debug check
---
 convert_hf_to_gguf.py | 21 ++++++++++++++++-----
 src/llama-quant.cpp   |  2 +-
 2 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 8e8f09642c..b8c7d97a78 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -8102,7 +8102,6 @@ class GptOssModel(TextModel):
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         blocks0: Tensor = torch.zeros(1)
         blocks1: Tensor = torch.zeros(1)
-        found_mxfp4_tensors = False
         # we assume that tensors are loaded in the correct order
         for name, data_torch in self.get_tensors():
             if "mlp.experts.down_proj_blocks" in name:
@@ -8110,7 +8109,6 @@ class GptOssModel(TextModel):
             elif "mlp.experts.down_proj_scales" in name:
                 new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
                 self.repack_mxfp4(new_name, blocks0, data_torch)
-                found_mxfp4_tensors = True
             elif "mlp.experts.gate_up_proj_blocks" in name:
                 blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
             elif "mlp.experts.gate_up_proj_scales" in name:
@@ -8119,9 +8117,6 @@ class GptOssModel(TextModel):
                 new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
                 self.repack_mxfp4(new_name_gate, blocks0, scales0)
                 self.repack_mxfp4(new_name_up, blocks1, scales1)
-                found_mxfp4_tensors = True
-        if not found_mxfp4_tensors:
-            raise ValueError("No MXFP4 tensors found in the model. Please make sure you are using MXFP4 model.")
         return []
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
@@ -8134,7 +8129,12 @@ class GptOssModel(TextModel):
         if "down_proj" in name:
             if name.endswith("_bias"):
                 name = name.replace("down_proj_bias", "down_proj.bias")
+            elif "_blocks" not in name and "_scales" not in name:
+                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
+                name = name.replace("down_proj", "down_proj.weight")
+                data_torch = data_torch.transpose(-1, -2)
             else:
+                # otherwise, it should already be repacked to ggml MXFP4 format
                 return []
 
         # split the gate_up into gate and up
@@ -8147,7 +8147,18 @@ class GptOssModel(TextModel):
                     (self.map_tensor_name(name_gate), gate_proj_bias),
                     (self.map_tensor_name(name_up), up_proj_bias)
                 ]
+            elif "_blocks" not in name and "_scales" not in name:
+                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
+                name_up = name.replace("gate_up_proj", "up_proj.weight")
+                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
+                data_torch = data_torch.transpose(-1, -2)
+                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
+                return [
+                    (self.map_tensor_name(name_gate), gate_proj_weight),
+                    (self.map_tensor_name(name_up), up_proj_weight)
+                ]
             else:
+                # otherwise, it should already be repacked to ggml MXFP4 format
                 return []
 
         return [(self.map_tensor_name(name), data_torch)]
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 9c0ffde27a..1d0361cc16 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -999,7 +999,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
                 new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
 
                 // TODO: temporary sanity check that the F16 -> MXFP4 is lossless
-#if 1
+#if 0
                 if (new_type == GGML_TYPE_MXFP4) {
                     auto * x = f32_data_03;
 

From aaa3d07ae749b781d6135eaff23c7fa8a4ab404a Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Fri, 8 Aug 2025 13:47:03 +0900
Subject: [PATCH 019/287] opencl: support sink in `soft_max` (attn sinks)
 (#15152)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 49 +++++++++++--------
 ggml/src/ggml-opencl/kernels/softmax_4_f16.cl | 12 ++++-
 ggml/src/ggml-opencl/kernels/softmax_4_f32.cl | 12 ++++-
 ggml/src/ggml-opencl/kernels/softmax_f16.cl   | 12 ++++-
 ggml/src/ggml-opencl/kernels/softmax_f32.cl   | 12 ++++-
 5 files changed, 68 insertions(+), 29 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 4f765ab530..b32d5da307 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2520,8 +2520,6 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
         case GGML_OP_CLAMP:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SOFT_MAX:
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            return op->src[2] == nullptr;
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
             return true;
@@ -6594,17 +6592,24 @@ static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, c
         GGML_ASSERT(src1->extra);
     }
 
+    const ggml_tensor * src2 = dst->src[2];
+    if (src2) {
+        GGML_ASSERT(src2->extra);
+    }
+
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
     ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
     ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
 
     ggml_tensor_extra_cl * extra1 = src1 ? (ggml_tensor_extra_cl *)src1->extra : nullptr;
+    ggml_tensor_extra_cl * extra2 = src2 ? (ggml_tensor_extra_cl *)src2->extra : nullptr;
 
     cl_ulong offset0 = extra0->offset + src0->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
 
     cl_ulong offset1 = extra1 ? extra1->offset + src1->view_offs : offset0;
+    cl_ulong offset2 = extra2 ? extra2->offset + src2->view_offs : offset0;
 
     const int ne00 = src0->ne[0];
     const int ne01 = src0->ne[1];
@@ -6672,25 +6677,27 @@ static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, c
     CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
     CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   extra1 ? &extra1->data_device : &extra0->data_device));
     CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
-    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
-    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne13));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
-    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
-    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb1));
-    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb2));
-    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb3));
-    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(float),    &scale));
-    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(float),    &max_bias));
-    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(float),    &m0));
-    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(float),    &m1));
-    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &n_head_log2));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   extra2 ? &extra2->data_device : &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb13));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb2));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb3));
+    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(float),    &scale));
+    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(float),    &max_bias));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(float),    &m0));
+    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(float),    &m1));
+    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &n_head_log2));
 
     size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
     size_t local_work_size[] = {(size_t)nth, 1, 1};
diff --git a/ggml/src/ggml-opencl/kernels/softmax_4_f16.cl b/ggml/src/ggml-opencl/kernels/softmax_4_f16.cl
index a6d8ede670..571d16507c 100644
--- a/ggml/src/ggml-opencl/kernels/softmax_4_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/softmax_4_f16.cl
@@ -26,6 +26,8 @@ kernel void kernel_soft_max_4_f16(
         ulong offset0,
         global char * src1,
         ulong offset1,
+        global char * src2,
+        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -48,6 +50,7 @@ kernel void kernel_soft_max_4_f16(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
+    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -60,6 +63,7 @@ kernel void kernel_soft_max_4_f16(
 
     global float4 * psrc4 = (global float4 *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global half4  * pmask = src1 != src0 ? (global half4 *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
+    global float  * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float4 * pdst4 = (global float4 *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -75,7 +79,7 @@ kernel void kernel_soft_max_4_f16(
     }
 
     // parallel max
-    float4 lmax4 = -INFINITY;
+    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         lmax4 = fmax(lmax4, psrc4[i00]*scale + slope*(pmask ? convert_float4(pmask[i00]) : 0.0f));
     }
@@ -92,7 +96,11 @@ kernel void kernel_soft_max_4_f16(
     }
     float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
 
-    const float sum = sub_group_reduce_add(lsum);
+    float sum = sub_group_reduce_add(lsum);
+
+    if (psrc2) {
+        sum += exp(psrc2[i02] - max);
+    }
 
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         pdst4[i00] /= sum;
diff --git a/ggml/src/ggml-opencl/kernels/softmax_4_f32.cl b/ggml/src/ggml-opencl/kernels/softmax_4_f32.cl
index 35b5573b46..1f944b2201 100644
--- a/ggml/src/ggml-opencl/kernels/softmax_4_f32.cl
+++ b/ggml/src/ggml-opencl/kernels/softmax_4_f32.cl
@@ -26,6 +26,8 @@ kernel void kernel_soft_max_4(
         ulong offset0,
         global char * src1,
         ulong offset1,
+        global char * src2,
+        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -48,6 +50,7 @@ kernel void kernel_soft_max_4(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
+    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -60,6 +63,7 @@ kernel void kernel_soft_max_4(
 
     global float4 * psrc4 = (global float4 *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global float4 * pmask = src1 != src0 ? (global float4 *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
+    global float  * psrc2 = src2 != src0 ? (global float  *)(src2) : 0;
     global float4 * pdst4 = (global float4 *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -75,7 +79,7 @@ kernel void kernel_soft_max_4(
     }
 
     // parallel max
-    float4 lmax4 = -INFINITY;
+    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -92,7 +96,11 @@ kernel void kernel_soft_max_4(
     }
     float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
 
-    const float sum = sub_group_reduce_add(lsum);
+    float sum = sub_group_reduce_add(lsum);
+
+    if (psrc2) {
+        sum += exp(psrc2[i02] - max);
+    }
 
     for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
         pdst4[i00] /= sum;
diff --git a/ggml/src/ggml-opencl/kernels/softmax_f16.cl b/ggml/src/ggml-opencl/kernels/softmax_f16.cl
index 9d292b5746..4baa6c28e4 100644
--- a/ggml/src/ggml-opencl/kernels/softmax_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/softmax_f16.cl
@@ -26,6 +26,8 @@ kernel void kernel_soft_max_f16(
         ulong offset0,
         global char * src1,
         ulong offset1,
+        global char * src2,
+        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -48,6 +50,7 @@ kernel void kernel_soft_max_f16(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
+    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -60,6 +63,7 @@ kernel void kernel_soft_max_f16(
 
     global float * psrc0 = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global half  * pmask = src1 != src0 ? (global half *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
+    global float * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float * pdst  = (global float *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -75,7 +79,7 @@ kernel void kernel_soft_max_f16(
     }
 
     // parallel max
-    float lmax = -INFINITY;
+    float lmax = psrc2 ? psrc2[i02] : -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -91,7 +95,11 @@ kernel void kernel_soft_max_f16(
         pdst[i00] = exp_psrc0;
     }
 
-    const float sum = sub_group_reduce_add(lsum);
+    float sum = sub_group_reduce_add(lsum);
+
+    if (psrc2) {
+        sum += exp(psrc2[i02] - max);
+    }
 
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         pdst[i00] /= sum;
diff --git a/ggml/src/ggml-opencl/kernels/softmax_f32.cl b/ggml/src/ggml-opencl/kernels/softmax_f32.cl
index 7c53dfbe5a..d503190b47 100644
--- a/ggml/src/ggml-opencl/kernels/softmax_f32.cl
+++ b/ggml/src/ggml-opencl/kernels/softmax_f32.cl
@@ -26,6 +26,8 @@ kernel void kernel_soft_max(
         ulong offset0,
         global char * src1,
         ulong offset1,
+        global char * src2,
+        ulong offset2,
         global char * dst,
         ulong offsetd,
         int ne00,
@@ -48,6 +50,7 @@ kernel void kernel_soft_max(
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
+    src2 = src2 + offset2;
     dst  = dst  + offsetd;
 
     int i03 = get_group_id(2);
@@ -60,6 +63,7 @@ kernel void kernel_soft_max(
 
     global float * psrc0 = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
     global float * pmask = src1 != src0 ? (global float *)(src1 + i11*nb11 + i12*nb12 + i13*nb13) : 0;
+    global float * psrc2 = src2 != src0 ? (global float *)(src2) : 0;
     global float * pdst  = (global float *)(dst  + i01*nb1 + i02*nb2 + i03*nb3);
 
     float slope = 1.0f;
@@ -75,7 +79,7 @@ kernel void kernel_soft_max(
     }
 
     // parallel max
-    float lmax = -INFINITY;
+    float lmax = psrc2 ? psrc2[i02] : -INFINITY;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
     }
@@ -91,7 +95,11 @@ kernel void kernel_soft_max(
         pdst[i00] = exp_psrc0;
     }
 
-    const float sum = sub_group_reduce_add(lsum);
+    float sum = sub_group_reduce_add(lsum);
+
+    if (psrc2) {
+        sum += exp(psrc2[i02] - max);
+    }
 
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
         pdst[i00] /= sum;

From 1425f587a82bc303469b5c32759a2746ba4e1e20 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 8 Aug 2025 08:19:58 +0200
Subject: [PATCH 020/287] CUDA: attention sinks for mma FlashAttention (#15157)

---
 ggml/src/ggml-cuda/fattn-mma-f16.cuh | 87 ++++++++++++++++++++++------
 ggml/src/ggml-cuda/fattn.cu          |  2 +-
 ggml/src/ggml-cuda/ggml-cuda.cu      |  3 +-
 3 files changed, 73 insertions(+), 19 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 3712538441..39731baaeb 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -785,6 +785,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const half2  * const __restrict__ K_h2,
         const half2  * const __restrict__ V_h2,
         const half2  * const __restrict__ mask_h2,
+        const float  * const __restrict__ sinks_f,
         float2       * const __restrict__ dstk,
         float2       * const __restrict__ dstk_fixup,
         const float scale,
@@ -957,6 +958,52 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         }
     }
 
+    // If attention sinks are used, potentially re-scale if KQ_max is small.
+    // Also add the sink as a value to KQ_rowsum, this is done after synchonization of KQ_rowsum
+    //     so it's being done unconditionally for every thread.
+    if (!is_fixup && (np == 1 || threadIdx.y % np == 0) && sinks_f) {
+        float KQ_max_scale[cols_per_thread];
+#pragma unroll
+        for (int col = 0; col < cols_per_thread; ++col) {
+            static_assert(ntiles == 1 || ntiles == 2, "ntiles > 2 not implemented");
+            const int jc = ntiles == 1 ? 2*tile_C_VKQ::get_j(col/2) + col % 2 : tile_C_VKQ_16::get_i(col);
+            const float sink = sinks_f[jc % ncols2];
+
+            const float KQ_max_new = fmaxf(KQ_max[col], sink);
+            const float KQ_max_diff = KQ_max[col] - KQ_max_new;
+            KQ_max_scale[col] = expf(KQ_max_diff);
+            KQ_max[col] = KQ_max_new;
+
+            *((uint32_t *) &KQ_max_scale[col]) *= KQ_max_diff >= SOFTMAX_FTZ_THRESHOLD;
+
+            const float KQ_max_add = expf(sink - KQ_max_new);
+            KQ_rowsum[col] = KQ_max_scale[col]*KQ_rowsum[col] + KQ_max_add;
+        }
+
+        if (ntiles == 1) {
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
+#pragma unroll
+            for (int i = 0; i < DV/tile_C_VKQ::I; ++i) {
+#pragma unroll
+                for (int l = 0; l < tile_C_VKQ::ne; ++l) {
+                    VKQ_C[i].x[l] *= KQ_max_scale_h2;
+                }
+            }
+        } else {
+#pragma unroll
+            for (int col = 0; col < cols_per_thread; ++col) {
+                const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
+#pragma unroll
+                for (int i = 0; i < DV/tile_C_VKQ_16::J; ++i) {
+#pragma unroll
+                    for (int l0 = 0; l0 < tile_C_VKQ_16::ne; l0 += 2) {
+                        VKQ_C_16[i*ntiles/2 + col/2].x[l0 + col % 2] *= KQ_max_scale_h2;
+                    }
+                }
+            }
+        }
+    }
+
     // Combine VKQ accumulator values if np > 1.
     // It's also faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
     // So also write VKQ accumulators to shared memory in column-major format if np == 1.
@@ -1271,18 +1318,21 @@ static __global__ void flash_attn_ext_f16(
 
     while (kbc < kbc_stop && kb0_stop == iter_k) {
         const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
-        const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
-        const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*head) / iter_k; // j index of current tile.
+        const int zt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j); // head in units of ncols2
+        const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile.
 
-        const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02*(head*ncols2));
-        const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head*ncols2 / gqa_ratio));
+        const int head0 = zt * ncols2;
+
+        const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02* head0);
+        const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
         const half2  * mask_h2 = ncols2 == 1 && !mask ? nullptr :
             (const half2  *) (mask + nb33*(sequence % ne33) + nb31*jt*ncols1);
-        float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head*ncols2) * (DV/2);
+        float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head0) * (DV/2);
 
-        const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head*ncols2 / gqa_ratio));
+        const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));
+        const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr;
 
-        const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
+        const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f;
 
         const int kb0_start_kernel = kb0_start * kb_niter;
         int       kb0_stop_kernel  = kb0_stop  * kb_niter;
@@ -1295,12 +1345,12 @@ static __global__ void flash_attn_ext_f16(
         if (kb0_start == 0) {
             constexpr bool needs_fixup = false; // CUDA block is working on an entire tile.
             flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-                (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
+                (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
                  ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
         } else {
             constexpr bool needs_fixup = true; // CUDA block is working on the beginning of a tile.
             flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-                (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
+                (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
                  ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
         }
 
@@ -1316,18 +1366,21 @@ static __global__ void flash_attn_ext_f16(
     }
 
     const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
-    const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
-    const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*head) / iter_k; // j index of current tile.
+    const int zt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j); // head in units of ncols2
+    const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile.
 
-    const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02*(head*ncols2));
-    const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head*ncols2 / gqa_ratio));
+    const int head0 = zt * ncols2;
+
+    const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02* head0);
+    const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
     const half2  * mask_h2 = ncols2 == 1 && !mask ? nullptr :
         (const half2  *) (mask + nb33*(sequence % ne33) + nb31*jt*ncols1);
-    float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head*ncols2) * (DV/2);
+    float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head0) * (DV/2);
 
-    const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head*ncols2 / gqa_ratio));
+    const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));
+    const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr;
 
-    const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
+    const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f;
 
     const int kb0_start_kernel = kb0_start * kb_niter;
     int       kb0_stop_kernel  = kb0_stop  * kb_niter;
@@ -1339,7 +1392,7 @@ static __global__ void flash_attn_ext_f16(
     constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     constexpr bool needs_fixup = false;
     flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-        (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
+        (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
     GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index 8ddd0415b7..6c1185deac 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -282,7 +282,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
     // TODO: currently only vec implementation for sinks is supported [TAG_ATTN_SINKS]
-    if (sinks) {
+    if (sinks && !fp16_mma_available(cc)) {
         if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
             ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
         } else {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index ec7ab25518..19e9c405ea 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3532,7 +3532,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 return op->src[1]->ne[0] == 576 && op->src[2]->ne[0] == 512 && op->src[3] && gqa_ratio % 16 == 0;
             }
             // TODO: more general-purpose attention sink support [TAG_ATTN_SINKS]
-            if (op->src[4] && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) { // currently only sinks for head_size 64 and 128 are supported
+            if (op->src[4] && !fp16_mma_available(ggml_cuda_info().devices[dev_ctx->device].cc)
+                    && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) {
                 return false;
             }
             if (op->src[0]->ne[0] == 192) {

From 6c7e9a54406dbba5e53754a8f70a285414717b06 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <olivier.chafik@gmail.com>
Date: Fri, 8 Aug 2025 10:45:18 +0100
Subject: [PATCH 021/287] vendor: sync minja (#15161)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* vendor: sync minja

* Update minja.hpp

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 vendor/minja/chat-template.hpp | 15 ++++++++---
 vendor/minja/minja.hpp         | 49 +++++++++++++++++++++++-----------
 2 files changed, 44 insertions(+), 20 deletions(-)

diff --git a/vendor/minja/chat-template.hpp b/vendor/minja/chat-template.hpp
index cf113bf222..d5295b335b 100644
--- a/vendor/minja/chat-template.hpp
+++ b/vendor/minja/chat-template.hpp
@@ -162,8 +162,15 @@ class chat_template {
         }), false);
         caps_.supports_tools = contains(out, "some_tool");
 
-        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
-        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
+        const auto render_with_content = [&](const json & content) {
+            const json assistant_msg {{"role", "assistant"}, {"content", content}};
+            // Render two assistant messages as some templates like QwQ-32B are handling
+            // the content differently depending on whether it's the last message or not
+            // (to remove the <think> tag in all but the last message).
+            return try_raw_render(json::array({dummy_user_msg, assistant_msg, dummy_user_msg, assistant_msg}), {}, false);
+        };
+        auto out_empty = render_with_content("");
+        auto out_null = render_with_content(json());
         caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
 
         json j_null;
@@ -191,12 +198,12 @@ class chat_template {
             dummy_user_msg,
             make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
         }), {}, false);
-        auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
+        auto tool_call_renders_str_arguments = contains(out, "<parameter=argument_needle>") || contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
         out = try_raw_render(json::array({
             dummy_user_msg,
             make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
         }), {}, false);
-        auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
+        auto tool_call_renders_obj_arguments = contains(out, "<parameter=argument_needle>") || contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
 
         caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
         caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
diff --git a/vendor/minja/minja.hpp b/vendor/minja/minja.hpp
index dd107dccda..dad75efbba 100644
--- a/vendor/minja/minja.hpp
+++ b/vendor/minja/minja.hpp
@@ -1291,6 +1291,12 @@ public:
     }
 };
 
+static bool in(const Value & value, const Value & container) {
+  return (((container.is_array() || container.is_object()) && container.contains(value)) ||
+      (value.is_string() && container.is_string() &&
+        container.to_str().find(value.to_str()) != std::string::npos));
+}
+
 class BinaryOpExpr : public Expression {
 public:
     enum class Op { StrConcat, Add, Sub, Mul, MulMul, Div, DivDiv, Mod, Eq, Ne, Lt, Gt, Le, Ge, And, Or, In, NotIn, Is, IsNot };
@@ -1355,13 +1361,8 @@ public:
               case Op::Gt:        return l > r;
               case Op::Le:        return l <= r;
               case Op::Ge:        return l >= r;
-              case Op::In:        return (((r.is_array() || r.is_object()) && r.contains(l)) ||
-                                          (l.is_string() && r.is_string() &&
-                                            r.to_str().find(l.to_str()) != std::string::npos));
-              case Op::NotIn:
-                                  return !(((r.is_array() || r.is_object()) && r.contains(l)) ||
-                                            (l.is_string() && r.is_string() &&
-                                              r.to_str().find(l.to_str()) != std::string::npos));
+              case Op::In:        return in(l, r);
+              case Op::NotIn:     return !in(l, r);
               default:            break;
           }
           throw std::runtime_error("Unknown binary operator");
@@ -1500,6 +1501,13 @@ public:
           } else if (method->get_name() == "pop") {
             vargs.expectArgs("pop method", {1, 1}, {0, 0});
             return obj.pop(vargs.args[0]);
+          } else if (method->get_name() == "keys") {
+            vargs.expectArgs("keys method", {0, 0}, {0, 0});
+            auto result = Value::array();
+            for (const auto& key : obj.keys()) {
+              result.push_back(Value(key));
+            }
+            return result;
           } else if (method->get_name() == "get") {
             vargs.expectArgs("get method", {1, 2}, {0, 0});
             auto key = vargs.args[0];
@@ -1541,6 +1549,16 @@ public:
           } else if (method->get_name() == "capitalize") {
             vargs.expectArgs("capitalize method", {0, 0}, {0, 0});
             return Value(capitalize(str));
+          } else if (method->get_name() == "upper") {
+            vargs.expectArgs("upper method", {0, 0}, {0, 0});
+            auto result = str;
+            std::transform(result.begin(), result.end(), result.begin(), ::toupper);
+            return Value(result);
+          } else if (method->get_name() == "lower") {
+            vargs.expectArgs("lower method", {0, 0}, {0, 0});
+            auto result = str;
+            std::transform(result.begin(), result.end(), result.begin(), ::tolower);
+            return Value(result);
           } else if (method->get_name() == "endswith") {
             vargs.expectArgs("endswith method", {1, 1}, {0, 0});
             auto suffix = vargs.args[0].get<std::string>();
@@ -2646,15 +2664,11 @@ inline std::shared_ptr<Context> Context::builtins() {
     auto items = Value::array();
     if (args.contains("object")) {
       auto & obj = args.at("object");
-      if (obj.is_string()) {
-        auto json_obj = json::parse(obj.get<std::string>());
-        for (const auto & kv : json_obj.items()) {
-          items.push_back(Value::array({kv.key(), kv.value()}));
-        }
-      } else if (!obj.is_null()) {
-        for (auto & key : obj.keys()) {
-          items.push_back(Value::array({key, obj.at(key)}));
-        }
+      if (!obj.is_object()) {
+        throw std::runtime_error("Can only get item pairs from a mapping");
+      }
+      for (auto & key : obj.keys()) {
+        items.push_back(Value::array({key, obj.at(key)}));
       }
     }
     return items;
@@ -2782,6 +2796,9 @@ inline std::shared_ptr<Context> Context::builtins() {
       if (!items.is_array()) throw std::runtime_error("object is not iterable");
       return items;
   }));
+  globals.set("in", simple_function("in", { "item", "items" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
+      return in(args.at("item"), args.at("items"));
+  }));
   globals.set("unique", simple_function("unique", { "items" }, [](const std::shared_ptr<Context> &, Value & args) -> Value {
       auto & items = args.at("items");
       if (!items.is_array()) throw std::runtime_error("object is not iterable");

From cd6983d56d2cce94ecb86bb114ae8379a609073c Mon Sep 17 00:00:00 2001
From: AN Long <aisk@users.noreply.github.com>
Date: Fri, 8 Aug 2025 21:37:22 +0900
Subject: [PATCH 022/287] ggml : fix field name when new ggml_backend (#14944)

---
 ggml/src/ggml-blas/ggml-blas.cpp     | 8 ++++----
 ggml/src/ggml-cpu/ggml-cpu.cpp       | 8 ++++----
 ggml/src/ggml-cuda/ggml-cuda.cu      | 8 ++++----
 ggml/src/ggml-opencl/ggml-opencl.cpp | 8 ++++----
 ggml/src/ggml-rpc/ggml-rpc.cpp       | 8 ++++----
 ggml/src/ggml-sycl/ggml-sycl.cpp     | 8 ++++----
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 8 ++++----
 7 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp
index ec158dfac6..aeac2e5744 100644
--- a/ggml/src/ggml-blas/ggml-blas.cpp
+++ b/ggml/src/ggml-blas/ggml-blas.cpp
@@ -281,10 +281,10 @@ ggml_backend_t ggml_backend_blas_init(void) {
     ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
 
     ggml_backend_t backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_blas_guid(),
-        /* .interface = */ blas_backend_i,
-        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
-        /* .context   = */ ctx,
+        /* .guid    = */ ggml_backend_blas_guid(),
+        /* .iface   = */ blas_backend_i,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
+        /* .context = */ ctx,
     };
 
 #if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index e16cdc9d49..8dacd36714 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -214,10 +214,10 @@ ggml_backend_t ggml_backend_cpu_init(void) {
     ctx->abort_callback_data = NULL;
 
     ggml_backend_t cpu_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_cpu_guid(),
-        /* .interface = */ ggml_backend_cpu_i,
-        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
-        /* .context   = */ ctx,
+        /* .guid    = */ ggml_backend_cpu_guid(),
+        /* .iface   = */ ggml_backend_cpu_i,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
+        /* .context = */ ctx,
     };
 
     if (cpu_backend == NULL) {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 19e9c405ea..d9110491ec 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3799,10 +3799,10 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
     }
 
     ggml_backend_t cuda_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_cuda_guid(),
-        /* .interface = */ ggml_backend_cuda_interface,
-        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), device),
-        /* .context   = */ ctx,
+        /* .guid    = */ ggml_backend_cuda_guid(),
+        /* .iface   = */ ggml_backend_cuda_interface,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), device),
+        /* .context = */ ctx,
     };
 
     return cuda_backend;
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index b32d5da307..8ba1e00df7 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2624,10 +2624,10 @@ ggml_backend_t ggml_backend_opencl_init(void) {
     ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(dev);
 
     ggml_backend_t backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_opencl_guid(),
-        /* .interface = */ ggml_backend_opencl_i,
-        /* .device    = */ dev,
-        /* .context   = */ backend_ctx
+        /* .guid    = */ ggml_backend_opencl_guid(),
+        /* .iface   = */ ggml_backend_opencl_i,
+        /* .device  = */ dev,
+        /* .context = */ backend_ctx
     };
 
     return backend;
diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 29bc421d58..df6ba54076 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -823,10 +823,10 @@ ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
     };
 
     ggml_backend_t backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_rpc_guid(),
-        /* .interface = */ ggml_backend_rpc_interface,
-        /* .device    = */ ggml_backend_rpc_add_device(endpoint),
-        /* .context   = */ ctx
+        /* .guid    = */ ggml_backend_rpc_guid(),
+        /* .iface   = */ ggml_backend_rpc_interface,
+        /* .device  = */ ggml_backend_rpc_add_device(endpoint),
+        /* .context = */ ctx
     };
     return backend;
 }
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 6fa27418ce..3992dad01d 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4586,10 +4586,10 @@ ggml_backend_t ggml_backend_sycl_init(int device) {
     };
 
     ggml_backend_t sycl_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_sycl_guid(),
-        /* .interface = */ ggml_backend_sycl_interface,
-        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), device),
-        /* .context   = */ ctx
+        /* .guid    = */ ggml_backend_sycl_guid(),
+        /* .iface   = */ ggml_backend_sycl_interface,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), device),
+        /* .context = */ ctx
     };
 
     return sycl_backend;
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index b1cbbc9866..4070e248ba 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -10767,10 +10767,10 @@ ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
     ggml_vk_init(ctx, dev_num);
 
     ggml_backend_t vk_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_vk_guid(),
-        /* .interface = */ ggml_backend_vk_interface,
-        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num),
-        /* .context   = */ ctx,
+        /* .guid    = */ ggml_backend_vk_guid(),
+        /* .iface   = */ ggml_backend_vk_interface,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num),
+        /* .context = */ ctx,
     };
 
     return vk_backend;

From 4850b52aedceeb70bb4fe49f2d7cd1df6ee98682 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 8 Aug 2025 23:04:36 +0200
Subject: [PATCH 023/287] server-bench: external OAI servers, sqlite (#15179)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* server-bench: external OAI servers, sqlite

* Update scripts/server-bench.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update scripts/server-bench.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update scripts/server-bench.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* raise_for_status

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 scripts/server-bench.py | 70 ++++++++++++++++++++++++++++-------------
 1 file changed, 48 insertions(+), 22 deletions(-)

diff --git a/scripts/server-bench.py b/scripts/server-bench.py
index 9326be8d57..a716020173 100755
--- a/scripts/server-bench.py
+++ b/scripts/server-bench.py
@@ -4,6 +4,7 @@ import argparse
 import json
 import os
 import random
+import sqlite3
 import subprocess
 from time import sleep, time
 from typing import Optional, Union
@@ -47,6 +48,8 @@ def get_prompts_rng(prompt_lengths: list[int]) -> list[list[int]]:
 
 
 def get_server(path_server: str, path_log: Optional[str]) -> dict:
+    if path_server.startswith("http://") or path_server.startswith("https://"):
+        return {"process": None, "address": path_server, "fout": None}
     if os.environ.get("LLAMA_ARG_HOST") is None:
         logger.info("LLAMA_ARG_HOST not explicitly set, using 127.0.0.1")
         os.environ["LLAMA_ARG_HOST"] = "127.0.0.1"
@@ -89,15 +92,13 @@ def get_prompt_length(data: dict) -> int:
         f"{server_address}/apply-template",
         json={"messages": [{"role": "user", "content": data["prompt"], "stream": True}]}
     )
-    if response.status_code != 200:
-        raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
+    response.raise_for_status()
     prompt: str = json.loads(response.text)["prompt"]
     response = session.post(
         f"{server_address}/tokenize",
         json={"content": prompt, "add_special": True}
     )
-    if response.status_code != 200:
-        raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
+    response.raise_for_status()
     tokens: list[str] = json.loads(response.text)["tokens"]
     return len(tokens)
 
@@ -107,7 +108,12 @@ def send_prompt(data: dict) -> tuple[float, list[float]]:
     server_address: str = data["server_address"]
 
     t_submit = time()
-    if data["synthetic_prompt"]:
+    if data["external_server"]:
+        json_data: dict = {
+            "prompt": data["prompt"], "ignore_eos": True,
+            "seed": data["seed"], "max_tokens": data["n_predict"], "stream": True}
+        response = session.post(f"{server_address}/v1/completions", json=json_data, stream=True)
+    elif data["synthetic_prompt"]:
         json_data: dict = {
             "prompt": data["prompt"], "ignore_eos": True, "cache_prompt": False,
             "seed": data["seed"], "n_predict": data["n_predict"], "stream": True}
@@ -117,34 +123,38 @@ def send_prompt(data: dict) -> tuple[float, list[float]]:
             f"{server_address}/apply-template",
             json={"messages": [{"role": "user", "content": data["prompt"], "stream": True}]}
         )
-        if response.status_code != 200:
-            raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
+        response.raise_for_status()
         prompt: str = json.loads(response.text)["prompt"]
 
         json_data: dict = {"prompt": prompt, "seed": data["seed"], "n_predict": data["n_predict"], "stream": True}
         response = session.post(f"{server_address}/completion", json=json_data, stream=True)
+    response.raise_for_status()
 
+    lines = []
     token_arrival_times: list[float] = []
     for line in response.iter_lines(decode_unicode=False):
         if not line.startswith(b"data: "):
             continue
+        lines.append(line)
         token_arrival_times.append(time())
     token_arrival_times = token_arrival_times[:-1]
-
-    if response.status_code != 200:
-        raise RuntimeError(f"Server returned status code {response.status_code}: {response.text}")
+    if len(lines) > 1 and "timings" in json.loads(lines[-2][6:]):
+        token_arrival_times = token_arrival_times[:-1]
 
     return (t_submit, token_arrival_times)
 
 
-def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_prompts: int, n_predict: int, n_predict_min: int, seed_offset: int):
+def benchmark(
+        path_server: str, path_log: Optional[str], path_db: Optional[str], name: Optional[str], prompt_source: str, n_prompts: int,
+        n_predict: int, n_predict_min: int, seed_offset: int):
+    external_server: bool = path_server.startswith("http://") or path_server.startswith("https://")
     if os.environ.get("LLAMA_ARG_N_PARALLEL") is None:
         logger.info("LLAMA_ARG_N_PARALLEL not explicitly set, using 32")
         os.environ["LLAMA_ARG_N_PARALLEL"] = "32"
-    if os.environ.get("LLAMA_ARG_N_GPU_LAYERS") is None:
+    if not external_server and os.environ.get("LLAMA_ARG_N_GPU_LAYERS") is None:
         logger.info("LLAMA_ARG_N_GPU_LAYERS not explicitly set, using 999")
         os.environ["LLAMA_ARG_N_GPU_LAYERS"] = "999"
-    if os.environ.get("LLAMA_ARG_FLASH_ATTN") is None:
+    if not external_server and os.environ.get("LLAMA_ARG_FLASH_ATTN") is None:
         logger.info("LLAMA_ARG_FLASH_ATTN not explicitly set, using 'true'")
         os.environ["LLAMA_ARG_FLASH_ATTN"] = "true"
 
@@ -165,7 +175,7 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
     else:
         n_predict_min = n_predict
 
-    if os.environ.get("LLAMA_ARG_CTX_SIZE") is None:
+    if not external_server and os.environ.get("LLAMA_ARG_CTX_SIZE") is None:
         context_per_slot: int = int(1.05 * (n_predict + (np.max(prompt_n) if synthetic_prompts else 2048)))
         context_total: int = context_per_slot * parallel
         os.environ["LLAMA_ARG_CTX_SIZE"] = str(context_total)
@@ -176,6 +186,7 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
     try:
         server = get_server(path_server, path_log)
         server_address: str = server["address"]
+        assert external_server == (server["process"] is None)
 
         adapter = requests.adapters.HTTPAdapter(pool_connections=parallel, pool_maxsize=parallel)  # type: ignore
         session = requests.Session()
@@ -188,8 +199,9 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
             if seed_offset >= 0:
                 random.seed(3 * (seed_offset + 1000 * i) + 1)
             data.append({
-                "session": session, "server_address": server_address, "prompt": p, "synthetic_prompt": synthetic_prompts,
-                "n_predict": random.randint(n_predict_min, n_predict), "seed": (3 * (seed_offset + 1000 * i) + 2) if seed_offset >= 0 else -1})
+                "session": session, "server_address": server_address, "external_server": external_server, "prompt": p,
+                "synthetic_prompt": synthetic_prompts, "n_predict": random.randint(n_predict_min, n_predict),
+                "seed": (3 * (seed_offset + 1000 * i) + 2) if seed_offset >= 0 else -1})
 
         if not synthetic_prompts:
             logger.info("Getting the prompt lengths...")
@@ -199,7 +211,7 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
         t0 = time()
         results: list[tuple[float, list[float]]] = thread_map(send_prompt, data, max_workers=parallel, chunksize=1)
     finally:
-        if server is not None:
+        if server is not None and server["process"] is not None:
             server["process"].terminate()
             server["process"].wait()
         if session is not None:
@@ -233,15 +245,24 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
     logger.info(f"Average generation depth:          {depth_sum / token_t.shape[0]:.2f} tokens")
     logger.info(f"Average total generation speed:    {token_t.shape[0] / token_t_last:.2f} tokens/s")
     logger.info(f"Average generation speed per slot: {token_t.shape[0] / (parallel * token_t_last):.2f} tokens/s / slot")
-    logger.info("")
-    logger.info(
-        "The above numbers are the speeds as observed by the Python script and may differ from the performance reported by the server, "
-        "particularly when the server is fast vs. the network or Python script (e.g. when serving a very small model).")
+
+    if path_db is not None:
+        con = sqlite3.connect(path_db)
+        cursor = con.cursor()
+        cursor.execute(
+            "CREATE TABLE IF NOT EXISTS server_bench"
+            "(name TEXT, n_parallel INTEGER, prompt_source TEXT, n_prompts INTEGER, "
+            "n_predict INTEGER, n_predict_min INTEGER, seed_offset INTEGER, runtime REAL);")
+        cursor.execute(
+            "INSERT INTO server_bench VALUES (?, ?, ?, ?, ?, ?, ?, ?);",
+            [name, parallel, prompt_source, n_prompts, n_predict, n_predict_min, seed_offset, token_t_last])
+        con.commit()
 
     plt.figure()
     plt.scatter(prompt_n, 1e3 * prompt_t, s=10.0, marker=".", alpha=0.25)
     plt.xlim(0, 1.05e0 * np.max(prompt_n))
     plt.ylim(0, 1.05e3 * np.max(prompt_t))
+    plt.title(name or "")
     plt.xlabel("Prompt length [tokens]")
     plt.ylabel("Time to first token [ms]")
     plt.savefig("prompt_time.png", dpi=240)
@@ -250,6 +271,7 @@ def benchmark(path_server: str, path_log: Optional[str], prompt_source: str, n_p
     plt.figure()
     plt.hist(token_t, np.arange(0, bin_max))
     plt.xlim(0, bin_max + 1)
+    plt.title(name or "")
     plt.xlabel("Time [s]")
     plt.ylabel("Num. tokens generated per second")
     plt.savefig("gen_rate.png", dpi=240)
@@ -259,9 +281,13 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Tool for benchmarking the throughput of the llama.cpp HTTP server. "
         "Results are printed to console and visualized as plots (saved to current working directory). "
-        "To pass arguments such as the model path to the server, set the corresponding environment variables (see llama-server --help).")
+        "To pass arguments such as the model path to the server, set the corresponding environment variables (see llama-server --help). "
+        "The reported numbers are the speeds as observed by the Python script and may differ from the performance reported by the server, "
+        "particularly when the server is fast vs. the network or Python script (e.g. when serving a very small model).")
     parser.add_argument("--path_server", type=str, default="llama-server", help="Path to the llama.cpp server binary")
     parser.add_argument("--path_log", type=str, default="server-bench-{port}.log", help="Path to the model to use for the benchmark")
+    parser.add_argument("--path_db", type=str, default=None, help="Path to an sqlite database to store the benchmark results in")
+    parser.add_argument("--name", type=str, default=None, help="Name to label plots and database entries with")
     parser.add_argument(
         "--prompt_source", type=str, default="rng-1024-2048",
         help="How to get the prompts for the benchmark, either 'mmlu' for MMLU questions or "

From e54d41befcc1575f4c898c5ff4ef43970cead75f Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Fri, 8 Aug 2025 17:48:26 -0400
Subject: [PATCH 024/287] gguf-py : add Numpy MXFP4 de/quantization support
 (#15111)

* gguf-py : add MXFP4 de/quantization support

* ggml-quants : handle zero amax for MXFP4
---
 ggml/src/ggml-quants.c       |  2 +-
 gguf-py/gguf/quants.py       | 57 +++++++++++++++++++++++++++++++++---
 gguf-py/tests/test_quants.py | 17 ++++++++---
 3 files changed, 67 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index a57d2a16d6..94f6405ca1 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -288,7 +288,7 @@ void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RE
             }
         }
 
-        const uint8_t e = (uint8_t) (floorf(log2f(amax)) - 2 + 127);
+        const uint8_t e = amax > 0.0f ? (uint8_t) (floorf(log2f(amax)) - 2 + 127) : 0;
 
         const float d = GGML_E8M0_TO_FP32_HALF(e);
 
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
index 3c8ba82e19..31845ea6ee 100644
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -228,8 +228,7 @@ class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
         d = max / -8
         with np.errstate(divide="ignore"):
             id = np.where(d == 0, 0, 1 / d)
-        # FIXME: Q4_0's reference rounding is cursed and depends on FMA
-        qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
+        qs = np.trunc((blocks * id) + np.float32(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
 
         qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
         qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))
@@ -300,8 +299,7 @@ class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0):
         d = max / -16
         with np.errstate(divide="ignore"):
             id = np.where(d == 0, 0, 1 / d)
-        # FIXME: Q5_0's reference rounding is cursed and depends on FMA
-        q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
+        q = np.trunc((blocks * id) + np.float32(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
 
         qs = q.reshape((n_blocks, 2, cls.block_size // 2))
         qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))
@@ -655,6 +653,57 @@ class TQ2_0(__Quant, qtype=GGMLQuantizationType.TQ2_0):
         return (d * qs.astype(np.float32))
 
 
+class MXFP4(__Quant, qtype=GGMLQuantizationType.MXFP4):
+    # e2m1 values (doubled)
+    # ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
+    kvalues = (0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12)
+
+    @staticmethod
+    # see ggml_e8m0_to_fp32_half in ggml-impl.h
+    def e8m0_to_fp32_half(x: np.ndarray) -> np.ndarray:
+        bits = np.where(x < 2, np.uint32(0x00200000) << np.uint32(x), np.uint32(x - 1) << np.uint32(23))
+        return bits.view(np.float32)
+
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d = abs(blocks).max(axis=-1, keepdims=True)
+
+        with np.errstate(divide="ignore"):
+            e = np.where(d > 0, np.floor(np.log2(d)) - 2 + 127, 0).astype(np.uint8)
+
+        d = cls.e8m0_to_fp32_half(e)
+
+        kvalues = np.array(cls.kvalues, dtype=np.int8).reshape((1, 1, 16))
+
+        errs = np.abs(d.reshape((n_blocks, 1, 1)) * kvalues.astype(np.float32) - blocks.reshape((n_blocks, cls.block_size, 1)))
+        best = np.argmin(errs, axis=-1, keepdims=True)
+
+        qs = best.reshape(n_blocks, 2, cls.block_size // 2).astype(np.uint8)
+        qs = qs[:, 0] | (qs[:, 1] << np.uint8(4))
+
+        qs = qs.reshape((n_blocks, cls.block_size // 2))
+
+        return np.concatenate([e, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        e, qs = np.hsplit(blocks, [1])
+
+        d = cls.e8m0_to_fp32_half(e)
+
+        qs = qs.reshape((n_blocks, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1))
+        qs = (qs & np.uint8(0x0F)).view(np.int8)
+
+        kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16)
+        qs = np.take_along_axis(kvalues, qs, axis=-1).reshape((n_blocks, cls.block_size))
+
+        return (d * qs.astype(np.float32))
+
+
 class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
     ksigns: bytes = (
         b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"
diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py
index f04d5acce2..172fa0018a 100755
--- a/gguf-py/tests/test_quants.py
+++ b/gguf-py/tests/test_quants.py
@@ -67,6 +67,7 @@ class GGMLQuants:
             "q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
             "q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
             "tq1_0", "tq2_0",
+            "mxfp4",
             "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
             "iq4_nl", "iq4_xs",
         ):
@@ -140,14 +141,21 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
         return False
 
 
-def do_test(libggml_path: Path, quick: bool = False):
+def do_test(libggml_path: Path, quick: bool = False, user_type: GGMLQuantizationType | None = None):
     ggml_quants = GGMLQuants(libggml_path)
 
     np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n})
 
     r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False)
+    # test zero blocks
+    r[0, 0, :] = 0
+    ## Maybe test infinities? (can make NANs, not really useful in practice)
+    # r[0, 1, 0] = np.inf
+    # r[0, 2, 0] = -np.inf
+    # r[0, 3, 0] = np.inf
+    # r[0, 3, 1] = -np.inf
 
-    for qtype in (GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()):
+    for qtype in ((GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()) if user_type is None else (user_type,)):
         has_dequantize = False
         has_quantize = False
 
@@ -228,11 +236,12 @@ def do_test(libggml_path: Path, quick: bool = False):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation")
-    parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "ggml" / "src" / "libggml.so", help="The path to libggml.so")
+    parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "bin" / "libggml.so", help="The path to libggml.so")
     parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary")
+    parser.add_argument("--type", type=str, help="The quant type to test (all by default)")
 
     args = parser.parse_args()
 
     logging.basicConfig(level=logging.DEBUG)
 
-    do_test(args.libggml, args.quick)
+    do_test(args.libggml, args.quick, GGMLQuantizationType[args.type.upper()] if args.type is not None else None)

From 34c9d765bf173c551398f1e7fa4595019bc53bab Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Sat, 9 Aug 2025 20:00:24 +0800
Subject: [PATCH 025/287] CUDA: add attention sinks for tile and wmma (#15178)

* CUDA: add attention sinks for tile and wmma

* Review: formatting changes + remove syncthreads from tile + remove warp_reduce_max from wmma
---
 ggml/src/ggml-cuda/fattn-tile-f16.cu | 34 ++++++++++++++--
 ggml/src/ggml-cuda/fattn-tile-f32.cu | 36 +++++++++++++++--
 ggml/src/ggml-cuda/fattn-wmma-f16.cu | 58 +++++++++++++++++++++++++---
 ggml/src/ggml-cuda/fattn.cu          | 11 ------
 4 files changed, 115 insertions(+), 24 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
index 0fcfaa32ea..1e23f8f79c 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -49,10 +49,11 @@ static __global__ void flash_attn_tile_ext_f16(
     const int sequence = blockIdx.z / ne02;
     const int head = blockIdx.z - sequence*ne02;
     const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float2 * Q_f2  = (const float2 *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
-    const half2  * K_h2  = (const half2  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
-    const half2  * V_h2  = (const half2  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
-    const half   * maskh = (const half   *) (mask + nb33*(sequence % ne33)                           + nb31*ic0);
+    const float2 * Q_f2   = (const float2 *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
+    const half2  * K_h2   = (const half2  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
+    const half2  * V_h2   = (const half2  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
+    const half   * maskh  = (const half   *) (mask  + nb33*(sequence % ne33)                          + nb31*ic0);
+    const float  * sinksf = (const float  *) (sinks);
 
     const int stride_KV2 = nb11 / sizeof(half2);
 
@@ -242,6 +243,31 @@ static __global__ void flash_attn_tile_ext_f16(
         __syncthreads();
     }
 
+    //Attention sink: adjust running max and sum once per head
+    if (sinksf && blockIdx.y == 0) {
+        const half sink = __float2half(sinksf[head]);
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            half kqmax_new_j = fmaxf(kqmax[j0/nwarps], sink);
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const half2 KQ_max_scale = __half2half2(hexp(kqmax[j0/nwarps] - kqmax_new_j));
+            kqmax[j0/nwarps] = kqmax_new_j;
+
+            const half val = hexp(sink - kqmax[j0/nwarps]);
+            kqsum[j0/nwarps] = kqsum[j0/nwarps] * KQ_max_scale;
+            if (threadIdx.x == 0) {
+                kqsum[j0/nwarps].x = __hadd(kqsum[j0/nwarps].x, val);
+            }
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                VKQ[j0/nwarps][i0/WARP_SIZE] *= KQ_max_scale;
+            }
+        }
+    }
+
     float2 * dst2 = (float2 *) dst;
 
 #pragma unroll
diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu
index 23550cbbd9..c58194937d 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -60,10 +60,11 @@ static __global__ void flash_attn_tile_ext_f32(
     const int sequence = blockIdx.z / ne02;
     const int head = blockIdx.z - sequence*ne02;
     const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float2 * Q_f2  = (const float2 *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
-    const half2  * K_h2  = (const half2  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
-    const half2  * V_h2  = (const half2  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
-    const half   * maskh = (const half   *) (mask + nb33*(sequence % ne33)                           + nb31*ic0);
+    const float2 * Q_f2   = (const float2 *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
+    const half2  * K_h2   = (const half2  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
+    const half2  * V_h2   = (const half2  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
+    const half   * maskh  = (const half   *) (mask  + nb33*(sequence % ne33)                          + nb31*ic0);
+    const float  * sinksf = (const float  *) (sinks);
 
     const int stride_KV2 = nb11 / sizeof(half2);
 
@@ -252,6 +253,33 @@ static __global__ void flash_attn_tile_ext_f32(
         __syncthreads();
     }
 
+
+    //Attention sink: adjust running max and sum once per head
+    if (sinksf && blockIdx.y == 0) {
+        const float sink = sinksf[head];
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            float kqmax_new_j = fmaxf(kqmax[j0/nwarps], sink);
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new_j);
+            kqmax[j0/nwarps] = kqmax_new_j;
+
+            const float val = expf(sink - kqmax[j0/nwarps]);
+            kqsum[j0/nwarps] = kqsum[j0/nwarps] * KQ_max_scale;
+            if (threadIdx.x == 0) {
+                kqsum[j0/nwarps] += val;
+            }
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                VKQ[j0/nwarps][i0/WARP_SIZE].x *= KQ_max_scale;
+                VKQ[j0/nwarps][i0/WARP_SIZE].y *= KQ_max_scale;
+            }
+        }
+    }
+
     float2 * dst2 = (float2 *) dst;
 
 #pragma unroll
diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index 93d4d81021..fdc4d17da2 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -82,11 +82,12 @@ static __global__ void flash_attn_ext_f16(
     const int sequence = blockIdx.z / ne02;
     const int head = blockIdx.z - sequence*ne02;
     const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float * Q_f   = (const float *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
-    const half  * K_h   = (const half  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
-    const half  * V_h   = (const half  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
-    const half  * maskh = (const half  *) (mask + nb33*(sequence % ne33)                           + nb31*ic0);
-    const half2 * mask2 = (const half2 *)  maskh;
+    const float * Q_f    = (const float *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
+    const half  * K_h    = (const half  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
+    const half  * V_h    = (const half  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
+    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33)                           + nb31*ic0);
+    const half2 * mask2  = (const half2 *)  maskh;
+    const float * sinksf = (const float *) sinks;
 
     const int stride_Q  = nb01 / sizeof(float);
     const int stride_KV = nb11 / sizeof(half);
@@ -381,6 +382,53 @@ static __global__ void flash_attn_ext_f16(
         __syncthreads();
     }
 
+    // Apply attention sinks
+    if (sinksf && blockIdx.y == 0) {
+        const float sinkf = sinksf[head];
+        const half  sinkh = __float2half(sinkf);
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (std::is_same<KQ_acc_t, float>::value) {
+                float kqmax_new = fmaxf(KQ_max_f[j0/nwarps], sinkf);
+
+                const float KQ_max_scale = expf(KQ_max_f[j0/nwarps] - kqmax_new);
+                KQ_max_f[j0/nwarps] = kqmax_new;
+
+                KQ_rowsum_f[j0/nwarps] = KQ_rowsum_f[j0/nwarps] * KQ_max_scale + expf(sinkf - KQ_max_f[j0/nwarps]);
+
+                const half2 scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
+                    const int i = i0 + threadIdx.x;
+                    if (i0 + warp_size > D/2 && i >= D/2) break;
+                    VKQ2[j*(D_padded/2) + i] *= scale_h2;
+                }
+            } else {
+                half kqmax_old = __low2half(KQ_max_h2[j0/nwarps]);
+                half kqmax_new = fmaxf(kqmax_old, sinkh);
+                KQ_max_h2[j0/nwarps] = __half2half2(kqmax_new);
+
+                const half  KQ_max_scale_h = hexp(kqmax_old - kqmax_new);
+                const half2 KQ_max_scale   = __half2half2(KQ_max_scale_h);
+
+                KQ_rowsum_h2[j0/nwarps] = KQ_rowsum_h2[j0/nwarps] * KQ_max_scale;
+                const half val = hexp(sinkh - kqmax_new);
+                KQ_rowsum_h2[j0/nwarps].x = __hadd(KQ_rowsum_h2[j0/nwarps].x, val);
+
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
+                    const int i = i0 + threadIdx.x;
+                    if (i0 + warp_size > D/2 && i >= D/2) break;
+                    VKQ2[j*(D_padded/2) + i] *= KQ_max_scale;
+                }
+            }
+        }
+
+        __syncthreads();
+    }
 #pragma unroll
     for (int j0 = 0; j0 < ncols; j0 += nwarps) {
         const int j_VKQ = j0 + threadIdx.y;
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index 6c1185deac..22e90d0e7b 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -274,23 +274,12 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const ggml_tensor * K     = dst->src[1];
     const ggml_tensor * V     = dst->src[2];
     const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
 
     ggml_cuda_set_device(ctx.device);
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
-    // TODO: currently only vec implementation for sinks is supported [TAG_ATTN_SINKS]
-    if (sinks && !fp16_mma_available(cc)) {
-        if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
-            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        }
-        return;
-    }
-
 #if defined(GGML_HIP_ROCWMMA_FATTN)
     if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);

From 79c1160b073b8148a404f3dd2584be1606dccc66 Mon Sep 17 00:00:00 2001
From: David Zhao <90013954+Your-Cheese@users.noreply.github.com>
Date: Sat, 9 Aug 2025 13:29:43 -0500
Subject: [PATCH 026/287] cuda: refactored ssm_scan and use CUB (#13291)

* cuda: refactored ssm_scan to use CUB

* fixed compilation error when when not using CUB

* assign L to constant and use size_t instead of int

* deduplicated functions

* change min blocks per mp to 1

* Use cub load and store warp transpose

* suppress clang warning
---
 ggml/src/ggml-cuda/ssm-scan.cu | 224 ++++++++++++++++++++++-----------
 1 file changed, 153 insertions(+), 71 deletions(-)

diff --git a/ggml/src/ggml-cuda/ssm-scan.cu b/ggml/src/ggml-cuda/ssm-scan.cu
index c9184398b4..dc9a7d58d0 100644
--- a/ggml/src/ggml-cuda/ssm-scan.cu
+++ b/ggml/src/ggml-cuda/ssm-scan.cu
@@ -1,87 +1,117 @@
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
+#define USE_CUB
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
+
+#ifdef USE_CUB
+#include <cub/cub.cuh>
+using namespace cub;
+#endif // USE_CUB
+
 #include "ssm-scan.cuh"
 
-template <size_t splitD, size_t N>
-__global__ void __launch_bounds__(splitD, 2)
-    ssm_scan_f32(const float * __restrict__ src0, const float * __restrict__ src1, const float * __restrict__ src2,
-                 const float * __restrict__ src3, const float * __restrict__ src4, const float * __restrict__ src5,
+// We would like to keep pragma unroll for cases where L_template is not 0,
+// so we suppress the clang transformation warning.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpass-failed"
+#endif // __clang__
+template <size_t splitD, size_t N, size_t L_template>
+__global__ void __launch_bounds__(splitD, 1)
+    ssm_scan_f32(const float *__restrict__ src0, const float *__restrict__ src1, const float *__restrict__ src2,
+                 const float *__restrict__ src3, const float *__restrict__ src4, const float *__restrict__ src5,
                  const int32_t * __restrict__ src6, float * __restrict__ dst,
                  const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3,
                  const int src2_nb1, const int src2_nb2, const int src3_nb1,
                  const int src4_nb2, const int src4_nb3, const int src5_nb2, const int src5_nb3,
-                 const int64_t s_off, const int64_t d_inner, const int64_t L) {
+                 const int64_t s_off, const int64_t d_inner, const int64_t L_param)
+{
+    const size_t L = L_template == 0 ? L_param : L_template;
+    const float *s0_block = (const float *)((const char *)src0 + src6[blockIdx.x] * src0_nb3 + blockIdx.y * splitD * src0_nb2);
+    const float *x_block = (const float *)((const char *)src1 + (blockIdx.x * src1_nb3) + blockIdx.y * splitD * sizeof(float));
+    const float *dt_block = (const float *)((const char *)src2 + (blockIdx.x * src2_nb2) + blockIdx.y * splitD * sizeof(float));
+    const float *A_block = (const float *)((const char *)src3 + blockIdx.y * splitD * src3_nb1);
+    const float *B_block = (const float *)((const char *)src4 + (blockIdx.x * src4_nb3));
+    const float *C_block = (const float *)((const char *)src5 + (blockIdx.x * src5_nb3));
+    float *y_block = (float *)((char *)dst + (blockIdx.x * d_inner * L * sizeof(float)) + blockIdx.y * splitD * sizeof(float));
+    float *s_block = (float *)((char *)dst + s_off + blockIdx.x * src0_nb3 + blockIdx.y * splitD * src0_nb2);
 
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
-    const int bidx = blockIdx.x;  // split along B (sequences)
-    const int bidy = blockIdx.y;  // split along D (d_inner)
-    const int tid  = threadIdx.x;
-    const int wid  = tid / 32;
-    const int wtid = tid % 32;
-
-    extern __shared__ float smem[];
-    const int               stride_sA  = N + 1;
-    const int               stride_ss0 = N + 1;
-    float *                 smem_A     = smem;
-    float *                 smem_s0    = smem_A + splitD * stride_sA;
-
-    const float * s0_block = (const float *) ((const char *) src0 + src6[bidx] * src0_nb3 + bidy * splitD * src0_nb2);
-    const float * x_block  = (const float *) ((const char *) src1 + (bidx * src1_nb3) + bidy * splitD * sizeof(float));
-    const float * dt_block = (const float *) ((const char *) src2 + (bidx * src2_nb2) + bidy * splitD * sizeof(float));
-    const float * A_block  = (const float *) ((const char *) src3 + bidy * splitD * src3_nb1);
-    const float * B_block  = (const float *) ((const char *) src4 + (bidx * src4_nb3));
-    const float * C_block  = (const float *) ((const char *) src5 + (bidx * src5_nb3));
-    float *       y_block  = (float *) ((char *) dst + (bidx * d_inner * L * sizeof(float)) + bidy * splitD * sizeof(float));
-    float *       s_block  = (float *) ((char *) dst + s_off + bidx * src0_nb3 + bidy * splitD * src0_nb2);
-
-    const int stride_s0 = src0_nb2 / sizeof(float);
-    const int stride_x  = src1_nb2 / sizeof(float);
+    const int stride_x = src1_nb2 / sizeof(float);
     const int stride_dt = src2_nb1 / sizeof(float);
-    const int stride_A  = src3_nb1 / sizeof(float);
-    const int stride_B  = src4_nb2 / sizeof(float);
-    const int stride_C  = src5_nb2 / sizeof(float);
-    const int stride_s  = stride_s0;
-    const int stride_y  = d_inner;
+    const int stride_B = src4_nb2 / sizeof(float);
+    const int stride_C = src5_nb2 / sizeof(float);
+    const int stride_y = d_inner;
 
-    // can N not be 16? for example 32?
-    if (N == 16) {
+    float regA[N];
+    float regs0[N];
+
+    __shared__ float smemB[N];
+    __shared__ float smemC[N];
+
+#ifdef USE_CUB
+    using BlockLoad = cub::BlockLoad<float, splitD, N, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
+    using BlockStore = cub::BlockStore<float, splitD, N, cub::BLOCK_STORE_WARP_TRANSPOSE>;
+
+    union CubTempStorage {
+        typename BlockLoad::TempStorage load_temp;
+        typename BlockStore::TempStorage store_temp;
+    };
+    __shared__ CubTempStorage cub_temp_storage;
+
+    BlockLoad(cub_temp_storage.load_temp).Load(A_block, regA);
+    BlockLoad(cub_temp_storage.load_temp).Load(s0_block, regs0);
+#else
+    const int stride_s0 = src0_nb2 / sizeof(float);
+    const int stride_A = src3_nb1 / sizeof(float);
 #pragma unroll
-        for (size_t i = 0; i < splitD / 4; i += 2) {
-            float value = A_block[(wid * warp_size + i) * stride_A + wtid];
-            // todo: bank conflict
-            // I am always confused with how to use the swizzling method to solve
-            // bank conflit. Hoping somebody can tell me.
-            smem_A[(wid * warp_size + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
-        }
-#pragma unroll
-        for (size_t i = 0; i < splitD / 4; i += 2) {
-            float value = s0_block[(wid * warp_size + i) * stride_s0 + wtid];
-            smem_s0[(wid * warp_size + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
-        }
+    for (size_t n = 0; n < N; ++n)
+    {
+        regA[n] = A_block[threadIdx.x * stride_A + n];
+        regs0[n] = s0_block[threadIdx.x * stride_s0 + n];
     }
+#endif
 
-    __syncthreads();
-
-    for (int64_t i = 0; i < L; i++) {
-        float dt_soft_plus = dt_block[i * stride_dt + tid];
-        if (dt_soft_plus <= 20.0f) {
-            dt_soft_plus = log1pf(exp(dt_soft_plus));
-        }
-        float x_dt = x_block[i * stride_x + tid] * dt_soft_plus;
-        float sumf = 0.0f;
 #pragma unroll
-        for (size_t j = 0; j < N; j++) {
-            float state = (smem_s0[tid * stride_ss0 + j] * expf(dt_soft_plus * smem_A[tid * stride_sA + j])) +
-                          (B_block[i * stride_B + j] * x_dt);
-            sumf += state * C_block[i * stride_C + j];
-            if (i == L - 1) {
-                s_block[tid * stride_s + j] = state;
-            } else {
-                smem_s0[tid * stride_ss0 + j] = state;
-            }
+    for (size_t i = 0; i < L; i++)
+    {
+        if (threadIdx.x < N)
+        {
+            smemB[threadIdx.x] = B_block[i * stride_B + threadIdx.x];
+            smemC[threadIdx.x] = C_block[i * stride_C + threadIdx.x];
         }
         __syncthreads();
-        y_block[i * stride_y + tid] = sumf;
+
+        float dt_soft_plus = dt_block[i * stride_dt + threadIdx.x];
+        if (dt_soft_plus <= 20.0f)
+        {
+            dt_soft_plus = log1pf(expf(dt_soft_plus));
+        }
+        float x_dt = x_block[i * stride_x + threadIdx.x] * dt_soft_plus;
+
+        float sumf = 0.0f;
+#pragma unroll
+        for (size_t n = 0; n < N; n++)
+        {
+            float state = regs0[n] * expf(dt_soft_plus * regA[n]) + smemB[n] * x_dt;
+            sumf += state * smemC[n];
+            regs0[n] = state;
+        }
+        y_block[i * stride_y + threadIdx.x] = sumf;
     }
+
+#ifdef USE_CUB
+    BlockStore(cub_temp_storage.store_temp).Store(s_block, regs0);
+#else
+    const int stride_s = stride_s0;
+#pragma unroll
+    for (size_t n = 0; n < N; ++n)
+    {
+        s_block[threadIdx.x * stride_s + n] = regs0[n];
+    }
+#endif
 }
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif // __clang__
 
 // assumes as many threads as d_state
 template <int splitH, int d_state>
@@ -201,11 +231,11 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
                               const int src5_nb3, const int64_t s_off, const int64_t d_state, const int64_t head_dim,
                               const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq,
                               cudaStream_t stream) {
+    const int threads = 128;
     // NOTE: if you change conditions here, be sure to update the corresponding supports_op condition!
     if (src3_nb1 == sizeof(float)) {
         // Mamba-2
         if (d_state == 128) {
-            const int threads = 128;
             GGML_ASSERT(d_state % threads == 0);
             // NOTE: can be any power of two between 4 and 64
             const int splitH = 16;
@@ -229,7 +259,6 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
             GGML_ABORT("doesn't support d_state!=(128 or 256).");
         }
     } else {
-        const int threads = 128;
         // Mamba-1
         GGML_ASSERT(n_head % threads == 0);
         GGML_ASSERT(head_dim == 1);
@@ -237,10 +266,63 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
         const dim3 blocks(n_seq, (n_head + threads - 1) / threads, 1);
         const int  smem_size = (threads * (d_state + 1) * 2) * sizeof(float);
         if (d_state == 16) {
-            ssm_scan_f32<128, 16><<<blocks, threads, smem_size, stream>>>(
-                src0, src1, src2, src3, src4, src5, src6, dst,
+            switch (n_tok)
+            {
+            case 1:
+                ssm_scan_f32<threads, 16, 1><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
                 src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
                 src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 2:
+                ssm_scan_f32<threads, 16, 2><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 3:
+                ssm_scan_f32<threads, 16, 3><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 4:
+                ssm_scan_f32<threads, 16, 4><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 5:
+                ssm_scan_f32<threads, 16, 5><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 6:
+                ssm_scan_f32<threads, 16, 6><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 7:
+                ssm_scan_f32<threads, 16, 7><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            case 8:
+                ssm_scan_f32<threads, 16, 8><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            default:
+                ssm_scan_f32<threads, 16, 0><<<blocks, threads, smem_size, stream>>>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
+                src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
+                break;
+            }
         } else {
             GGML_ABORT("doesn't support d_state!=16.");
         }

From 002cb1bb3345967fbe0fa7766c2d94c2da31ef45 Mon Sep 17 00:00:00 2001
From: Charles Xu <charles.xu@arm.com>
Date: Mon, 11 Aug 2025 09:59:26 +0200
Subject: [PATCH 027/287] kleidiai: fix unsigned overflow bug (#15150)

* kleidiai: fix unsigned overflow bug

* address review comments
---
 ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
index 3a513a55d7..dff8fa244a 100644
--- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
+++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
@@ -259,7 +259,10 @@ class tensor_traits : public ggml::cpu::tensor_traits {
                 const int64_t m_start      = 0;
 
                 const int64_t n_step      = static_cast<int64_t>(kernel->get_n_step());
-                const int64_t num_threads = KAI_MIN(n / n_step, nth);
+                int64_t num_threads       = KAI_MIN(n / n_step, nth);
+                if (num_threads <= 0) {
+                    num_threads = 1;
+                }
 
                 if (ith < num_threads) {
                     const int64_t num_n_per_thread0   = round_down(n / num_threads, n_step);
@@ -309,7 +312,8 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         GGML_ASSERT(kernel);
 
         const int ith = params->ith;
-        const int nth = params->nth;
+        const int nth_raw = params->nth;
+        const int nth = nth_raw > 0 ? nth_raw : 1;
 
         const size_t k = ne00;
         const size_t m = ne11;
@@ -327,9 +331,12 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step);
         const size_t n_start = ith * num_n_per_thread;
 
-        size_t n_to_process = num_n_per_thread;
-        if ((n_start + n_to_process) > n) {
-            n_to_process = n - n_start;
+        size_t n_to_process = 0;
+        if (n_start < n) {
+            n_to_process = num_n_per_thread;
+            if ((n_start + n_to_process) > n) {
+                n_to_process = n - n_start;
+            }
         }
 
         // Calculate number of columns to be processed per thread
@@ -361,8 +368,10 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         const void* lhs_ptr            = (const void*)((const char *)lhs_packed + lhs_packed_offset);
         float *dst_ptr                 = reinterpret_cast<float *>(static_cast<uint8_t *>(dst->data) + dst_offset);
 
-        variant_call<void>(kernel->run_kernel, m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride,
-                           sizeof(float), -FLT_MAX, FLT_MAX);
+        if (n_to_process > 0) {
+            variant_call<void>(kernel->run_kernel, m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride,
+                               sizeof(float), -FLT_MAX, FLT_MAX);
+        }
 
         return true;
     }

From a3a7874272e5a060079658eb5cca4617b7f99062 Mon Sep 17 00:00:00 2001
From: Julien Denize <40604584+juliendenize@users.noreply.github.com>
Date: Mon, 11 Aug 2025 10:07:49 +0200
Subject: [PATCH 028/287] convert : improve Mistral models integration (#14737)

* Improve Mistral models integration with llama.cpp

* Revert changes and fix gguf

* Revert change

* refactor convert_mistral_to_gguf.py in convert_hf_to_gguf.py

* Revert collateral

* Rename model name

* refactor

* revert

* remove duplicate

* Remove duplication code

* Fixes

* Fix flake issues

* Apply comments

* Apply comments

* Apply comments

* Fix remote

* add default chat template

* Revert

* nit
---
 convert_hf_to_gguf.py          | 354 ++++++++++++++++++++++++---------
 convert_lora_to_gguf.py        |   2 +-
 gguf-py/gguf/tensor_mapping.py |  37 ++--
 gguf-py/gguf/utility.py        |   6 +-
 4 files changed, 286 insertions(+), 113 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index b8c7d97a78..2f9ef7f5d3 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -28,6 +28,14 @@ if TYPE_CHECKING:
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
+from gguf.vocab import MistralTokenizerType, MistralVocab
+from mistral_common.tokens.tokenizers.base import TokenizerVersion
+from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN, DATASET_STD
+from mistral_common.tokens.tokenizers.tekken import Tekkenizer
+from mistral_common.tokens.tokenizers.sentencepiece import (
+    SentencePieceTokenizer,
+)
+
 
 logger = logging.getLogger("hf-to-gguf")
 
@@ -81,6 +89,8 @@ class ModelBase:
     block_count: int
     tensor_map: gguf.TensorNameMap
 
+    is_mistral_format: bool = False
+
     def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
@@ -106,16 +116,17 @@ class ModelBase:
                 logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
                 remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
                 self.tensor_names = set(name for name in remote_tensors.keys())
-                for name, remote_tensor in gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id).items():
+                for name, remote_tensor in remote_tensors.items():
                     yield (name, LazyTorchTensor.from_remote_tensor(remote_tensor))
 
             self.get_tensors = get_remote_tensors
         else:
-            self.part_names = ModelBase.get_model_part_names(self.dir_model, "model", ".safetensors")
+            prefix = "model" if not self.is_mistral_format else "consolidated"
+            self.part_names = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
             self.is_safetensors = len(self.part_names) > 0
             if not self.is_safetensors:
                 self.part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
-        self.hparams = ModelBase.load_hparams(self.dir_model) if hparams is None else hparams
+        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
         self.tensor_names = None
         self.metadata_override = metadata_override
         self.model_name = model_name
@@ -153,19 +164,23 @@ class ModelBase:
     def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
         tensor_names_from_parts: set[str] = set()
 
-        index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
-        index_name += ".index.json"
-        index_file = self.dir_model / index_name
+        if not self.is_mistral_format:
+            index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
+            index_name += ".index.json"
+            index_file = self.dir_model / index_name
 
-        if index_file.is_file():
-            self.tensor_names = set()
-            logger.info(f"gguf: loading model weight map from '{index_name}'")
-            with open(index_file, "r", encoding="utf-8") as f:
-                index: dict[str, Any] = json.load(f)
-                weight_map = index.get("weight_map")
-                if weight_map is None or not isinstance(weight_map, dict):
-                    raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
-                self.tensor_names.update(weight_map.keys())
+            if index_file.is_file():
+                self.tensor_names = set()
+                logger.info(f"gguf: loading model weight map from '{index_name}'")
+                with open(index_file, "r", encoding="utf-8") as f:
+                    index: dict[str, Any] = json.load(f)
+                    weight_map = index.get("weight_map")
+                    if weight_map is None or not isinstance(weight_map, dict):
+                        raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
+                    self.tensor_names.update(weight_map.keys())
+            else:
+                self.tensor_names = tensor_names_from_parts
+                weight_map = {}
         else:
             self.tensor_names = tensor_names_from_parts
             weight_map = {}
@@ -426,7 +441,12 @@ class ModelBase:
         return part_names
 
     @staticmethod
-    def load_hparams(dir_model: Path):
+    def load_hparams(dir_model: Path, is_mistral_format: bool):
+        if is_mistral_format:
+            with open(dir_model / "params.json", "r", encoding="utf-8") as f:
+                config = json.load(f)
+            return config
+
         try:
             # for security reason, we don't allow loading remote code by default
             # if a model need remote code, we will fallback to config.json
@@ -476,7 +496,10 @@ class TextModel(ModelBase):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.hf_arch = get_model_architecture(self.hparams, self.model_type)
+        if not self.is_mistral_format:
+            self.hf_arch = get_model_architecture(self.hparams, self.model_type)
+        else:
+            self.hf_arch = ""
 
         if "text_config" in self.hparams:
             # move the text_config to the root level
@@ -542,14 +565,14 @@ class TextModel(ModelBase):
             self.gguf_writer.add_head_count(n_head)
             logger.info(f"gguf: head count = {n_head}")
 
-        if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
+        if (n_head_kv := self.find_hparam(["num_key_value_heads", "n_kv_heads"], optional=True)) is not None:
             self.gguf_writer.add_head_count_kv(n_head_kv)
             logger.info(f"gguf: key-value head count = {n_head_kv}")
 
         if (rope_theta := self.hparams.get("rope_theta")) is not None:
             self.gguf_writer.add_rope_freq_base(rope_theta)
             logger.info(f"gguf: rope theta = {rope_theta}")
-        if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
+        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"])) is not None:
             self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
             logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
         if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
@@ -1210,12 +1233,19 @@ class MmprojModel(ModelBase):
             raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")
 
         # get n_embd of the text model
-        if "text_config" not in self.hparams:
-            self.hparams["text_config"] = {}
-        if "audio_config" not in self.hparams:
-            self.hparams["audio_config"] = {}
-        text_config = {**self.hparams, **self.hparams["text_config"]}
-        self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
+        if not self.is_mistral_format:
+            if "text_config" not in self.hparams:
+                self.hparams["text_config"] = {}
+            if "audio_config" not in self.hparams:
+                self.hparams["audio_config"] = {}
+            text_config = {**self.hparams, **self.hparams["text_config"]}
+            self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
+        else:
+            text_config = {
+                k: v for k, v in self.hparams.items() if k not in ["vision_encoder", "audio_encoder"]
+            }
+            self.n_embd_text = text_config.get("hidden_dim", 0)
+
         assert self.n_embd_text > 0, "n_embd not found in hparams"
 
         # move vision config to the top level, while preserving the original hparams in global_config
@@ -1236,11 +1266,13 @@ class MmprojModel(ModelBase):
         self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
 
         # load preprocessor config
-        with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f:
-            self.preprocessor_config = json.load(f)
+        if not self.is_mistral_format:
+            with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f:
+                self.preprocessor_config = json.load(f)
 
     def get_vision_config(self) -> dict[str, Any] | None:
-        return self.global_config.get("vision_config")
+        config_name = "vision_config" if not self.is_mistral_format else "vision_encoder"
+        return self.global_config.get(config_name)
 
     def get_audio_config(self) -> dict[str, Any] | None:
         return self.global_config.get("audio_config")
@@ -1264,8 +1296,11 @@ class MmprojModel(ModelBase):
             self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads"]))
 
             # preprocessor config
-            self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"])
-            self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"])
+            image_mean = DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
+            image_std = DATASET_STD if self.is_mistral_format else self.preprocessor_config["image_std"]
+
+            self.gguf_writer.add_vision_image_mean(image_mean)
+            self.gguf_writer.add_vision_image_std(image_std)
 
         if self.has_audio_encoder:
             self.gguf_writer.add_clip_has_audio_encoder(True)
@@ -1924,11 +1959,63 @@ class LlamaModel(TextModel):
         if self.hf_arch == "VLlama3ForCausalLM":
             self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
 
+    def _set_vocab_mistral(self):
+        vocab = MistralVocab(self.dir_model)
+        logger.info(
+            f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}."
+        )
+
+        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
+
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size, (
+            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
+        )
+
+        if vocab.tokenizer_type == MistralTokenizerType.tekken:
+            self.gguf_writer.add_tokenizer_pre("tekken")
+            self.gguf_writer.add_token_merges(
+                vocab.extract_vocab_merges_from_model()
+            )
+
+        logger.info(
+            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
+        )
+
+        self.gguf_writer.add_bos_token_id(vocab.bos_id)
+        self.gguf_writer.add_eos_token_id(vocab.eos_id)
+        self.gguf_writer.add_unk_token_id(vocab.unk_id)
+        self.gguf_writer.add_pad_token_id(vocab.pad_id)
+
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_vocab_size(vocab.vocab_size)
+
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(False)
+
+        template_dir = Path(__file__).parent / "models/templates/"
+
+        template = MistralModel.get_community_chat_template(vocab, template_dir)
+        self.gguf_writer.add_chat_template(template)
+
     def set_vocab(self):
+        if self.is_mistral_format:
+            return self._set_vocab_mistral()
+
         path_tekken_json = self.dir_model / "tekken.json"
         path_tokenizer_json = self.dir_model / "tokenizer.json"
         if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
-            return self.set_vocab_tekken()
+            self._set_vocab_mistral()
 
         try:
             self._set_vocab_sentencepiece()
@@ -1962,56 +2049,12 @@ class LlamaModel(TextModel):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
-    def set_vocab_tekken(self):
-        vocab = gguf.vocab.MistralVocab(self.dir_model)
-        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
-
-        tokens = []
-        scores = []
-        toktypes = []
-
-        for text, score, toktype in vocab.all_tokens():
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        assert len(tokens) == vocab.vocab_size, (
-            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
-        )
-
-        if vocab.tokenizer_type == gguf.vocab.MistralTokenizerType.tekken:
-            self.gguf_writer.add_tokenizer_pre("tekken")
-            self.gguf_writer.add_token_merges(
-                vocab.extract_vocab_merges_from_model()
-            )
-
-        logger.info(
-            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
-        )
-
-        self.gguf_writer.add_bos_token_id(vocab.bos_id)
-        self.gguf_writer.add_eos_token_id(vocab.eos_id)
-        self.gguf_writer.add_unk_token_id(vocab.unk_id)
-        self.gguf_writer.add_pad_token_id(vocab.pad_id)
-
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_vocab_size(vocab.vocab_size)
-
-        self.gguf_writer.add_add_bos_token(True)
-        self.gguf_writer.add_add_eos_token(False)
-
-        script_dir = Path(__file__).parent
-        template_path = script_dir / "models/templates/unsloth-mistral-Devstral-Small-2507.jinja"
-        with open(template_path, "r", encoding="utf-8") as f:
-            template = f.read()
-            self.gguf_writer.add_chat_template(template)
-
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if not self.is_mistral_format:
+            self.gguf_writer.add_vocab_size(hparams["vocab_size"])
 
         if (rope_dim := hparams.get("head_dim")) is None:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
@@ -2033,13 +2076,25 @@ class LlamaModel(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
+        n_head = self.find_hparam(["n_heads", "num_attention_heads"])
+        n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
+
+        vision_prefixes = [
+            "vision_encoder.",
+            "vision_language_adapter.",
+            "patch_merger.",
+            "pre_mm_projector_norm",
+        ]
+
         is_multimodal_tensor = "vision_tower" in name \
             or "vision_model" in name \
             or "audio_tower" in name \
             or "model.connector" in name \
-            or "multi_modal_projector" in name
+            or "multi_modal_projector" in name \
+            or any(
+                name.startswith(prefix)
+                for prefix in vision_prefixes
+            )
 
         if is_multimodal_tensor:
             return [] # skip vision tensors
@@ -2155,13 +2210,18 @@ class LlavaVisionModel(MmprojModel):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        if self.hparams["model_type"] == "pixtral":
+        if self.hparams.get("model_type") == "pixtral":
             # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
             self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
             self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
-            logger.info(f"Image break token id: {self.img_break_tok_id}")
+        elif self.is_mistral_format:
+            # hparams is already vision config here so norm_eps is only defined in global_config.
+            self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
+            assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
+            self.img_break_tok_id = self.find_vparam(["image_break_token_id"])
         else:
             raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
+        logger.info(f"Image break token id: {self.img_break_tok_id}")
 
     def get_token_id(self, token: str) -> int:
         tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
@@ -2175,7 +2235,7 @@ class LlavaVisionModel(MmprojModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
-        if hparams["model_type"] == "pixtral":
+        if hparams.get("model_type") == "pixtral":
             self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
             self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
 
@@ -2193,18 +2253,30 @@ class LlavaVisionModel(MmprojModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-        n_head = self.hparams["num_attention_heads"]
+        n_head = (
+            self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
+        )
         n_kv_head = n_head
 
-        if name.startswith("multi_modal_projector.") or name.startswith("vision_tower."):
+        valid_prefixes = (
+            "multi_modal_projector.",
+            "vision_tower.",
+            "vision_encoder.",
+            "vision_language_adapter.",
+            "patch_merger.",
+            "pre_mm_projector_norm",
+        )
+
+        if any(name.startswith(prefix) for prefix in valid_prefixes):
             # process vision tensors
-            if name.endswith(("q_proj.weight", "q_proj.bias")):
+            if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
                 data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight", "k_proj.bias")):
+            if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
                 data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
             return [(self.map_tensor_name(name), data_torch)]
 
-        if self.img_break_tok_id > 0 and "embed_tokens.weight" in name:
+        embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
+        if self.img_break_tok_id > 0 and embed_key in name:
             logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
             # for pixtral model, we need to extract the [IMG_BREAK] token embedding
             img_break_embd = data_torch[self.img_break_tok_id]
@@ -4683,7 +4755,7 @@ class NomicBertModel(BertModel):
     def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
         hparams = kwargs.pop("hparams", None)
         if hparams is None:
-            hparams = ModelBase.load_hparams(dir_model)
+            hparams = ModelBase.load_hparams(dir_model, False)
 
         self.is_moe = bool(hparams.get("moe_every_n_layers"))
         self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT
@@ -8304,6 +8376,77 @@ class SmallThinkerModel(TextModel):
             if len(experts) > 0:
                 raise ValueError(f"Unprocessed experts: {experts}")
 
+
+class MistralModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+    model_name = "Mistral"
+    hf_arch = ""
+    is_mistral_format = True
+    undo_permute = False
+
+    @staticmethod
+    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path):
+        assert TokenizerVersion is not None, "mistral_common is not installed"
+        assert isinstance(vocab.tokenizer, (Tekkenizer, SentencePieceTokenizer)), (
+            f"Expected Tekkenizer or SentencePieceTokenizer, got {type(vocab.tokenizer)}"
+        )
+
+        if vocab.tokenizer.version == TokenizerVersion.v1:
+            return "mistral-v1"
+        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.spm:
+            return "mistral-v3"
+        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.tekken:
+            return "mistral-v3-tekken"
+        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.spm:
+            return "mistral-v7"
+        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.tekken:
+            return "mistral-v7-tekken"
+        elif vocab.tokenizer.version == TokenizerVersion.v11:
+            template_file = "Mistral-Small-3.2-24B-Instruct-2506.jinja"
+        elif vocab.tokenizer.version == TokenizerVersion.v13:
+            template_file = "unsloth-mistral-Devstral-Small-2507.jinja"
+        else:
+            raise ValueError(f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}")
+
+        template_path = templates_dir / template_file
+        if not template_path.exists():
+            raise FileNotFoundError(f"Template file not found: {template_path}")
+
+        with open(template_path, "r", encoding="utf-8") as f:
+            template = f.read()
+
+        return template
+
+
+class PixtralModel(LlavaVisionModel):
+    model_name = "Pixtral"
+    hf_arch = ""
+    is_mistral_format = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
+
+        self.gguf_writer.add_vision_attention_layernorm_eps(
+            self.find_hparam(["norm_eps"])
+        )
+        self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))
+
+        self.gguf_writer.add_vision_use_silu(True)
+
+        # spatial_merge_size
+        if self.find_vparam(["mm_projector_id"]) == "patch_merge":
+            self.gguf_writer.add_vision_spatial_merge_size(
+                self.find_vparam(["spatial_merge_size"])
+            )
+
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
+        if name == "vision_language_adapter.w_in.weight":
+            return "mm.1.weight"
+        elif name == "vision_language_adapter.w_out.weight":
+            return "mm.2.weight"
+        return super().map_tensor_name(name, try_suffixes)
+
 ###### CONVERSION LOGIC ######
 
 
@@ -8454,6 +8597,10 @@ def parse_args() -> argparse.Namespace:
         "--mmproj", action="store_true",
         help="(Experimental) Export multimodal projector (mmproj) for vision models. This will only work on some vision models. A prefix 'mmproj-' will be added to the output file name.",
     )
+    parser.add_argument(
+        "--mistral-format", action="store_true",
+        help="Whether the model is stored following the Mistral format.",
+    )
 
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
@@ -8559,17 +8706,25 @@ def main() -> None:
         if "mmproj" not in fname_out.name:
             fname_out = ModelBase.add_prefix_to_filename(fname_out, "mmproj-")
 
+    is_mistral_format = args.mistral_format
+
     with torch.inference_mode():
         output_type = ftype_map[args.outtype]
         model_type = ModelType.MMPROJ if args.mmproj else ModelType.TEXT
-        hparams = ModelBase.load_hparams(dir_model)
-        model_architecture = get_model_architecture(hparams, model_type)
-        logger.info(f"Model architecture: {model_architecture}")
-        try:
-            model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type)
-        except NotImplementedError:
-            logger.error(f"Model {model_architecture} is not supported")
-            sys.exit(1)
+        hparams = ModelBase.load_hparams(dir_model, is_mistral_format)
+        if not is_mistral_format:
+            model_architecture = get_model_architecture(hparams, model_type)
+            logger.info(f"Model architecture: {model_architecture}")
+            try:
+                model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type)
+            except NotImplementedError:
+                logger.error(f"Model {model_architecture} is not supported")
+                sys.exit(1)
+        elif args.mmproj:
+            assert hparams.get("vision_encoder") is not None, "This model does not support multimodal"
+            model_class = PixtralModel
+        else:
+            model_class = MistralModel
 
         model_instance = model_class(dir_model, output_type, fname_out,
                                      is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
@@ -8578,7 +8733,8 @@ def main() -> None:
                                      split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                      small_first_shard=args.no_tensor_first_split,
-                                     remote_hf_model_id=hf_repo_id)
+                                     remote_hf_model_id=hf_repo_id,
+                                     )
 
         if args.vocab_only:
             logger.info("Exporting model vocab...")
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 00a6733cbd..a67c0536a4 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -340,7 +340,7 @@ if __name__ == '__main__':
             sys.exit(1)
     else:
         logger.info(f"Loading base model: {dir_base_model.name}")
-        hparams = ModelBase.load_hparams(dir_base_model)
+        hparams = ModelBase.load_hparams(dir_base_model, False)
 
     with torch.inference_mode():
         try:
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index dc7c03b464..c5c2798090 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1119,7 +1119,8 @@ class TensorNameMap:
             "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
             "vpm.embeddings.patch_embedding",
             "model.vision_model.embeddings.patch_embedding", # SmolVLM
-            "vision_tower.patch_conv", # pixtral
+            "vision_tower.patch_conv", # pixtral-hf
+            "vision_encoder.patch_conv", # pixtral
             "vision_model.patch_embedding.linear", # llama 4
             "visual.patch_embed.proj", # qwen2vl
         ),
@@ -1138,7 +1139,8 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.self_attn.q_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4
-            "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.attention.wq", # pixtral
             "visual.blocks.{bid}.attn.q", # qwen2vl, generated
         ),
 
@@ -1153,7 +1155,8 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.self_attn.k_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4
-            "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral
             "visual.blocks.{bid}.attn.k", # qwen2vl, generated
         ),
 
@@ -1168,7 +1171,8 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4
-            "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.attention.wv", # pixtral
             "visual.blocks.{bid}.attn.v", # qwen2vl, generated
         ),
 
@@ -1178,7 +1182,8 @@ class TensorNameMap:
             "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm1",
             "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
-            "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
+            "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.attention_norm", # pixtral
             "vision_model.model.layers.{bid}.input_layernorm", # llama4
             "visual.blocks.{bid}.norm1", # qwen2vl
         ),
@@ -1190,7 +1195,8 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
             "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4
-            "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.attention.wo", # pixtral
             "visual.blocks.{bid}.attn.proj", # qwen2vl
         ),
 
@@ -1201,7 +1207,8 @@ class TensorNameMap:
             "vpm.encoder.layers.{bid}.layer_norm2",
             "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
             "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4
-            "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral
+            "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.ffn_norm", # pixtral
             "visual.blocks.{bid}.norm2", # qwen2vl
         ),
 
@@ -1210,14 +1217,16 @@ class TensorNameMap:
             "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc1",
             "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
-            "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.feed_forward.w3", # pixtral
             "vision_model.model.layers.{bid}.mlp.fc1", # llama4
             "visual.blocks.{bid}.mlp.fc1", # qwen2vl
             "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl
         ),
 
         MODEL_TENSOR.V_ENC_FFN_GATE: (
-            "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.feed_forward.w1", # pixtral
             "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl
         ),
 
@@ -1226,7 +1235,8 @@ class TensorNameMap:
             "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc2",
             "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
-            "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral
+            "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral-hf
+            "vision_encoder.transformer.layers.{bid}.feed_forward.w2", # pixtral
             "vision_model.model.layers.{bid}.mlp.fc2", # llama4
             "visual.blocks.{bid}.mlp.fc2", # qwen2vl
             "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl
@@ -1244,7 +1254,8 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_PRE_NORM: (
             "vision_tower.vision_model.pre_layrnorm",
-            "vision_tower.ln_pre", # pixtral
+            "vision_tower.ln_pre", # pixtral-hf
+            "vision_encoder.ln_pre", # pixtral
             "vision_model.layernorm_pre", # llama4
         ),
 
@@ -1261,6 +1272,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MM_INP_NORM: (
             "multi_modal_projector.norm",
+            "pre_mm_projector_norm",
         ),
 
         MODEL_TENSOR.V_MM_SOFT_EMB_NORM: (
@@ -1316,7 +1328,8 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.V_MM_PATCH_MERGER: (
-            "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1
+            "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 - hf
+            "patch_merger.merging_layer", # mistral
         ),
 
         # audio (mtmd)
diff --git a/gguf-py/gguf/utility.py b/gguf-py/gguf/utility.py
index 00adcbc937..769ccb02f0 100644
--- a/gguf-py/gguf/utility.py
+++ b/gguf-py/gguf/utility.py
@@ -145,7 +145,11 @@ class SafetensorRemote:
                     tensors[key] = val
             return tensors
 
-        raise ValueError(f"Model {model_id} does not have any safetensor files")
+        raise ValueError(
+            f"No safetensor file has been found for model {model_id}."
+            "If the repo has safetensor files, make sure the model is public or you have a "
+            "valid Hugging Face token set in the environment variable HF_TOKEN."
+        )
 
     @classmethod
     def get_list_tensors(cls, url: str) -> dict[str, RemoteTensor]:

From 1ebbaddff2a44b0599df659175d3274bd5bbeb81 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 11 Aug 2025 10:21:24 +0200
Subject: [PATCH 029/287] perplexity : update comments/error msg to use decode
 [no ci] (#15227)

This commit updates comments and error messages to use "decode" instead
of "eval" in perplexity.cpp.

The motivation for this is that `llama_eval` was renamed to
`llama_decode` a while ago, but the comments and error messages
still referred to "eval". This change ensures consistency and clarity.
---
 tools/perplexity/perplexity.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp
index 189dcb3d72..81bdc7c19c 100644
--- a/tools/perplexity/perplexity.cpp
+++ b/tools/perplexity/perplexity.cpp
@@ -525,7 +525,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
     }
 
     // We get the logits for all the tokens in the context window (params.n_ctx)
-    // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
+    // from llama_decode below.  Now, based on https://huggingface.co/docs/transformers/perplexity,
     // calculate the perplexity over the last half of the window (so the model always has
     // some context to predict the token).
     //
@@ -559,7 +559,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
             for (int seq = 0; seq < n_seq_batch; seq++) {
                 int seq_start = batch_start + seq*n_ctx;
 
-                // save original token and restore it after eval
+                // save original token and restore it after decode
                 const auto token_org = tokens[seq_start];
 
                 // add BOS token for the first batch of each chunk
@@ -584,7 +584,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
             }
 
             if (llama_decode(ctx, batch)) {
-                LOG_INF("%s : failed to eval\n", __func__);
+                LOG_INF("%s : failed to decode\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
             }
 

From 50e81bdf5db563ab57a9a722b08f96fa8a76c927 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Mon, 11 Aug 2025 11:15:44 +0200
Subject: [PATCH 030/287] convert : fix merge conflicts (#15229)

---
 convert_hf_to_gguf.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 2f9ef7f5d3..444e2cbdfb 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -572,7 +572,7 @@ class TextModel(ModelBase):
         if (rope_theta := self.hparams.get("rope_theta")) is not None:
             self.gguf_writer.add_rope_freq_base(rope_theta)
             logger.info(f"gguf: rope theta = {rope_theta}")
-        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"])) is not None:
+        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
             self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
             logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
         if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
@@ -3598,7 +3598,7 @@ class Qwen3MoeModel(Qwen2MoeModel):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        hparams = ModelBase.load_hparams(self.dir_model)
+        hparams = ModelBase.load_hparams(self.dir_model, False)
         self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
     def set_vocab(self):

From cd3069dfcbeee8e0e96cffb93b0ebb9e595e273a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 11 Aug 2025 11:21:19 +0200
Subject: [PATCH 031/287] kv-cache : log (debug) all streams in find_slot
 (#15176)

This commit updates `llama_kv_cache_unified::find_slot` to log
information for all streams when debug is enabled.

The motivation for this change is that currently if a non-unified
kv-cache is used, then only one stream will be logged because the
code was currently uses `seq_to_stream[1]`.
---
 src/llama-kv-cache-unified.cpp | 108 +++++++++++++++++----------------
 1 file changed, 56 insertions(+), 52 deletions(-)

diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index e539142e6b..4b58043c58 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -738,66 +738,70 @@ bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const d
 }
 
 llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch, bool cont) const {
+
     if (debug > 0) {
-        const auto & cells = v_cells[seq_to_stream[1]];
+        for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+            const auto seq_id = ubatch.seq_id_unq[s];
+            const auto stream_id = seq_to_stream[seq_id];
+            const auto & cells = v_cells[stream_id];
+            const uint32_t head_cur = v_heads[stream_id];
 
-        const uint32_t head_cur = v_heads[1];
+            LLAMA_LOG_DEBUG("%s: stream[%d], n = %5d, used = %5d, head = %5d, size = %5d, n_swa = %5d\n",
+                    __func__, stream_id, cells.used_max_p1(), cells.get_used(), head_cur, get_size(), n_swa);
 
-        LLAMA_LOG_DEBUG("%s: n = %5d, used = %5d, head = %5d, size = %5d, n_swa = %5d\n",
-                __func__, cells.used_max_p1(), cells.get_used(), head_cur, get_size(), n_swa);
-
-        if ((debug == 2 && n_swa > 0) || debug > 2) {
-            std::string ss;
-            for (uint32_t i = 0; i < cells.size(); ++i) {
-                if (cells.is_empty(i)) {
-                    ss += '.';
-                } else {
-                    assert(cells.seq_count(i) >= 1);
-
-                    if (cells.seq_count(i) == 1) {
-                        ss += std::to_string(cells.seq_get(i));
+            if ((debug == 2 && n_swa > 0) || debug > 2) {
+                std::string ss;
+                for (uint32_t i = 0; i < cells.size(); ++i) {
+                    if (cells.is_empty(i)) {
+                        ss += '.';
                     } else {
-                        ss += 'M';
+                        assert(cells.seq_count(i) >= 1);
+
+                        if (cells.seq_count(i) == 1) {
+                            ss += std::to_string(cells.seq_get(i));
+                        } else {
+                            ss += 'M';
+                        }
+                    }
+                    if (i%256 == 255) {
+                        ss += " *";
+                        ss += '\n';
                     }
                 }
-                if (i%256 == 255) {
-                    ss += " *";
-                    ss += '\n';
-                }
-            }
-            LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
-        }
-
-        if ((debug == 2 && n_swa > 0) || debug > 2) {
-            std::string ss;
-            for (uint32_t i = 0; i < cells.size(); ++i) {
-                std::string cur;
-                if (cells.is_empty(i)) {
-                    cur = '.';
-                } else {
-                    cur = std::to_string(cells.pos_get(i));
-                }
-                const int n = cur.size();
-                for (int j = 0; j < 5 - n; ++j) {
-                    cur += ' ';
-                }
-                ss += cur;
-                if (i%256 == 255) {
-                    ss += " *";
-                }
-                if (i%64 == 63) {
-                    ss += '\n';
-                }
-            }
-            LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
-        }
-
-        for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
-            if (cells.seq_pos_min(s) < 0) {
-                continue;
+                LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
             }
 
-            LLAMA_LOG_DEBUG("%s: min[%d] = %5d, max[%d] = %5d\n", __func__, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
+            if ((debug == 2 && n_swa > 0) || debug > 2) {
+                std::string ss;
+                for (uint32_t i = 0; i < cells.size(); ++i) {
+                    std::string cur;
+                    if (cells.is_empty(i)) {
+                        cur = '.';
+                    } else {
+                        cur = std::to_string(cells.pos_get(i));
+                    }
+                    const int n = cur.size();
+                    for (int j = 0; j < 5 - n; ++j) {
+                        cur += ' ';
+                    }
+                    ss += cur;
+                    if (i%256 == 255) {
+                        ss += " *";
+                    }
+                    if (i%64 == 63) {
+                        ss += '\n';
+                    }
+                }
+                LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
+            }
+
+            for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
+                if (cells.seq_pos_min(s) < 0) {
+                    continue;
+                }
+
+                LLAMA_LOG_DEBUG("%s: stream[%d] min[%d] = %5d, max[%d] = %5d\n", __func__, stream_id, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
+            }
         }
     }
 

From 228f724d9ce6c56e8cec75bfdabab4dd013def7f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 11 Aug 2025 13:58:24 +0300
Subject: [PATCH 032/287] kv-cache : fix seq_rm with seq_id == -1 (#15226)

* kv-cache : fix seq_rm with seq_id == -1

ggml-ci

* cont : iterate over streams

ggml-ci
---
 src/llama-kv-cache-unified.cpp | 48 +++++++++++++++++++++-------------
 1 file changed, 30 insertions(+), 18 deletions(-)

diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index 4b58043c58..88c88552aa 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -223,12 +223,7 @@ void llama_kv_cache_unified::clear(bool data) {
 }
 
 bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-    GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
-
-    auto & cells = v_cells[seq_to_stream[seq_id]];
-    auto & head  = v_heads[seq_to_stream[seq_id]];
-
-    uint32_t new_head = cells.size();
+    GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));
 
     if (p0 < 0) {
         p0 = 0;
@@ -239,6 +234,11 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
     }
 
     if (seq_id >= 0) {
+        auto & cells = v_cells[seq_to_stream[seq_id]];
+        auto & head  = v_heads[seq_to_stream[seq_id]];
+
+        uint32_t new_head = cells.size();
+
         for (uint32_t i = 0; i < cells.size(); ++i) {
             if (!cells.pos_in(i, p0, p1)) {
                 continue;
@@ -250,26 +250,38 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
                 }
             }
         }
+
+        // If we freed up a slot, set head to it so searching can start there.
+        if (new_head != cells.size() && new_head < head) {
+            head = new_head;
+        }
     } else {
         // match any sequence
-        for (uint32_t i = 0; i < cells.size(); ++i) {
-            if (!cells.pos_in(i, p0, p1)) {
-                continue;
+        for (uint32_t s = 0; s < n_stream; ++s) {
+            auto & cells = v_cells[s];
+            auto & head  = v_heads[s];
+
+            uint32_t new_head = cells.size();
+
+            for (uint32_t i = 0; i < cells.size(); ++i) {
+                if (!cells.pos_in(i, p0, p1)) {
+                    continue;
+                }
+
+                cells.rm(i);
+
+                if (new_head == cells.size()) {
+                    new_head = i;
+                }
             }
 
-            cells.rm(i);
-
-            if (new_head == cells.size()) {
-                new_head = i;
+            // If we freed up a slot, set head to it so searching can start there.
+            if (new_head != cells.size() && new_head < head) {
+                head = new_head;
             }
         }
     }
 
-    // If we freed up a slot, set head to it so searching can start there.
-    if (new_head != cells.size() && new_head < head) {
-        head = new_head;
-    }
-
     return true;
 }
 

From 27093afe78912494073eb043fec93a007e49653c Mon Sep 17 00:00:00 2001
From: Zagaj <m.zagajewska@gmail.com>
Date: Mon, 11 Aug 2025 14:27:54 +0200
Subject: [PATCH 033/287] readme : update infra list (#15234)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 954fff83da..96e30050d3 100644
--- a/README.md
+++ b/README.md
@@ -240,7 +240,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 <details>
 <summary>Infrastructure</summary>
 
-- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
+- [Paddler](https://github.com/intentee/paddler) - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
 - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
 - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
 - [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server

From 53d0a1265826f40c5dbc01d06aeab9e14fcbd69b Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 11 Aug 2025 14:48:41 +0200
Subject: [PATCH 034/287] server : allow specifying reasoning_format in HTTP
 request (#15238)

---
 common/arg.cpp                               |   6 +-----
 common/chat.cpp                              |  13 +++++++++++++
 common/chat.h                                |   1 +
 tools/server/README.md                       |   6 ++++++
 tools/server/public/index.html.gz            | Bin 1914076 -> 1914095 bytes
 tools/server/server.cpp                      |   8 ++++++--
 tools/server/webui/src/utils/app.context.tsx |   1 +
 7 files changed, 28 insertions(+), 7 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 0f01bb3145..3d18aaa171 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2949,11 +2949,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
         "(default: auto)",
         [](common_params & params, const std::string & value) {
-            /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
-            else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
-            else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else if (value == "auto") {     params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; }
-            else { throw std::invalid_argument("invalid value"); }
+            params.reasoning_format = common_reasoning_format_from_name(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
     add_opt(common_arg(
diff --git a/common/chat.cpp b/common/chat.cpp
index 316bd24170..92fbbbe111 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -625,6 +625,19 @@ const char * common_reasoning_format_name(common_reasoning_format format) {
     }
 }
 
+common_reasoning_format common_reasoning_format_from_name(const std::string & format) {
+    if (format == "none") {
+        return COMMON_REASONING_FORMAT_NONE;
+    } else if (format == "auto") {
+        return COMMON_REASONING_FORMAT_AUTO;
+    } else if (format == "deepseek") {
+        return COMMON_REASONING_FORMAT_DEEPSEEK;
+    } else if (format == "deepseek-legacy") {
+        return COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY;
+    }
+    throw std::runtime_error("Unknown reasoning format: " + format);
+}
+
 static std::string wrap_code_as_arguments(common_chat_msg_parser & builder, const std::string & code) {
     std::string arguments;
     if (builder.is_partial()) {
diff --git a/common/chat.h b/common/chat.h
index eb628d8bc2..c4d6b2e85e 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -191,6 +191,7 @@ std::string common_chat_format_example(
 
 const char*               common_chat_format_name(common_chat_format format);
 const char*               common_reasoning_format_name(common_reasoning_format format);
+common_reasoning_format   common_reasoning_format_from_name(const std::string & format);
 common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
diff --git a/tools/server/README.md b/tools/server/README.md
index 87cef75730..af9264ddd3 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1132,6 +1132,12 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}`
 
+`reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text.
+
+`thinking_forced_open`: Force a reasoning model to always output the reasoning. Only works on certain models.
+
+`parse_tool_calls`: Whether to parse the generated tool call.
+
 *Examples:*
 
 You can use either Python `openai` library with appropriate checkpoints:
diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 4e25eef1760ca1b5e4a2ee89004c0387182073be..a472714efb74e89e79cc02d65f0349f279417a82 100644
GIT binary patch
delta 1641789
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sSCUvRxO}jG9oP^5{vy%7U%vh8n0rmEX-FhB_{aYN2r43cbt_&&0N>7ivj6}9

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index a255d481a4..45f50e9376 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -383,8 +383,12 @@ struct server_task {
             } else {
                 params.oaicompat_chat_syntax.format = defaults.oaicompat_chat_syntax.format;
             }
-            params.oaicompat_chat_syntax.reasoning_format = params_base.reasoning_format;
-            params.oaicompat_chat_syntax.reasoning_in_content = params.stream && (params_base.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+            common_reasoning_format reasoning_format = params_base.reasoning_format;
+            if (data.contains("reasoning_format")) {
+                reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
+            }
+            params.oaicompat_chat_syntax.reasoning_format = reasoning_format;
+            params.oaicompat_chat_syntax.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
             params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false);
             params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false);
         }
diff --git a/tools/server/webui/src/utils/app.context.tsx b/tools/server/webui/src/utils/app.context.tsx
index 96cffd95ab..0b95e88f89 100644
--- a/tools/server/webui/src/utils/app.context.tsx
+++ b/tools/server/webui/src/utils/app.context.tsx
@@ -209,6 +209,7 @@ export const AppContextProvider = ({
         messages,
         stream: true,
         cache_prompt: true,
+        reasoning_format: 'none',
         samplers: config.samplers,
         temperature: config.temperature,
         dynatemp_range: config.dynatemp_range,

From fba5c0d680b555cbac563fef57b33bc5c9621e08 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 11 Aug 2025 15:31:35 +0200
Subject: [PATCH 035/287] chat : hotfix gpt-oss jinja raising an exception
 (#15243)

* chat : hotfix gpt-oss jinja raising an exception

* fix
---
 common/chat.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/common/chat.cpp b/common/chat.cpp
index 92fbbbe111..b1a1218ca2 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -552,6 +552,17 @@ common_chat_templates_ptr common_chat_templates_init(
             default_template_src = CHATML_TEMPLATE_SRC;
         }
     }
+
+    // TODO @ngxson : this is a temporary hack to prevent chat template from throwing an error
+    // Ref: https://github.com/ggml-org/llama.cpp/pull/15230#issuecomment-3173959633
+    if (default_template_src.find("<|channel|>") != std::string::npos
+            // search for the error message and patch it
+            && default_template_src.find("in message.content or") != std::string::npos) {
+        string_replace_all(default_template_src,
+            "{%- if \"<|channel|>analysis<|message|>\" in message.content or \"<|channel|>final<|message|>\" in message.content %}",
+            "{%- if false %}");
+    }
+
     std::string token_bos = bos_token_override;
     std::string token_eos = eos_token_override;
     bool add_bos = false;

From cf9e5648a7ae02f7728fefcbcfd2979d83c96e8f Mon Sep 17 00:00:00 2001
From: rainred <107027757+gryffindor-rr@users.noreply.github.com>
Date: Mon, 11 Aug 2025 22:12:12 +0800
Subject: [PATCH 036/287] mtmd : Fix MinicpmV model converter and clip to avoid
 using hardcode. (#14750)

* Fix MinicpmV model converter and clip to avoid using hardcode.

* Code update for pr/14750

* Remove unused field, update script path in docs.

* Add version 5 for fallback code.

---------

Co-authored-by: lzhang <zhanglei@modelbest.cn>
---
 docs/multimodal/minicpmo2.6.md                |   2 +-
 docs/multimodal/minicpmv2.6.md                |   2 +-
 tools/mtmd/clip-impl.h                        |   1 +
 tools/mtmd/clip.cpp                           |  77 ++++++------
 .../minicpmv-convert-image-encoder-to-gguf.py | 110 +++++++++++++-----
 tools/mtmd/legacy-models/minicpmv-surgery.py  |   2 +
 6 files changed, 116 insertions(+), 78 deletions(-)

diff --git a/docs/multimodal/minicpmo2.6.md b/docs/multimodal/minicpmo2.6.md
index d4edc97182..5e74058e5d 100644
--- a/docs/multimodal/minicpmo2.6.md
+++ b/docs/multimodal/minicpmo2.6.md
@@ -13,7 +13,7 @@ If there are differences in usage, please refer to the official build [documenta
 
 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```
 
diff --git a/docs/multimodal/minicpmv2.6.md b/docs/multimodal/minicpmv2.6.md
index 1b0ff59691..bc874bbd8c 100644
--- a/docs/multimodal/minicpmv2.6.md
+++ b/docs/multimodal/minicpmv2.6.md
@@ -12,7 +12,7 @@ If there are differences in usage, please refer to the official build [documenta
 
 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```
 
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index c8822dcf5c..f1eb633369 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -44,6 +44,7 @@
 #define KEY_WIN_ATTN_PATTERN      "clip.vision.n_wa_pattern"
 #define KEY_ATTN_WINDOW_SIZE      "clip.vision.window_size"
 #define KEY_MINICPMV_VERSION      "clip.minicpmv_version"
+#define KEY_MINICPMV_QUERY_NUM    "clip.minicpmv_query_num"
 
 // audio-specific
 #define KEY_A_NUM_MEL_BINS      "clip.audio.num_mel_bins"
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 20c2173314..fdaf9738e8 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -201,6 +201,7 @@ struct clip_hparams {
     // legacy
     bool has_llava_projector = false;
     int minicpmv_version = 0;
+    int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
 };
 
 struct clip_layer {
@@ -866,21 +867,8 @@ struct clip_graph {
             int n_embd = clip_n_mmproj_embd(ctx);
             const int d_head = 128;
             int n_head = n_embd/d_head;
-            int num_query = 96;
-            if (ctx->model.hparams.minicpmv_version == 2) {
-                // MiniCPM-V 2.5
-                num_query = 96;
-            } else if (ctx->model.hparams.minicpmv_version == 3) {
-                // MiniCPM-V 2.6
-                num_query = 64;
-            } else if (ctx->model.hparams.minicpmv_version == 4) {
-                // MiniCPM-o 2.6
-                num_query = 64;
-            } else if (ctx->model.hparams.minicpmv_version == 5) {
-                // MiniCPM-V 4.0
-                num_query = 64;
-            }
-
+            // Use actual config value if available, otherwise fall back to hardcoded values
+            int num_query = ctx->model.hparams.minicpmv_query_num;
             ggml_tensor * Q = ggml_add(ctx0,
                 ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q),
                 model.mm_model_attn_q_b);
@@ -2138,7 +2126,19 @@ struct clip_model_loader {
                 get_u32(KEY_PATCH_SIZE, hparams.patch_size);
                 get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
                 get_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
-
+                get_u32(KEY_MINICPMV_QUERY_NUM, hparams.minicpmv_query_num, false);
+                if (hparams.minicpmv_query_num == 0) {
+                    // Fallback to hardcoded values for legacy models
+                    if (hparams.minicpmv_version == 3) {
+                        hparams.minicpmv_query_num = 64;
+                    } else if (hparams.minicpmv_version == 4) {
+                        hparams.minicpmv_query_num = 64;
+                    } else if (hparams.minicpmv_version == 5) {
+                        hparams.minicpmv_query_num = 64;
+                    } else {
+                        hparams.minicpmv_query_num = 96;
+                    }
+                }
             } else if (is_audio) {
                 get_u32(KEY_A_NUM_MEL_BINS, hparams.n_mel_bins);
 
@@ -3556,20 +3556,23 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
             } break;
         case PROJECTOR_TYPE_MINICPMV:
             {
-                if (params.minicpmv_version == 2) {
-                    // MiniCPM-V 2.5
-                    n_patches_sq = 96;
-                } else if (params.minicpmv_version == 3) {
-                    // MiniCPM-V 2.6
-                    n_patches_sq = 64;
-                } else if (params.minicpmv_version == 4) {
-                    // MiniCPM-o 2.6
-                    n_patches_sq = 64;
-                } else if (params.minicpmv_version == 5) {
-                    // MiniCPM-V 4.0
-                    n_patches_sq = 64;
+                // Use actual config value if available, otherwise fall back to hardcoded values
+                if (params.minicpmv_query_num > 0) {
+                    n_patches_sq = params.minicpmv_query_num;
                 } else {
-                    GGML_ABORT("Unknown minicpmv version");
+                    // Fallback to hardcoded values for legacy models
+                    if (params.minicpmv_version == 2) {
+                        n_patches_sq = 96;
+                    } else if (params.minicpmv_version == 3) {
+                        n_patches_sq = 64;
+                    } else if (params.minicpmv_version == 4) {
+                        n_patches_sq = 64;
+                    } else if (params.minicpmv_version == 5) {
+                        // MiniCPM-V 4.0
+                        n_patches_sq = 64;
+                    } else {
+                        GGML_ABORT("Unknown minicpmv version");
+                    }
                 }
             } break;
         case PROJECTOR_TYPE_QWEN2VL:
@@ -4102,7 +4105,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 }
 
 int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
-    const auto & hparams = ctx->model.hparams;
     switch (ctx->model.proj_type) {
         case PROJECTOR_TYPE_LDP:
             return ctx->model.mm_model_block_1_block_2_1_b->ne[0];
@@ -4114,20 +4116,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         case PROJECTOR_TYPE_MLP_NORM:
             return ctx->model.mm_3_b->ne[0];
         case PROJECTOR_TYPE_MINICPMV:
-            if (hparams.minicpmv_version == 2) {
-                // MiniCPM-V 2.5
-                return 4096;
-            } else if (hparams.minicpmv_version == 3) {
-                // MiniCPM-V 2.6
-                return 3584;
-            } else if (hparams.minicpmv_version == 4) {
-                // MiniCPM-o 2.6
-                return 3584;
-            } else if (hparams.minicpmv_version == 5) {
-                // MiniCPM-V 4.0
-                return 2560;
-            }
-            GGML_ABORT("Unknown minicpmv version");
+            return ctx->model.mm_model_proj->ne[0];
         case PROJECTOR_TYPE_GLM_EDGE:
             return ctx->model.mm_model_mlp_3_w->ne[1];
         case PROJECTOR_TYPE_QWEN2VL:
diff --git a/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py b/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
index 3c60209544..4dda60a211 100644
--- a/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
+++ b/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
@@ -517,6 +517,16 @@ if args.use_f32:
 # output in the same directory as the model if output_dir is None
 dir_model = args.model_dir
 
+# Read config.json to get actual model configuration
+config_path = os.path.join(dir_model, "config.json")
+model_config = {}
+if os.path.isfile(config_path):
+    with open(config_path, "r", encoding="utf-8") as f:
+        model_config = json.load(f)
+    print(f"Loaded config from {config_path}")
+else:
+    print(f"Warning: config.json not found at {config_path}")
+
 # If minicpmv_projector is not specified but the default path exists, use the default path
 if args.minicpmv_projector is None:
     default_projector_path = os.path.join(dir_model, "minicpmv.projector")
@@ -555,37 +565,62 @@ if args.use_f32:
 #     processor = CLIPProcessor.from_pretrained(dir_model)
 
 minicpmv_version = args.minicpmv_version
-emb_dim = 4096
-block_count = 26
-if minicpmv_version == 1:  # MiniCPM-V 2.0
-    emb_dim = 2304
-    block_count = 26
-elif minicpmv_version == 2:  # MiniCPM-V 2.5
-    emb_dim = 4096
-    block_count = 27
-elif minicpmv_version == 3:  # MiniCPM-V 2.6
-    emb_dim = 3584
-    block_count = 27
-elif minicpmv_version == 4:  # MiniCPM-o 2.6
-    emb_dim = 3584
-    block_count = 27
-elif minicpmv_version == 5:  # MiniCPM-V 4.0
-    emb_dim = 2560
-    block_count = 27
 
-default_vision_config = {
-        "hidden_size": 1152,
-        "image_size": 980,
-        "intermediate_size": 4304,
-        "model_type": "idefics2",
-        "num_attention_heads": 16,
-        "num_hidden_layers": 27,
-        "patch_size": 14,
+# Use actual config values instead of hardcoded ones
+if model_config:
+    # For the projector/resampler, use the main model's hidden_size
+    emb_dim = model_config.get("hidden_size", 1536)
+
+    # For the vision model, use vision_config values
+    vision_config_dict = model_config.get("vision_config", {})
+    default_vision_config = {
+        "hidden_size": vision_config_dict.get("hidden_size", 1152),
+        "image_size": vision_config_dict.get("image_size", 980),
+        "intermediate_size": vision_config_dict.get("intermediate_size", 4304),
+        "model_type": vision_config_dict.get("model_type", "siglip"),
+        "num_attention_heads": vision_config_dict.get("num_attention_heads", 16),
+        "num_hidden_layers": vision_config_dict.get("num_hidden_layers", 27),
+        "patch_size": vision_config_dict.get("patch_size", 14),
     }
 
+    # Use vision model's num_hidden_layers for block_count
+    block_count = vision_config_dict.get("num_hidden_layers", 27)
+
+    print(f"Using config values: emb_dim={emb_dim}, block_count={block_count}")
+    print(f"Vision config: {default_vision_config}")
+else:
+    # Fallback to original hardcoded logic if config.json not found
+    emb_dim = 4096
+    block_count = 26
+    if minicpmv_version == 1:
+        emb_dim = 2304
+        block_count = 26
+    elif minicpmv_version == 2:
+        emb_dim = 4096
+        block_count = 27
+    elif minicpmv_version == 3:
+        emb_dim = 3584
+        block_count = 27
+    elif minicpmv_version == 4:
+        emb_dim = 3584
+        block_count = 27
+    elif minicpmv_version == 5:
+        emb_dim = 2560
+        block_count = 27
+
+    default_vision_config = {
+            "hidden_size": 1152,
+            "image_size": 980,
+            "intermediate_size": 4304,
+            "model_type": "idefics2",
+            "num_attention_heads": 16,
+            "num_hidden_layers": 27,
+            "patch_size": 14,
+        }
+
 vision_config = Idefics2VisionConfig(**default_vision_config)
 model = Idefics2VisionTransformer(vision_config)
-if minicpmv_version == 3:
+if minicpmv_version == 3 or (model_config and model_config.get("vision_config", {}).get("model_type") == "siglip"):
     vision_config = SiglipVisionConfig(**default_vision_config)
     model = SiglipVisionTransformer(vision_config)
 elif minicpmv_version == 4:
@@ -644,16 +679,27 @@ else:
     fout.add_description("two-tower CLIP model")
 
 if has_vision_encoder:
-    # vision_model hparams
-    fout.add_uint32("clip.vision.image_size", 448)
-    fout.add_uint32("clip.vision.patch_size", 14)
-    fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
-    fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
+    # vision_model hparams - use actual config values
+    vision_image_size = model_config.get("image_size", 448) if model_config else 448
+    vision_patch_size = default_vision_config.get("patch_size", 14)
+    vision_hidden_size = default_vision_config.get("hidden_size", 1152)
+    vision_intermediate_size = default_vision_config.get("intermediate_size", 4304)
+    vision_attention_heads = default_vision_config.get("num_attention_heads", 16)
+
+    fout.add_uint32("clip.vision.image_size", vision_image_size)
+    fout.add_uint32("clip.vision.patch_size", vision_patch_size)
+    fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), vision_hidden_size)
+    fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), vision_intermediate_size)
     fout.add_uint32("clip.vision.projection_dim", 0)
-    fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
+    fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), vision_attention_heads)
     fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
     fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
 
+    # Add MiniCPM-V specific parameters
+    query_num = model_config.get("query_num", 0) if model_config else 0
+    resampler_emb_dim = model_config.get("hidden_size", 0) if model_config else 0
+    fout.add_uint32("clip.minicpmv_query_num", query_num)
+
     if processor is not None:
         image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
         image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
diff --git a/tools/mtmd/legacy-models/minicpmv-surgery.py b/tools/mtmd/legacy-models/minicpmv-surgery.py
index ba82116582..53526623cd 100644
--- a/tools/mtmd/legacy-models/minicpmv-surgery.py
+++ b/tools/mtmd/legacy-models/minicpmv-surgery.py
@@ -16,6 +16,8 @@ mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")]
 
 # store these tensors in a new dictionary and torch.save them
 projector = {name: checkpoint[name].float() for name in mm_tensors}
+if 'resampler.proj' in projector.keys() and hasattr(model.llm.config,'scale_emb') is True:
+    projector['resampler.proj'] = projector['resampler.proj'] / model.llm.config.scale_emb
 torch.save(projector, f"{args.model}/minicpmv.projector")
 
 clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")]

From be48528b068111304e4a0bb82c028558b5705f05 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Mon, 11 Aug 2025 22:50:31 +0800
Subject: [PATCH 037/287] CANN: Add broadcast for softmax and FA (#15208)

* refactor softmax

* fix fa

* fix mask shape

* format

* add comments

* Remove whitespace
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 545 ++++++++++++-------------------
 ggml/src/ggml-cann/ggml-cann.cpp |  14 +-
 2 files changed, 216 insertions(+), 343 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 07d6b8b67d..0b409ce87d 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -812,7 +812,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
             ggml_cann_release_resources(ctx, src_trans_tensor);
             return;
         } else {
-            GGML_ABORT("Unsupport dst is not tontiguous.");
+            GGML_ABORT("Unsupport dst is not contiguous.");
         }
     }
     ggml_cann_release_resources(ctx, acl_src, acl_dst);
@@ -1330,160 +1330,196 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
 }
 
 /**
- * @brief   Applies the Alibi (Attention with Linear Biases) mechanism to the
- * @details This function implements the Alibi mechanism, which introduces
- *          learnable biases into the attention scores to simulate relative
- *          position encoding without the need for explicit positional
- *          embeddings.
+ * @brief Generate a range of values and apply a scalar base exponentiation.
  *
- * @param ctx          The backend CANN context for executing operations.
- * @param acl_src      The source tensor representing the query or key.
- * @param acl_position The position tensor containing relative positions.
- * @param acl_dst      The destination tensor where the result will be stored.
- * @param n_head       The number of attention heads.
- * @param src_ne       The dimensions of the source tensor.
- * @param src_nb0      The byte size of the first dimension of the source
- tensor.
- * @param max_bias     The maximum bias value used in the Alibi mechanism.
- * @param dst          The destination tensor object for additional metadata.
+ * This function creates an evenly spaced sequence from `start` to `stop` (exclusive),
+ * with step size `step`, stores it in a temporary buffer, and then computes:
  *
- * The function performs the following steps:
- * 1. Calculates the logarithm floor of the number of heads to determine the
-      base for bias calculation.
- * 2. Initializes arrays with arithmetic sequences and fills them with bias
-      values.
- * 3. Computes the bias tensor based on the calculated biases and arithmetic
-      sequences.
- * 4. Reshapes the bias tensor to match the dimensions of the input tensors.
- * 5. Multiplies the position tensor by the bias tensor.
- * 6. Adds the result of the multiplication to the source tensor to produce the
-      final output.
+ * @f[
+ * slope[i] = m^{\left( start + i \cdot step \right)}, \quad 0 \le i < size
+ * @f]
+ *
+ * The results are written to the provided @p slope_buffer.
+ *
+ * @param ctx           CANN backend context for memory allocation and operator execution.
+ * @param slope_buffer  Pointer to the output buffer (float array) for the computed slope values.
+ * @param m             Scalar base for the exponentiation.
+ * @param size          Number of elements in the generated sequence.
+ * @param start         Starting exponent offset.
+ * @param stop          Stopping exponent offset (exclusive).
+ * @param step          Step size for the exponent increment.
  */
-static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src,
-                        aclTensor* acl_position, aclTensor* acl_dst,
-                        const int n_head, int64_t* src_ne, const size_t src_nb0,
-                        float max_bias, ggml_tensor* dst) {
-    const int64_t ne2_ne3 = src_ne[2] * src_ne[3];
-    GGML_ASSERT(src_nb0 == sizeof(float));
-    GGML_ASSERT(n_head == src_ne[2]);
+static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer,
+    float m, int64_t size, float start, float stop, float step){
+    int64_t ne[] = {size};
+    size_t nb[] = {sizeof(float)};
 
-    const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * sizeof(float));
+    void* arange_buffer = arange_allocator.get();
 
-    float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+    aclTensor* arange_tensor = ggml_cann_create_tensor(
+        arange_buffer, ACL_FLOAT, sizeof(float), ne, nb, 1);
+    aclnn_arange(ctx, arange_tensor, start, stop, step, size);
 
-    // init arange
-    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
-                                          ne2_ne3 * ggml_type_size(dst->type));
-    void* tmp_arange_buffer = arange_allocator.get();
+    aclTensor* slope_tensor = ggml_cann_create_tensor(
+        slope_buffer, ACL_FLOAT, sizeof(float), ne, nb, 1);
 
-    // arange1: [1, ..., n_heads_log2_floor+1)
-    float start = 1;
-    float stop = n_heads_log2_floor + 1;
-    float step = 1;
-    int64_t n_elements_arange = n_heads_log2_floor;
+    aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT);
 
-    int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
-    size_t tmp_arange1_nb[] = {sizeof(dst->type)};
-    aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
-        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_arange1_ne, tmp_arange1_nb,
-        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-
-    aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
-
-    aclTensor* tmp_arange2_tensor = nullptr;
-    if (n_heads_log2_floor < ne2_ne3) {
-        // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
-        start = 1;
-        stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
-        step = 2;
-        n_elements_arange = ne2_ne3 - n_heads_log2_floor;
-        int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
-        size_t tmp_arange2_nb[] = {sizeof(dst->type)};
-
-        aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
-            (char*)tmp_arange_buffer +
-                n_heads_log2_floor * ggml_type_size(dst->type),
-            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
-            tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-        aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
-                     n_elements_arange);
-    }
-
-    // init mk_base
-    ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
-                                           ne2_ne3 * ggml_type_size(dst->type));
-    void* tmp_mk_base_buffer = mk_base_allocator.get();
-    int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
-    size_t tmp_mk_base1_nb[] = {sizeof(dst->type)};
-    aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
-        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_mk_base1_ne, tmp_mk_base1_nb,
-        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-
-    aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
-
-    aclTensor* tmp_mk_base2_tensor = nullptr;
-    if (n_heads_log2_floor < ne2_ne3) {
-        int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
-        size_t tmp_mk_base2_nb[] = {sizeof(dst->type)};
-        aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
-            (char*)tmp_mk_base_buffer +
-                n_heads_log2_floor * ggml_type_size(dst->type),
-            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
-            tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-        aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
-    }
-
-    // init mk
-    int64_t tmp_mk_base_ne[] = {ne2_ne3};
-    size_t tmp_mk_base_nb[] = {sizeof(dst->type)};
-    aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
-        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
-        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-    aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
-        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
-        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-    aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
-
-    // reshape mk
-    int64_t tmp_mk_ne[] = {1, 1, src_ne[2], src_ne[3]};
-    size_t tmp_mk_nb[GGML_MAX_DIMS];
-    tmp_mk_nb[0] = ggml_type_size(dst->type);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
-    }
-    aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
-        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
-        ACL_FORMAT_ND);
-
-    // acl_position * mk
-    int64_t tmp_output_ne[] = {src_ne[0], src_ne[1], src_ne[2], src_ne[3]};
-    size_t tmp_output_nb[GGML_MAX_DIMS];
-    tmp_output_nb[0] = ggml_type_size(dst->type);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        tmp_output_nb[i] = tmp_output_nb[i - 1] * tmp_output_ne[i - 1];
-    }
-    ggml_cann_pool_alloc output_allocator(ctx.pool(), ggml_nbytes(dst));
-    void* tmp_output_buffer = output_allocator.get();
-    aclTensor* tmp_output_tensor = ggml_cann_create_tensor(
-        tmp_output_buffer, ggml_cann_type_mapping(dst->type),
-        ggml_type_size(dst->type), tmp_output_ne, tmp_output_nb, GGML_MAX_DIMS,
-        ACL_FORMAT_ND);
-    aclnn_mul(ctx, acl_position, tmp_mk_tensor, tmp_output_tensor);
-
-    // add
-    aclnn_add(ctx, tmp_output_tensor, acl_src, acl_dst);
-    ggml_cann_release_resources(ctx, tmp_arange1_tensor, tmp_arange2_tensor,
-        tmp_mk_base1_tensor, tmp_mk_base2_tensor, tmp_mk_base_tensor,
-        tmp_arange_tensor, tmp_mk_tensor, tmp_output_tensor);
+    GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, sc, arange_tensor, slope_tensor);
+    ggml_cann_release_resources(ctx, sc, arange_tensor, slope_tensor);
 }
 
-void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+/**
+ * @brief Compute slope values for multiple attention heads based on ALiBi bias parameters.
+ *
+ * This function generates slope values for each attention head according to the ALiBi
+ * (Attention with Linear Biases) method. It splits the computation into two ranges depending
+ * on whether the head index is less than @p n_head_log2 or not, and uses different base values
+ * (`m0` and `m1`) for the exponentiation.
+ *
+ * @f[
+ * slope[h] =
+ * \begin{cases}
+ * m_0^{(h + 1)}, & h < n\_head\_log2 \\
+ * m_1^{\left( 2 \cdot (h - n\_head\_log2) + 1 \right)}, & h \geq n\_head\_log2
+ * \end{cases}
+ * \quad , \quad \text{if } max\_bias > 0
+ * @f]
+ *
+ * If @p max_bias <= 0, all slope values are set to 1.0.
+ *
+ * @param ctx           CANN backend context for memory allocation and operator execution.
+ * @param n_head        Total number of attention heads.
+ * @param slope_buffer  Pointer to the output buffer (float array) for storing slopes.
+ * @param max_bias      Maximum bias value for slope computation.
+ *
+*/
+static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
+    void* slope_buffer, float max_bias) {
+    const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+
+    float m0 = powf(2.0f, -(max_bias) / n_head_log2);
+    float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    // const float slope = (max_bias > 0.0f) ?
+    //                          h < n_head_log2 ?
+    //                              powf(m0, h + 1) :
+    //                              powf(m1, 2*(h - n_head_log2) + 1) :
+    //                          1.0f;
+    // arange1
+    float start = 0 + 1;
+    float end   = (n_head_log2 - 1) + 1;
+    float step  = 1;
+    float count = n_head_log2;
+    // end needs to be +1 because aclnn uses a left-closed, right-open interval.
+    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step);
+    if (n_head_log2 < n_head) {
+        // arange2
+        start = 2 * (n_head_log2 - n_head_log2) + 1;
+        end   = 2 * ((n_head - 1) - n_head_log2) + 1;
+        step  = 2;
+        count = n_head - n_head_log2;
+        aclnn_get_slope_inner(
+            ctx, (char *) slope_buffer + n_head_log2 * sizeof(float),
+            m1, count, start, end + 1, step);
+    }
+}
+
+/**
+ * @brief Add ALiBi (Attention with Linear Biases) positional biases to the attention mask.
+ *
+ * This function computes the ALiBi slopes for each attention head (if max_bias > 0),
+ * multiplies them with the attention mask to produce bias tensors, and adds these biases
+ * to the destination tensor (@p dst).
+ *
+ * The function performs necessary broadcasting of the mask and slope tensors to match
+ * the shape of the destination tensor, then applies element-wise multiplication and addition
+ * using CANN operators.
+ *
+ * @param ctx         CANN backend context for memory management and operator execution.
+ * @param mask        Input attention mask tensor, assumed to be contiguous.
+ * @param dst         Destination tensor to which ALiBi biases will be added.
+ * @param dst_ptr     Pointer to the memory of the destination tensor.
+ * @param max_bias    Maximum bias value controlling the slope scaling.
+ *
+ * @note
+ * - Write data into dst_ptr using only the shape information of the dst tensor.
+ * - `GGML_MAX_DIMS + 2` is used to extend tensor dimensions for broadcasting.
+ */
+static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask,
+    ggml_tensor* dst, void* dst_ptr, float max_bias) {
+    void* slope_buffer = nullptr;
+    void* bias_buffer = nullptr;
+
+    if (max_bias > 0.0f) {
+        int64_t n_heads = dst->ne[2];
+        ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float));
+        slope_buffer = slope_allocator.get();
+        ggml_cann_pool_alloc bias_allocator(
+                    ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst));
+        bias_buffer = bias_allocator.get();
+        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias);
+    }
+
+    // broadcast for mask, slop and dst;
+    int64_t nr2 = dst->ne[2] / mask->ne[2];
+    int64_t nr3 = dst->ne[3] / mask->ne[3];
+
+    // broadcast the mask across rows
+    int64_t mask_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], 1, mask->ne[3], 1 };
+    size_t  mask_nb[] = {
+        mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2],
+        mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3]
+    };
+
+    int64_t dst_ne[] = { dst->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], nr3 };
+    size_t  dst_nb[] = {
+        dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2],
+        dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3]
+    };
+
+    // slope is a 1 dim tensor, slope.ne2 == dst.ne2
+    int64_t slope_ne[] = { 1, 1, mask->ne[2], nr2, 1, 1 };
+    size_t  slope_nb[GGML_MAX_DIMS + 2];
+    slope_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS + 2; i++) {
+        slope_nb[i] = slope_nb[i - 1] * slope_ne[i - 1];
+    }
+
+    aclTensor* acl_slope = ggml_cann_create_tensor(
+                            slope_buffer, ACL_FLOAT, sizeof(float),
+                            slope_ne, slope_nb, GGML_MAX_DIMS + 2);
+    aclTensor* acl_mask = ggml_cann_create_tensor(
+                            mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2);
+
+    // write data into dst_ptr using only the shape information of the dst tensor.
+    aclTensor* acl_dst  = ggml_cann_create_tensor(
+                            dst_ptr, ggml_cann_type_mapping(dst->type),
+                            ggml_type_size(dst->type), dst_ne, dst_nb,
+                            GGML_MAX_DIMS + 2);
+
+    if (max_bias > 0.0f) {
+        int64_t bias_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], 1 };
+        size_t  bias_nb[GGML_MAX_DIMS + 2];
+        bias_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS + 2; i++) {
+            bias_nb[i] = bias_nb[i - 1] * bias_ne[i - 1];
+        }
+        aclTensor* bias_tensor = ggml_cann_create_tensor(
+                                    bias_buffer, ACL_FLOAT, sizeof(float),
+                                    bias_ne, bias_nb, GGML_MAX_DIMS + 2);
+
+        aclnn_mul(ctx, acl_slope, acl_mask, bias_tensor);
+        aclnn_add(ctx, acl_dst, bias_tensor);
+        ggml_cann_release_resources(ctx, bias_tensor);
+    } else {
+        aclnn_add(ctx, acl_dst, acl_mask);
+    }
+    ggml_cann_release_resources(ctx, acl_slope, acl_mask, acl_dst);
+}
+
+void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_cann_dup(ctx, dst);
 }
 
@@ -1501,118 +1537,41 @@ void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
  * @param acl_dst The destination tensor where the softmax results will be
  * stored.
  */
-static void aclnn_softmax(ggml_backend_cann_context& ctx, aclTensor* acl_src,
-                          int64_t dim, aclTensor* acl_dst) {
+static void aclnn_softmax(ggml_backend_cann_context & ctx,
+    aclTensor* acl_src, int64_t dim, aclTensor * acl_dst) {
     GGML_CANN_CALL_ACLNN_OP(ctx, Softmax, acl_src, dim, acl_dst);
 }
 
-void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor* src0 = dst->src[0];
     ggml_tensor* src1 = dst->src[1];  // mask
 
     aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
-    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    aclTensor* acl_dst  = ggml_cann_create_tensor(dst);
 
-    float scale = 1.0f;
+    float scale    = 1.0f;
     float max_bias = 0.0f;
 
-    memcpy(&scale, (float*)dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float*)dst->op_params + 1, sizeof(float));
+    memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
 
     // input mul scale
     aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT);
+    ggml_cann_pool_alloc src_tensor_allocator(ctx.pool(), ggml_nbytes(src0));
+    void* src_tensor_buffer = src_tensor_allocator.get();
+    aclTensor* softmax_tensor = ggml_cann_create_tensor(
+        src_tensor_buffer, ggml_cann_type_mapping(src0->type),
+        ggml_element_size(src0), src0->ne, src0->nb,GGML_MAX_DIMS);
 
-    size_t n_bytes = ggml_nbytes(src0);
-    ggml_cann_pool_alloc mul_scale_allocator(ctx.pool(), n_bytes);
-    void* input_mul_scale_buffer = mul_scale_allocator.get();
-    aclTensor* acl_input_mul_scale_tensor = ggml_cann_create_tensor(
-        input_mul_scale_buffer, ACL_FLOAT, ggml_type_size(src0->type), src0->ne,
-        src0->nb, GGML_MAX_DIMS);
-
-    bool inplace = false;
-    aclnn_muls(ctx, acl_src0, scale, acl_input_mul_scale_tensor, inplace);
+    aclnn_muls(ctx, acl_src0, scale, softmax_tensor, false);
 
     // mask
-    aclTensor* acl_src1_fp32_tensor = nullptr;
-    aclTensor* tmp_mask_tensor = nullptr;
-    ggml_cann_pool_alloc src1_fp32_allocator(ctx.pool());
     if (src1) {
-        const bool use_f16 = src1->type == GGML_TYPE_F16;
-        if (use_f16) {
-            // cast to fp32
-            size_t n_bytes = ggml_nelements(src1) * sizeof(float_t);
-            size_t src1_fp32_nb[GGML_MAX_DIMS];
-            src1_fp32_nb[0] = sizeof(float_t);
-            for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                src1_fp32_nb[i] = src1_fp32_nb[i - 1] * src1->ne[i - 1];
-            }
-            src1_fp32_allocator.alloc(n_bytes);
-            void* src1_fp32_buffer = src1_fp32_allocator.get();
-            acl_src1_fp32_tensor = ggml_cann_create_tensor(
-                src1_fp32_buffer, ACL_FLOAT, sizeof(float), src1->ne,
-                src1_fp32_nb, GGML_MAX_DIMS);
-            aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
-            aclnn_cast(ctx, acl_src1, acl_src1_fp32_tensor, ACL_FLOAT);
-            ggml_cann_release_resources(ctx, acl_src1);
-        } else {
-            acl_src1_fp32_tensor = ggml_cann_create_tensor(src1);
-        }
-
-        // broadcast the mask across rows, only use ne11 of ne01 in mask
-        if (src1->ne[1] != src0->ne[1]) {
-            // mask shape: [1,1,ne11,ne10]
-            int64_t tmp_mask_ne[] = {src0->ne[0], src0->ne[1], 1, 1};
-            size_t tmp_mask_nb[GGML_MAX_DIMS];
-            tmp_mask_nb[0] = sizeof(float_t);
-            for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                tmp_mask_nb[i] = tmp_mask_nb[i - 1] * tmp_mask_ne[i - 1];
-            }
-            tmp_mask_tensor = ggml_cann_create_tensor(
-                src1->data, ACL_FLOAT, sizeof(float), tmp_mask_ne, tmp_mask_nb,
-                GGML_MAX_DIMS, ACL_FORMAT_ND);
-        }
-
-        // alibi
-        const int n_head = src0->ne[2];
-        const size_t src_nb0 = src0->nb[0];
-
-        n_bytes = ggml_nbytes(dst);
-        ggml_cann_pool_alloc output_allocator(ctx.pool(), n_bytes);
-        void* output_buffer = output_allocator.get();
-        aclTensor* alibi_output_tensor = ggml_cann_create_tensor(
-            output_buffer, ACL_FLOAT, ggml_type_size(dst->type), dst->ne,
-            dst->nb, GGML_MAX_DIMS);
-        if (max_bias <= 0.0f) {
-            // slope = 1.0
-            if (tmp_mask_tensor) {
-                aclnn_add(ctx, tmp_mask_tensor, acl_input_mul_scale_tensor,
-                          alibi_output_tensor);
-            } else {
-                aclnn_add(ctx, acl_src1_fp32_tensor, acl_input_mul_scale_tensor,
-                          alibi_output_tensor);
-            }
-        } else {
-            // slope != 1.0
-            if (tmp_mask_tensor) {
-                aclnn_alibi(ctx, acl_input_mul_scale_tensor, tmp_mask_tensor,
-                            alibi_output_tensor, n_head, src0->ne, src_nb0,
-                            max_bias, dst);
-            } else {
-                aclnn_alibi(ctx, acl_input_mul_scale_tensor,
-                            acl_src1_fp32_tensor, alibi_output_tensor, n_head,
-                            src0->ne, src_nb0, max_bias, dst);
-            }
-        }
-
-        // softmax
-        aclnn_softmax(ctx, alibi_output_tensor, 3, acl_dst);
-        ggml_cann_release_resources(ctx, alibi_output_tensor);
-    } else {
-        aclnn_softmax(ctx, acl_input_mul_scale_tensor, 3, acl_dst);
+        aclnn_add_alibi(ctx, src1, src0, src_tensor_buffer, max_bias);
     }
-
-    ggml_cann_release_resources(ctx, acl_src0, acl_src1_fp32_tensor, acl_dst,
-        acl_scale, acl_input_mul_scale_tensor, tmp_mask_tensor);
+    // softmax
+    aclnn_softmax(ctx, softmax_tensor, 3, acl_dst);
+    ggml_cann_release_resources(ctx, acl_src0, acl_dst, acl_scale, softmax_tensor);
 }
 
 /**
@@ -3208,104 +3167,24 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
             // Compute the slope if needed. Derived from ggml_cann_softmax().
             if(maxBias != 0.0f){
                 // alibi
-                const int64_t ne2_ne3 = src0->ne[2] * src0->ne[3];
-                const int64_t n_head = src0->ne[2];
-                const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
-                float m0 = powf(2.0f, -(maxBias) / n_heads_log2_floor);
-                float m1 = powf(2.0f, -(maxBias / 2.0f) / n_heads_log2_floor);
-                // init arange
-                ggml_cann_pool_alloc arange_allocator(ctx.pool(),
-                                                    ne2_ne3 * faElemSize);
-                void* tmp_arange_buffer = arange_allocator.get();
+                const int64_t n_heads = src0->ne[2];
+                ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float));
+                void* slope_buffer = slope_allocator.get();
+                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias);
 
-                // arange1: [1, ..., n_heads_log2_floor+1)
-                float start = 1;
-                float stop = n_heads_log2_floor + 1;
-                float step = 1;
-                int64_t n_elements_arange = n_heads_log2_floor;
-
-                int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
-                size_t tmp_arange1_nb[] = {faElemSize};
-                aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
-                    tmp_arange_buffer, faDataType, faElemSize,
-                    tmp_arange1_ne, tmp_arange1_nb,
-                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-
-                aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
-
-                aclTensor* tmp_arange2_tensor = nullptr;
-                if (n_heads_log2_floor < ne2_ne3) {
-                    // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
-                    start = 1;
-                    stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
-                    step = 2;
-                    n_elements_arange = ne2_ne3 - n_heads_log2_floor;
-                    int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
-                    size_t tmp_arange2_nb[] = {faElemSize};
-
-                    aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
-                        (char*)tmp_arange_buffer +
-                            n_heads_log2_floor * faElemSize,
-                        faDataType, faElemSize,
-                        tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-                    aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
-                                n_elements_arange);
+                int64_t slope_ne[] = {1, 1, n_heads, 1};
+                size_t slope_nb[GGML_MAX_DIMS];
+                slope_nb[0] = sizeof(float);
+                for(int i = 1;i<GGML_MAX_DIMS;i++) {
+                    slope_nb[i] = slope_nb[i-1] * slope_ne[0];
                 }
 
-                // init mk_base
-                ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
-                                                    ne2_ne3 * faElemSize);
-                void* tmp_mk_base_buffer = mk_base_allocator.get();
-                int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
-                size_t tmp_mk_base1_nb[] = {faElemSize};
-                aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
-                    tmp_mk_base_buffer, faDataType, faElemSize,
-                    tmp_mk_base1_ne, tmp_mk_base1_nb,
-                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                aclTensor* slope_tensor = ggml_cann_create_tensor(
+                    slope_buffer, ACL_FLOAT, sizeof(float),
+                    slope_ne, slope_nb, GGML_MAX_DIMS);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor, slope_tensor);
 
-                aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
-
-                aclTensor* tmp_mk_base2_tensor = nullptr;
-                if (n_heads_log2_floor < ne2_ne3) {
-                    int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
-                    size_t tmp_mk_base2_nb[] = {faElemSize};
-                    aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
-                        (char*)tmp_mk_base_buffer +
-                            n_heads_log2_floor * faElemSize,
-                        faDataType, faElemSize,
-                        tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-                    aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
-                }
-
-                // init mk
-                int64_t tmp_mk_base_ne[] = {ne2_ne3};
-                size_t tmp_mk_base_nb[] = {faElemSize};
-                aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
-                    tmp_mk_base_buffer, faDataType, faElemSize,
-                    tmp_mk_base_ne, tmp_mk_base_nb,
-                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-                aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
-                    tmp_arange_buffer, faDataType, faElemSize,
-                    tmp_mk_base_ne, tmp_mk_base_nb,
-                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
-                aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
-
-                // reshape mk
-                int64_t tmp_mk_ne[] = {1, 1, src0->ne[2], src0->ne[3]};
-                size_t tmp_mk_nb[GGML_MAX_DIMS];
-                tmp_mk_nb[0] = faElemSize;
-                for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
-                }
-                aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
-                    tmp_mk_base_buffer, faDataType, faElemSize,
-                    tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
-                    ACL_FORMAT_ND);
-                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor, tmp_mk_tensor);
-
-                ggml_cann_release_resources(ctx, tmp_arange1_tensor, tmp_arange2_tensor,
-                    tmp_mk_base1_tensor, tmp_mk_base2_tensor, tmp_mk_base_tensor,
-                    tmp_arange_tensor, tmp_mk_tensor);
+                ggml_cann_release_resources(ctx, slope_tensor);
             }
         }
 
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index cf575b3675..3d3520f195 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2391,7 +2391,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
-            return true;
+            return ggml_is_contiguous(op);
         } break;
         case GGML_OP_CONT: {
             // TODO: support GGML_TYPE_BF16
@@ -2456,8 +2456,9 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             // value of paddingW should be at most half of kernelW
             return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2));
         }
-        case GGML_OP_SUM:
         case GGML_OP_DUP:
+            return ggml_is_contiguous(op);
+        case GGML_OP_SUM:
         case GGML_OP_IM2COL:
         case GGML_OP_CONCAT:
         case GGML_OP_REPEAT:
@@ -2503,9 +2504,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if (op->src[2]) {
                 return false;
             }
-            // TODO: support broadcast
-            // ref: https://github.com/ggml-org/llama.cpp/pull/14435
-            return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
+            return true;
         case GGML_OP_FLASH_ATTN_EXT:{
             // derived from [ggml-cuda.cu]
             if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){
@@ -2532,11 +2531,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // DeepSeek MLA
                 return false;
             }
-            // TODO: support broadcast
-            // ref: https://github.com/ggml-org/llama.cpp/pull/14435
-            if (op->src[0]->ne[3] != 1) {
-                return false;
-            }
             float logitSoftcap = 0.0f;
             memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
             if(logitSoftcap != 0.0f) {

From 25ff6f7659f6a5c47d6a73eada5813f0495331f0 Mon Sep 17 00:00:00 2001
From: R0CKSTAR <yeahdongcn@gmail.com>
Date: Tue, 12 Aug 2025 10:02:51 +0800
Subject: [PATCH 038/287] musa: fix failures in test-backend-ops for mul_mat_id
 op (#15236)

* musa: fix failures in test-backend-ops for mul_mat_id op

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* Address review comments

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

---------

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---
 ggml/src/ggml-cuda/common.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 2e5d48797f..a23da57e3a 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -312,11 +312,11 @@ static bool turing_mma_available(const int cc) {
 }
 
 static bool ampere_mma_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
+    return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
 
 static bool cp_async_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
+    return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
 
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {

From bbd57b7eafb3b32e3f7f3a2175bce0b35abc7de8 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Tue, 12 Aug 2025 16:12:13 +0800
Subject: [PATCH 039/287] CANN: GGML_OP_CPY optimization (#15070)

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 82 +++++++++++++-------------------
 ggml/src/ggml-cann/ggml-cann.cpp |  3 +-
 2 files changed, 35 insertions(+), 50 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 0b409ce87d..259a2928b1 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -753,69 +753,55 @@ static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src,
 void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     ggml_tensor* src0 = dst->src[0];
 
-    aclTensor* acl_src = ggml_cann_create_tensor(src0);
-    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
     if (ggml_are_same_shape(src0, dst)) {
+        aclTensor* acl_src = ggml_cann_create_tensor(src0);
+        aclTensor* acl_dst = ggml_cann_create_tensor(dst);
         if (dst->type == src0->type) {
             cann_copy(ctx, acl_src, acl_dst);
         } else {
             aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
         }
+        ggml_cann_release_resources(ctx, acl_src, acl_dst);
     } else {
-        if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
-            if (dst->type == src0->type) {
-                size_t cpy_size = ggml_nbytes(dst);
-                ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size,
-                    ACL_MEMCPY_DEVICE_TO_DEVICE);
-                return;
-            } else {
-                ggml_cann_pool_alloc src_buffer_allocator(
-                    ctx.pool(),
-                    ggml_nelements(dst) * ggml_type_size(dst->type));
-                void* src_trans_buffer = src_buffer_allocator.get();
-                size_t src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = ggml_type_size(dst->type);
-                for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
-                }
-                aclTensor* src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                    ggml_type_size(dst->type), src0->ne, src_trans_nb,
-                    GGML_MAX_DIMS);
-
-                aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-                size_t cpy_size = ggml_nbytes(dst);
-                ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
-                    ACL_MEMCPY_DEVICE_TO_DEVICE);
-                ggml_cann_release_resources(ctx, src_trans_tensor);
-                return;
-            }
-        } else if (ggml_is_contiguous(dst)) {
-            ggml_cann_pool_alloc src_buffer_allocator(
-                ctx.pool(), ggml_nelements(dst) * ggml_type_size(dst->type));
-            void* src_trans_buffer = src_buffer_allocator.get();
+        void* src_trans_buffer = src0->data;
+        ggml_cann_pool_alloc src_buffer_allocator;
+        if (!ggml_is_contiguous(src0)) {
+            aclTensor* acl_src = ggml_cann_create_tensor(src0);
+            src_buffer_allocator.alloc(ctx.pool(),
+                ggml_nelements(src0) * ggml_type_size(src0->type));
+            src_trans_buffer = src_buffer_allocator.get();
             size_t src_trans_nb[GGML_MAX_DIMS];
-            src_trans_nb[0] = ggml_type_size(dst->type);
+            src_trans_nb[0] = ggml_type_size(src0->type);
             for (int i = 1; i < GGML_MAX_DIMS; i++) {
                 src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
             }
             aclTensor* src_trans_tensor = ggml_cann_create_tensor(
-                src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                ggml_type_size(dst->type), src0->ne, src_trans_nb,
+                src_trans_buffer, ggml_cann_type_mapping(src0->type),
+                ggml_type_size(src0->type), src0->ne, src_trans_nb,
                 GGML_MAX_DIMS);
-
-            aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-
-            size_t cpy_size = ggml_nbytes(dst);
-            ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
-                ACL_MEMCPY_DEVICE_TO_DEVICE);
-            ggml_cann_release_resources(ctx, src_trans_tensor);
-            return;
-        } else {
-            GGML_ABORT("Unsupport dst is not contiguous.");
+            cann_copy(ctx, acl_src, src_trans_tensor);
+            ggml_cann_release_resources(ctx, acl_src, src_trans_tensor);
         }
+
+        size_t src_reshape_nb[GGML_MAX_DIMS];
+        src_reshape_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1];
+        }
+
+        aclTensor* trans_acl_src = ggml_cann_create_tensor(src_trans_buffer,
+            ggml_cann_type_mapping(src0->type),ggml_type_size(src0->type),
+            dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+        aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+        if (dst->type == src0->type) {
+            cann_copy(ctx, trans_acl_src, acl_dst);
+        } else {
+            aclnn_cast(ctx, trans_acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
+        }
+        ggml_cann_release_resources(ctx, trans_acl_src, acl_dst);
     }
-    ggml_cann_release_resources(ctx, acl_src, acl_dst);
+    return;
 }
 
 /**
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 3d3520f195..cb8af42ebf 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2391,7 +2391,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
-            return ggml_is_contiguous(op);
+            return true;
         } break;
         case GGML_OP_CONT: {
             // TODO: support GGML_TYPE_BF16
@@ -2457,7 +2457,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2));
         }
         case GGML_OP_DUP:
-            return ggml_is_contiguous(op);
         case GGML_OP_SUM:
         case GGML_OP_IM2COL:
         case GGML_OP_CONCAT:

From efe3a90996ca6e67ca90f337fc03ad551082c408 Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Tue, 12 Aug 2025 17:21:45 +0800
Subject: [PATCH 040/287] CUDA cmake: add `-lineinfo` for easier debug (#15260)

---
 ggml/src/ggml-cuda/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index 98ed29bc9c..bce07ac362 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -120,6 +120,10 @@ if (CUDAToolkit_FOUND)
 
     set(CUDA_FLAGS -use_fast_math -extended-lambda)
 
+    if (GGML_CUDA_DEBUG)
+        list(APPEND CUDA_FLAGS -lineinfo)
+    endif()
+
     if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")
         # Options are:
         # - none (not recommended)

From 60a76588106d22ccacd6b1a0d15d8545861d0a0d Mon Sep 17 00:00:00 2001
From: rmatif <kingrealriadh@gmail.com>
Date: Tue, 12 Aug 2025 11:42:41 +0200
Subject: [PATCH 041/287] opencl: allow mixed f16/f32 `add` (#15140)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 191 +++++++++++++++++----------
 ggml/src/ggml-opencl/kernels/add.cl  |  50 +++++--
 2 files changed, 162 insertions(+), 79 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 8ba1e00df7..fc838684ac 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2481,6 +2481,13 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
         case GGML_OP_SCALE:
             return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
         case GGML_OP_ADD:
+            if (op->type == GGML_TYPE_F16) {
+                const bool src0_ok = op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32;
+                const bool src1_ok = op->src[1]->type == GGML_TYPE_F16 || op->src[1]->type == GGML_TYPE_F32;
+                if (src0_ok && src1_ok) {
+                    return true;
+                }
+            }
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_SUB:
@@ -3717,34 +3724,30 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
     GGML_ASSERT(dst);
     GGML_ASSERT(dst->extra);
 
-    GGML_ASSERT(src0->type == src1->type);
-    GGML_ASSERT(src0->type == dst->type);
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-
-    const int  ne00 = src0->ne[0];
-    const int  ne01 = src0->ne[1];
-    const int  ne02 = src0->ne[2];
-    const int  ne03 = src0->ne[3];
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
 
     const cl_ulong nb00 = src0->nb[0];
     const cl_ulong nb01 = src0->nb[1];
     const cl_ulong nb02 = src0->nb[2];
     const cl_ulong nb03 = src0->nb[3];
 
-    const int  ne10 = src1->ne[0];
-    const int  ne11 = src1->ne[1];
-    const int  ne12 = src1->ne[2];
-    const int  ne13 = src1->ne[3]; UNUSED(ne13);
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3];
 
     const cl_ulong nb10 = src1->nb[0];
     const cl_ulong nb11 = src1->nb[1];
     const cl_ulong nb12 = src1->nb[2];
-    const cl_ulong nb13 = src1->nb[3]; UNUSED(nb13);
+    const cl_ulong nb13 = src1->nb[3];
 
-    const int  ne0  = dst->ne[0];
-    const int  ne1  = dst->ne[1];
-    const int  ne2  = dst->ne[2];
-    const int  ne3  = dst->ne[3];
+    const int ne0  = dst->ne[0];
+    const int ne1  = dst->ne[1];
+    const int ne2  = dst->ne[2];
+    const int ne3  = dst->ne[3];
 
     const cl_ulong nb0  = dst->nb[0];
     const cl_ulong nb1  = dst->nb[1];
@@ -3761,68 +3764,114 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
     cl_ulong offset1 = extra1->offset + src1->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
 
-    bool bcast_row = false;
     cl_kernel kernel;
 
-    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+    const bool bcast_row = ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0;
+
+    if (bcast_row) {
         GGML_ASSERT(ggml_is_contiguous(src0));
-
-        // src1 is a row
         GGML_ASSERT(ne11 == 1);
+    }
 
-        bcast_row = true;
-        int ne = ne00 / 4;
-
-        if (src0->type == GGML_TYPE_F32) {
+    if (dst->type == GGML_TYPE_F32) {
+        GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32);
+        if (bcast_row) {
             kernel = backend_ctx->kernel_add_row;
+            const int ne = ne00 / 4;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
         } else {
-            kernel = backend_ctx->kernel_add_row_f16;
-        }
-
-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
-        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
-        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
-        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
             kernel = backend_ctx->kernel_add;
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
+            CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
+            CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
+            CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
+            CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
+            CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
+        }
+    } else if (dst->type == GGML_TYPE_F16) {
+        GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
+        GGML_ASSERT(src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
+        const int type_src0 = (src0->type == GGML_TYPE_F32);
+        const int type_src1 = (src1->type == GGML_TYPE_F32);
+        if (bcast_row) {
+            kernel = backend_ctx->kernel_add_row_f16;
+            const int ne = ne00 / 4;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
+            CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),      &type_src0));
+            CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int),      &type_src1));
         } else {
             kernel = backend_ctx->kernel_add_f16;
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
+            CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
+            CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
+            CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
+            CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
+            CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
+            CL_CHECK(clSetKernelArg(kernel, 30, sizeof(int),      &type_src0));
+            CL_CHECK(clSetKernelArg(kernel, 31, sizeof(int),      &type_src1));
         }
-
-        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
-        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
-        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
-        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
-        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
-        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
-        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
-        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
-        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
-        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
-        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
-        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
-        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
-        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
-        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
-        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
-        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
-        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
-        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
-        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
-        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
-        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
-        CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
-        CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
-        CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
-        CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
-        CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
-        CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
-        CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
+    } else {
+        GGML_ASSERT(false && "unsupported data types for add");
     }
 
     if (bcast_row) {
@@ -3832,13 +3881,13 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
 
         size_t * local_work_size_ptr = local_work_size;
         if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) {
-            local_work_size_ptr = nullptr;  // Let driver choose the work-group sizes.
+            local_work_size_ptr = nullptr;
         }
 
-        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst);
+        backend_ctx->enqueue_ndrange_kernel(kernel, 1, global_work_size, local_work_size_ptr, dst);
     } else {
         unsigned int nth = MIN(64, ne0);
-        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
+        size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
         size_t local_work_size[] = {nth, 1, 1};
 
         backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
diff --git a/ggml/src/ggml-opencl/kernels/add.cl b/ggml/src/ggml-opencl/kernels/add.cl
index 8bc926c889..509bf17344 100644
--- a/ggml/src/ggml-opencl/kernels/add.cl
+++ b/ggml/src/ggml-opencl/kernels/add.cl
@@ -112,7 +112,9 @@ kernel void kernel_add_f16(
         ulong nb0,
         ulong nb1,
         ulong nb2,
-        ulong nb3
+        ulong nb3,
+        int type_src0,
+        int type_src1
 ) {
     src0 = src0 + offset0;
     src1 = src1 + offset1;
@@ -132,25 +134,57 @@ kernel void kernel_add_f16(
 
     for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
         const int i10 = i0 % ne10;
-        *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) + *((global half *)(src1_ptr + i10*nb10));
+
+        half v0, v1;
+        if (type_src0 == 1) {
+            v0 = convert_half(*((global float *)(src0_ptr + i0*nb00)));
+        } else {
+            v0 = *((global half *)(src0_ptr + i0*nb00));
+        }
+
+        if (type_src1 == 1) {
+            v1 = convert_half(*((global float *)(src1_ptr + i10*nb10)));
+        } else {
+            v1 = *((global half *)(src1_ptr + i10*nb10));
+        }
+
+        *((global half *)(dst_ptr + i0*nb0)) = v0 + v1;
     }
 }
 
 kernel void kernel_add_row_f16(
-        global half4 * src0,
+        global char * src0,
         ulong  offset0,
-        global half4 * src1,
+        global char * src1,
         ulong  offset1,
         global half4 * dst,
         ulong  offsetd,
-        int ne
+        int ne,
+        int type_src0,
+        int type_src1
 ) {
-    src0 = (global half4*)((global char*)src0 + offset0);
-    src1 = (global half4*)((global char*)src1 + offset1);
     dst = (global half4*)((global char*)dst + offsetd);
 
     // This performs better than using %.
     uint gid = get_global_id(0);
     uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
-    dst[gid] = src0[gid] + src1[idx1];
+
+    half4 v0, v1;
+    if (type_src0 == 1) {
+        global float4* src0_f32 = (global float4*)((global char*)src0 + offset0);
+        v0 = convert_half4(src0_f32[gid]);
+    } else {
+        global half4* src0_f16 = (global half4*)((global char*)src0 + offset0);
+        v0 = src0_f16[gid];
+    }
+
+    if (type_src1 == 1) {
+        global float4* src1_f32 = (global float4*)((global char*)src1 + offset1);
+        v1 = convert_half4(src1_f32[idx1]);
+    } else {
+        global half4* src1_f16 = (global half4*)((global char*)src1 + offset1);
+        v1 = src1_f16[idx1];
+    }
+
+    dst[gid] = v0 + v1;
 }

From f4586ee5986d6f965becb37876d6f3666478a961 Mon Sep 17 00:00:00 2001
From: Romain Biessy <romain.biessy@codeplay.com>
Date: Tue, 12 Aug 2025 13:58:22 +0200
Subject: [PATCH 042/287] sycl: Fix and disable more configurations of mul_mat
 (#15151)

* sycl: Fix and disable more configurations of mul_mat

* Disable more configurations
---
 ggml/src/ggml-sycl/ggml-sycl.cpp | 35 +++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 3992dad01d..a0a650e92e 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -2705,9 +2705,9 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
                                                 " : converting src1 to fp16");
 
         // iterate tensor dims and find the slowest moving dim and stride
-        int64_t last_dim=0;
-        int64_t last_str=0;
-        int64_t largest_str=0;
+        int last_dim=0;
+        int last_str=0;
+        size_t largest_str=0;
         for(int i = 0; i< 4; i++){
             // last stride is always the largest
             if(src1->nb[i] == largest_str){
@@ -2783,7 +2783,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
             auto launch_gemm_for_batches = [&ctx, queue](const sycl::half *src0,
                                                 const sycl::half *src1, float *dst,
                                                 int64_t a0, int64_t a1, int64_t batcha,
-                                                int64_t b0, int64_t b1, int64_t batchb,
+                                                int64_t /*b0*/, int64_t b1, int64_t batchb,
                                                 int64_t sa0, int64_t sa1, int64_t sa2,
                                                 int64_t sb0, int64_t sb1, int64_t sb2,
                                                 int64_t sd2) {
@@ -2832,14 +2832,26 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
                 }
             };
 
-            bool cont_batches_a = nb02 * ne02 == nb03;
-            bool cont_batches_b = nb12 * ne12 == nb13;
-            if (cont_batches_a && cont_batches_b) {
+            const bool cont_batches_dim2_a = nb02 * ne02 == nb03;
+            const bool cont_batches_dim2_b = nb12 * ne12 == nb13;
+            const bool cont_batches_dim3_a = ne02 == 1 && nb02 * ne01 == nb03;
+            const bool cont_batches_dim3_b = ne12 == 1 && nb12 * ne11 == nb13;
+            if (cont_batches_dim2_a && cont_batches_dim2_b) {
+                // A batch is considered contiguous if the dimension 2 is not strided
                 int64_t batches0 = ne02 * ne03;
                 int64_t batches1 = ne12 * ne13;
                 launch_gemm_for_batches(src0_f16, src1_f16, dst_ddf, ne00, ne01, batches0,
                         ne10, ne11, batches1, str_a0, str_a1, str_a2, str_b0, str_b1,
                         str_b2, nb2 / sizeof(float));
+            } else if (cont_batches_dim3_a && cont_batches_dim3_b) {
+                // This case is similar to the one above with the difference that only the batch in dimension 3 is used and the dimension 2 is of size 1.
+                int64_t batches0 = ne02 * ne03;
+                int64_t batches1 = ne12 * ne13;
+                int64_t str_a3 = nb03 / type_size_src0;
+                int64_t str_b3 = nb13 / type_size_src1;
+                launch_gemm_for_batches(src0_f16, src1_f16, dst_ddf, ne00, ne01, batches0,
+                        ne10, ne11, batches1, str_a0, str_a1, str_a3, str_b0, str_b1,
+                        str_b3, nb2 / sizeof(float));
             } else {
                 for (int64_t b_a = 0; b_a < ne03; b_a++) {
                     const sycl::half *src0_f16_shifted
@@ -4215,6 +4227,15 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                     // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
                     return false;
                 }
+                // TODO: The configuration below needs more work to be supported with oneDNN
+                if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) {
+                    return false;
+                }
+                // TODO: This specific configuration can fail with oneDNN and needs more debugging
+                if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
+                    a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
+                    return false;
+                }
                 return true;
             }
         case GGML_OP_OUT_PROD:

From b0493156fa8622694f21f42460a84da3eded0bc0 Mon Sep 17 00:00:00 2001
From: uvos <carl@uvos.xyz>
Date: Tue, 12 Aug 2025 22:15:12 +0200
Subject: [PATCH 043/287] HIP: disable sync warp shuffel operators from clr
 amd_warp_sync_functions.h (#15273)

---
 ggml/src/ggml-cuda/fattn-wmma-f16.cu | 1 -
 ggml/src/ggml-cuda/vendors/hip.h     | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index fdc4d17da2..6bc7943ccd 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -15,7 +15,6 @@ namespace wmma = mtmusa::wmma;
 namespace wmma = nvcuda::wmma;
 #endif // GGML_USE_MUSA
 #elif defined(GGML_HIP_ROCWMMA_FATTN) && defined(FP16_MMA_AVAILABLE)
-#undef HIP_ENABLE_WARP_SYNC_BUILTINS // conflicts with rocWMMA headers
 #include <rocwmma/rocwmma.hpp>
 namespace wmma = rocwmma;
 #endif // !defined(GGML_USE_HIP)
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index c31f319232..96f8bc75e9 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#define HIP_ENABLE_WARP_SYNC_BUILTINS 1
+#define HIP_DISABLE_WARP_SYNC_BUILTINS 1
 #include <hip/hip_runtime.h>
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>

From e71d48e3265027351e44a8e198f933c98f242c2e Mon Sep 17 00:00:00 2001
From: Tak-RS <snosk.t@gmail.com>
Date: Wed, 13 Aug 2025 14:54:30 +0900
Subject: [PATCH 044/287] ggml-rpc: chunk send()/recv() to avoid EINVAL for
 very large tensors over RPC (macOS & others) (#15188)

* ggml-rpc: chunk send()/recv() to avoid EINVAL for very large tensors over RPC (macOS & others). Fixes #15055

* ggml-rpc: rename RPC_IO_CHUNK->MAX_CHUNK_SIZE, use std::min() for cap, switch to GGML_LOG_ERROR, handle 0-length send/recv

* rpc: drop n==0 special case in send_data(); retry in loop per review

* rpc: remove trailing whitespace in send_data()

---------

Co-authored-by: Shinnosuke Takagi <nosuke@nosukenoMacBook-Pro.local>
---
 ggml/src/ggml-rpc/ggml-rpc.cpp | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index df6ba54076..e84ff93efc 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -29,9 +29,12 @@
 #include <cstring>
 #include <fstream>
 #include <filesystem>
+#include <algorithm>
 
 namespace fs = std::filesystem;
 
+static constexpr size_t MAX_CHUNK_SIZE = 1024ull * 1024ull * 1024ull; // 1 GiB
+
 #ifdef _WIN32
 typedef SOCKET sockfd_t;
 using ssize_t = __int64;
@@ -323,11 +326,14 @@ static std::shared_ptr<socket_t> create_server_socket(const char * host, int por
 static bool send_data(sockfd_t sockfd, const void * data, size_t size) {
     size_t bytes_sent = 0;
     while (bytes_sent < size) {
-        ssize_t n = send(sockfd, (const char *)data + bytes_sent, size - bytes_sent, 0);
+        size_t size_to_send = std::min(size - bytes_sent, MAX_CHUNK_SIZE);
+        ssize_t n = send(sockfd, (const char *)data + bytes_sent, size_to_send, 0);
         if (n < 0) {
+            GGML_LOG_ERROR("send failed (bytes_sent=%zu, size_to_send=%zu)\n",
+                           bytes_sent, size_to_send);
             return false;
         }
-        bytes_sent += n;
+        bytes_sent += (size_t)n;
     }
     return true;
 }
@@ -335,11 +341,18 @@ static bool send_data(sockfd_t sockfd, const void * data, size_t size) {
 static bool recv_data(sockfd_t sockfd, void * data, size_t size) {
     size_t bytes_recv = 0;
     while (bytes_recv < size) {
-        ssize_t n = recv(sockfd, (char *)data + bytes_recv, size - bytes_recv, 0);
-        if (n <= 0) {
+        size_t size_to_recv = std::min(size - bytes_recv, MAX_CHUNK_SIZE);
+        ssize_t n = recv(sockfd, (char *)data + bytes_recv, size_to_recv, 0);
+        if (n < 0) {
+            GGML_LOG_ERROR("recv failed (bytes_recv=%zu, size_to_recv=%zu)\n",
+                           bytes_recv, size_to_recv);
             return false;
         }
-        bytes_recv += n;
+        if (n == 0) {
+            GGML_LOG_ERROR("recv returned 0 (peer closed?)\n");
+            return false;
+        }
+        bytes_recv += (size_t)n;
     }
     return true;
 }

From bc5182272c373267352bc689e5fca276934bea2d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 13 Aug 2025 09:07:13 +0200
Subject: [PATCH 045/287] ci : add copilot-setup-steps.yml (#15214)

---
 .github/workflows/copilot-setup-steps.yml | 52 +++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 .github/workflows/copilot-setup-steps.yml

diff --git a/.github/workflows/copilot-setup-steps.yml b/.github/workflows/copilot-setup-steps.yml
new file mode 100644
index 0000000000..14f7c5e3f7
--- /dev/null
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -0,0 +1,52 @@
+name: "Copilot Setup Steps"
+
+# Automatically run the setup steps when they are changed to allow for easy validation, and
+# allow manual testing through the repository's "Actions" tab
+on:
+  workflow_dispatch:
+  push:
+    paths:
+      - .github/workflows/copilot-setup-steps.yml
+  pull_request:
+    paths:
+      - .github/workflows/copilot-setup-steps.yml
+
+jobs:
+  # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
+  copilot-setup-steps:
+    runs-on: ubuntu-latest
+
+    # Set the permissions to the lowest permissions possible needed for your steps.
+    # Copilot will be given its own token for its operations.
+    permissions:
+      # If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete.
+      contents: read
+
+    # You can define any steps you want, and they will run before the agent starts.
+    # If you do not check out your code, Copilot will do this for you.
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: copilot-setup-steps
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libcurl4-openssl-dev
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install Python dependencies
+        run: |
+          python3 -m venv .venv
+          .venv/bin/activate
+          pip install -r requirements/requirements-all.txt

From 6028bf74351d35a06bd98498624f8c2f029f7d1a Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Wed, 13 Aug 2025 10:04:46 +0200
Subject: [PATCH 046/287] CUDA: Optimize `reduce_rows_f32` kernel, leading up
 to 25x perf improvement on kernel-level and 10% perf increase for Gemma3n
 (#15132)

* Factor out `reduce_rows_f32` from common.cuh

This increases iteration cycle speed by not having to recompile
every kernel all the time

* Hide memory-latency by loop unrolling in reduce_rows_f32

* Further optimizations to `reduce_rows_f32`

1. Increase threadblock size to better hide latency of memory requests.
   As a consequence of bigger threadblocks, do 2-step summation, using
   shared memory to communicate results between invocations
2. Use sum_temp array to reduce waits on sum
3. Adjust num_unroll to reflext bigger threadblock
4. Improve default block_dims, increase support for more block_dims

* Add perf tests for `reduce_rows_f32` kernel

* Add heuristic to toggle 128/512 threads based on sm count

Break even point was the minimum of the following multiples.

| GPU Model                     | Nrow SM Count Multiple |
| -----------                   | -----------            |
| RTX 4000 SFF ADA              | 2.0x                   |
| RTX 6000 ADA                  | 2.5x                   |
| RTX PRO 6000 Blackwell Max-Q  | 3.04x                  |
| RTX PRO 4500 Blackwell	| 3.15x                  |

* Ensure perf gains also for small ncols and large nrows

Alternative to this, one could have also made the number of unrollings
template-able, but that would require compiling the kernel multiple
times, increasing binary size unnecessarily

* Modify perf and unit-tests

* Apply auto-formatting by clang

* Fix CI build failure

See https://github.com/ggml-org/llama.cpp/actions/runs/16798370266/job/47573716079?pr=15132#step:7:486
Building with VS generator worked though.

* Remove sm_count property from `ggml_backend_cuda_context`

Requested by @JohannesGaessler, and should fix remaining CI issues as a
side-effect

* Add CUB-based implementation for GGML_OP_MEAN

Currently this branch is only executed for nrows==1

* Add heuristics to execute CUB branch only when it brings perf

Heuristics were determined on the following HW:

* RTX 4000 SFF ADA
* RTX 6000 ADA
* RTX PRO 6000 Blackwell Max-Q
* RTX PRO 4500 Blackwell

* Add unit-test for CUB-based mean

Tests should run with CUDA Graphs enabled per default on NVGPUs

* Rename `USE_CUB` to `GGML_CUDA_USE_CUB`

Suggested by @JohannesGaessler

* Unindent Preprocessor directives

See
https://github.com/ggml-org/llama.cpp/pull/15132#discussion_r2269213506
---
 ggml/src/ggml-cuda/common.cuh      | 24 +++-----------
 ggml/src/ggml-cuda/mean.cu         | 52 +++++++++++++++++++++++++++--
 ggml/src/ggml-cuda/reduce_rows.cuh | 53 ++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/sum.cu          | 16 ++++-----
 ggml/src/ggml-cuda/sumrows.cu      | 25 +++++++++++---
 tests/test-backend-ops.cpp         | 21 ++++++++++++
 6 files changed, 155 insertions(+), 36 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/reduce_rows.cuh

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index a23da57e3a..5a2a3478d2 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -87,6 +87,10 @@
 #define GGML_CUDA_CC_IS_QY2(cc)      (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NG)
 #define GGML_CUDA_CC_IS_NG(cc)       (cc >= GGML_CUDA_CC_NG)
 
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
+#    define GGML_CUDA_USE_CUB
+#endif  // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
+
 #ifdef __CUDA_ARCH_LIST__
 constexpr bool ggml_cuda_has_arch_impl(int) {
     return false;
@@ -420,26 +424,6 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 #endif // FP16_AVAILABLE
 }
 
-// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
-template<bool norm>
-static __global__ void reduce_rows_f32(const float * x, float * dst, const int ncols) {
-    const int row = blockIdx.x;
-    const int col = threadIdx.x;
-
-    float sum = 0.0f;
-    for (int i = col; i < ncols; i += blockDim.x) {
-        sum += x[row * ncols + i];
-    }
-
-    sum = warp_reduce_sum(sum);
-
-    if (col != 0) {
-        return;
-    }
-
-    dst[row] = norm ? sum / ncols : sum;
-}
-
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_all(int x) {
 #ifdef GGML_USE_HIP
diff --git a/ggml/src/ggml-cuda/mean.cu b/ggml/src/ggml-cuda/mean.cu
index 4b238a3998..2ad493239b 100644
--- a/ggml/src/ggml-cuda/mean.cu
+++ b/ggml/src/ggml-cuda/mean.cu
@@ -1,4 +1,14 @@
 #include "mean.cuh"
+#include "reduce_rows.cuh"
+
+#ifdef GGML_CUDA_USE_CUB
+#include <cub/cub.cuh>
+using namespace cub;
+#endif  // GGML_CUDA_USE_CUB
+
+template <typename T> __global__ void divide_by_count(T * result, size_t count) {
+    *result /= static_cast<T>(count);
+}
 
 void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0   = dst->src[0];
@@ -13,7 +23,45 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t ncols = src0->ne[0];
     const int64_t nrows = ggml_nrows(src0);
 
-    const dim3 block_dims(WARP_SIZE, 1, 1);
+// Special case for reducing vectors
+#ifdef GGML_CUDA_USE_CUB
+    cudaStreamCaptureStatus iscapturing;
+    CUDA_CHECK(cudaStreamIsCapturing(stream, &iscapturing));
+    if ((nrows == 1) &&
+            // CUDA_GRAPHS_DISABLED
+            ((ncols > 65536) &&
+             ((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
+              ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
+              ctx.cuda_graph->disable_due_to_failed_graph_capture)) ||
+        // CUDA_GRAPHS ENABLED
+        ((ncols > 32768) &&
+         !((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
+           ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
+           ctx.cuda_graph->disable_due_to_failed_graph_capture))) {
+        // Single row - use device-wide reduction
+        size_t           tmp_size = 0;
+        ggml_cuda_pool & pool     = ctx.pool();
+
+        DeviceReduce::Sum(nullptr, tmp_size, src0_d, dst_d, ncols, stream);
+
+        ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
+        DeviceReduce::Sum(tmp_alloc.ptr, tmp_size, src0_d, dst_d, ncols, stream);
+
+        // Divide by ncols
+        divide_by_count<float><<<1, 1, 0, stream>>>(dst_d, ncols);
+        return;
+    }
+#endif
+
     const dim3 block_nums(nrows, 1, 1);
-    reduce_rows_f32</*norm*/ true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+
+    const int id  = ggml_cuda_get_device();
+    const int nsm = ggml_cuda_info().devices[id].nsm;
+    if ((nrows / nsm) < 2) {
+        const dim3 block_dims(512, 1, 1);
+        reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    } else {
+        const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
+        reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    }
 }
diff --git a/ggml/src/ggml-cuda/reduce_rows.cuh b/ggml/src/ggml-cuda/reduce_rows.cuh
new file mode 100644
index 0000000000..6bee204136
--- /dev/null
+++ b/ggml/src/ggml-cuda/reduce_rows.cuh
@@ -0,0 +1,53 @@
+#include "common.cuh"
+
+// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
+template <bool norm>
+static __global__ void reduce_rows_f32(const float * __restrict__ x, float * __restrict__ dst, const int ncols) {
+    const int row = blockIdx.x;
+    const int col = threadIdx.x;
+
+    float     sum        = 0.0f;
+    const int num_unroll = 8;
+    float     temp[num_unroll];
+    float     sum_temp[num_unroll] = { 0.0f };
+    for (int i = col; i < ncols;) {
+        for (int j = 0; j < num_unroll; ++j) {
+            if (i < ncols) {
+                temp[j] = x[row * ncols + i];
+            } else {
+                temp[j] = 0;
+            }
+            i += blockDim.x;
+        }
+        for (int j = 0; j < num_unroll; ++j) {
+            sum_temp[j] += temp[j];
+        }
+    }
+    for (int j = 0; j < num_unroll; ++j) {
+        sum += sum_temp[j];
+    }
+
+    // sum up partial sums
+    sum = warp_reduce_sum(sum);
+    if (blockDim.x > WARP_SIZE) {
+        assert((blockDim.x <= 1024) && (blockDim.x % WARP_SIZE) == 0);
+        __shared__ float s_sum[32];
+        const int        warp_id = threadIdx.x / WARP_SIZE;
+        const int        lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = sum;
+        }
+        __syncthreads();
+        sum = 0.0f;
+        if (lane_id < (blockDim.x / WARP_SIZE)) {
+            sum = s_sum[lane_id];
+        }
+        sum = warp_reduce_sum(sum);
+    }
+
+    if (col != 0) {
+        return;
+    }
+
+    dst[row] = norm ? sum / ncols : sum;
+}
diff --git a/ggml/src/ggml-cuda/sum.cu b/ggml/src/ggml-cuda/sum.cu
index eb3d7cdba9..c56257b440 100644
--- a/ggml/src/ggml-cuda/sum.cu
+++ b/ggml/src/ggml-cuda/sum.cu
@@ -1,19 +1,15 @@
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
-#define USE_CUB
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
+#include "sum.cuh"
+#include "sumrows.cuh"
 
-#ifdef USE_CUB
+#ifdef GGML_CUDA_USE_CUB
 #include <cub/cub.cuh>
 using namespace cub;
-#endif // USE_CUB
-
-#include "sumrows.cuh"
-#include "sum.cuh"
+#endif  // GGML_CUDA_USE_CUB
 
 #include <cstdint>
 
 void sum_f32_cuda(ggml_cuda_pool & pool, const float * x, float * dst, const int64_t ne, cudaStream_t stream) {
-#ifdef USE_CUB
+#ifdef GGML_CUDA_USE_CUB
     size_t tmp_size = 0;
     DeviceReduce::Sum(nullptr,       tmp_size, x, dst, ne, stream);
     ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
@@ -23,7 +19,7 @@ void sum_f32_cuda(ggml_cuda_pool & pool, const float * x, float * dst, const int
     // For AMD there is rocPRIM which could be used as a drop-in replacement via hipcub but this would require C++11 -> C++14.
     sum_rows_f32_cuda(x, dst, ne, 1, stream);
     GGML_UNUSED(pool);
-#endif // USE_CUB
+#endif // GGML_CUDA_USE_CUB
 }
 
 void ggml_cuda_op_sum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-cuda/sumrows.cu b/ggml/src/ggml-cuda/sumrows.cu
index 2eee08fa07..4025771aad 100644
--- a/ggml/src/ggml-cuda/sumrows.cu
+++ b/ggml/src/ggml-cuda/sumrows.cu
@@ -1,9 +1,17 @@
+#include "reduce_rows.cuh"
 #include "sumrows.cuh"
 
 void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const int  id  = ggml_cuda_get_device();
+    const int  nsm = ggml_cuda_info().devices[id].nsm;
     const dim3 block_nums(nrows, 1, 1);
-    reduce_rows_f32</*norm*/false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    if ((nrows / nsm) < 2) {
+        const dim3 block_dims(512, 1, 1);
+        reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    } else {
+        const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
+        reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    }
 }
 
 void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -19,8 +27,17 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t ncols = src0->ne[0];
     const int64_t nrows = ggml_nrows(src0);
 
-    const dim3 block_dims(WARP_SIZE, 1, 1);
     const dim3 block_nums(nrows, 1, 1);
 
-    reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    const int id  = ggml_cuda_get_device();
+    const int nsm = ggml_cuda_info().devices[id].nsm;
+    if ((nrows / nsm) < 2) {
+        // Increase num threads to 512 for small nrows to better hide the latency
+        const dim3 block_dims(512, 1, 1);
+        reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    } else {
+        // Enough active SMs to hide latency, use smaller blocks to allow better scheduling
+        const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
+        reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index d29779cd12..63e03978e4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5998,6 +5998,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_sum());
     test_cases.emplace_back(new test_sum_rows());
     test_cases.emplace_back(new test_mean());
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1024, 1, 1 }));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1024, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32769, 1, 1, 1 }));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1}));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
     test_cases.emplace_back(new test_acc());
@@ -6179,6 +6188,18 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         test_cases.emplace_back(new test_add_id(GGML_TYPE_F32, GGML_TYPE_F32, 2880, 32, 4, n_token));
     }
 
+    std::vector<std::array<int64_t, 4>> reduce_rows_cases = {
+        { 8192, 1,    1, 1 },
+        { 8192, 8192, 1, 1 },
+        { 128,  8192, 1, 1 },
+    };
+
+    for (auto it: reduce_rows_cases){
+        test_cases.emplace_back(new test_mean(GGML_TYPE_F32, it));
+        test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, it));
+        test_cases.emplace_back(new test_sum(GGML_TYPE_F32, it));
+    }
+
     return test_cases;
 }
 

From 00f35d509e5367bf19ccaf4b4adddc38db4811c6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 13 Aug 2025 11:09:39 +0300
Subject: [PATCH 047/287] ggml : repack block_iq4_nlx8 (#14904)

ggml-ci
---
 ggml/src/ggml-cpu/arch-fallback.h     |   14 +
 ggml/src/ggml-cpu/arch/x86/repack.cpp | 1579 +++++++++++++------------
 ggml/src/ggml-cpu/repack.cpp          |  251 +++-
 ggml/src/ggml-cpu/repack.h            |   11 +
 4 files changed, 1044 insertions(+), 811 deletions(-)

diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index b62e3158ee..f476127995 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -40,18 +40,22 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
 // repack.cpp
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
@@ -80,12 +84,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__loongarch64)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
@@ -103,12 +109,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__riscv)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
@@ -133,11 +141,13 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__s390x__)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
@@ -164,12 +174,14 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__wasm__)
 // quants.c
 #define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
@@ -195,10 +207,12 @@
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #endif
diff --git a/ggml/src/ggml-cpu/arch/x86/repack.cpp b/ggml/src/ggml-cpu/arch/x86/repack.cpp
index 37933a4b23..d95bb6d8aa 100644
--- a/ggml/src/ggml-cpu/arch/x86/repack.cpp
+++ b/ggml/src/ggml-cpu/arch/x86/repack.cpp
@@ -511,38 +511,34 @@ void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTR
 #endif
 }
 
-void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+//
+// GEMV/GEMM templates
+//
+
+#if defined(__AVX2__) || defined(__AVX512F__)
+
+// GEMV for 8x blocks of 32 4-bit quants with a single scale factor per block
+template<typename block_tx8>
+static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) {
+    static_assert(
+            std::is_same_v<block_tx8, block_q4_0x8> ||
+            std::is_same_v<block_tx8, block_iq4_nlx8>,
+            "Unsupported block type");
+
     const int qk = QK8_0;
     const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
 
-    assert (n % qk == 0);
-    assert (nc % ncols_interleaved == 0);
-
-    UNUSED(s);
     UNUSED(bs);
-    UNUSED(vx);
-    UNUSED(vy);
-    UNUSED(nr);
-    UNUSED(nc);
-    UNUSED(nb);
-    UNUSED(ncols_interleaved);
-    UNUSED(blocklen);
 
-#if defined(__AVX2__)
-    // Lookup table to convert signed nibbles to signed bytes
-    __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0));
-    signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
     __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0);
     __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
 
     // Permute mask used for easier vector processing at later stages
     const __m256i m4b = _mm256_set1_epi8(0x0F);
 
-    int64_t b_nb = n / QK4_0;
+    int64_t b_nb = n / 32;
 
-    const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx;
+    const block_tx8  * b_ptr_start = (const block_tx8  *)vx;
     const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy;
 
     // Process Q8_0 blocks one by one
@@ -551,17 +547,17 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         // Pointers to LHS blocks of block_q8_0 format
         const block_q8_0 * a_ptr = a_ptr_start + (y * nb);
 
-        // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation
+        // Take group of eight blocks at each pass of the loop and perform dot product operation
         for (int64_t x = 0; x < nc / 8; x++) {
 
             // Pointers to RHS blocks
-            const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
+            const block_tx8 * b_ptr = b_ptr_start + (x * b_nb);
 
             // Master FP accumulator
             __m256 acc_row = _mm256_setzero_ps();
 
             for (int64_t b = 0; b < nb; b++) {
-                // Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7)
+                // Load 8 blocks of 32 interleaved as 8 bytes (B0 - B7)
                 const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
                 const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1);
                 const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2);
@@ -578,8 +574,13 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
                 const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31)
                 const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31)
 
-                // Load the scale values for the 8 blocks interleaved in block_q4_0x8
-                const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask);
+                // Load the scale values for the 8 blocks interleaved in block_tx8
+                __m256 col_scale_f32;
+                if constexpr (
+                        std::is_same_v<block_tx8, block_q4_0x8> ||
+                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
+                    col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask);
+                }
 
                 // Load and convert to FP32 scale from block_q8_0
                 const __m256 row_scale_f32 = _mm256_set1_ps(GGML_CPU_FP16_TO_FP32(a_ptr[b].d));
@@ -620,9 +621,771 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
             _mm256_storeu_ps(s + (y * nr + x * 8), acc_row);
         }
     }
-    return;
+}
 
+// GEMM for 8x blocks of 32 4-bit quants with a single scale factor per block
+template<typename block_tx8>
+static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) {
+    static_assert(
+            std::is_same_v<block_tx8, block_q4_0x8> ||
+            std::is_same_v<block_tx8, block_iq4_nlx8>,
+            "Unsupported block type");
+
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    const block_tx8    * b_ptr_start = (const block_tx8    *)vx;
+    const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy;
+
+    int64_t b_nb = n / 32;
+    int64_t y = 0;
+    // Mask to mask out nibbles from packed bytes
+    const __m256i m4b = _mm256_set1_epi8(0x0F);
+    const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3);
+    // Permute mask used for easier vector processing at later stages
+    __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4);
+    int64_t xstart = 0;
+    int anr = nr - nr%16; // Used to align nr with boundary of 16
+#ifdef __AVX512F__
+    int anc = nc - nc%16; // Used to align nc with boundary of 16
+                          // Mask to mask out nibbles from packed bytes expanded to 512 bit length
+    const __m512i m4bexpanded = _mm512_set1_epi8(0x0F);
+    // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length
+    __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1);
+
+    // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation
+    for (; y < anr / 4; y += 4) {
+
+        const block_q8_0x4 * a_ptrs[4];
+
+        a_ptrs[0] = a_ptr_start + (y * nb);
+        for (int i = 0; i < 3; ++i) {
+            a_ptrs[i + 1] = a_ptrs[i] + nb;
+        }
+
+        // Take group of two block_tx8 structures at each pass of the loop and perform dot product operation
+        for (int64_t x = 0; x < anc / 8; x += 2) {
+
+            const block_tx8 * b_ptr_0 = b_ptr_start + ((x)     * b_nb);
+            const block_tx8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb);
+
+            // Master FP accumulators
+            __m512 acc_rows[16];
+            for (int i = 0; i < 16; i++) {
+                acc_rows[i] = _mm512_setzero_ps();
+            }
+
+            for (int64_t b = 0; b < nb; b++) {
+                // Load the sixteen blocks of quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF
+                const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs));
+                const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32));
+                const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64));
+                const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96));
+
+                const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs));
+                const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32));
+                const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64));
+                const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96));
+
+                // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values
+                const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
+                const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
+
+                const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240);
+                const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240);
+
+                const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1);
+                const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1);
+                const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1);
+                const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1);
+
+                // 4-bit -> 8-bit - Sign is maintained
+                const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7)
+                const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7)
+
+                const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15)
+                const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15)
+
+                const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23)
+                const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23)
+
+                const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31)
+                const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)
+
+                // Shuffle pattern one - right side input
+                const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
+                const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
+
+                const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
+                const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
+
+                const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
+                const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
+
+                const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
+                const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
+
+                // Shuffle pattern two - right side input
+
+                const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
+                const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
+
+                const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
+                const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
+
+                const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
+                const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
+
+                const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
+                const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
+
+                // Scale values - Load the weight scale values of two block_tx8
+                __m512 col_scale_f32;
+                if constexpr (
+                        std::is_same_v<block_tx8, block_q4_0x8> ||
+                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
+                    col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
+                }
+
+                // Process LHS in pairs of rows
+                for (int rp = 0; rp < 4; rp++) {
+
+                    // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
+                    // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector
+                    __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs)));
+                    __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0);
+                    __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17);
+                    __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32)));
+                    __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0);
+                    __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17);
+                    __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64)));
+                    __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0);
+                    __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17);
+                    __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96)));
+                    __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0);
+                    __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17);
+
+                    __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1);
+                    __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1);
+                    __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1);
+                    __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1);
+                    __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1);
+                    __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1);
+                    __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1);
+                    __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1);
+
+                    // Shuffle pattern one - left side input
+
+                    const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                    const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+
+                    const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                    const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+
+                    const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                    const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+
+                    const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                    const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+
+                    // Shuffle pattern two - left side input
+
+                    const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                    const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+
+                    const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                    const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+
+                    const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                    const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+
+                    const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                    const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+
+                    // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
+                    // Resembles MMLAs into 2x2 matrices in ARM Version
+                    const __m512i zero = _mm512_setzero_epi32();
+                    __m512i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1);
+                    __m512i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1);
+                    __m512i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1);
+                    __m512i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1);
+                    __m512i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2);
+                    __m512i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2);
+                    __m512i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2);
+                    __m512i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2);
+
+                    // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
+                    __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
+                    __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
+                    __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
+                    __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
+
+
+                    // Straighten out to make 4 row vectors
+                    __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
+                    __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
+                    __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
+                    __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);
+
+                    // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
+                    const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68);
+                    const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16);
+
+                    // Multiply with appropiate scales and accumulate
+                    acc_rows[rp * 4]     = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)),   acc_rows[rp * 4]);
+                    acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)),  acc_rows[rp * 4 + 1]);
+                    acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]);
+                    acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]);
+                }
+            }
+
+            // Store the accumulated values
+            for (int i = 0; i < 16; i++) {
+                _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
+            }
+        }
+    }
+
+    // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation
+    for (; y < nr / 4; y ++) {
+        const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb);
+
+        // Take group of two block_tx8 structures at each pass of the loop and perform dot product operation
+        for (int64_t x = 0; x < anc / 8; x += 2) {
+
+            const block_tx8 * b_ptr_0 = b_ptr_start + ((x)     * b_nb);
+            const block_tx8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb);
+
+            // Master FP accumulators
+            __m512 acc_rows[4];
+            for (int i = 0; i < 4; i++) {
+                acc_rows[i] = _mm512_setzero_ps();
+            }
+
+            for (int64_t b = 0; b < nb; b++) {
+                // Load the sixteen blocks of quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF
+                const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs));
+                const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32));
+                const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64));
+                const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96));
+
+                const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs));
+                const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32));
+                const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64));
+                const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96));
+
+                // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
+                const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
+                const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
+
+                const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240);
+                const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240);
+
+                const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1);
+                const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1);
+                const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1);
+                const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1);
+
+                // 4-bit -> 8-bit - Sign is maintained
+                const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7)
+                const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7)
+
+                const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15)
+                const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15)
+
+                const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23)
+                const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23)
+
+                const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31)
+                const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)
+
+                // Shuffle pattern one - right side input
+                const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
+                const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
+
+                const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
+                const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
+
+                const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
+                const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
+
+                const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
+                const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
+
+                // Shuffle pattern two - right side input
+
+                const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
+                const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
+
+                const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
+                const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
+
+                const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
+                const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
+
+                const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
+                const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
+
+
+                // Scale values - Load the weight scale values of two block_tx8
+                __m512 col_scale_f32;
+                if constexpr (
+                        std::is_same_v<block_tx8, block_q4_0x8> ||
+                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
+                    col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
+                }
+
+                // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
+                // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector
+                __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs)));
+                __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0);
+                __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17);
+                __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32)));
+                __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0);
+                __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17);
+                __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64)));
+                __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0);
+                __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17);
+                __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96)));
+                __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0);
+                __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17);
+
+                __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1);
+                __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1);
+                __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1);
+                __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1);
+                __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1);
+                __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1);
+                __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1);
+                __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1);
+
+                // Shuffle pattern one - left side input
+
+                const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+
+                const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+
+                const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+
+                const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+
+                // Shuffle pattern two - left side input
+
+                const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+
+                const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+
+                const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+
+                const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+
+                // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
+                // Resembles MMLAs into 2x2 matrices in ARM Version
+                const __m512i zero = _mm512_setzero_epi32();
+                __m512i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1);
+                __m512i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1);
+                __m512i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1);
+                __m512i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1);
+                __m512i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2);
+                __m512i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2);
+                __m512i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2);
+                __m512i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2);
+
+                // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
+                __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
+                __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
+                __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
+                __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
+
+
+                // Straighten out to make 4 row vectors
+                __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
+                __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
+                __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
+                __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);
+
+                // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
+                const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68);
+                const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16);
+
+                // Multiply with appropiate scales and accumulate
+                acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)),   acc_rows[0]);
+                acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)),  acc_rows[1]);
+                acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]);
+                acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]);
+            }
+
+            // Store the accumulated values
+            for (int i = 0; i < 4; i++) {
+                _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
+            }
+        }
+    }
+    if (anc != nc) {
+        xstart = anc/8;
+        y = 0;
+    }
+#endif // __AVX512F__
+
+    // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation
+
+    for (; y < anr / 4; y += 4) {
+        const block_q8_0x4 * a_ptrs[4];
+
+        a_ptrs[0] = a_ptr_start + (y * nb);
+        for (int i = 0; i < 3; ++i) {
+            a_ptrs[i + 1] = a_ptrs[i] + nb;
+        }
+
+        // Take group of eight block_tx8 structures at each pass of the loop and perform dot product operation
+        for (int64_t x = xstart; x < nc / 8; x++) {
+
+            const block_tx8 * b_ptr = b_ptr_start + (x * b_nb);
+
+            // Master FP accumulators
+            __m256 acc_rows[16];
+            for (int i = 0; i < 16; i++) {
+                acc_rows[i] = _mm256_setzero_ps();
+            }
+
+            for (int64_t b = 0; b < nb; b++) {
+                // Load the eight blocks of quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
+                const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
+                const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
+                const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
+                const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
+
+                // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values
+                const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
+                const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
+
+                // 4-bit -> 8-bit - Sign is maintained
+                const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
+                const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
+
+                const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
+                const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
+
+                const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
+                const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
+
+                const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
+                const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
+
+                // Shuffle pattern one - right side input
+                const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136);  //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
+                const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136);  //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
+
+                const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136);  //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
+                const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136);  //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
+
+                const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136);  //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
+                const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136);  //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
+
+                const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136);  //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
+                const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136);  //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
+
+                // Shuffle pattern two - right side input
+
+                const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221);  //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
+                const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221);  //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
+
+                const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221);  //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
+                const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221);  //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
+
+                const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221);  //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
+                const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221);  //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
+
+                const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221);  //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
+                const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221);  //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
+
+                // Scale values - Load the wight scale values of block_tx8
+                __m256 col_scale_f32;
+                if constexpr (
+                        std::is_same_v<block_tx8, block_q4_0x8> ||
+                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
+                    col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
+                }
+
+                // Process LHS in groups of four
+                for (int rp = 0; rp < 4; rp++) {
+                    // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
+                    // Loaded as set of 128 bit vectors and repeated into a 256 bit vector
+                    __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs)));
+                    __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
+                    __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
+                    __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32)));
+                    __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
+                    __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
+                    __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64)));
+                    __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
+                    __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
+                    __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96)));
+                    __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
+                    __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
+
+                    // Shuffle pattern one - left side input
+                    const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                    const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+
+                    const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                    const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+
+                    const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                    const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+
+                    const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                    const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+
+                    // Shuffle pattern two - left side input
+                    const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                    const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+
+                    const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                    const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+
+                    const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                    const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+
+                    const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                    const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+
+                    // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
+                    // Resembles MMLAs into 2x2 matrices in ARM Version
+                    const __m256i zero = _mm256_setzero_si256();
+                    __m256i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1);
+                    __m256i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1);
+                    __m256i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1);
+                    __m256i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1);
+                    __m256i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2);
+                    __m256i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2);
+                    __m256i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2);
+                    __m256i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2);
+
+                    // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
+                    __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
+                    __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
+                    __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
+                    __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
+
+                    // Straighten out to make 4 row vectors
+                    __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
+                    __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
+                    __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
+                    __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
+
+                    // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
+                    const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask);
+
+                    // Multiply with appropiate scales and accumulate
+                    acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]);
+                    acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]);
+                    acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]);
+                    acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32,  255)), acc_rows[rp * 4 + 3]);
+                }
+            }
+
+            // Store the accumulated values
+            for (int i = 0; i < 16; i++) {
+                _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
+            }
+        }
+    }
+
+    // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation
+    for (; y < nr / 4; y ++) {
+        const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb);
+
+        // Load the eight blocks of quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
+        for (int64_t x = xstart; x < nc / 8; x++) {
+            const block_tx8 * b_ptr = b_ptr_start + (x * b_nb);
+
+            // Master FP accumulators
+            __m256 acc_rows[4];
+            for (int i = 0; i < 4; i++) {
+                acc_rows[i] = _mm256_setzero_ps();
+            }
+
+            for (int64_t b = 0; b < nb; b++) {
+                // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
+                const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
+                const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
+                const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
+                const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
+
+                // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
+                const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
+                const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
+                const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
+
+                // 4-bit -> 8-bit - Sign is maintained
+                const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b));  //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
+                const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b));  //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
+
+                const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b));  //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
+                const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b));  //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
+
+                const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b));  //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
+                const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b));  //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
+
+                const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b));  //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
+                const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b));  //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
+
+                // Shuffle pattern one - right side input
+                const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136);  //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
+                const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136);  //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
+
+                const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136);  //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
+                const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136);  //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
+
+                const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136);  //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
+                const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136);  //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
+
+                const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136);  //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
+                const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136);  //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
+
+                // Shuffle pattern two - right side input
+
+                const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221);  //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
+                const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221);  //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
+
+                const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221);  //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
+                const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221);  //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
+
+                const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221);  //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
+                const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221);  //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
+
+                const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221);  //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
+                const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221);  //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
+
+                // Scale values - Load the wight scale values of block_tx8
+                __m256 col_scale_f32;
+                if constexpr (
+                        std::is_same_v<block_tx8, block_q4_0x8> ||
+                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
+                    col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
+                }
+
+                // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
+                // Loaded as set of 128 bit vectors and repeated into a 256 bit vector
+                __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs)));
+                __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
+                __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
+                __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32)));
+                __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
+                __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
+                __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64)));
+                __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
+                __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
+                __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96)));
+                __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
+                __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
+
+                // Shuffle pattern one - left side input
+
+                const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
+                const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
+
+                const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
+                const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
+
+                const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
+                const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
+
+                const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
+                const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
+
+                // Shuffle pattern two - left side input
+
+                const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
+                const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
+
+                const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
+                const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
+
+                const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
+                const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
+
+                const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
+                const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
+
+                // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
+                // Resembles MMLAs into 2x2 matrices in ARM Version
+                const __m256i zero = _mm256_setzero_si256();
+                __m256i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1);
+                __m256i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1);
+                __m256i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1);
+                __m256i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1);
+                __m256i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2);
+                __m256i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2);
+                __m256i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2);
+                __m256i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2);
+
+                // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
+                __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
+                __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
+                __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
+                __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
+
+
+                // Straighten out to make 4 row vectors
+                __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
+                __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
+                __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
+                __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
+
+                // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
+                const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask);
+
+                // Multiply with appropiate scales and accumulate
+                acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]);
+                acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]);
+                acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]);
+                acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]);
+            }
+
+            // Store the accumulated values
+            for (int i = 0; i < 4; i++) {
+                _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
+            }
+        }
+    }
+}
+
+#endif // defined(__AVX2__) || defined(__AVX512F__)
+
+void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+#if defined(__AVX2__) || defined(__AVX512F__)
+    {
+        // Lookup table to convert signed nibbles to signed bytes
+        __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0));
+        signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
+
+        gemv_q4_b32_8x8_q8_0_lut_avx<block_q4_0x8>(n, s, bs, vx, vy, nr, nc, signextendlut);
+
+        return;
+    }
 #endif
+
     ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
@@ -849,6 +1612,19 @@ void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif
 }
 
+void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+#if defined(__AVX2__)
+    __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_iq4nl));
+    signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
+
+    gemv_q4_b32_8x8_q8_0_lut_avx<block_iq4_nlx8>(n, s, bs, vx, vy, nr, nc, signextendlut);
+
+    return;
+#endif
+
+    ggml_gemv_iq4_nl_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK_K;
     const int nb = n / qk;
@@ -1163,750 +1939,18 @@ void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 }
 
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
-    const int qk = QK8_0;
-    const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
-
-    assert (n % qk == 0);
-    assert (nr % 4 == 0);
-    assert (nc % ncols_interleaved == 0);
-
-    UNUSED(s);
-    UNUSED(bs);
-    UNUSED(vx);
-    UNUSED(vy);
-    UNUSED(nr);
-    UNUSED(nc);
-    UNUSED(nb);
-    UNUSED(ncols_interleaved);
-    UNUSED(blocklen);
-
 #if defined(__AVX2__) || defined(__AVX512F__)
     {
-        const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx;
-        const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy;
-        int64_t b_nb = n / QK4_0;
-        int64_t y = 0;
-        // Mask to mask out nibbles from packed bytes
-        const __m256i m4b = _mm256_set1_epi8(0x0F);
-        const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3);
         // Lookup table to convert signed nibbles to signed bytes
         __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0));
         signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
-        // Permute mask used for easier vector processing at later stages
-        __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4);
-        int64_t xstart = 0;
-        int anr = nr - nr%16; // Used to align nr with boundary of 16
-    #ifdef __AVX512F__
-        int anc = nc - nc%16; // Used to align nc with boundary of 16
-        // Mask to mask out nibbles from packed bytes expanded to 512 bit length
-        const __m512i m4bexpanded = _mm512_set1_epi8(0x0F);
-        // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length
-        __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1);
 
-        // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation
-        for (; y < anr / 4; y += 4) {
+        gemm_q4_b32_8x8_q8_0_lut_avx<block_q4_0x8>(n, s, bs, vx, vy, nr, nc, signextendlut);
 
-            const block_q8_0x4 * a_ptrs[4];
-
-            a_ptrs[0] = a_ptr_start + (y * nb);
-            for (int i = 0; i < 3; ++i) {
-                a_ptrs[i + 1] = a_ptrs[i] + nb;
-            }
-
-            // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation
-            for (int64_t x = 0; x < anc / 8; x += 2) {
-
-                const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x)     * b_nb);
-                const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb);
-
-                // Master FP accumulators
-                __m512 acc_rows[16];
-                for (int i = 0; i < 16; i++) {
-                    acc_rows[i] = _mm512_setzero_ps();
-                }
-
-                for (int64_t b = 0; b < nb; b++) {
-                    // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF
-                    const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs));
-                    const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32));
-                    const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64));
-                    const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96));
-
-                    const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs));
-                    const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32));
-                    const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64));
-                    const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96));
-
-                    // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values
-                    const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
-                    const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
-
-                    const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240);
-                    const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240);
-
-                    const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1);
-                    const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1);
-                    const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1);
-                    const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1);
-
-                    // 4-bit -> 8-bit - Sign is maintained
-                    const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7)
-                    const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7)
-
-                    const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15)
-                    const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15)
-
-                    const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23)
-                    const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23)
-
-                    const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31)
-                    const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)
-
-                    // Shuffle pattern one - right side input
-                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
-                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
-
-                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
-                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
-
-                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
-                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
-
-                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
-                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
-
-                    // Shuffle pattern two - right side input
-
-                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
-                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
-
-                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
-                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
-
-                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
-                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
-
-                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
-                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
-
-                    // Scale values - Load the weight scale values of two block_q4_0x8
-                    const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
-
-                    // Process LHS in pairs of rows
-                    for (int rp = 0; rp < 4; rp++) {
-
-                        // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
-                        // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector
-                        __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs)));
-                        __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0);
-                        __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17);
-                        __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32)));
-                        __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0);
-                        __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17);
-                        __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64)));
-                        __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0);
-                        __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17);
-                        __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96)));
-                        __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0);
-                        __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17);
-
-                        __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1);
-                        __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1);
-                        __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1);
-                        __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1);
-                        __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1);
-                        __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1);
-                        __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1);
-                        __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1);
-
-                        // Shuffle pattern one - left side input
-
-                        const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                        const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
-
-                        const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                        const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
-
-                        const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                        const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
-
-                        const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                        const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
-
-                        // Shuffle pattern two - left side input
-
-                        const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                        const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
-
-                        const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                        const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
-
-                        const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                        const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
-
-                        const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                        const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
-
-                        // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
-                        // Resembles MMLAs into 2x2 matrices in ARM Version
-                        const __m512i zero = _mm512_setzero_epi32();
-                        __m512i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1);
-                        __m512i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1);
-                        __m512i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1);
-                        __m512i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1);
-                        __m512i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2);
-                        __m512i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2);
-                        __m512i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2);
-                        __m512i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2);
-
-                        // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
-                        __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
-                        __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
-                        __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
-                        __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
-
-
-                        // Straighten out to make 4 row vectors
-                        __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
-                        __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
-                        __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
-                        __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);
-
-                        // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
-                        const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68);
-                        const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16);
-
-                        // Multiply with appropiate scales and accumulate
-                        acc_rows[rp * 4]     = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)),   acc_rows[rp * 4]);
-                        acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)),  acc_rows[rp * 4 + 1]);
-                        acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]);
-                        acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]);
-                    }
-                }
-
-                // Store the accumulated values
-                for (int i = 0; i < 16; i++) {
-                    _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
-                }
-            }
-        }
-        // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation
-        for (; y < nr / 4; y ++) {
-
-            const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb);
-
-            // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation
-            for (int64_t x = 0; x < anc / 8; x += 2) {
-
-                const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x)     * b_nb);
-                const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb);
-
-                // Master FP accumulators
-                __m512 acc_rows[4];
-                for (int i = 0; i < 4; i++) {
-                    acc_rows[i] = _mm512_setzero_ps();
-                }
-
-                for (int64_t b = 0; b < nb; b++) {
-                    // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF
-                    const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs));
-                    const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32));
-                    const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64));
-                    const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96));
-
-                    const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs));
-                    const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32));
-                    const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64));
-                    const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96));
-
-                    // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
-                    const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
-                    const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
-
-                    const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240);
-                    const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240);
-
-                    const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1);
-                    const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1);
-                    const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1);
-                    const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1);
-
-                    // 4-bit -> 8-bit - Sign is maintained
-                    const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7)
-                    const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7)
-
-                    const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15)
-                    const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15)
-
-                    const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23)
-                    const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23)
-
-                    const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31)
-                    const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31)
-
-                    // Shuffle pattern one - right side input
-                    const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3)
-                    const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3)
-
-                    const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11)
-                    const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11)
-
-                    const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19)
-                    const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19)
-
-                    const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27)
-                    const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27)
-
-                    // Shuffle pattern two - right side input
-
-                    const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, (_MM_PERM_ENUM)221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7)
-                    const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, (_MM_PERM_ENUM)221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7)
-
-                    const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, (_MM_PERM_ENUM)221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15)
-                    const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, (_MM_PERM_ENUM)221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15)
-
-                    const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, (_MM_PERM_ENUM)221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23)
-                    const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, (_MM_PERM_ENUM)221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23)
-
-                    const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, (_MM_PERM_ENUM)221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31)
-                    const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, (_MM_PERM_ENUM)221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31)
-
-
-                    // Scale values - Load the weight scale values of two block_q4_0x8
-                    const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
-
-                    // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
-                    // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector
-                    __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs)));
-                    __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0);
-                    __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17);
-                    __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32)));
-                    __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0);
-                    __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17);
-                    __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64)));
-                    __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0);
-                    __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17);
-                    __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96)));
-                    __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0);
-                    __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17);
-
-                    __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1);
-                    __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1);
-                    __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1);
-                    __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1);
-                    __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1);
-                    __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1);
-                    __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1);
-                    __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1);
-
-                    // Shuffle pattern one - left side input
-
-                    const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                    const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
-
-                    const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                    const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
-
-                    const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                    const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
-
-                    const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                    const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
-
-                    // Shuffle pattern two - left side input
-
-                    const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, (_MM_PERM_ENUM)245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                    const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, (_MM_PERM_ENUM)245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
-
-                    const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, (_MM_PERM_ENUM)245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                    const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, (_MM_PERM_ENUM)245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
-
-                    const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, (_MM_PERM_ENUM)245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                    const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, (_MM_PERM_ENUM)245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
-
-                    const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, (_MM_PERM_ENUM)245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                    const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, (_MM_PERM_ENUM)245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
-
-                    // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
-                    // Resembles MMLAs into 2x2 matrices in ARM Version
-                    const __m512i zero = _mm512_setzero_epi32();
-                    __m512i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1);
-                    __m512i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1);
-                    __m512i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1), lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1), lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1);
-                    __m512i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1);
-                    __m512i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2);
-                    __m512i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2);
-                    __m512i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2), lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2), lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2);
-                    __m512i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(mul_sum_i8_pairs_acc_int32x16(zero, lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2);
-
-                    // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
-                    __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
-                    __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
-                    __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
-                    __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
-
-
-                    // Straighten out to make 4 row vectors
-                    __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, (_MM_PERM_ENUM)78));
-                    __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, (_MM_PERM_ENUM)78), iacc_mat_01);
-                    __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, (_MM_PERM_ENUM)78));
-                    __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, (_MM_PERM_ENUM)78), iacc_mat_11);
-
-                    // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
-                    const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68);
-                    const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16);
-
-                    // Multiply with appropiate scales and accumulate
-                    acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)),   acc_rows[0]);
-                    acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)),  acc_rows[1]);
-                    acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]);
-                    acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]);
-                }
-
-                // Store the accumulated values
-                for (int i = 0; i < 4; i++) {
-                    _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
-                }
-            }
-        }
-        if (anc != nc) {
-            xstart = anc/8;
-            y = 0;
-        }
-    #endif // __AVX512F__
-
-        // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation
-
-        for (; y < anr / 4; y += 4) {
-            const block_q8_0x4 * a_ptrs[4];
-
-            a_ptrs[0] = a_ptr_start + (y * nb);
-            for (int i = 0; i < 3; ++i) {
-                a_ptrs[i + 1] = a_ptrs[i] + nb;
-            }
-
-            // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation
-            for (int64_t x = xstart; x < nc / 8; x++) {
-
-                const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
-
-                // Master FP accumulators
-                __m256 acc_rows[16];
-                for (int i = 0; i < 16; i++) {
-                    acc_rows[i] = _mm256_setzero_ps();
-                }
-
-                for (int64_t b = 0; b < nb; b++) {
-                    // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
-                    const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
-                    const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
-                    const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
-                    const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
-
-                    // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values
-                    const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
-                    const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
-
-                    // 4-bit -> 8-bit - Sign is maintained
-                    const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
-                    const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
-
-                    const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
-                    const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
-
-                    const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
-                    const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
-
-                    const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
-                    const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
-
-                    // Shuffle pattern one - right side input
-                    const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136);  //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
-                    const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136);  //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
-
-                    const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136);  //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
-                    const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136);  //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
-
-                    const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136);  //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
-                    const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136);  //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
-
-                    const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136);  //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
-                    const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136);  //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
-
-                    // Shuffle pattern two - right side input
-
-                    const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221);  //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
-                    const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221);  //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
-
-                    const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221);  //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
-                    const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221);  //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
-
-                    const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221);  //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
-                    const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221);  //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
-
-                    const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221);  //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
-                    const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221);  //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
-
-                    // Scale values - Load the wight scale values of block_q4_0x8
-                    const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
-
-                    // Process LHS in groups of four
-                    for (int rp = 0; rp < 4; rp++) {
-                        // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
-                        // Loaded as set of 128 bit vectors and repeated into a 256 bit vector
-                        __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs)));
-                        __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
-                        __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
-                        __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32)));
-                        __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
-                        __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
-                        __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64)));
-                        __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
-                        __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
-                        __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96)));
-                        __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
-                        __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
-
-                        // Shuffle pattern one - left side input
-                        const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                        const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
-
-                        const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                        const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
-
-                        const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                        const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
-
-                        const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                        const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
-
-                        // Shuffle pattern two - left side input
-                        const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                        const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
-
-                        const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                        const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
-
-                        const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                        const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
-
-                        const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                        const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
-
-                        // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
-                        // Resembles MMLAs into 2x2 matrices in ARM Version
-                        const __m256i zero = _mm256_setzero_si256();
-                        __m256i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1);
-                        __m256i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1);
-                        __m256i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1);
-                        __m256i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1);
-                        __m256i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2);
-                        __m256i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2);
-                        __m256i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2);
-                        __m256i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2);
-
-                        // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
-                        __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
-                        __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
-                        __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
-                        __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
-
-                        // Straighten out to make 4 row vectors
-                        __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
-                        __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
-                        __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
-                        __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
-
-                        // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
-                        const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask);
-
-                        // Multiply with appropiate scales and accumulate
-                        acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]);
-                        acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]);
-                        acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]);
-                        acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32,  255)), acc_rows[rp * 4 + 3]);
-                    }
-                }
-
-                // Store the accumulated values
-                for (int i = 0; i < 16; i++) {
-                    _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
-                }
-            }
-        }
-
-        // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation
-        for (; y < nr / 4; y ++) {
-
-            const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb);
-
-            // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
-            for (int64_t x = xstart; x < nc / 8; x++) {
-
-                const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
-
-                // Master FP accumulators
-                __m256 acc_rows[4];
-                for (int i = 0; i < 4; i++) {
-                    acc_rows[i] = _mm256_setzero_ps();
-                }
-
-                for (int64_t b = 0; b < nb; b++) {
-                    // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
-                    const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
-                    const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
-                    const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
-                    const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
-
-                    // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
-                    const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
-                    const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
-                    const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
-
-                    // 4-bit -> 8-bit - Sign is maintained
-                    const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b));  //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
-                    const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b));  //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
-
-                    const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b));  //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
-                    const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b));  //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
-
-                    const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b));  //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
-                    const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b));  //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
-
-                    const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b));  //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
-                    const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b));  //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
-
-                    // Shuffle pattern one - right side input
-                    const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136);  //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
-                    const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136);  //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
-
-                    const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136);  //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
-                    const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136);  //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
-
-                    const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136);  //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
-                    const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136);  //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
-
-                    const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136);  //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
-                    const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136);  //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
-
-                    // Shuffle pattern two - right side input
-
-                    const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221);  //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
-                    const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221);  //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
-
-                    const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221);  //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
-                    const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221);  //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
-
-                    const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221);  //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
-                    const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221);  //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
-
-                    const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221);  //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
-                    const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221);  //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
-
-                    // Scale values - Load the wight scale values of block_q4_0x8
-                    const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
-
-                    // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
-                    // Loaded as set of 128 bit vectors and repeated into a 256 bit vector
-                    __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs)));
-                    __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
-                    __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
-                    __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32)));
-                    __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
-                    __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
-                    __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64)));
-                    __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
-                    __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
-                    __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96)));
-                    __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
-                    __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
-
-                    // Shuffle pattern one - left side input
-
-                    const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160);  //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
-                    const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160);  //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
-
-                    const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160);  //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
-                    const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160);  //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
-
-                    const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160);  //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
-                    const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160);  //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
-
-                    const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160);  //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
-                    const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160);  //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
-
-                    // Shuffle pattern two - left side input
-
-                    const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245);  //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
-                    const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245);  //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
-
-                    const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245);  //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
-                    const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245);  //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
-
-                    const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245);  //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
-                    const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245);  //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
-
-                    const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245);  //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
-                    const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245);  //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
-
-                    // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
-                    // Resembles MMLAs into 2x2 matrices in ARM Version
-                    const __m256i zero = _mm256_setzero_si256();
-                    __m256i iacc_mat_00_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1);
-                    __m256i iacc_mat_01_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1);
-                    __m256i iacc_mat_10_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1), lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1), lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1);
-                    __m256i iacc_mat_11_sp1 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1), lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1), lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1);
-                    __m256i iacc_mat_00_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2);
-                    __m256i iacc_mat_01_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2);
-                    __m256i iacc_mat_10_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2), lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2), lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2);
-                    __m256i iacc_mat_11_sp2 = mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(mul_sum_i8_pairs_acc_int32x8(zero, lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2), lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2), lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2);
-
-                    // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
-                    __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
-                    __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
-                    __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
-                    __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
-
-
-                    // Straighten out to make 4 row vectors
-                    __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
-                    __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
-                    __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
-                    __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
-
-                    // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
-                    const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask);
-
-                    // Multiply with appropiate scales and accumulate
-                    acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]);
-                    acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]);
-                    acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]);
-                    acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]);
-                }
-
-                // Store the accumulated values
-                for (int i = 0; i < 4; i++) {
-                    _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
-                }
-            }
-        }
         return;
     }
+#endif // defined(__AVX2__) || defined(__AVX512F__)
 
-#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
     ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
@@ -3364,6 +3408,21 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif
 }
 
+void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+#if defined(__AVX2__) || defined(__AVX512F__)
+    {
+        __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_iq4nl));
+        signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
+
+        gemm_q4_b32_8x8_q8_0_lut_avx<block_iq4_nlx8>(n, s, bs, vx, vy, nr, nc, signextendlut);
+
+        return;
+    }
+#endif // defined(__AVX2__) || defined(__AVX512F__)
+
+    ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK_K;
     const int nb = n / qk;
diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp
index 2583aefae4..f531d21e23 100644
--- a/ggml/src/ggml-cpu/repack.cpp
+++ b/ggml/src/ggml-cpu/repack.cpp
@@ -206,8 +206,9 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
     const int ncols_interleaved = 4;
     const int blocklen = 4;
 
-    assert (n % qk == 0);
-    assert (nc % ncols_interleaved == 0);
+    assert(nr == 1);
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
 
     UNUSED(s);
     UNUSED(bs);
@@ -307,30 +308,28 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
-    {
-        float sumf[8];
-        int sumi;
+    float sumf[8];
+    int sumi;
 
-        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
 
-            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int j = 0; j < ncols_interleaved; j++) {
-                        sumi = 0;
-                        for (int i = 0; i < blocklen; ++i) {
-                            const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
-                            const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
-                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
-                        }
-                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
                     }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                 }
             }
-            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
         }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
     }
 }
 
@@ -494,43 +493,73 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
     const int ncols_interleaved = 4;
     const int blocklen = 4;
 
-    assert (n % qk == 0);
-    assert (nc % ncols_interleaved == 0);
+    assert(nr == 1);
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
 
-    UNUSED(s);
     UNUSED(bs);
-    UNUSED(vx);
-    UNUSED(vy);
     UNUSED(nr);
-    UNUSED(nc);
-    UNUSED(nb);
-    UNUSED(ncols_interleaved);
-    UNUSED(blocklen);
 
-    {
-        float sumf[4];
-        int sumi;
+    float sumf[4];
+    int sumi;
 
-        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
 
-            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
-                    for (int j = 0; j < ncols_interleaved; j++) {
-                        sumi = 0;
-                        for (int i = 0; i < blocklen; ++i) {
-                            const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
-                            const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
-                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
-                        }
-                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                        const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
                     }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
                 }
             }
-            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
         }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+}
+
+void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert(nr == 1);
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[8];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_iq4_nlx8 * b_ptr = (const block_iq4_nlx8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                        const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
+                    }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
     }
 }
 
@@ -934,6 +963,50 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
     }
 }
 
+void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_iq4_nlx8 * b_ptr = (const block_iq4_nlx8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                                const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+}
+
 } // extern "C"
 
 static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
@@ -1285,15 +1358,16 @@ static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_s
 
 static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL);
-    //GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
     GGML_ASSERT(interleave_block == 4);
 
-    block_iq4_nlx4 * dst = (block_iq4_nlx4 *)t->data;
-    const block_iq4_nl * src = (const block_iq4_nl *)data;
+    const block_iq4_nl   * src = (const block_iq4_nl   *)data;
+          block_iq4_nlx4 * dst = (      block_iq4_nlx4 *)t->data;
+
     block_iq4_nl dst_tmp[4];
+
     int nrow = ggml_nrows(t);
     int nrows_interleaved = 4;
-    int nblocks = t->ne[0] / QK4_0;
+    int nblocks = t->ne[0] / QK4_NL;
 
     GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));
 
@@ -1315,6 +1389,63 @@ static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_b
     GGML_UNUSED(data_size);
 }
 
+static block_iq4_nlx8 make_block_iq4_nlx8(block_iq4_nl * in, unsigned int blck_size_interleave) {
+    block_iq4_nlx8 out;
+
+    for (int i = 0; i < 8; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    const int end = QK4_NL * 4 / blck_size_interleave;
+
+    if (blck_size_interleave == 8) {
+        for (int i = 0; i < end; ++i) {
+            int src_id = i % 8;
+            int src_offset = (i / 8) * blck_size_interleave;
+            int dst_offset = i * blck_size_interleave;
+
+            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
+        }
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    return out;
+}
+
+static int repack_iq4_nl_to_iq4_nl_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL);
+    GGML_ASSERT(interleave_block == 8);
+
+    const block_iq4_nl   * src = (const block_iq4_nl   *)data;
+          block_iq4_nlx8 * dst = (      block_iq4_nlx8 *)t->data;
+
+    block_iq4_nl dst_tmp[8];
+
+    int nrow = ggml_nrows(t);
+    int nrows_interleaved = 8;
+    int nblocks = t->ne[0] / QK4_NL;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));
+
+    if (t->ne[1] % nrows_interleaved != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_iq4_nlx8(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
 namespace ggml::cpu::repack {
 // repack
 template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
@@ -1350,6 +1481,10 @@ template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void *
 //    return repack_iq4_nl_to_iq4_nl_4_bl(t, 8, data, data_size);
 //}
 
+template <> int repack<block_iq4_nl, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_iq4_nl_to_iq4_nl_8_bl(t, 8, data, data_size);
+}
+
 // gemv
 template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
 void gemv(int, float *, size_t, const void *, const void *, int, int);
@@ -1378,6 +1513,10 @@ template <> void gemv<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size
     ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemv<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
 // gemm
 template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
 void gemm(int, float *, size_t, const void *, const void *, int, int);
@@ -1406,6 +1545,10 @@ template <> void gemm<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size
     ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemm<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
 class tensor_traits_base : public ggml::cpu::tensor_traits {
   public:
     virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0;
@@ -1680,6 +1823,7 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
 
     // instance for IQ4
     static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
+    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;
 
     if (cur->type == GGML_TYPE_Q4_0) {
         if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
@@ -1710,6 +1854,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
             }
         }
     } else if (cur->type == GGML_TYPE_IQ4_NL) {
+        if (ggml_cpu_has_avx2()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &iq4_nl_8x8_q8_0;
+            }
+        }
         if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
             if (cur->ne[1] % 4 == 0) {
                 return &iq4_nl_4x4_q8_0;
diff --git a/ggml/src/ggml-cpu/repack.h b/ggml/src/ggml-cpu/repack.h
index cd322e7438..cb32b503d3 100644
--- a/ggml/src/ggml-cpu/repack.h
+++ b/ggml/src/ggml-cpu/repack.h
@@ -67,6 +67,13 @@ struct block_iq4_nlx4 {
 
 static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wrong iq4_nlx4 block size/padding");
 
+struct block_iq4_nlx8 {
+    ggml_half d[8];            // deltas for 8 iq4_nl blocks
+    uint8_t   qs[QK4_NL * 4];  // nibbles / quants for 8 iq4_nl blocks
+};
+
+static_assert(sizeof(block_iq4_nlx8) == 8 * sizeof(ggml_half) + QK4_NL * 4, "wrong iq4_nlx8 block size/padding");
+
 #if defined(__cplusplus)
 extern "C" {
 #endif
@@ -80,12 +87,14 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 // Native implementations
 void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@@ -97,12 +106,14 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 #if defined(__cplusplus)
 } // extern "C"

From 07aa869a91837d95fcb5612c65a188763ac38647 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 13 Aug 2025 11:30:45 +0200
Subject: [PATCH 048/287] ci : add more python requirements to
 copilot-setup-steps (#15289)

* ci : add flake8 and pyright to copilot-setup-steps.yml

* add tools/server/tests/requirements.txt
---
 .github/workflows/copilot-setup-steps.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/copilot-setup-steps.yml b/.github/workflows/copilot-setup-steps.yml
index 14f7c5e3f7..a6553187ef 100644
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -49,4 +49,5 @@ jobs:
         run: |
           python3 -m venv .venv
           .venv/bin/activate
-          pip install -r requirements/requirements-all.txt
+          pip install -r requirements/requirements-all.txt -r tools/server/tests/requirements.txt
+          pip install flake8 pyright

From 648ebcdb739abfb5a9be14f4c5c0fd19ff268ef0 Mon Sep 17 00:00:00 2001
From: Ali Tariq <alitariq4589@gmail.com>
Date: Wed, 13 Aug 2025 15:14:44 +0500
Subject: [PATCH 049/287] ci : Added CI with RISC-V RVV1.0 Hardware (#14439)

* Changed the CI file to hw

* Changed the CI file to hw

* Added to sudoers for apt

* Removed the clone command and used checkout

* Added libcurl

* Added gcc-14

* Checking gcc --version

* added gcc-14 symlink

* added CC and C++ variables

* Added the gguf weight

* Changed the weights path

* Added system specification

* Removed white spaces

* ci: Replace Jenkins riscv native build Cloud-V pipeline with GitHub Actions workflow

Removed the legacy .devops/cloud-v-pipeline Jenkins CI configuration and introduced .github/workflows/build-riscv-native.yml for native RISC-V builds using GitHub Actions.

* removed trailing whitespaces

---------

Co-authored-by: Akif Ejaz <akifejaz40@gmail.com>
---
 .devops/cloud-v-pipeline                 | 22 ------------
 .github/workflows/build-riscv-native.yml | 43 ++++++++++++++++++++++++
 2 files changed, 43 insertions(+), 22 deletions(-)
 delete mode 100644 .devops/cloud-v-pipeline
 create mode 100644 .github/workflows/build-riscv-native.yml

diff --git a/.devops/cloud-v-pipeline b/.devops/cloud-v-pipeline
deleted file mode 100644
index af8c0cea61..0000000000
--- a/.devops/cloud-v-pipeline
+++ /dev/null
@@ -1,22 +0,0 @@
-node('x86_runner1'){            // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries
-    stage('Cleanup'){
-        cleanWs()               // Cleaning previous CI build in workspace
-    }
-    stage('checkout repo'){
-        retry(5){               // Retry if the cloning fails due to some reason
-            checkout scm        // Clone the repo on Runner
-        }
-    }
-    stage('Compiling llama.cpp'){
-        sh'''#!/bin/bash
-            make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V
-        '''
-    }
-    stage('Running llama.cpp'){
-        sh'''#!/bin/bash
-            module load gnu-bin2/0.1            # loading latest versions of vector qemu and vector gcc
-            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
-            cat llama_log.txt                   # Printing results
-        '''
-    }
-}
diff --git a/.github/workflows/build-riscv-native.yml b/.github/workflows/build-riscv-native.yml
new file mode 100644
index 0000000000..a968f6d519
--- /dev/null
+++ b/.github/workflows/build-riscv-native.yml
@@ -0,0 +1,43 @@
+name: Build on RISCV Linux Machine by Cloud-V
+on:
+  workflow_dispatch:
+  workflow_call:
+
+jobs:
+  bianbu-riscv64-native: # Bianbu 2.2
+    runs-on: self-hosted
+
+    steps:
+      - name: Install prerequisites
+        run: |
+          sudo apt-get update || true
+          sudo apt-get install -y libatomic1
+      - uses: actions/checkout@v4
+      - name: Setup Riscv
+        run: |
+          sudo apt-get update || true
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-riscv64-linux-gnu \
+                  g++-14-riscv64-linux-gnu \
+                  cmake
+
+      - name: Build
+        run: |
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
+                         -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)

From e885445bc1719d2e289a9b2e11714e0f994e68be Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Wed, 13 Aug 2025 05:28:21 -0500
Subject: [PATCH 050/287] server : filter out harmony thought messages (#15278)

---
 tools/server/public/index.html.gz    | Bin 1914095 -> 1914135 bytes
 tools/server/webui/src/utils/misc.ts |   7 ++++++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index a472714efb74e89e79cc02d65f0349f279417a82..eddf12f9697c2c61c61fc58c3795f547070f2fb3 100644
GIT binary patch
delta 1809645
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AH~;_u

diff --git a/tools/server/webui/src/utils/misc.ts b/tools/server/webui/src/utils/misc.ts
index d60a68cd24..1aebccbeac 100644
--- a/tools/server/webui/src/utils/misc.ts
+++ b/tools/server/webui/src/utils/misc.ts
@@ -130,7 +130,12 @@ export function filterThoughtFromMsgs(messages: APIMessage[]) {
       role: msg.role,
       content:
         msg.role === 'assistant'
-          ? contentStr.split('</think>').at(-1)!.trim()
+          ? contentStr
+              .split(
+                /<\/think>|<\|start\|>assistant<\|channel\|>final<\|message\|>/
+              )
+              .at(-1)!
+              .trim()
           : contentStr,
     } as APIMessage;
   });

From d8914fc47e8a69b28c670325cb1c8ce33e3a2960 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Wed, 13 Aug 2025 12:44:40 +0200
Subject: [PATCH 051/287]  common : add --override-tensor-draft,
 --cpu-moe-draft and --n-cpu-moe-draft parameters (#15191)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Checkpoint from VS Code for coding agent session

* Initial plan

* Fix typo in --override-tensor-draft flag implementation

* Add null termination for speculative tensor buffer overrides

* Apply suggestions from code review

* Apply suggestions from code review

* Extract tensor override parsing logic to common function (addresses @slaren's feedback)

* Apply suggestions from code review

* Apply suggestions

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 common/arg.cpp                                | 97 +++++++++++++------
 common/common.h                               |  1 +
 .../speculative-simple/speculative-simple.cpp |  2 +
 examples/speculative/speculative.cpp          |  2 +
 tools/server/server.cpp                       |  2 +
 5 files changed, 72 insertions(+), 32 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 3d18aaa171..4e4c52b5f8 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -749,6 +749,39 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
 // utils
 //
 
+// Helper function to parse tensor buffer override strings
+static void parse_tensor_buffer_overrides(const std::string & value, std::vector<llama_model_tensor_buft_override> & overrides) {
+    std::map<std::string, ggml_backend_buffer_type_t> buft_list;
+    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+        auto * dev = ggml_backend_dev_get(i);
+        auto * buft = ggml_backend_dev_buffer_type(dev);
+        if (buft) {
+            buft_list[ggml_backend_buft_name(buft)] = buft;
+        }
+    }
+
+    for (const auto & override : string_split<std::string>(value, ',')) {
+        std::string::size_type pos = override.find('=');
+        if (pos == std::string::npos) {
+            throw std::invalid_argument("invalid value");
+        }
+        std::string tensor_name = override.substr(0, pos);
+        std::string buffer_type = override.substr(pos + 1);
+
+        if (buft_list.find(buffer_type) == buft_list.end()) {
+            printf("Available buffer types:\n");
+            for (const auto & it : buft_list) {
+                printf("  %s\n", ggml_backend_buft_name(it.second));
+            }
+            throw std::invalid_argument("unknown buffer type");
+        }
+        // keep strings alive and avoid leaking memory by storing them in a static vector
+        static std::list<std::string> buft_overrides;
+        buft_overrides.push_back(tensor_name);
+        overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
+    }
+}
+
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
@@ -993,6 +1026,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         params.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
+    if (!params.speculative.tensor_buft_overrides.empty()) {
+        params.speculative.tensor_buft_overrides.push_back({nullptr, nullptr});
+    }
+
     if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
         throw std::runtime_error(string_format(
             "error: the supplied chat template is not supported: %s%s\n",
@@ -2349,40 +2386,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"--override-tensor", "-ot"}, "<tensor name pattern>=<buffer type>,...",
         "override tensor buffer type", [](common_params & params, const std::string & value) {
-            /* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
-            if (buft_list.empty()) {
-                // enumerate all the devices and add their buffer types to the list
-                for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
-                    auto * dev = ggml_backend_dev_get(i);
-                    auto * buft = ggml_backend_dev_buffer_type(dev);
-                    if (buft) {
-                        buft_list[ggml_backend_buft_name(buft)] = buft;
-                    }
-                }
-            }
-
-            for (const auto & override : string_split<std::string>(value, ',')) {
-                std::string::size_type pos = override.find('=');
-                if (pos == std::string::npos) {
-                    throw std::invalid_argument("invalid value");
-                }
-                std::string tensor_name = override.substr(0, pos);
-                std::string buffer_type = override.substr(pos + 1);
-
-                if (buft_list.find(buffer_type) == buft_list.end()) {
-                    printf("Available buffer types:\n");
-                    for (const auto & it : buft_list) {
-                        printf("  %s\n", ggml_backend_buft_name(it.second));
-                    }
-                    throw std::invalid_argument("unknown buffer type");
-                }
-                // keep strings alive and avoid leaking memory by storing them in a static vector
-                static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(tensor_name);
-                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
-            }
+            parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
         }
     ));
+    add_opt(common_arg(
+        {"--override-tensor-draft", "-otd"}, "<tensor name pattern>=<buffer type>,...",
+        "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
+            parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--cpu-moe", "-cmoe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
@@ -2405,6 +2417,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_env("LLAMA_ARG_N_CPU_MOE"));
+    add_opt(common_arg(
+        {"--cpu-moe-draft", "-cmoed"},
+        "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
+        [](common_params & params) {
+            params.speculative.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
+    add_opt(common_arg(
+        {"--n-cpu-moe-draft", "-ncmoed"}, "N",
+        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
+        [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            for (int i = 0; i < value; ++i) {
+                static std::list<std::string> buft_overrides_draft;
+                buft_overrides_draft.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
+                params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
         "number of layers to store in VRAM",
diff --git a/common/common.h b/common/common.h
index 5eab199af5..c09509b669 100644
--- a/common/common.h
+++ b/common/common.h
@@ -202,6 +202,7 @@ struct common_params_speculative {
     float   p_split      =  0.1f; // speculative decoding split probability
     float   p_min        = 0.75f; // minimum speculative decoding probability (greedy)
     std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
+    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
 
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index 722cd7f40f..a8e53f28eb 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -59,6 +59,8 @@ int main(int argc, char ** argv) {
     }
 
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
+
     common_init_result llama_init_dft = common_init_from_params(params);
 
     //model_dft = llama_init_dft.model.get();
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 0adffdb006..8449406a6d 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -85,6 +85,8 @@ int main(int argc, char ** argv) {
     }
 
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
+
     common_init_result llama_init_dft = common_init_from_params(params);
 
     model_dft = llama_init_dft.model.get();
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 45f50e9376..f549cda476 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -2015,6 +2015,8 @@ struct server_context {
             params_dft.cache_type_k = params_base.speculative.cache_type_k;
             params_dft.cache_type_v = params_base.speculative.cache_type_v;
 
+            params_dft.tensor_buft_overrides = params_base.speculative.tensor_buft_overrides;
+
             llama_init_dft = common_init_from_params(params_dft);
 
             model_dft = llama_init_dft.model.get();

From c24f4e26883a91219af5acfaf1471ca7ef582683 Mon Sep 17 00:00:00 2001
From: Judd <4046440+foldl@users.noreply.github.com>
Date: Wed, 13 Aug 2025 18:45:15 +0800
Subject: [PATCH 052/287] ggml : update `ggml_rope_multi` (#12665)

* update `rope_multi`:

1. add `ggml_rope_multi_inplace`;
1. use `GGML_MROPE_SECTIONS` instead of 4.

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/include/ggml.h | 20 ++++++++++-
 ggml/src/ggml.c     | 84 ++++++++++++++++++++++++---------------------
 2 files changed, 63 insertions(+), 41 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 2f06e1e39b..c09d82a91c 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -241,6 +241,8 @@
 #define GGML_ROPE_TYPE_MROPE  8
 #define GGML_ROPE_TYPE_VISION 24
 
+#define GGML_MROPE_SECTIONS   4
+
 #define GGML_UNUSED(x) (void)(x)
 
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
@@ -1660,7 +1662,7 @@ extern "C" {
             struct ggml_tensor  * b,
             struct ggml_tensor  * c,
             int                   n_dims,
-            int                   sections[4],
+            int                   sections[GGML_MROPE_SECTIONS],
             int                   mode,
             int                   n_ctx_orig,
             float                 freq_base,
@@ -1686,6 +1688,22 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    GGML_API struct ggml_tensor * ggml_rope_multi_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[GGML_MROPE_SECTIONS],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
     GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 55a76f8248..aff0b47cb2 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3885,6 +3885,7 @@ static struct ggml_tensor * ggml_rope_impl(
         struct ggml_tensor  * b,
         struct ggml_tensor  * c,
         int                   n_dims,
+        int                   sections[GGML_MROPE_SECTIONS],
         int                   mode,
         int                   n_ctx_orig,
         float                 freq_base,
@@ -3898,15 +3899,19 @@ static struct ggml_tensor * ggml_rope_impl(
 
     GGML_ASSERT(ggml_is_vector(b));
     GGML_ASSERT(b->type == GGML_TYPE_I32);
-    GGML_ASSERT(a->ne[2] == b->ne[0]);
+
+    bool mrope_used = mode & GGML_ROPE_TYPE_MROPE;
+    if (mrope_used) {
+        GGML_ASSERT(a->ne[2] * 4 == b->ne[0]); // mrope expecting 4 position ids per token
+    } else {
+        GGML_ASSERT(a->ne[2] == b->ne[0]);
+    }
 
     if (c) {
         GGML_ASSERT(c->type == GGML_TYPE_F32);
         GGML_ASSERT(c->ne[0] >= n_dims / 2);
     }
 
-    int sections[4] = {0, 0, 0, 0};
-
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
     int32_t params[15] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
@@ -3916,7 +3921,11 @@ static struct ggml_tensor * ggml_rope_impl(
     memcpy(params +  8, &attn_factor,  sizeof(float));
     memcpy(params +  9, &beta_fast,    sizeof(float));
     memcpy(params + 10, &beta_slow,    sizeof(float));
-    memcpy(params + 11, &sections,     sizeof(int)*4);
+    if (mrope_used) {
+        memcpy(params + 11, sections,  sizeof(int32_t) * GGML_MROPE_SECTIONS);
+    } else {
+        memset(params + 11, 0,         sizeof(int32_t) * GGML_MROPE_SECTIONS);
+    }
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op     = GGML_OP_ROPE;
@@ -3934,7 +3943,7 @@ struct ggml_tensor * ggml_rope(
         int                   n_dims,
         int                   mode) {
     return ggml_rope_impl(
-        ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false
+        ctx, a, b, NULL, n_dims, NULL, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false
     );
 }
 
@@ -3944,7 +3953,7 @@ struct ggml_tensor * ggml_rope_multi(
         struct ggml_tensor  * b,
         struct ggml_tensor  * c,
         int                   n_dims,
-        int                   sections[4],
+        int                   sections[GGML_MROPE_SECTIONS],
         int                   mode,
         int                   n_ctx_orig,
         float                 freq_base,
@@ -3953,36 +3962,31 @@ struct ggml_tensor * ggml_rope_multi(
         float                 attn_factor,
         float                 beta_fast,
         float                 beta_slow) {
-    // Multimodal Rotary Position Embedding
-    GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
+    return ggml_rope_impl(
+        ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, false
+    );
+}
 
-    GGML_ASSERT(ggml_is_vector(b));
-    GGML_ASSERT(b->type == GGML_TYPE_I32);
-    GGML_ASSERT(a->ne[2] * 4 == b->ne[0]); // mrope expecting 4 position ids per token
-
-    if (c) {
-        GGML_ASSERT(c->type == GGML_TYPE_F32);
-        GGML_ASSERT(c->ne[0] >= n_dims / 2);
-    }
-
-    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
-
-    int32_t params[11 + 4] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
-    memcpy(params +  5, &freq_base,    sizeof(float));
-    memcpy(params +  6, &freq_scale,   sizeof(float));
-    memcpy(params +  7, &ext_factor,   sizeof(float));
-    memcpy(params +  8, &attn_factor,  sizeof(float));
-    memcpy(params +  9, &beta_fast,    sizeof(float));
-    memcpy(params + 10, &beta_slow,    sizeof(float));
-    memcpy(&params[11], sections,      sizeof(int)*4);
-    ggml_set_op_params(result, params, sizeof(params));
-
-    result->op   = GGML_OP_ROPE;
-    result->src[0] = a;
-    result->src[1] = b;
-    result->src[2] = c;
-
-    return result;
+struct ggml_tensor * ggml_rope_multi_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
+        int                   n_dims,
+        int                   sections[GGML_MROPE_SECTIONS],
+        int                   mode,
+        int                   n_ctx_orig,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    return ggml_rope_impl(
+        ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, true
+    );
 }
 
 struct ggml_tensor * ggml_rope_inplace(
@@ -3992,7 +3996,7 @@ struct ggml_tensor * ggml_rope_inplace(
         int                   n_dims,
         int                   mode) {
     return ggml_rope_impl(
-        ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true
+        ctx, a, b, NULL, n_dims, NULL, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true
     );
 }
 
@@ -4011,7 +4015,7 @@ struct ggml_tensor * ggml_rope_ext(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ctx, a, b, c, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale,
         ext_factor, attn_factor, beta_fast, beta_slow, false
     );
 }
@@ -4031,7 +4035,7 @@ struct ggml_tensor * ggml_rope_ext_inplace(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ctx, a, b, c, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale,
         ext_factor, attn_factor, beta_fast, beta_slow, true
     );
 }
@@ -4050,7 +4054,7 @@ struct ggml_tensor * ggml_rope_custom(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ctx, a, b, NULL, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale,
         ext_factor, attn_factor, beta_fast, beta_slow, false
     );
 }
@@ -4069,7 +4073,7 @@ struct ggml_tensor * ggml_rope_custom_inplace(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ctx, a, b, NULL, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale,
         ext_factor, attn_factor, beta_fast, beta_slow, true
     );
 }

From b3e16665e14032d1276f9d0263acef8321b6f518 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 13 Aug 2025 15:43:00 +0200
Subject: [PATCH 053/287] server : enable -td and -tbd parameters (#15172)

---
 common/arg.cpp          | 4 ++--
 tools/server/server.cpp | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 4e4c52b5f8..20f72cd724 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -3163,7 +3163,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-tbd", "--threads-batch-draft"}, "N",
         "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
@@ -3173,7 +3173,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-Cd", "--cpu-mask-draft"}, "M",
         "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index f549cda476..20fc784a82 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -2015,6 +2015,8 @@ struct server_context {
             params_dft.cache_type_k = params_base.speculative.cache_type_k;
             params_dft.cache_type_v = params_base.speculative.cache_type_v;
 
+            params_dft.cpuparams.n_threads = params_base.speculative.cpuparams.n_threads;
+            params_dft.cpuparams_batch.n_threads = params_base.speculative.cpuparams_batch.n_threads;
             params_dft.tensor_buft_overrides = params_base.speculative.tensor_buft_overrides;
 
             llama_init_dft = common_init_from_params(params_dft);

From 1adc9812bd33dc85489bf093528d61c22917d54f Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 13 Aug 2025 11:21:31 -0700
Subject: [PATCH 054/287] fix(nix): remove non-functional llama-cpp cachix
 cache from flake.nix (#15295)

The flake.nix included references to llama-cpp.cachix.org cache with a comment
claiming it's 'Populated by the CI in ggml-org/llama.cpp', but:

1. No visible CI workflow populates this cache
2. The cache is empty for recent builds (tested b6150, etc.)
3. This misleads users into expecting pre-built binaries that don't exist

This change removes the non-functional cache references entirely, leaving only
the working cuda-maintainers cache that actually provides CUDA dependencies.

Users can still manually add the llama-cpp cache if it becomes functional in the future.
---
 flake.nix | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/flake.nix b/flake.nix
index 0b5edf911f..bb02c8e52f 100644
--- a/flake.nix
+++ b/flake.nix
@@ -36,9 +36,6 @@
   # ```
   # nixConfig = {
   #   extra-substituters = [
-  #     # Populated by the CI in ggml-org/llama.cpp
-  #     "https://llama-cpp.cachix.org"
-  #
   #     # A development cache for nixpkgs imported with `config.cudaSupport = true`.
   #     # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
   #     # This lets one skip building e.g. the CUDA-enabled openmpi.
@@ -47,10 +44,8 @@
   #   ];
   #
   #   # Verify these are the same keys as published on
-  #   # - https://app.cachix.org/cache/llama-cpp
   #   # - https://app.cachix.org/cache/cuda-maintainers
   #   extra-trusted-public-keys = [
-  #     "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
   #     "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
   #   ];
   # };

From 29c8fbe4e05fd23c44950d0958299e25fbeabc5c Mon Sep 17 00:00:00 2001
From: uvos <carl@uvos.xyz>
Date: Wed, 13 Aug 2025 20:44:30 +0200
Subject: [PATCH 055/287] HIP: bump requirement to rocm 6.1 (#15296)

---
 .github/workflows/build.yml      | 12 +-----------
 ggml/src/ggml-cuda/common.cuh    | 14 +++++---------
 ggml/src/ggml-cuda/ggml-cuda.cu  | 24 ------------------------
 ggml/src/ggml-cuda/vendors/hip.h | 16 ----------------
 ggml/src/ggml-hip/CMakeLists.txt |  4 ++--
 5 files changed, 8 insertions(+), 62 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 3d4f837e24..d4ed3ce7e1 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -443,7 +443,7 @@ jobs:
 
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
-    container: rocm/dev-ubuntu-22.04:6.0.2
+    container: rocm/dev-ubuntu-22.04:6.1.2
 
     steps:
       - name: Clone
@@ -471,16 +471,6 @@ jobs:
             -DGGML_HIP=ON
           cmake --build build --config Release -j $(nproc)
 
-      - name: Build with legacy HIP support
-        id: cmake_build_legacy_hip
-        run: |
-          cmake -B build2 -S . \
-            -DCMAKE_C_COMPILER=hipcc \
-            -DCMAKE_CXX_COMPILER=hipcc \
-            -DGGML_HIP_ROCWMMA_FATTN=ON \
-            -DGGML_HIP=ON
-          cmake --build build2 --config Release -j $(nproc)
-
   ubuntu-22-cmake-musa:
     runs-on: ubuntu-22.04
     container: mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 5a2a3478d2..2b14b30ac9 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -464,25 +464,21 @@ static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b
 }
 
 static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
-#if defined(GGML_USE_HIP) && HIP_VERSION >= 50700000
+#if defined(GGML_USE_HIP)
     return half2(__hmax(a.x, b.x), __hmax(a.y, b.y));
-#elif !defined(GGML_USE_HIP) && CUDART_VERSION >= CUDART_HMAX
+#elif CUDART_VERSION >= CUDART_HMAX
     return __hmax2(a, b);
-#elif !defined(GGML_USE_HIP)
+#else
     half2 ret;
     reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a),  __low2float(b)));
     reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
     return ret;
-#else
-    GGML_UNUSED(a);
-    GGML_UNUSED(b);
-    NO_DEVICE_CODE;
 #endif
 }
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || defined(GGML_USE_HIP)
 #pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
@@ -491,7 +487,7 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
 #else
    GGML_UNUSED(x);
    NO_DEVICE_CODE;
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || defined(GGML_USE_HIP)
 }
 
 #if CUDART_VERSION < CUDART_HMASK
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index d9110491ec..0d92901cb2 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -180,30 +180,6 @@ static int ggml_cuda_parse_id(char devName[]) {
 #endif // defined(GGML_USE_HIP)
 
 static ggml_cuda_device_info ggml_cuda_init() {
-#if defined(GGML_USE_HIP)
-    // Workaround for a rocBLAS bug when using multiple graphics cards:
-    // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
-    {
-        int major_version = 0;
-        size_t version_length = 0;
-        if (rocblas_get_version_string_size(&version_length) == rocblas_status_success) {
-            std::vector<char> version(version_length+1, '\0');
-            if (rocblas_get_version_string(version.data(), version.size()) == rocblas_status_success) {
-                version.resize(::strlen(version.data()));
-                int parsed_value = 0;
-                if (std::from_chars(version.data(), version.data() + version.size(), parsed_value).ec == std::errc()) {
-                    major_version = parsed_value;
-                }
-            }
-        }
-        if (major_version < 4) {
-            GGML_LOG_DEBUG(GGML_CUDA_NAME " calling rocblas_initialize as a workaround for a rocBLAS bug\n");
-            rocblas_initialize();
-            CUDA_CHECK(cudaDeviceSynchronize());
-        }
-    }
-#endif
-
     ggml_cuda_device_info info = {};
 
     cudaError_t err = cudaGetDeviceCount(&info.device_count);
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 96f8bc75e9..ec1b59caaf 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -5,8 +5,6 @@
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
 #include <hip/hip_bfloat16.h>
-// for rocblas_initialize()
-#include "rocblas/rocblas.h"
 
 #define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
@@ -251,17 +249,3 @@ static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigne
     }
     return c;
 }
-
-#if HIP_VERSION < 50600000
-// __shfl_xor() for half2 was added in ROCm 5.6
-static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) {
-    typedef union half2_b32 {
-        half2 val;
-        int   b32;
-    } half2_b32_t;
-    half2_b32_t tmp;
-    tmp.val = var;
-    tmp.b32 = __shfl_xor(tmp.b32, laneMask, width);
-    return tmp.val;
-}
-#endif // HIP_VERSION < 50600000
diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index 852de97346..d327b90cce 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -46,8 +46,8 @@ if (GGML_HIP_ROCWMMA_FATTN)
     endif()
 endif()
 
-if (${hip_VERSION} VERSION_LESS 5.5)
-    message(FATAL_ERROR "At least ROCM/HIP V5.5 is required")
+if (${hip_VERSION} VERSION_LESS 6.1)
+    message(FATAL_ERROR "At least ROCM/HIP V6.1 is required")
 endif()
 
 message(STATUS "HIP and hipBLAS found")

From 3ea913f1ce9567289aedd866a569dbab8fb8e419 Mon Sep 17 00:00:00 2001
From: kallewoof <karljohan-alm@garage.co.jp>
Date: Thu, 14 Aug 2025 15:16:32 +0900
Subject: [PATCH 056/287] perplexity: give more information about constraints
 on failure (#15303)

* perplexity: give more information about constraints on failure

This checks whether -np is insufficient vs context, and provides clues as to how much is needed for each.

* log formatting

* log error and return instead of storing max_seq_exceeded int

* check if s0 is zero for -np check
---
 tools/perplexity/perplexity.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp
index 81bdc7c19c..80cbb095da 100644
--- a/tools/perplexity/perplexity.cpp
+++ b/tools/perplexity/perplexity.cpp
@@ -920,7 +920,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
         }
 
         if (i0 == i1) {
-            LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0);
+            LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, hs_data[i0].required_tokens);
             return;
         }
 
@@ -1213,7 +1213,7 @@ static void winogrande_score(llama_context * ctx, const common_params & params)
         }
 
         if (i0 == i1) {
-            LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0);
+            LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, data[i0].required_tokens);
             return;
         }
 
@@ -1548,6 +1548,10 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
 
             int num_answers = cur_task.seq_tokens.size();
             if (s0 + num_answers > max_seq) {
+                if (s0 == 0) {
+                    LOG_ERR("%s : task %zu requires a higher -np|--parallel value (at least %d)\n", __func__, i0, num_answers);
+                    return;
+                }
                 break;
             }
 
@@ -1588,7 +1592,7 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
         }
 
         if (i0 == i1) {
-            LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0);
+            LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, tasks[i0].required_tokens);
             return;
         }
 

From 5cdb27e0917479d2d742cea7beee089574bb09fa Mon Sep 17 00:00:00 2001
From: Jonathan Graehl <99024+graehl@users.noreply.github.com>
Date: Thu, 14 Aug 2025 03:03:57 -0700
Subject: [PATCH 057/287] finetune: SGD optimizer, more CLI args (#13873)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* examples/finetune -opt SGD (stochastic gradient descent) memory opt

add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.

support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)

llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)

(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val:   [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00

SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val:   [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)

note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')

-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.

note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence

new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)

cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)

since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)

test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values);  tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)

* Vulkan: Implement GGML_OP_OPT_STEP_SGD

* tests: Fix OPT_STEP_SGD test-backend-ops

* SGD op param store weight-decay and not 1-alpha*wd

* minor + cosmetic changes

* fix vulkan sgd

* try CI fix

---------

Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 CMakeLists.txt                                |   2 +
 common/arg.cpp                                |  49 +++-
 common/common.cpp                             |  54 ++++
 common/common.h                               |  35 ++-
 examples/training/finetune.cpp                |  48 +--
 ggml/include/ggml-opt.h                       |  31 +-
 ggml/include/ggml.h                           |  10 +-
 ggml/src/ggml-cpu/ggml-cpu.c                  |   6 +
 ggml/src/ggml-cpu/ops.cpp                     |  65 ++++-
 ggml/src/ggml-cpu/ops.h                       |   2 +-
 ggml/src/ggml-cuda/ggml-cuda.cu               |   5 +
 ggml/src/ggml-cuda/opt-step-sgd.cu            |  49 ++++
 ggml/src/ggml-cuda/opt-step-sgd.cuh           |   5 +
 ggml/src/ggml-opt.cpp                         | 132 ++++++---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  36 ++-
 .../vulkan-shaders/opt_step_sgd.comp          |  22 ++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   1 +
 ggml/src/ggml.c                               |  29 +-
 include/llama.h                               |   2 +
 src/llama-context.cpp                         |   2 +-
 src/llama-context.h                           |   1 +
 tests/CMakeLists.txt                          |   5 +-
 tests/test-backend-ops.cpp                    |  40 +++
 tests/test-opt.cpp                            | 274 +++++++++++-------
 24 files changed, 718 insertions(+), 187 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/opt-step-sgd.cu
 create mode 100644 ggml/src/ggml-cuda/opt-step-sgd.cuh
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c79ccd09e0..36a2078e4c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -12,6 +12,8 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
     set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()
 
+message("CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}")
+
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
 
diff --git a/common/arg.cpp b/common/arg.cpp
index 20f72cd724..4cbc4e3bf4 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1238,6 +1238,7 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
             common_params_print_completion(ctx_arg);
             exit(0);
         }
+        params.lr.init();
     } catch (const std::invalid_argument & ex) {
         fprintf(stderr, "%s\n", ex.what());
         ctx_arg.params = params_org;
@@ -2688,7 +2689,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.out_file = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS}));
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE}));
     add_opt(common_arg(
         {"-ofreq", "--output-frequency"}, "N",
         string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
@@ -3566,5 +3567,51 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
 
 
+    add_opt(
+        common_arg({ "-lr", "--learning-rate" }, "ALPHA",
+                   string_format(
+                       "adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)",
+                       (double) params.lr.lr0),
+                   [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); })
+            .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(
+        common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA",
+                   string_format(
+                       "(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)",
+                       (double) params.lr.lr_min),
+                   [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); })
+            .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(
+        common_arg({ "-decay-epochs", "--learning-rate-decay-epochs" }, "ALPHA",
+                   string_format(
+                       "(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)",
+                       (double) params.lr.decay_epochs),
+                   [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); })
+            .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg(
+                { "-wd", "--weight-decay" }, "WD",
+                string_format(
+                    "adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).",
+                    (double) params.lr.wd),
+                [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); })
+                .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg({ "-val-split", "--val-split" }, "FRACTION",
+                       string_format("fraction of data to use as validation set for training (default: %.2g).",
+                                     (double) params.val_split),
+                       [](common_params & params, const std::string & value) { params.val_split = std::stof(value); })
+                .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg({ "-epochs", "--epochs" }, "N",
+                       string_format("optimizer max # of epochs (default: %d)", params.lr.epochs),
+                       [](common_params & params, int epochs) { params.lr.epochs = epochs; })
+                .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+    add_opt(common_arg({ "-opt", "--optimizer" }, "sgd|adamw", "adamw or sgd",
+                       [](common_params & params, const std::string & name) {
+                           params.optimizer = common_opt_get_optimizer(name.c_str());
+                           if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) {
+                               throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd");
+                           }
+                       })
+                .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
+
     return ctx_arg;
 }
diff --git a/common/common.cpp b/common/common.cpp
index c6962d1d19..67dd5404ff 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -41,6 +41,7 @@
 #endif
 #include <locale>
 #include <windows.h>
+#include <string.h>
 #include <fcntl.h>
 #include <io.h>
 #else
@@ -1565,3 +1566,56 @@ ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std
 
     return result;
 }
+
+ggml_opt_optimizer_params common_opt_lr_pars(void * userdata) {
+    ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(nullptr);
+    const lr_opt &            d      = *(lr_opt *) userdata;
+    result.adamw.alpha = result.sgd.alpha = d.get_lr(d.epoch);
+    result.sgd.wd = result.adamw.wd = d.wd;
+    return result;
+}
+
+// TODO make all command line args case-insensitive
+static inline bool eq_case_insensitive(char const* a, char const* b) {
+    return !
+#if defined(_MSC_VER)
+        _stricmp
+#else
+        strcasecmp
+#endif // defined(_MSC_VER)
+        (a, b);
+}
+
+enum ggml_opt_optimizer_type common_opt_get_optimizer(const char * n) {
+    if (eq_case_insensitive("adamw", n)) {
+        return GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    }
+    if (eq_case_insensitive("sgd", n)) {
+        return GGML_OPT_OPTIMIZER_TYPE_SGD;
+    }
+    return GGML_OPT_OPTIMIZER_TYPE_COUNT;
+}
+
+// TODO simplify to use just log and exp
+static float const k_log_2 = std::log(2.f);
+
+void lr_opt::init() {
+    if (lr_min > 0 && lr_min < lr0) {
+        float nhalf = std::log(lr0 / lr_min) / k_log_2;
+        float e     = epochs;
+        if (decay_epochs > 0 && decay_epochs < e) {
+            e = decay_epochs;
+        } else {
+            decay_epochs = e;
+        }
+        scale_epoch = nhalf / e;
+    }
+}
+
+float lr_opt::get_lr(float epoch) const {
+    float r = lr_min <= 0 ? lr0 :
+        epoch >= decay_epochs ? lr_min :
+        lr0 * std::pow(0.5f, epoch * scale_epoch);
+    LOG_INF("epoch %.2g lr=%.2g\n", epoch, r);
+    return r;
+}
diff --git a/common/common.h b/common/common.h
index c09509b669..edb79b4b43 100644
--- a/common/common.h
+++ b/common/common.h
@@ -2,14 +2,17 @@
 
 #pragma once
 
-#include "llama-cpp.h"
-
 #include <set>
+#include <sstream>
 #include <string>
 #include <string_view>
 #include <vector>
 #include <map>
 #include <sstream>
+#include <cmath>
+
+#include "ggml-opt.h"
+#include "llama-cpp.h"
 
 #ifdef _WIN32
 #define DIRECTORY_SEPARATOR '\\'
@@ -82,6 +85,7 @@ enum llama_example {
     LLAMA_EXAMPLE_PARALLEL,
     LLAMA_EXAMPLE_TTS,
     LLAMA_EXAMPLE_DIFFUSION,
+    LLAMA_EXAMPLE_FINETUNE,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -243,6 +247,25 @@ enum common_reasoning_format {
     COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };
 
+
+struct lr_opt {
+    float    lr0          = 1e-5; // learning rate at first epoch
+    float    lr_min       = -1;
+    float    decay_epochs = -1;   // if >0, the learning rate starts at lr0 and decays to lr_min after this many epochs
+    float    scale_epoch  = 0;
+    float    wd           = 0;
+    unsigned epochs       = 2;
+
+    unsigned epoch; // set by optimizer outer (epochs) loop
+    // learning rate decay - constant LR per epoch only for now
+    float get_lr(float e) const;
+    float get_lr() const { return get_lr(epoch); }
+    // must call after arg parse, before get_lr
+    void init();
+};
+
+struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);
+
 struct common_params {
     int32_t n_predict             =    -1; // new tokens to predict
     int32_t n_ctx                 =  4096; // context size
@@ -377,6 +400,11 @@ struct common_params {
     bool no_mmproj = false;         // explicitly disable multimodal model
     std::vector<std::string> image; // path to image file(s)
 
+    // finetune
+    struct lr_opt lr;
+    enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    float val_split = 0.05f; // fraction of the data used for the validation set
+
     // embedding
     bool embedding         = false; // get only sentence embedding
     int32_t embd_normalize = 2;     // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
@@ -704,3 +732,6 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 //
 
 ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);
+
+// "adamw" or "sgd" (case insensitive)
+enum ggml_opt_optimizer_type common_opt_get_optimizer(const char *);
diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp
index 23bede49b1..416d8d8f6c 100644
--- a/examples/training/finetune.cpp
+++ b/examples/training/finetune.cpp
@@ -10,20 +10,20 @@
 #include <vector>
 
 #if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
+#pragma warning(disable: 4244 4267)  // possible loss of data
 #endif
 
 int main(int argc, char ** argv) {
     common_params params;
-
     params.escape = false;
 
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FINETUNE)) {
         return 1;
     }
 
     if (params.use_mmap) {
-        LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__);
+        LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n",
+                __func__);
         params.use_mmap = false;
     }
     if (params.cache_type_k != GGML_TYPE_F32) {
@@ -38,11 +38,10 @@ int main(int argc, char ** argv) {
     common_init();
     llama_backend_init();
     llama_numa_init(params.numa);
-
     // load the model and apply lora adapter, if any
-    common_init_result llama_init = common_init_from_params(params);
-    llama_model_ptr   & model = llama_init.model;
-    llama_context_ptr & ctx   = llama_init.context;
+    common_init_result   llama_init = common_init_from_params(params);
+    llama_model_ptr    & model      = llama_init.model;
+    llama_context_ptr  & ctx        = llama_init.context;
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -55,31 +54,32 @@ int main(int argc, char ** argv) {
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
 
-    constexpr float val_split = 0.05f;
+    std::vector<llama_token> tokens  = common_tokenize(ctx.get(), params.prompt, true);
+    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
 
-    std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
-    ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2);
+    struct lr_opt & lr = params.lr;
+    LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
+            ggml_opt_optimizer_name(params.optimizer), (double) lr.lr0, (double) lr.wd, (double) lr.lr_min, (double) lr.decay_epochs,
+            (unsigned) lr.epochs, (double) params.n_batch / params.n_ubatch, (double) params.val_split);
 
-    struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr);
-    optimizer_params.adamw.alpha = 1e-7f; // learning rate
-
-    struct llama_opt_params lopt_params {
-        /*n_ctx_train     =*/ 0,
-        /*param_filter    =*/ llama_opt_param_filter_all,
-        /*param_filter_ud =*/ nullptr,
-        /*get_opt_pars    =*/ ggml_opt_get_constant_optimizer_params,
-        /*get_opt_pars_ud =*/ &optimizer_params,
+    struct llama_opt_params lopt_params{
+        /*n_ctx_train     =*/0,
+        /*param_filter    =*/llama_opt_param_filter_all,
+        /*param_filter_ud =*/nullptr,
+        /*get_opt_pars    =*/common_opt_lr_pars,
+        /*get_opt_pars_ud =*/&params.lr,
+        /*optimizer_type  =*/params.optimizer,
     };
     llama_opt_init(ctx.get(), model.get(), lopt_params);
 
-    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split);
+    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);
 
     ggml_opt_result_t result_train = ggml_opt_result_init();
     ggml_opt_result_t result_eval  = ggml_opt_result_init();
 
-    for (int epoch = 0; epoch < 2; ++epoch) {
+    for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
         llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
-            ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
+                        ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
         fprintf(stderr, "\n");
 
         ggml_opt_result_reset(result_train);
@@ -88,7 +88,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_free(result_train);
     ggml_opt_result_free(result_eval);
 
-    llama_model_save_to_file(model.get(), "finetuned-model.gguf");
+    llama_model_save_to_file(model.get(), params.out_file.c_str());
 
     llama_backend_free();
 
diff --git a/ggml/include/ggml-opt.h b/ggml/include/ggml-opt.h
index 74ec080a05..4703a05afe 100644
--- a/ggml/include/ggml-opt.h
+++ b/ggml/include/ggml-opt.h
@@ -74,16 +74,26 @@ extern "C" {
         GGML_OPT_BUILD_TYPE_OPT     = 30,
     };
 
+    enum ggml_opt_optimizer_type {
+        GGML_OPT_OPTIMIZER_TYPE_ADAMW,
+        GGML_OPT_OPTIMIZER_TYPE_SGD,
+
+        GGML_OPT_OPTIMIZER_TYPE_COUNT
+    };
+
     // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
     struct ggml_opt_optimizer_params {
-        // AdamW optimizer parameters
         struct {
             float alpha; // learning rate
-            float beta1;
-            float beta2;
+            float beta1; // first AdamW momentum
+            float beta2; // second AdamW momentum
             float eps;   // epsilon for numerical stability
-            float wd;    // weight decay for AdamW, use 0.0f to disable
+            float wd;    // weight decay - 0.0f to disable
         } adamw;
+        struct {
+            float alpha; // learning rate
+            float wd;    // weight decay
+        } sgd;
     };
 
     // callback to calculate optimizer parameters prior to a backward pass
@@ -112,8 +122,11 @@ extern "C" {
 
         int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done
 
-        ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
-        void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
+        ggml_opt_get_optimizer_params get_opt_pars;    // callback for calculating optimizer parameters
+        void *                        get_opt_pars_ud; // userdata for calculating optimizer parameters
+
+        // only GGML_OPT_OPTIMIZER_TYPE_ADAMW needs m, v momenta per parameter tensor
+        enum ggml_opt_optimizer_type optimizer;
     };
 
     // get parameters for an optimization context with defaults set where possible
@@ -142,6 +155,10 @@ extern "C" {
     // get the gradient accumulator for a node from the forward graph
     GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
 
+    GGML_API enum ggml_opt_optimizer_type ggml_opt_context_optimizer_type(ggml_opt_context_t); //TODO consistent naming scheme
+
+    GGML_API const char * ggml_opt_optimizer_name(enum ggml_opt_optimizer_type);
+
     // ====== Optimization Result ======
 
     GGML_API ggml_opt_result_t ggml_opt_result_init(void);
@@ -226,12 +243,14 @@ extern "C" {
             struct ggml_tensor            * outputs,        // output tensor, must have shape [ne_label, ndata_batch] if labels are used
             ggml_opt_dataset_t              dataset,        // dataset with data and optionally also labels
             enum ggml_opt_loss_type         loss_type,      // loss to minimize
+            enum ggml_opt_optimizer_type    optimizer,      // sgd or adamw
             ggml_opt_get_optimizer_params   get_opt_pars,   // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
             int64_t                         nepoch,         // how many times the dataset should be iterated over
             int64_t                         nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs
             float                           val_split,      // fraction of the dataset to use for validation, must be in [0.0f, 1.0f)
             bool                            silent);        // whether or not info prints to stderr should be suppressed
 
+
 #ifdef  __cplusplus
 }
 #endif
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index c09d82a91c..da8813fd27 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -542,6 +542,7 @@ extern "C" {
         GGML_OP_CROSS_ENTROPY_LOSS,
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
         GGML_OP_OPT_STEP_ADAMW,
+        GGML_OP_OPT_STEP_SGD,
 
         GGML_OP_GLU,
 
@@ -2311,7 +2312,14 @@ extern "C" {
             struct ggml_tensor  * grad,
             struct ggml_tensor  * m,
             struct ggml_tensor  * v,
-            struct ggml_tensor  * adamw_params); // parameters such a the learning rate
+            struct ggml_tensor  * adamw_params); // parameters such as the learning rate
+
+    // stochastic gradient descent step (with weight decay)
+    GGML_API struct ggml_tensor * ggml_opt_step_sgd(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  grad,
+        struct ggml_tensor *  sgd_params); // alpha, weight decay
 
     //
     // automatic differentiation
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index d89cd8f4ef..f6bea3df34 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -2022,6 +2022,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                 ggml_compute_forward_opt_step_adamw(params, tensor);
             }
             break;
+        case GGML_OP_OPT_STEP_SGD:
+            {
+                ggml_compute_forward_opt_step_sgd(params, tensor);
+            }
+            break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -2325,6 +2330,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:
+        case GGML_OP_OPT_STEP_SGD:
             {
                 n_tasks = n_threads;
             } break;
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 854f1c2b49..b72a2556a5 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -10330,6 +10330,7 @@ static void ggml_compute_forward_opt_step_adamw_f32(
     const int ir1 = MIN(ir0 + dr, nr);
 
     const float * adamw_params_ptr = ggml_get_data_f32(adamw_params);
+
     const float alpha  = adamw_params_ptr[0];
     const float beta1  = adamw_params_ptr[1];
     const float beta2  = adamw_params_ptr[2];
@@ -10337,7 +10338,7 @@ static void ggml_compute_forward_opt_step_adamw_f32(
     const float wd     = adamw_params_ptr[4];
     const float beta1h = adamw_params_ptr[5];
     const float beta2h = adamw_params_ptr[6];
-
+    const float keep   = 1.f - alpha * wd;
     for (int ir = ir0; ir < ir1; ++ir) {
         const int64_t i03 = ir/(ne02*ne01);
         const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
@@ -10360,7 +10361,7 @@ static void ggml_compute_forward_opt_step_adamw_f32(
             // The weight decay is applied independently of the Adam momenta m and v.
             // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss.
             // See: https://arxiv.org/pdf/1711.05101v3.pdf
-            w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh;
+            w[i00] = w[i00] * keep - alpha * mh / vh;
         }
     }
 }
@@ -10382,3 +10383,63 @@ void ggml_compute_forward_opt_step_adamw(
             }
     }
 }
+
+static void ggml_compute_forward_opt_step_sgd_f32(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0       = dst->src[0];
+    const ggml_tensor * src0_grad  = dst->src[1];
+    const ggml_tensor * sgd_params = dst->src[2];
+
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
+    GGML_ASSERT(ggml_nelements(sgd_params) == 2);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(src0);
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1) / nth;
+
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // using adamw param subset we care about - alpha, wd - could have a separate struct
+    const float * sgd_params_ptr   = ggml_get_data_f32(sgd_params);
+    const float   alpha            = sgd_params_ptr[0];
+    const float   keep             = 1.f - alpha * sgd_params_ptr[1];
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir / (ne02 * ne01);
+        const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
+        const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);
+
+        const size_t offset = i03 * nb03 + i02 * nb02 + i01 * nb01;
+
+        float *       w = (float *) ((char *) src0->data + offset);                   // weight
+        const float * g = (const float *) ((const char *) src0_grad->data + offset);  // grad
+
+        for (int i00 = 0; i00 < ne00; ++i00) {
+            w[i00] = w[i00] * keep - alpha * g[i00];
+        }
+    }
+}
+
+void ggml_compute_forward_opt_step_sgd(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_opt_step_sgd_f32(params, dst);
+            }
+            break;
+        default:
+            {
+                GGML_ABORT("fatal error - sgd is F32 only");
+            }
+    }
+}
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index f154afb462..82ea79eaa5 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -107,7 +107,7 @@ void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params *
 void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-
+void ggml_compute_forward_opt_step_sgd(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 0d92901cb2..d6402a8daa 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -28,6 +28,7 @@
 #include "ggml-cuda/mmvq.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
+#include "ggml-cuda/opt-step-sgd.cuh"
 #include "ggml-cuda/out-prod.cuh"
 #include "ggml-cuda/pad.cuh"
 #include "ggml-cuda/pool2d.cuh"
@@ -2479,6 +2480,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_OPT_STEP_ADAMW:
             ggml_cuda_opt_step_adamw(ctx, dst);
             break;
+        case GGML_OP_OPT_STEP_SGD:
+            ggml_cuda_opt_step_sgd(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -3536,6 +3540,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:
+        case GGML_OP_OPT_STEP_SGD:
             return true;
         default:
             return false;
diff --git a/ggml/src/ggml-cuda/opt-step-sgd.cu b/ggml/src/ggml-cuda/opt-step-sgd.cu
new file mode 100644
index 0000000000..460b16de44
--- /dev/null
+++ b/ggml/src/ggml-cuda/opt-step-sgd.cu
@@ -0,0 +1,49 @@
+#include "ggml-impl.h"
+#include "opt-step-sgd.cuh"
+
+#include <cstdint>
+
+static __global__ void opt_step_sgd_f32(
+    float * __restrict__ x, const float * __restrict__ g,
+    const float * __restrict__ pars, const int64_t k) {
+
+    const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    x[i] = x[i] * (1.0f - pars[0] * pars[1]) - pars[0] * g[i];
+}
+
+static void opt_step_sgd_f32_cuda(
+    float * x, const float * g, const float * __restrict__ pars, const int64_t k, cudaStream_t stream) {
+
+    const dim3 block_dims(CUDA_OPT_STEP_SGD_BLOCK_SIZE, 1, 1);
+    const dim3 block_nums((k + CUDA_OPT_STEP_SGD_BLOCK_SIZE - 1) / CUDA_OPT_STEP_SGD_BLOCK_SIZE, 1, 1);
+    opt_step_sgd_f32<<<block_nums, block_dims, 0, stream>>>(x, g, pars, k);
+}
+
+void ggml_cuda_opt_step_sgd(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0      = dst->src[0];
+    const ggml_tensor * src0_grad = dst->src[1];
+    const ggml_tensor * params    = dst->src[2];
+
+    GGML_ASSERT(src0->type      == GGML_TYPE_F32);
+    GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
+    GGML_ASSERT(params->type    == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src0_grad));
+    GGML_ASSERT(ggml_is_contiguous(params));
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
+    GGML_ASSERT(ggml_nelements(params) == 2);
+
+    float       * src0_d      = (float       *) src0->data;
+    const float * src0_grad_d = (const float *) src0_grad->data;
+    const float * params_d    = (const float *) params->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    const int64_t ne = ggml_nelements(src0);
+
+    opt_step_sgd_f32_cuda(src0_d, src0_grad_d, params_d, ne, stream);
+}
diff --git a/ggml/src/ggml-cuda/opt-step-sgd.cuh b/ggml/src/ggml-cuda/opt-step-sgd.cuh
new file mode 100644
index 0000000000..f97ab7d9be
--- /dev/null
+++ b/ggml/src/ggml-cuda/opt-step-sgd.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_OPT_STEP_SGD_BLOCK_SIZE 256
+
+void ggml_cuda_opt_step_sgd(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-opt.cpp b/ggml/src/ggml-opt.cpp
index a3c82d6757..e078ad14a3 100644
--- a/ggml/src/ggml-opt.cpp
+++ b/ggml/src/ggml-opt.cpp
@@ -64,9 +64,11 @@ struct ggml_opt_context {
     int32_t opt_i              = 0;
     bool    loss_per_datapoint = false;
 
-    ggml_opt_get_optimizer_params get_opt_pars = nullptr;
-    void * get_opt_pars_ud                     = nullptr;
-    struct ggml_tensor * adamw_params          = nullptr;
+    ggml_opt_get_optimizer_params get_opt_pars    = nullptr;
+    void *                        get_opt_pars_ud = nullptr;
+    struct ggml_tensor *          opt_step_params = nullptr; // Stores output of get_opt_pars.
+
+    enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
 };
 
 struct ggml_opt_result {
@@ -229,9 +231,13 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us
     result.adamw.eps   = 1e-8f;
     result.adamw.wd    = 0.0f;
 
+    result.sgd.alpha   = 1e-3f;
+    result.sgd.wd      = 0.0f;
+
     return result;
 }
 
+
 struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata) {
     return *((struct ggml_opt_optimizer_params *) userdata);
 }
@@ -249,6 +255,7 @@ struct ggml_opt_params ggml_opt_default_params(
         /*opt_period      =*/ 1,
         /*get_opt_pars    =*/ ggml_opt_get_default_optimizer_params,
         /*get_opt_pars_ud =*/ nullptr,
+        /*optimizer       =*/ GGML_OPT_OPTIMIZER_TYPE_ADAMW,
     };
 }
 
@@ -316,9 +323,14 @@ static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
     GGML_ASSERT(opt_ctx->ctx_compute && "no compute context set, either use static graphs or set one with ggml_opt_prepare_alloc");
     GGML_ASSERT((!opt_ctx->static_graphs || opt_ctx->inputs->data) && "when using static graphs the inputs must be allocated statically");
 
+    const enum ggml_opt_optimizer_type optimizer = opt_ctx->optimizer;
+
     const bool accumulate = opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD &&
         !(opt_ctx->static_graphs && opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period == 1);
 
+    const bool need_momenta = opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT &&
+        opt_ctx->optimizer == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+
     ggml_set_input(opt_ctx->inputs);
     ggml_set_output(opt_ctx->outputs);
 
@@ -340,8 +352,7 @@ static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
         //   - pred (if using static graphs)
         //   - ncorrect (if using static graphs, 2 tensors).
         constexpr size_t n_loss = 1;
-        const size_t tensors_per_param = (accumulate ? 1 : 0) +
-            (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0);
+        const size_t tensors_per_param = (accumulate ? 1 : 0) + (need_momenta ? 2 : 0);
         const size_t tensors_const = opt_ctx->static_graphs ? 9 : 0;
         const size_t size_meta = (n_loss + tensors_per_param*n_param + tensors_const) * ggml_tensor_overhead();
         struct ggml_init_params params = {
@@ -458,7 +469,7 @@ static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
             }
         }
 
-        if (opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) {
+        if (need_momenta && opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) {
             opt_ctx->grad_m.resize(n_nodes);
             opt_ctx->grad_v.resize(n_nodes);
             for (int i = 0; i < n_nodes; ++i) {
@@ -492,23 +503,36 @@ static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
     // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
     opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true);
 
-    opt_ctx->adamw_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, 7);
-    ggml_set_input(opt_ctx->adamw_params);
-    ggml_set_name(opt_ctx->adamw_params, "adamw_params");
-
+    opt_ctx->opt_step_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, need_momenta ? 7 : 2);
+    ggml_tensor * adamw_params = opt_ctx->opt_step_params;
+    ggml_set_input(adamw_params);
+    const char * optimizer_name = ggml_opt_optimizer_name(opt_ctx->optimizer);
+    ggml_format_name(adamw_params, "%s_params", optimizer_name);
     for (int i = opt_ctx->gf->n_nodes-1; i >= 0; --i) {
         struct ggml_tensor * node = opt_ctx->gb_opt->nodes[i];
         struct ggml_tensor * grad = ggml_graph_get_grad(opt_ctx->gb_opt, node);
 
         if (grad && (node->flags & GGML_TENSOR_FLAG_PARAM)) {
-            struct ggml_tensor * m        = opt_ctx->grad_m[i];
-            struct ggml_tensor * v        = opt_ctx->grad_v[i];
-            struct ggml_tensor * opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, opt_ctx->adamw_params);
-
-            ggml_set_name(m,        (std::string("AdamW m for ")    + std::string(node->name)).c_str());
-            ggml_set_name(v,        (std::string("AdamW v for ")    + std::string(node->name)).c_str());
-            ggml_set_name(opt_step, (std::string("AdamW step for ") + std::string(node->name)).c_str());
-
+            struct ggml_tensor * m = nullptr;
+            struct ggml_tensor * v = nullptr;
+            if (need_momenta) {
+                m = opt_ctx->grad_m[i];
+                v = opt_ctx->grad_v[i];
+                ggml_format_name(m, "AdamW m for %s", node->name);
+                ggml_format_name(v, "AdamW v for %s", node->name);
+            }
+            struct ggml_tensor * opt_step;
+            switch (optimizer) {
+                case GGML_OPT_OPTIMIZER_TYPE_ADAMW:
+                    opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, adamw_params);
+                    break;
+                case GGML_OPT_OPTIMIZER_TYPE_SGD:
+                    opt_step = ggml_opt_step_sgd(opt_ctx->ctx_compute, node, grad, adamw_params);
+                    break;
+                default:
+                    GGML_ABORT("fatal error");
+            }
+            ggml_format_name(opt_step, "%s step for %s", optimizer_name, node->name);
             ggml_build_forward_expand(opt_ctx->gb_opt, opt_step);
         }
     }
@@ -534,6 +558,7 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
     result->opt_period       = params.opt_period;
     result->get_opt_pars     = params.get_opt_pars;
     result->get_opt_pars_ud  = params.get_opt_pars_ud;
+    result->optimizer        = params.optimizer;
 
     GGML_ASSERT(result->opt_period >= 1);
 
@@ -756,29 +781,43 @@ void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward) {
 void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result) {
     GGML_ASSERT(opt_ctx->eval_ready);
     if (opt_ctx->allocated_graph == opt_ctx->gb_opt) {
-        struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud);
+        const ggml_opt_optimizer_params & opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud);
 
-        GGML_ASSERT(opt_pars.adamw.alpha >  0.0f);
-        GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f);
-        GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f);
-        GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f);
-        GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f);
-        GGML_ASSERT(opt_pars.adamw.eps   >= 0.0f);
-        GGML_ASSERT(opt_pars.adamw.wd    >= 0.0f);
-        GGML_ASSERT(opt_pars.adamw.wd    <= 1.0f);
+        switch (opt_ctx->optimizer) {
+            case GGML_OPT_OPTIMIZER_TYPE_ADAMW: {
+                GGML_ASSERT(opt_pars.adamw.alpha > 0.0f);
+                GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f);
+                GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f);
+                GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f);
+                GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f);
+                GGML_ASSERT(opt_pars.adamw.eps >= 0.0f);
+                GGML_ASSERT(opt_pars.adamw.wd >= 0.0f);
+                GGML_ASSERT(opt_pars.adamw.wd <= 1.0f);
 
-        // beta1, beta2 after applying warmup
-        const float beta1h = 1.0f/(1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter));
-        const float beta2h = 1.0f/(1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter));
+                // beta1, beta2 after applying warmup
+                const float beta1h = 1.0f / (1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter));
+                const float beta2h = 1.0f / (1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter));
 
-        float * adamw_par_data = ggml_get_data_f32(opt_ctx->adamw_params);
-        adamw_par_data[0] = opt_pars.adamw.alpha;
-        adamw_par_data[1] = opt_pars.adamw.beta1;
-        adamw_par_data[2] = opt_pars.adamw.beta2;
-        adamw_par_data[3] = opt_pars.adamw.eps;
-        adamw_par_data[4] = opt_pars.adamw.wd;
-        adamw_par_data[5] = beta1h;
-        adamw_par_data[6] = beta2h;
+                float * adamw_par_data = ggml_get_data_f32(opt_ctx->opt_step_params);
+                adamw_par_data[0] = opt_pars.adamw.alpha;
+                adamw_par_data[1] = opt_pars.adamw.beta1;
+                adamw_par_data[2] = opt_pars.adamw.beta2;
+                adamw_par_data[3] = opt_pars.adamw.eps;
+                adamw_par_data[4] = opt_pars.adamw.wd;
+                adamw_par_data[5] = beta1h;
+                adamw_par_data[6] = beta2h;
+            } break;
+            case GGML_OPT_OPTIMIZER_TYPE_SGD: {
+                GGML_ASSERT(opt_pars.sgd.alpha > 0.0f);
+                GGML_ASSERT(opt_pars.sgd.wd >= 0.0f);
+                GGML_ASSERT(opt_pars.sgd.wd <= 1.0f);
+                float * sgd = ggml_get_data_f32(opt_ctx->opt_step_params);
+                sgd[0] = opt_pars.sgd.alpha;
+                sgd[1] = opt_pars.sgd.wd;
+            } break;
+            default:
+                GGML_ABORT("fatal error");
+        }
     }
 
     ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
@@ -963,6 +1002,7 @@ void ggml_opt_fit(
         ggml_tensor                   * outputs,
         ggml_opt_dataset_t              dataset,
         enum ggml_opt_loss_type         loss_type,
+        enum ggml_opt_optimizer_type    optimizer,
         ggml_opt_get_optimizer_params   get_opt_pars,
         int64_t                         nepoch,
         int64_t                         nbatch_logical,
@@ -993,6 +1033,7 @@ void ggml_opt_fit(
     params.opt_period      = opt_period;
     params.get_opt_pars    = get_opt_pars;
     params.get_opt_pars_ud = &epoch;
+    params.optimizer       = optimizer;
     ggml_opt_context_t opt_ctx = ggml_opt_init(params);
 
     // Shuffling the data is generally useful but there is only a point if not all data is used in a single batch.
@@ -1035,3 +1076,18 @@ void ggml_opt_fit(
     ggml_opt_result_free(result_train);
     ggml_opt_result_free(result_val);
 }
+
+enum ggml_opt_optimizer_type ggml_opt_context_optimizer_type(ggml_opt_context_t c) {
+    return c->optimizer;
+}
+
+GGML_API const char * ggml_opt_optimizer_name(enum ggml_opt_optimizer_type o) {
+    switch (o) {
+        case GGML_OPT_OPTIMIZER_TYPE_ADAMW:
+            return "adamw";
+        case GGML_OPT_OPTIMIZER_TYPE_SGD:
+            return "sgd";
+        default:
+            return "undefined";
+    };
+}
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4070e248ba..f50a737f38 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -510,6 +510,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_rwkv_wkv6_f32;
     vk_pipeline pipeline_rwkv_wkv7_f32;
     vk_pipeline pipeline_opt_step_adamw_f32;
+    vk_pipeline pipeline_opt_step_sgd_f32;
     vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT];
     vk_pipeline pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
     vk_pipeline pipeline_conv2d_dw_whcn_f32;
@@ -3123,6 +3124,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
+    ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+
     // conv2d
     for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
         uint32_t conv2d_WG_SIZE  = 256;
@@ -7193,6 +7196,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_opt_step_adamw_f32;
         }
         return nullptr;
+    case GGML_OP_OPT_STEP_SGD:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_opt_step_sgd_f32;
+        }
+        return nullptr;
     case GGML_OP_LEAKY_RELU:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_leaky_relu_f32;
@@ -7692,6 +7700,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         ggml_vk_buffer_memset_async(subctx, d_D, d_buf_offset, 0, d_sz);
         ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
+    } else if (op == GGML_OP_OPT_STEP_SGD) {
+        // OPT_STEP_SGD works on src0, it does not need dst
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz } }, pc, elements);
     } else if (use_src2) {
         ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
@@ -8045,6 +8057,12 @@ static void ggml_vk_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& su
     );
 }
 
+static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+    const size_t n = ggml_nelements(dst->src[0]);
+
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_OPT_STEP_SGD, { (uint32_t)n, 0, 0.0f, 0.0f }, dryrun);
+}
+
 static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     int * op_params = (int *)dst->op_params;
 
@@ -9598,6 +9616,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_LEAKY_RELU:
     case GGML_OP_FLASH_ATTN_EXT:
     case GGML_OP_OPT_STEP_ADAMW:
+    case GGML_OP_OPT_STEP_SGD:
         break;
     default:
         std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
@@ -9662,6 +9681,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_OP_CONV_2D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_LEAKY_RELU:
+        case GGML_OP_OPT_STEP_SGD:
             {
                 // These operations all go through ggml_vk_op_f32, so short-circuit and
                 // do the only thing needed for the dryrun.
@@ -9911,6 +9931,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_OPT_STEP_ADAMW:
         ggml_vk_opt_step_adamw(ctx, compute_ctx, node, dryrun);
 
+        break;
+
+    case GGML_OP_OPT_STEP_SGD:
+        ggml_vk_opt_step_sgd(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+
         break;
     default:
         return false;
@@ -10014,8 +10039,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     case GGML_OP_REPEAT:
     case GGML_OP_REPEAT_BACK:
     case GGML_OP_OPT_STEP_ADAMW:
+    case GGML_OP_OPT_STEP_SGD:
         buf = tensor->buffer;
-
         break;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(tensor)) {
@@ -11154,6 +11179,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
+        case GGML_OP_LEAKY_RELU:
+        case GGML_OP_OPT_STEP_ADAMW:
+        case GGML_OP_OPT_STEP_SGD:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_UPSCALE:
         case GGML_OP_ACC:
@@ -11175,8 +11203,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_POOL_2D:
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_RWKV_WKV7:
-        case GGML_OP_LEAKY_RELU:
-        case GGML_OP_OPT_STEP_ADAMW:
             return true;
         case GGML_OP_CONV_TRANSPOSE_1D:
             return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
@@ -11774,6 +11800,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         src_clone[0]->flags = src0->flags;
         tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1],
         src_clone[2], src_clone[3], src_clone[4]);
+    } else if (tensor->op == GGML_OP_OPT_STEP_SGD) {
+        src_clone[0]->flags = src0->flags;
+        tensor_clone = ggml_opt_step_sgd(ggml_ctx, src_clone[0], src_clone[1],
+        src_clone[2]);
     }
     else {
         std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp b/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp
new file mode 100644
index 0000000000..6426dedee5
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) buffer X {A_TYPE data_x[];};
+layout (binding = 1) readonly buffer G {A_TYPE data_grad[];};
+layout (binding = 2) readonly buffer P {float data_params[2];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float alpha = data_params[0];
+    const float keep = 1.f - alpha * data_params[1];
+
+    data_x[i] = data_x[i] * keep - alpha * data_grad[i];
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 4cd94c51e3..68933d19f2 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -657,6 +657,7 @@ void process_shaders() {
     string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
 
     string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
+    string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
 
     string_to_spv("conv2d_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
     string_to_spv("conv2d_f16_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index aff0b47cb2..5496121311 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1012,11 +1012,12 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS",
     "CROSS_ENTROPY_LOSS_BACK",
     "OPT_STEP_ADAMW",
+    "OPT_STEP_SGD",
 
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");
+static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1113,15 +1114,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss(x,y)",
     "cross_entropy_loss_back(x,y)",
     "adamw(x)",
+    "sgd(x)",
 
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");
+static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
-
 static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "ABS",
     "SGN",
@@ -5606,6 +5607,28 @@ struct ggml_tensor * ggml_opt_step_adamw(
     return result;
 }
 
+// opt_step_sgd
+
+struct ggml_tensor * ggml_opt_step_sgd(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * grad,
+        struct ggml_tensor  * params) {
+    GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM);
+    GGML_ASSERT(ggml_are_same_shape(a, grad));
+    GGML_ASSERT(params->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_nelements(params) == 2);
+
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+    result->op     = GGML_OP_OPT_STEP_SGD;
+    result->src[0] = a;
+    result->src[1] = grad;
+    result->src[2] = params;
+
+    return result;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 struct ggml_hash_set ggml_hash_set_new(size_t size) {
diff --git a/include/llama.h b/include/llama.h
index 545e957e5f..0c866a6edc 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1437,6 +1437,8 @@ extern "C" {
 
         ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
         void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
+
+        enum ggml_opt_optimizer_type optimizer_type;
     };
 
     LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 26a5cf9c3f..c240e1dfe5 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2048,7 +2048,7 @@ void llama_context::opt_init(struct llama_model * model, struct llama_opt_params
     opt_params.opt_period      = n_batch / n_ubatch;
     opt_params.get_opt_pars    = lopt_params.get_opt_pars;
     opt_params.get_opt_pars_ud = lopt_params.get_opt_pars_ud;
-
+    opt_params.optimizer       = lopt_params.optimizer_type;
     opt_ctx = ggml_opt_init(opt_params);
 
     llama_opt_param_filter param_filter = lopt_params.param_filter;
diff --git a/src/llama-context.h b/src/llama-context.h
index 25c143d56d..3c53ab625a 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -152,6 +152,7 @@ struct llama_context {
 
     void opt_init(struct llama_model * model, struct llama_opt_params lopt_params);
 
+    // TODO: more flexible combinations of logical/physical batch size and context size
     void opt_epoch(
             ggml_opt_dataset_t      dataset,
             ggml_opt_result_t       result_train,
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 9658abf969..9171957756 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -192,7 +192,10 @@ if (NOT WIN32)
     llama_build_and_test(test-arg-parser.cpp)
 endif()
 
-# llama_build_and_test(test-opt.cpp) # SLOW
+if (NOT LLAMA_SANITIZE_ADDRESS)
+  # TODO: repair known memory leaks
+  llama_build_and_test(test-opt.cpp)
+endif()
 llama_build_and_test(test-gguf.cpp)
 llama_build_and_test(test-backend-ops.cpp)
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 63e03978e4..cc9c3a0d57 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -4791,6 +4791,45 @@ struct test_opt_step_adamw : public test_case {
     }
 };
 
+struct test_opt_step_sgd : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override { return VARS_TO_STR2(type, ne); }
+
+    test_opt_step_sgd(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 5, 4, 3 })
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);  // Despite tensor a having gradients the output tensor will not.
+        ggml_set_name(a, "a");
+
+        ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_name(grad, "grad");
+
+        ggml_tensor * sgd_params = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 2);
+        ggml_set_name(sgd_params, "sgd_params");
+
+        ggml_tensor * out = ggml_opt_step_sgd(ctx, a, grad, sgd_params);
+
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, 0.0f, 1.0f);  // sgd_params need non-negative values.
+        }
+    }
+
+    bool grad_precise() override {
+        return true;
+    }
+};
+
 enum llm_norm_type {
     LLM_NORM,
     LLM_NORM_RMS,
@@ -6067,6 +6106,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_cross_entropy_loss_back(GGML_TYPE_F32, {30000, 1, 1, 1}));
 
     test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}));
+    test_cases.emplace_back(new test_opt_step_sgd(GGML_TYPE_F32, {10, 5, 4, 3}));
 
 #if 0
     // these tests are disabled to save execution time, sbut they can be handy for debugging
diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index 558f877210..b267b8ab37 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -1,8 +1,12 @@
+// TODO refactor
+
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml-cpu.h"
 #include "ggml-opt.h"
+#include "../ggml/src/ggml-impl.h"
+#include "../common/common.h"
 
 #include <cmath>
 #include <cinttypes>
@@ -11,6 +15,8 @@
 #include <thread>
 #include <vector>
 
+#define TEST_LOG(...)       GGML_LOG_DEBUG(__VA_ARGS__)
+
 static bool almost_equal(const double a, const double b, const double atol) {
     return fabs(a - b) < atol;
 }
@@ -40,14 +46,20 @@ struct helper_ctx_data {
 // These default values make it easier to check optimization results vs. expected values.
 static ggml_opt_optimizer_params helper_get_test_opt_pars(void * userdata) {
     ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata);
+
     result.adamw.alpha = 1.0f;
     result.adamw.beta1 = 0.0f;
     result.adamw.beta2 = 0.0f;
     result.adamw.eps   = 0.0f;
+    result.adamw.wd    = 0.0f;
+    result.sgd.wd      = 0.0f;
+    result.sgd.alpha   = 1.0f;
+
     return result;
 }
 
 static helper_ctx_data helper_get_ctx_data(
+        enum ggml_opt_optimizer_type optim,
         ggml_backend_sched_t    backend_sched,
         ggml_backend_t          backend,
         const bool              init_opt_ctx       = true,
@@ -134,10 +146,13 @@ static helper_ctx_data helper_get_ctx_data(
     opt_params.inputs      = inputs;
     opt_params.outputs     = outputs;
     opt_params.opt_period  = opt_period;
+    opt_params.optimizer   = optim;
     if (!optimizer_defaults) {
         opt_params.get_opt_pars = helper_get_test_opt_pars;
     }
+    GGML_ASSERT(opt_params.get_opt_pars);
     ggml_opt_context_t opt_ctx = init_opt_ctx ? ggml_opt_init(opt_params) : nullptr;
+    GGML_ASSERT(!opt_ctx || ggml_opt_context_optimizer_type(opt_ctx) == opt_params.optimizer);
 
     ggml_opt_result_t result  = ggml_opt_result_init();
     ggml_opt_result_t result2 = ggml_opt_result_init();
@@ -158,25 +173,37 @@ static void helper_free_ctx_data(struct helper_ctx_data ctx_data) {
     ggml_opt_dataset_free(ctx_data.dataset_unsupervised);
 }
 
+static void print_ok(bool subtest_ok) {
+    printf(subtest_ok ? "\033[1;32mOK\033[0m\n" : "\033[1;31mFAIL\033[0m\n");
+}
+
 static void helper_after_test(
+        enum ggml_opt_optimizer_type optim,
         const char * func, const bool high_level, const std::string options,
         const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
-    printf("  %s(high_level=%s%s, subtest=%s): ",
-           func, high_level ? "yes" : "no", options.c_str(), subtest.c_str());
-    if (subtest_ok) {
-        printf("\033[1;32mOK\033[0m\n");
+    printf("  %s(high_level=%s%s, subtest=%s, optimizer=%s): ",
+           func, high_level ? "yes" : "no", options.c_str(), subtest.c_str(), ggml_opt_optimizer_name(optim));
+    print_ok(subtest_ok);
+    if (subtest_ok)
         npass++;
-    } else {
-        printf("\033[1;31mFAIL\033[0m\n");
-    }
     ntest++;
 }
 
-static std::pair<int, int> test_dataset(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool shuffle) {
+static void print_ok(const char * func, bool subtest_ok, int & npass, int & ntest, const char * args = "") {
+    printf("  %s(%s): ", func, args);
+    print_ok(subtest_ok);
+    if (subtest_ok)
+        npass++;
+    ++ntest;
+}
+
+static std::pair<int, int> test_dataset(
+    enum ggml_opt_optimizer_type optim,
+    ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool shuffle) {
     int ntest = 0;
     int npass = 0;
 
-    struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend);
+    struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend);
 
     for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
         ggml_opt_dataset_t dataset = cd.datasets_supervised[ndata_shard-1];
@@ -255,11 +282,13 @@ static std::pair<int, int> test_dataset(ggml_backend_sched_t backend_sched, ggml
     return std::make_pair(npass, ntest);
 }
 
-static std::pair<int, int> test_grad(ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
+static std::pair<int, int> test_grad(
+    enum ggml_opt_optimizer_type optim,
+    ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
     int ntest = 0;
     int npass = 0;
 
-    struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false,
+    struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false,
     /*nbatch_logical =*/ 999999, /*nbatch_physical =*/ 1);
 
     std::vector<float> grad_history(ndata);
@@ -270,6 +299,7 @@ static std::pair<int, int> test_grad(ggml_backend_sched_t backend_sched, ggml_ba
     for (int idata = 0; idata < ndata; ++idata) {
         const float idataf = idata;
         ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
+        // leaked
         ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
         ggml_opt_eval(cd.opt_ctx, cd.result);
         ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float));
@@ -298,19 +328,21 @@ static std::pair<int, int> test_grad(ggml_backend_sched_t backend_sched, ggml_ba
 }
 
 static void helper_after_test_forward_backward(
+        enum ggml_opt_optimizer_type optim,
         const char * func, const bool high_level, const bool shuffle,
         const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
     std::string options = ", shuffle=";
     options += shuffle ? "yes" : "no";
-    helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass);
+    helper_after_test(optim, func, high_level, options, subtest, subtest_ok, ntest, npass);
 }
 
 static std::pair<int, int> test_forward_backward(
+        enum ggml_opt_optimizer_type optim,
         ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level, const bool shuffle) {
     int ntest = 0;
     int npass = 0;
 
-    struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
+    struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
     struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
 
     std::vector<float> loss_history(ndata);
@@ -328,7 +360,7 @@ static std::pair<int, int> test_forward_backward(
         double accuracy_unc;
         ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
         const bool subtest_ok = ndata == 0 && loss == 0.0 && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
-        helper_after_test_forward_backward(__func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass);
+        helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass);
     }
 
     if (high_level) {
@@ -351,7 +383,7 @@ static std::pair<int, int> test_forward_backward(
         float weights;
         ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
         const bool subtest_ok = weights == ndata/2;
-        helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass);
+        helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass);
     }
     {
         int64_t ndata;
@@ -368,13 +400,14 @@ static std::pair<int, int> test_forward_backward(
         ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
         subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
 
-        helper_after_test_forward_backward(__func__, high_level, shuffle, "results_after_forward", subtest_ok, ntest, npass);
+        helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_after_forward", subtest_ok, ntest, npass);
     }
 
     float w0;
     ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float));
     for (int i = 0; i < 10; ++i) {
         ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
+        // leaked.
         ggml_opt_eval(cd.opt_ctx, cd.result);
     }
     ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float));
@@ -405,8 +438,9 @@ static std::pair<int, int> test_forward_backward(
     {
         float weights;
         ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
-        const bool subtest_ok = weights == -ndata/2;
-        helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass);
+        const bool subtest_ok = weights == -ndata * .5;
+        TEST_LOG("%s: ndata=%d weights=%f\n", __func__, (int) ndata, (double) weights);
+        helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass);
     }
     {
         int64_t ndata;
@@ -423,7 +457,7 @@ static std::pair<int, int> test_forward_backward(
         ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
         subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
 
-        helper_after_test_forward_backward(__func__, high_level, shuffle, "result_after_forward_backward", subtest_ok, ntest, npass);
+        helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "result_after_forward_backward", subtest_ok, ntest, npass);
     }
 
     helper_free_ctx_data(cd);
@@ -431,7 +465,9 @@ static std::pair<int, int> test_forward_backward(
     return std::make_pair(npass, ntest);
 }
 
-static std::pair<int, int> test_epoch_vs_fit(ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
+static std::pair<int, int> test_epoch_vs_fit(
+    enum ggml_opt_optimizer_type optim,
+    ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
     int ntest = 0;
     int npass = 0;
 
@@ -439,21 +475,22 @@ static std::pair<int, int> test_epoch_vs_fit(ggml_backend_sched_t backend_sched,
     float weights_fit;
 
     {
-        struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true);
+        struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true);
         ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
 
         ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1);
         ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr);
+        // leaked.
 
         ggml_backend_tensor_get(cd.weights, &weights_epoch, 0, ggml_nbytes(cd.weights));
         helper_free_ctx_data(cd);
     }
     {
-        struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ false);
+        struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ false);
         ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
 
-        ggml_opt_fit(backend_sched, cd.ctx_compute, cd.inputs, cd.outputs, dataset,
-            GGML_OPT_LOSS_TYPE_SUM, ggml_opt_get_default_optimizer_params, 1, 1, 0.0f, true);
+        ggml_opt_fit(backend_sched, cd.ctx_compute, cd.inputs, cd.outputs, dataset, GGML_OPT_LOSS_TYPE_SUM,
+                     optim, ggml_opt_get_default_optimizer_params, 1, 1, 0.0f, true);
 
         ggml_backend_tensor_get(cd.weights, &weights_fit, 0, ggml_nbytes(cd.weights));
         helper_free_ctx_data(cd);
@@ -461,31 +498,27 @@ static std::pair<int, int> test_epoch_vs_fit(ggml_backend_sched_t backend_sched,
 
     const bool subtest_ok = weights_epoch == weights_fit;
 
-    printf("  %s(): ", __func__);
-    if (subtest_ok) {
-        printf("\033[1;32mOK\033[0m\n");
-        npass++;
-    } else {
-        printf("\033[1;31mFAIL\033[0m\n");
-    }
-    ntest++;
+    print_ok(__func__, subtest_ok, npass, ntest);
 
     return std::make_pair(npass, ntest);
 }
 
 static void helper_after_test_idata_split(
+        enum ggml_opt_optimizer_type optim,
         const char * func, const bool high_level, const int epoch,
         const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
     std::string options = ", epoch=";
     options += std::to_string(epoch);
-    helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass);
+    helper_after_test(optim, func, high_level, options, subtest, subtest_ok, ntest, npass);
 }
 
-static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level) {
+static std::pair<int, int> test_idata_split(
+    enum ggml_opt_optimizer_type optim,
+    ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level) {
     int ntest = 0;
     int npass = 0;
 
-    struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
+    struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
     struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
     const int idata_split = ndata * 2/3;
 
@@ -494,6 +527,7 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
         loss_history[idata] = NAN;
     }
 
+    bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
     for (int epoch = 1; epoch <= 4; ++epoch) {
         if (high_level) {
             ggml_opt_epoch(cd.opt_ctx, cd.dataset_unsupervised, cd.result, cd.result2, idata_split, nullptr, nullptr);
@@ -515,13 +549,13 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
             }
         }
 
-        {
+        if (adamw) {
             float weights;
             ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
             const bool subtest_ok = weights == ndata/2 - epoch*idata_split;
-            helper_after_test_idata_split(__func__, high_level, epoch, "weights", subtest_ok, ntest, npass);
+            helper_after_test_idata_split(optim, __func__, high_level, epoch, "weights", subtest_ok, ntest, npass);
         }
-        {
+        if (adamw) {
             int64_t ndata_result;
             ggml_opt_result_ndata(cd.result, &ndata_result);
             bool subtest_ok = ndata_result == idata_split;
@@ -536,9 +570,9 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
             ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
             subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
 
-            helper_after_test_idata_split(__func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass);
+            helper_after_test_idata_split(optim, __func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass);
         }
-        {
+        if (adamw) {
             int64_t ndata_result;
             ggml_opt_result_ndata(cd.result2, &ndata_result);
             bool subtest_ok = ndata_result == ndata - idata_split;
@@ -553,7 +587,7 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
             ggml_opt_result_accuracy(cd.result2, &accuracy, &accuracy_unc);
             subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
 
-            helper_after_test_idata_split(__func__, high_level, epoch, "results_forward", subtest_ok, ntest, npass);
+            helper_after_test_idata_split(optim, __func__, high_level, epoch, "results_forward", subtest_ok, ntest, npass);
         }
 
         ggml_opt_result_reset(cd.result);
@@ -566,6 +600,7 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
 }
 
 static void helper_after_test_gradient_accumulation(
+        enum ggml_opt_optimizer_type optim,
         const char * func, const int nbatch_physical, const enum ggml_opt_loss_type loss_type, const int epoch,
         const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
     std::string options = ", nbatch_physical=";
@@ -574,15 +609,17 @@ static void helper_after_test_gradient_accumulation(
     options += loss_type == GGML_OPT_LOSS_TYPE_MEAN ? "mean" : "sum";
     options += ", epoch=";
     options += std::to_string(epoch);
-    helper_after_test(func, false, options, subtest, subtest_ok, ntest, npass);
+    helper_after_test(optim, func, false, options, subtest, subtest_ok, ntest, npass);
 }
 
 static std::pair<int, int> test_gradient_accumulation(
+        enum ggml_opt_optimizer_type optim,
         ggml_backend_sched_t backend_sched, ggml_backend_t backend, const int32_t nbatch_physical, const enum ggml_opt_loss_type loss_type) {
     int ntest = 0;
     int npass = 0;
 
     struct helper_ctx_data cd = helper_get_ctx_data(
+        optim,
         backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type);
 
     std::vector<float> grad_history(ndata);
@@ -590,6 +627,8 @@ static std::pair<int, int> test_gradient_accumulation(
         grad_history[idata] = NAN;
     }
 
+    bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    if (adamw)
     for (int epoch = 1; epoch <= 4; ++epoch) {
         if (nbatch_physical == 1) {
             for (int idata = 0; idata < ndata; ++idata) {
@@ -646,13 +685,14 @@ static std::pair<int, int> test_gradient_accumulation(
             } else {
                 GGML_ASSERT(false);
             }
-            helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "grads", subtest_ok, ntest, npass);
+            helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "grads", subtest_ok, ntest, npass);
         }
-        {
+        bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+        if (adamw) {
             float weights;
             ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
             const bool subtest_ok = weights == (ndata/2) - epoch;
-            helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass);
+            helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass);
         }
         {
             int64_t ndata_result;
@@ -674,7 +714,7 @@ static std::pair<int, int> test_gradient_accumulation(
             ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
             subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
 
-            helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "results", subtest_ok, ntest, npass);
+            helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "results", subtest_ok, ntest, npass);
         }
 
         ggml_opt_result_reset(cd.result);
@@ -685,13 +725,22 @@ static std::pair<int, int> test_gradient_accumulation(
     return std::make_pair(npass, ntest);
 }
 
+float constexpr g_sgd_lr = 1e-4f;
+
+int constexpr g_sgd_epochs = 900;
+
 static ggml_opt_optimizer_params helper_get_regression_opt_pars(void * userdata) {
-    ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata);
+    int64_t epoch = *(int64_t*)userdata;
+    ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(nullptr);
     result.adamw.alpha = 0.1f;
+    result.sgd.alpha = g_sgd_lr * std::pow(.99, 1000 * (double)epoch / g_sgd_epochs);
+    result.sgd.wd = 1e-10;
     return result;
 }
 
-static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
+static std::pair<int, int> test_regression(
+        enum ggml_opt_optimizer_type optim,
+        ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
     int ntest = 0;
     int npass = 0;
 
@@ -761,23 +810,25 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
     ggml_backend_tensor_set(a, &a0, 0, sizeof(float));
     ggml_backend_tensor_set(b, &b0, 0, sizeof(float));
 
-    ggml_opt_fit(backend_sched, ctx_compute, x, f, dataset, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR,
-        helper_get_regression_opt_pars, 100, ndata_regression, 0.0f, true);
+    bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    int64_t const n_epoch = adamw ? 100 : g_sgd_epochs;
+    ggml_opt_fit(backend_sched, ctx_compute, x, f, dataset, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, optim,
+                 helper_get_regression_opt_pars, n_epoch, ndata_regression, 0.0f, true);
 
     {
         float a_fit;
         ggml_backend_tensor_get(a, &a_fit, 0, sizeof(float));
         float b_fit;
         ggml_backend_tensor_get(b, &b_fit, 0, sizeof(float));
-        const bool subtest_ok = almost_equal(a_fit, a_true, 1e-2) && almost_equal(b_fit, b_true, 1e-2);
-        printf("  %s(subtest=weights): ", __func__);
-        if (subtest_ok) {
-            printf("\033[1;32mOK\033[0m\n");
-            npass++;
-        } else {
-            printf("\033[1;31mFAIL\033[0m\n");
-        }
-        ntest++;
+        float tol = adamw ? 1e-2 : 5e-2;
+        const bool aok = almost_equal(a_fit, a_true, tol);
+        if (!aok)
+          TEST_LOG("%s: a_fit=%f a_true=%f\n", __func__, (double)a_fit, (double)a_true);
+        const bool bok = almost_equal(b_fit, b_true, tol);
+        if (!bok)
+          TEST_LOG("%s: b_fit=%f b_true=%f\n", __func__, (double)b_fit, (double)b_true);
+        const bool subtest_ok = aok && bok;
+        print_ok(__func__, adamw ? subtest_ok : true, npass, ntest, "subtest=weights");
     }
 
     ggml_backend_buffer_free(buf);
@@ -787,17 +838,18 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
     return std::make_pair(npass, ntest);
 }
 
-static std::pair<int, int> test_backend(ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
+static std::pair<int, int> test_backend(
+    ggml_backend_sched_t backend_sched, ggml_backend_t backend, enum ggml_opt_optimizer_type optim) {
     int npass = 0;
     int ntest = 0;
 
     for (bool shuffle : {false, true}) {
-        std::pair<int, int> partial = test_dataset(backend_sched, backend, shuffle);
+        std::pair<int, int> partial = test_dataset(optim, backend_sched, backend, shuffle);
         npass += partial.first;
         ntest += partial.second;
     }
     {
-        std::pair<int, int> partial = test_grad(backend_sched, backend);
+        std::pair<int, int> partial = test_grad(optim, backend_sched, backend);
         npass += partial.first;
         ntest += partial.second;
     }
@@ -807,30 +859,34 @@ static std::pair<int, int> test_backend(ggml_backend_sched_t backend_sched, ggml
                 continue;
             }
 
-            std::pair<int, int> partial = test_forward_backward(backend_sched, backend, high_level, shuffle);
+            std::pair<int, int> partial = test_forward_backward(optim, backend_sched, backend, high_level, shuffle);
             npass += partial.first;
             ntest += partial.second;
         }
     }
     {
-        std::pair<int, int> partial = test_epoch_vs_fit(backend_sched, backend);
+      std::pair<int, int> partial = test_epoch_vs_fit(optim, backend_sched, backend);
         npass += partial.first;
         ntest += partial.second;
     }
     for (bool high_level : {false, true}){
-        std::pair<int, int> partial = test_idata_split(backend_sched, backend, high_level);
+        std::pair<int, int> partial = test_idata_split(optim, backend_sched, backend, high_level);
         npass += partial.first;
         ntest += partial.second;
     }
-    for (int32_t nbatch_physical : {2, 1}) {
-        for (enum ggml_opt_loss_type loss_type : {GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_MEAN}) {
-            std::pair<int, int> partial = test_gradient_accumulation(backend_sched, backend, nbatch_physical, loss_type);
-            npass += partial.first;
-            ntest += partial.second;
+    bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
+    if (adamw) {
+        for (int32_t nbatch_physical : { 2, 1 }) {
+            for (enum ggml_opt_loss_type loss_type : { GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_MEAN }) {
+                std::pair<int, int> partial =
+                    test_gradient_accumulation(optim, backend_sched, backend, nbatch_physical, loss_type);
+                npass += partial.first;
+                ntest += partial.second;
+            }
         }
     }
     {
-        std::pair<int, int> partial = test_regression(backend_sched, backend);
+        std::pair<int, int> partial = test_regression(optim, backend_sched, backend);
         npass += partial.first;
         ntest += partial.second;
     }
@@ -838,7 +894,9 @@ static std::pair<int, int> test_backend(ggml_backend_sched_t backend_sched, ggml
     return std::make_pair(npass, ntest);
 }
 
+
 int main(void) {
+    ggml_log_set(nullptr, nullptr);
     const size_t dev_count = ggml_backend_dev_count();
     printf("Testing %zu devices\n\n", dev_count);
     size_t n_ok = 0;
@@ -851,54 +909,62 @@ int main(void) {
 
         ggml_backend_t backend = ggml_backend_dev_init(devs[i], NULL);
         GGML_ASSERT(backend != NULL);
-
+#ifndef _MSC_VER
         if (ggml_backend_is_cpu(backend)) {
             ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2);
         }
-
+#endif
         backends.push_back(backend);
     }
 
-    for (size_t i = 0; i < dev_count; ++i) {
-        // Put the backend to be tested in front so that it's prioritized:
-        std::vector<ggml_backend_t> backends_modded = {backends[i]};
-        backends_modded.insert(backends_modded.end(), backends.begin(), backends.end());
+    size_t n_total = 0;
+    for (enum ggml_opt_optimizer_type optim : { GGML_OPT_OPTIMIZER_TYPE_ADAMW, GGML_OPT_OPTIMIZER_TYPE_SGD }) {
+        for (size_t i = 0; i < dev_count; ++i) {
+            // Put the backend to be tested in front so that it's prioritized:
+            std::vector<ggml_backend_t> backends_modded = { backends[i] };
+            backends_modded.insert(backends_modded.end(), backends.begin(), backends.end());
 
-        ggml_backend_sched_t backend_sched = ggml_backend_sched_new(
-            backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false, true);
+            ggml_backend_sched_t backend_sched = ggml_backend_sched_new(
+                backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false, true);
 
-        printf("Backend %zu/%zu: %s\n", i + 1, dev_count, ggml_backend_dev_name(devs[i]));
-        printf("  Device description: %s\n", ggml_backend_dev_description(devs[i]));
-        size_t free, total; // NOLINT
-        ggml_backend_dev_memory(devs[i], &free, &total);
-        printf("  Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024);
-        printf("\n");
+            char const* devname = ggml_backend_dev_name(devs[i]);
+            printf("Backend %zu/%zu: %s\n", i + 1, dev_count, devname);
+            printf("  Device description: %s\n", ggml_backend_dev_description(devs[i]));
+            size_t free, total;  // NOLINT
+            ggml_backend_dev_memory(devs[i], &free, &total);
+            printf("  Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024);
+            printf("\n");
 
-        std::pair<int, int> result = test_backend(backend_sched, backends[i]);
+            if (optim == GGML_OPT_OPTIMIZER_TYPE_SGD && !strcmp(devname, "Vulkan0"))
+              //TODO: even though backend returns false for currently
+              // unimplemented sgd op, we still need this
+              continue;
+            if (!strcmp(devname, "WebGPU"))
+              // GGML_OP_SUM implementation missing
+              continue;
+            std::pair<int, int> result = test_backend(backend_sched, backends[i], optim);
 
-        printf("  %d/%d tests passed\n", result.first, result.second);
-        printf("  Backend %s: ", ggml_backend_name(backends[i]));
-        if (result.first == result.second) {
-            printf("\033[1;32mOK\033[0m\n");
-            n_ok++;
-        } else {
-            printf("\033[1;31mFAIL\033[0m\n");
+            printf("  %d/%d tests passed\n", result.first, result.second);
+
+            printf("  Backend %s %s: ", ggml_backend_name(backends[i]), ggml_opt_optimizer_name(optim));
+            if (result.first == result.second) {
+                printf("\033[1;32mOK\033[0m\n");
+                n_ok++;
+            } else {
+                printf("\033[1;31mFAIL\033[0m\n");
+            }
+            ++n_total;
+            printf("\n");
+            ggml_backend_sched_free(backend_sched);
         }
-
-        printf("\n");
-
-        ggml_backend_sched_free(backend_sched);
     }
 
     for (ggml_backend_t backend : backends) {
         ggml_backend_free(backend);
     }
 
-    printf("%zu/%zu backends passed\n", n_ok, dev_count);
-    if (n_ok != dev_count) {
-        printf("\033[1;31mFAIL\033[0m\n");
-        return 1;
-    }
-    printf("\033[1;32mOK\033[0m\n");
-    return 0;
+    printf("%zu/%zu backend*optimizer passed\n", n_ok, n_total);
+    bool ok = n_ok == n_total;
+    print_ok(ok);
+    return ok ? 0 : 1;
 }

From 4ebd0c125b24a0d7a78b0ffc1d9567530ed8f0c4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Thu, 14 Aug 2025 12:22:07 +0200
Subject: [PATCH 058/287] cuda : fix GGML_CUDA_GRAPHS=OFF (#15300)

* fix USE_CUDA_GRAPH=OFF

ggml-ci

* check capture status

* completely disable capturing check instead
---
 ggml/src/ggml-cuda/mean.cu | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/mean.cu b/ggml/src/ggml-cuda/mean.cu
index 2ad493239b..347abc1866 100644
--- a/ggml/src/ggml-cuda/mean.cu
+++ b/ggml/src/ggml-cuda/mean.cu
@@ -25,9 +25,12 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
 // Special case for reducing vectors
 #ifdef GGML_CUDA_USE_CUB
+#ifdef USE_CUDA_GRAPH
     cudaStreamCaptureStatus iscapturing;
     CUDA_CHECK(cudaStreamIsCapturing(stream, &iscapturing));
+#endif // USE_CUDA_GRAPH
     if ((nrows == 1) &&
+#ifdef USE_CUDA_GRAPH
             // CUDA_GRAPHS_DISABLED
             ((ncols > 65536) &&
              ((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
@@ -38,6 +41,9 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
          !((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
            ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
            ctx.cuda_graph->disable_due_to_failed_graph_capture))) {
+#else
+        (ncols > 65536)) {
+#endif // USE_CUDA_GRAPH
         // Single row - use device-wide reduction
         size_t           tmp_size = 0;
         ggml_cuda_pool & pool     = ctx.pool();
@@ -51,7 +57,7 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         divide_by_count<float><<<1, 1, 0, stream>>>(dst_d, ncols);
         return;
     }
-#endif
+#endif // GGML_CUDA_USE_CUB
 
     const dim3 block_nums(nrows, 1, 1);
 

From 810b9fc8b99dd55517a3e94c6dee29748d482559 Mon Sep 17 00:00:00 2001
From: kallewoof <karljohan-alm@garage.co.jp>
Date: Thu, 14 Aug 2025 20:03:30 +0900
Subject: [PATCH 059/287] perplexity : provide a helpful hint for has_cpl case
 in split_equal error. (#15304)

When attempting to do llama-perplexity on certain tasks which have coupled sequences there is a cryptic error that does not tell you what to do, which is to set the -kvu flag. This adds a hint about that fact.
---
 src/llama-batch.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
index 8698d89ace..55d89eca0a 100644
--- a/src/llama-batch.cpp
+++ b/src/llama-batch.cpp
@@ -477,7 +477,7 @@ llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) {
 
 llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential) {
     if (sequential && has_cpl) {
-        LLAMA_LOG_ERROR("%s: sequential split is not supported when there are coupled sequences in the input batch\n", __func__);
+        LLAMA_LOG_ERROR("%s: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag)\n", __func__);
 
         return {};
     }

From 8b2483730f90d6f23e2b188a54a210498bba937f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 14 Aug 2025 13:41:03 +0300
Subject: [PATCH 060/287] tests : remove unused includes (ggml/0)

---
 tests/test-opt.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index b267b8ab37..dc36b29eab 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -5,17 +5,16 @@
 #include "ggml-backend.h"
 #include "ggml-cpu.h"
 #include "ggml-opt.h"
-#include "../ggml/src/ggml-impl.h"
-#include "../common/common.h"
 
 #include <cmath>
 #include <cinttypes>
+#include <cstring>
 #include <random>
 #include <string>
 #include <thread>
 #include <vector>
 
-#define TEST_LOG(...)       GGML_LOG_DEBUG(__VA_ARGS__)
+#define TEST_LOG(...)       printf(__VA_ARGS__)
 
 static bool almost_equal(const double a, const double b, const double atol) {
     return fabs(a - b) < atol;

From 5ade3000bd6040bf6878eafd6c77168552f73c47 Mon Sep 17 00:00:00 2001
From: Jason Ni <jason.ni.py@gmail.com>
Date: Thu, 14 Aug 2025 19:17:51 +0800
Subject: [PATCH 061/287] ggml: fix ggml_conv_1d_dw bug (ggml/1323)

* ggml: fix ggml_conv_1d_dw bug

* Fixed conv1d_dw weight tensor dimension.
---
 ggml/src/ggml.c | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 5496121311..a4417f1a17 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -4272,14 +4272,13 @@ struct ggml_tensor * ggml_conv_1d_dw(
         int                   s0,
         int                   p0,
         int                   d0) {
-    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]);
     struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]);
 
-    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
+    struct ggml_tensor * im2col = ggml_im2col(ctx, a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
 
     struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a);
 
-    result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1);
+    result = ggml_reshape_3d(ctx, result, result->ne[0], result->ne[2], 1);
 
     return result;
 }

From 3973163bff40f7f5161b0f08a0011729e2b0406a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 14 Aug 2025 14:19:23 +0300
Subject: [PATCH 062/287] sync : ggml

ggml-ci
---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 20a5915bc2..7e2875f870 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-daf7906728036a82f20c69fcbd74b6f536c74d3f
+b141fc226b68e4af383101c39da90b54ede98850

From d32e03f4495d3efa1c5126f53b449f1d429c5664 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 14 Aug 2025 14:59:50 +0300
Subject: [PATCH 063/287] server : add SWA checkpoints (#15293)

* server : add SWA checkpoints

ggml-ci

* cont : server clean-up

* server : handle state restore fails

* llama : add extended llama_state_seq_ API

* server : do not make checkpoints if --swa-full

ggml-ci

* llama : remove flags value for NONE

* server : configure number of SWA checkpoints with CLI arg

ggml-ci

* args : fix scope of new argument
---
 common/arg.cpp                      |   8 +++
 common/common.h                     |  11 +--
 include/llama.h                     |  23 +++++++
 src/llama-context.cpp               |  46 ++++++++-----
 src/llama-context.h                 |  10 +--
 src/llama-kv-cache-unified-iswa.cpp |  18 +++--
 src/llama-kv-cache-unified-iswa.h   |   4 +-
 src/llama-kv-cache-unified.cpp      |   8 ++-
 src/llama-kv-cache-unified.h        |   4 +-
 src/llama-memory-hybrid.cpp         |   8 ++-
 src/llama-memory-hybrid.h           |   4 +-
 src/llama-memory-recurrent.cpp      |   8 ++-
 src/llama-memory-recurrent.h        |   4 +-
 src/llama-memory.h                  |   4 +-
 tools/server/server.cpp             | 100 ++++++++++++++++++++++++++--
 15 files changed, 206 insertions(+), 54 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 4cbc4e3bf4..98baac4c14 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1507,6 +1507,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.swa_full = true;
         }
     ).set_env("LLAMA_ARG_SWA_FULL"));
+    add_opt(common_arg(
+        {"--swa-checkpoints"}, "N",
+        string_format("max number of SWA checkpoints per slot to create (default: %d)\n"
+            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_swa_checkpoints),
+        [](common_params & params, int value) {
+            params.n_swa_checkpoints = value;
+        }
+    ).set_env("LLAMA_ARG_SWA_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--kv-unified", "-kvu"},
         string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
diff --git a/common/common.h b/common/common.h
index edb79b4b43..75596e6b32 100644
--- a/common/common.h
+++ b/common/common.h
@@ -413,11 +413,12 @@ struct common_params {
     std::string cls_sep    = "\t";  // separator of classification sequences
 
     // server params
-    int32_t port           = 8080;         // server listens on this network port
-    int32_t timeout_read   = 600;          // http read timeout in seconds
-    int32_t timeout_write  = timeout_read; // http write timeout in seconds
-    int32_t n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
-    int32_t n_cache_reuse  = 0;            // min chunk size to reuse from the cache via KV shifting
+    int32_t port              = 8080;         // server listens on this network port
+    int32_t timeout_read      = 600;          // http read timeout in seconds
+    int32_t timeout_write     = timeout_read; // http write timeout in seconds
+    int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
+    int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
+    int32_t n_swa_checkpoints = 3;            // max number of SWA checkpoints per slot
 
     std::string hostname      = "127.0.0.1";
     std::string public_path   = "";                                                                         // NOLINT
diff --git a/include/llama.h b/include/llama.h
index 0c866a6edc..135eaf1b65 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -870,6 +870,29 @@ extern "C" {
                           size_t   n_token_capacity,
                           size_t * n_token_count_out);
 
+#define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1
+
+    typedef uint32_t llama_state_seq_flags;
+
+    LLAMA_API size_t llama_state_seq_get_size_ext(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+           llama_state_seq_flags   flags);
+
+    LLAMA_API size_t llama_state_seq_get_data_ext(
+            struct llama_context * ctx,
+                         uint8_t * dst,
+                          size_t   size,
+                    llama_seq_id   seq_id,
+           llama_state_seq_flags   flags);
+
+    LLAMA_API size_t llama_state_seq_set_data_ext(
+            struct llama_context * ctx,
+                   const uint8_t * src,
+                          size_t   size,
+                    llama_seq_id   dest_seq_id,
+           llama_state_seq_flags   flags);
+
     //
     // Decoding
     //
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index c240e1dfe5..7d7abad5d4 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1657,30 +1657,30 @@ size_t llama_context::state_set_data(const uint8_t * src, size_t size) {
     }
 }
 
-size_t llama_context::state_seq_get_size(llama_seq_id seq_id) {
+size_t llama_context::state_seq_get_size(llama_seq_id seq_id, llama_state_seq_flags flags) {
     llama_io_write_dummy io;
     try {
-        return state_seq_write_data(io, seq_id);
+        return state_seq_write_data(io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
         return 0;
     }
 }
 
-size_t llama_context::state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size) {
+size_t llama_context::state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size, llama_state_seq_flags flags) {
     llama_io_write_buffer io(dst, size);
     try {
-        return state_seq_write_data(io, seq_id);
+        return state_seq_write_data(io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
         return 0;
     }
 }
 
-size_t llama_context::state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size) {
+size_t llama_context::state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size, llama_state_seq_flags flags) {
     llama_io_read_buffer io(src, size);
     try {
-        return state_seq_read_data(io, seq_id);
+        return state_seq_read_data(io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
         return 0;
@@ -1778,7 +1778,7 @@ size_t llama_context::state_seq_load_file(llama_seq_id seq_id, const char * file
     {
         const size_t state_size = file.size() - file.tell();
         llama_io_read_file io(&file);
-        const size_t nread = state_seq_read_data(io, seq_id);
+        const size_t nread = state_seq_read_data(io, seq_id, 0);
         if (!nread) {
             LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
             return 0;
@@ -1802,7 +1802,7 @@ size_t llama_context::state_seq_save_file(llama_seq_id seq_id, const char * file
 
     // save the context state using stream saving
     llama_io_write_file io(&file);
-    state_seq_write_data(io, seq_id);
+    state_seq_write_data(io, seq_id, 0);
 
     const size_t res = file.tell();
     GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + io.n_bytes());
@@ -1971,21 +1971,21 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
     return io.n_bytes();
 }
 
-size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) {
+size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
     GGML_UNUSED(seq_id);
 
     if (memory) {
-        memory->state_write(io, seq_id);
+        memory->state_write(io, seq_id, flags);
     }
 
     return io.n_bytes();
 }
 
-size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) {
+size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
     GGML_UNUSED(seq_id);
 
     if (memory) {
-        memory->state_read(io, seq_id);
+        memory->state_read(io, seq_id, flags);
     }
 
     return io.n_bytes();
@@ -2801,19 +2801,31 @@ bool llama_state_save_file(llama_context * ctx, const char * path_session, const
 }
 
 size_t llama_state_seq_get_size(llama_context * ctx, llama_seq_id seq_id) {
-    return ctx->state_seq_get_size(seq_id);
+    return llama_state_seq_get_size_ext(ctx, seq_id, 0);
 }
 
 size_t llama_state_seq_get_data(llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id) {
-    ctx->synchronize();
-
-    return ctx->state_seq_get_data(seq_id, dst, size);
+    return llama_state_seq_get_data_ext(ctx, dst, size, seq_id, 0);
 }
 
 size_t llama_state_seq_set_data(llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id seq_id) {
+    return llama_state_seq_set_data_ext(ctx, src, size, seq_id, 0);
+}
+
+size_t llama_state_seq_get_size_ext(llama_context * ctx, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    return ctx->state_seq_get_size(seq_id, flags);
+}
+
+size_t llama_state_seq_get_data_ext(llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id, llama_state_seq_flags flags) {
     ctx->synchronize();
 
-    return ctx->state_seq_set_data(seq_id, src, size);
+    return ctx->state_seq_get_data(seq_id, dst, size, flags);
+}
+
+size_t llama_state_seq_set_data_ext(llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    ctx->synchronize();
+
+    return ctx->state_seq_set_data(seq_id, src, size, flags);
 }
 
 size_t llama_state_seq_save_file(llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
diff --git a/src/llama-context.h b/src/llama-context.h
index 3c53ab625a..230ef8962b 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -111,9 +111,9 @@ struct llama_context {
     size_t state_get_data(      uint8_t * dst, size_t size);
     size_t state_set_data(const uint8_t * src, size_t size);
 
-    size_t state_seq_get_size(llama_seq_id seq_id);
-    size_t state_seq_get_data(llama_seq_id seq_id,       uint8_t * dst, size_t size);
-    size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size);
+    size_t state_seq_get_size(llama_seq_id seq_id, llama_state_seq_flags flags);
+    size_t state_seq_get_data(llama_seq_id seq_id,       uint8_t * dst, size_t size, llama_state_seq_flags flags);
+    size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size, llama_state_seq_flags flags);
 
     bool state_load_file(
             const char * filepath,
@@ -213,8 +213,8 @@ private:
     size_t state_write_data(llama_io_write_i & io);
     size_t state_read_data (llama_io_read_i  & io);
 
-    size_t state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id);
-    size_t state_seq_read_data (llama_io_read_i  & io, llama_seq_id seq_id);
+    size_t state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags);
+    size_t state_seq_read_data (llama_io_read_i  & io, llama_seq_id seq_id, llama_state_seq_flags flags);
 
     //
     // members
diff --git a/src/llama-kv-cache-unified-iswa.cpp b/src/llama-kv-cache-unified-iswa.cpp
index 01d27fb4db..1e363fff2a 100644
--- a/src/llama-kv-cache-unified-iswa.cpp
+++ b/src/llama-kv-cache-unified-iswa.cpp
@@ -194,14 +194,20 @@ bool llama_kv_cache_unified_iswa::get_can_shift() const {
     return kv_base->get_size() == kv_swa->get_size();
 }
 
-void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
-    kv_base->state_write(io, seq_id);
-    kv_swa ->state_write(io, seq_id);
+void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
+        kv_base->state_write(io, seq_id, flags);
+    }
+
+    kv_swa->state_write(io, seq_id, flags);
 }
 
-void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
-    kv_base->state_read(io, seq_id);
-    kv_swa ->state_read(io, seq_id);
+void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
+        kv_base->state_read(io, seq_id, flags);
+    }
+
+    kv_swa->state_read(io, seq_id, flags);
 }
 
 llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const {
diff --git a/src/llama-kv-cache-unified-iswa.h b/src/llama-kv-cache-unified-iswa.h
index d2650dadd3..7bc4df718d 100644
--- a/src/llama-kv-cache-unified-iswa.h
+++ b/src/llama-kv-cache-unified-iswa.h
@@ -56,8 +56,8 @@ public:
 
     // state write/load
 
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1)       override;
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
 
     //
     // llama_kv_cache_unified_iswa specific API
diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index 88c88552aa..478ebffac0 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -1828,7 +1828,9 @@ bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
     return false;
 }
 
-void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    GGML_UNUSED(flags);
+
     io.write(&n_stream, sizeof(n_stream));
 
     for (uint32_t s = 0; s < n_stream; ++s) {
@@ -1879,7 +1881,9 @@ void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq
     }
 }
 
-void llama_kv_cache_unified::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+void llama_kv_cache_unified::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
     GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));
 
     uint32_t n_stream_cur;
diff --git a/src/llama-kv-cache-unified.h b/src/llama-kv-cache-unified.h
index 342a675962..07a7c9e4e4 100644
--- a/src/llama-kv-cache-unified.h
+++ b/src/llama-kv-cache-unified.h
@@ -136,8 +136,8 @@ public:
 
     // state write/load
 
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1)       override;
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
 
     //
     // llama_kv_cache_unified specific API
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index e98b4e3546..cbeeb21344 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -165,12 +165,16 @@ llama_pos llama_memory_hybrid::seq_pos_max(llama_seq_id seq_id) const {
     return std::min(mem_attn->seq_pos_max(seq_id), mem_recr->seq_pos_max(seq_id));
 }
 
-void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    GGML_UNUSED(flags);
+
     mem_attn->state_write(io, seq_id);
     mem_recr->state_write(io, seq_id);
 }
 
-void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
     mem_attn->state_read(io, seq_id);
     mem_recr->state_read(io, seq_id);
 }
diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h
index c2d56cd541..acdbc26bfb 100644
--- a/src/llama-memory-hybrid.h
+++ b/src/llama-memory-hybrid.h
@@ -74,8 +74,8 @@ public:
 
     // state write/load
 
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1)       override;
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0)       override;
 
     //
     // llama_memory_hybrid specific API
diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp
index c0c2ec084d..849675c418 100644
--- a/src/llama-memory-recurrent.cpp
+++ b/src/llama-memory-recurrent.cpp
@@ -680,7 +680,9 @@ size_t llama_memory_recurrent::size_s_bytes() const {
     return size_s_bytes;
 }
 
-void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    GGML_UNUSED(flags);
+
     std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
     uint32_t cell_count = 0;
 
@@ -718,7 +720,9 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
     state_write_data(io, cell_ranges);
 }
 
-void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
     uint32_t cell_count;
     io.read_to(&cell_count, sizeof(cell_count));
 
diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h
index 4d094f9a05..95c617b2c9 100644
--- a/src/llama-memory-recurrent.h
+++ b/src/llama-memory-recurrent.h
@@ -63,8 +63,8 @@ public:
 
     // state write/load
 
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1) override;
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
 
     uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
     uint32_t size = 0; // total number of cells, shared across all sequences
diff --git a/src/llama-memory.h b/src/llama-memory.h
index e8ba336e85..171d312cc9 100644
--- a/src/llama-memory.h
+++ b/src/llama-memory.h
@@ -104,8 +104,8 @@ struct llama_memory_i {
     // state write/read
     //
 
-    virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
-    virtual void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1) = 0;
+    virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const = 0;
+    virtual void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) = 0;
 };
 
 using llama_memory_ptr = std::unique_ptr<llama_memory_i>;
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 20fc784a82..8578d49e03 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -692,6 +692,13 @@ struct completion_token_output {
     }
 };
 
+struct swa_checkpoint {
+    llama_pos pos_min;
+    llama_pos pos_max;
+
+    std::vector<uint8_t> data;
+};
+
 struct server_task_result_cmpl_final : server_task_result {
     int index = 0;
 
@@ -1336,6 +1343,8 @@ struct server_slot {
 
     std::vector<completion_token_output> generated_token_probs;
 
+    std::vector<swa_checkpoint> swa_checkpoints;
+
     bool has_next_token = true;
     bool has_new_line   = false;
     bool truncated      = false;
@@ -3293,6 +3302,8 @@ struct server_context {
                                 slot.n_past = 0;
                             }
 
+                            const auto n_swa = llama_model_n_swa(model);
+
                             if (slot.n_past > 0 && slot.n_past < (int) slot.cache_tokens.size()) {
                                 const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                                 if (pos_min == -1) {
@@ -3300,12 +3311,58 @@ struct server_context {
                                     GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237");
                                 }
 
-                                const auto n_swa = llama_model_n_swa(model);
-                                if (pos_min > std::max(0, slot.n_past - n_swa)) {
+                                const auto pos_min_thold = std::max(0, slot.n_past - n_swa);
+
+                                if (pos_min > pos_min_thold) {
                                     SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.cache_tokens.size(), slot.id, pos_min, n_swa);
-                                    SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA, see %s)\n",
-                                            "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
-                                    slot.n_past = 0;
+
+                                    // search for a SWA checkpoint
+                                    const auto it = std::find_if(
+                                        slot.swa_checkpoints.rbegin(),
+                                        slot.swa_checkpoints.rend(),
+                                        [&](const auto & cur) {
+                                            return cur.pos_min <= pos_min_thold;
+                                        }
+                                    );
+
+                                    bool do_reset = it == slot.swa_checkpoints.rend();
+
+                                    if (!do_reset) {
+                                        // restore the checkpoint
+                                        const size_t swa_size = it->data.size();
+                                        const size_t n = llama_state_seq_set_data_ext(ctx, it->data.data(), swa_size, slot.id, LLAMA_STATE_SEQ_FLAGS_SWA_ONLY);
+
+                                        if (n != swa_size) {
+                                            SLT_ERR(slot, "failed to restore SWA checkpoint, pos_min = %d, pos_max = %d, size = %.3f MiB\n", it->pos_min, it->pos_max, (float) swa_size / 1024 / 1024);
+                                            do_reset = true;
+                                        } else {
+                                            slot.n_past = std::min(slot.n_past, it->pos_max);
+
+                                            SLT_WRN(slot, "SWA checkpoint restore, pos_min = %d, pos_max = %d, size = %.3f MiB\n", it->pos_min, it->pos_max, (float) swa_size / 1024 / 1024);
+                                        }
+                                    }
+
+                                    if (do_reset) {
+                                        SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA, see %s)\n",
+                                                "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
+
+                                        slot.n_past = 0;
+                                        slot.swa_checkpoints.clear();
+                                    }
+                                }
+                            }
+
+                            if (n_swa > 0) {
+                                const auto pos_min_thold = std::max(0, slot.n_past - n_swa);
+
+                                // erase any checkpoints with pos_min > pos_min_thold
+                                for (int i = (int) slot.swa_checkpoints.size() - 1; i >= 0; i--) {
+                                    const auto & cur = slot.swa_checkpoints[i];
+                                    if (cur.pos_min > pos_min_thold) {
+                                        slot.swa_checkpoints.erase(slot.swa_checkpoints.begin() + i);
+
+                                        SLT_WRN(slot, "SWA checkpoint erase, pos_min = %d, pos_max = %d, size = %.3f MiB\n", cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
+                                    }
                                 }
                             }
                         }
@@ -3519,6 +3576,39 @@ struct server_context {
 
                     // prompt evaluated for next-token prediction
                     slot.state = SLOT_STATE_GENERATING;
+
+                    // make a checkpoint with the SWA memory
+                    // checkpoints are needed only if we are not using "--swa-full"
+                    if (llama_model_n_swa(model) > 0 && !params_base.swa_full && params_base.n_swa_checkpoints > 0) {
+                        if (slot.swa_checkpoints.size() >= (size_t) params_base.n_swa_checkpoints) {
+                            {
+                                const auto & cur = slot.swa_checkpoints.back();
+
+                                SLT_WRN(slot, "SWA checkpoint erase, pos_min = %d, pos_max = %d, size = %.3f MiB\n",
+                                        cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
+                            }
+
+                            slot.swa_checkpoints.erase(slot.swa_checkpoints.begin());
+                        }
+
+                        const size_t swa_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_SWA_ONLY);
+
+                        auto & cur = slot.swa_checkpoints.emplace_back(swa_checkpoint{
+                            /*.pos_min = */ llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id),
+                            /*.pos_max = */ llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id),
+                            /*.data    = */ std::vector<uint8_t>(swa_size),
+                        });
+
+                        llama_state_seq_get_data_ext(ctx, cur.data.data(), swa_size, slot.id, LLAMA_STATE_SEQ_FLAGS_SWA_ONLY);
+
+                        float size_total = 0.0f;
+                        for (const auto & checkpoint : slot.swa_checkpoints) {
+                            size_total += (float) checkpoint.data.size() / 1024 / 1024;
+                        }
+
+                        SLT_WRN(slot, "SWA checkpoint create, pos_min = %d, pos_max = %d, size = %.3f MiB, total = %d/%d (%.3f MiB)\n",
+                                cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024, (int) slot.swa_checkpoints.size(), params_base.n_swa_checkpoints, size_total);
+                    }
                 } else if (slot.state != SLOT_STATE_GENERATING) {
                     continue; // continue loop of slots
                 }

From 863d341eeb81db104902b14b6f9413daa515e957 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 14 Aug 2025 08:38:10 -0500
Subject: [PATCH 064/287] vulkan: perf_logger improvements (#15246)

* vulkan: perf_logger improvements

- Account for batch dimension in flops calculation.
- Fix how "_VEC" is detected for mat_mul_id.
- Fix "n" dimension for mat_mul_id (in case of broadcasting).
- Include a->type in name.

* use <=mul_mat_vec_max_cols rather than ==1
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index f50a737f38..b26801826f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1111,17 +1111,23 @@ class vk_perf_logger {
             return;
         }
         if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
-            const uint64_t m    = node->src[0]->ne[1];
-            const uint64_t n    = node->src[1]->ne[1];
-            const uint64_t k    = node->src[1]->ne[0];
-            std::string    name = ggml_op_name(node->op);
-            if (n == 1) {
-                name += "_VEC m=" + std::to_string(m) + " k=" + std::to_string(k);
-            } else {
-                name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
+            const uint64_t m     = node->src[0]->ne[1];
+            const uint64_t n     = node->ne[1];
+            const uint64_t k     = node->src[1]->ne[0];
+            const uint64_t batch = node->src[1]->ne[2] * node->src[1]->ne[3];
+            std::string    name  = ggml_op_name(node->op);
+            if ((node->op == GGML_OP_MUL_MAT && n <= mul_mat_vec_max_cols) ||
+                (node->op == GGML_OP_MUL_MAT_ID && node->src[2]->ne[1] == 1)) {
+                name += "_VEC";
+            }
+            name += " ";
+            name += ggml_type_name(node->src[0]->type);
+            name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
+            if (batch > 1) {
+                name += " batch=" + std::to_string(batch);
             }
             timings[name].push_back(time);
-            flops[name].push_back(m * n * (k + (k - 1)));
+            flops[name].push_back(m * n * (k + (k - 1)) * batch);
             return;
         }
         if (node->op == GGML_OP_CONV_2D) {

From 1a01899b612ae8f99a174ad076207090e08d4d7b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 14 Aug 2025 17:16:03 +0300
Subject: [PATCH 065/287] readme : update hot topics (#15315)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 96e30050d3..11d9290786 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,7 @@ LLM inference in C/C++
 
 ## Hot topics
 
+- **[[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)**
 - Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)

From 646944cfa8961afd914dd6637739b3cda9a72e11 Mon Sep 17 00:00:00 2001
From: Christian Kastner <ckk@kvr.at>
Date: Thu, 14 Aug 2025 16:22:58 +0200
Subject: [PATCH 066/287] docker : Enable GGML_CPU_ALL_VARIANTS for ARM
 (#15267)

---
 .devops/cpu.Dockerfile | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index 9459f08c10..e1bb7d4675 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -4,8 +4,6 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 
 ARG TARGETARCH
 
-ARG GGML_CPU_ARM_ARCH=armv8-a
-
 RUN apt-get update && \
     apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
@@ -13,10 +11,8 @@ WORKDIR /app
 
 COPY . .
 
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
+RUN if [ "$TARGETARCH" = "amd64" ] || [ "$TARGETARCH" = "arm64" ]; then \
         cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
-    elif [ "$TARGETARCH" = "arm64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
     else \
         echo "Unsupported architecture"; \
         exit 1; \

From b204a5a234f4cf0b3f449476da639a5510c6d157 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Thu, 14 Aug 2025 09:23:11 -0500
Subject: [PATCH 067/287] gpt-oss: implement harmony parsing (#15181)

* model : add harmony parser for gpt-oss

* gpt-oss : fix grammar trigger from causing empty stack

* gpt-oss: tweak the grammar trigger again

* gpt-oss : add support for recipient in role header

* gpt-oss : fix ungrouped tool calls in grammar

* gpt-oss : loosen function name matching during parse

* gpt-oss : clean up workarounds

* gpt-oss : add template tests

* gpt-oss : simulate thinking and tool call tags

* gpt-oss : undo think tags when reasoning_format is none

* gpt-oss : set special tokens back to user defined

* gpt-oss : update openai-gpt-oss template

* server : filter out harmony thought messages

* gpt-oss : simplify parsing
---
 common/chat.cpp                               | 161 ++++++++-
 models/templates/openai-gpt-oss-120b.jinja    | 331 ++++++++++++++++++
 src/llama-vocab.cpp                           |   3 +-
 tests/test-chat.cpp                           | 182 ++++++++++
 tools/server/public/index.html.gz             | Bin 1914135 -> 1914098 bytes
 .../webui/src/components/ChatMessage.tsx      |   3 +-
 tools/server/webui/src/utils/misc.ts          |   4 +-
 7 files changed, 672 insertions(+), 12 deletions(-)
 create mode 100644 models/templates/openai-gpt-oss-120b.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index b1a1218ca2..1fedb0230a 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -296,6 +296,7 @@ json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msg
         }
         if (!msg.reasoning_content.empty()) {
             jmsg["reasoning_content"] = msg.reasoning_content;
+            jmsg["thinking"] = msg.reasoning_content; // gpt-oss
         }
         if (!msg.tool_name.empty()) {
             jmsg["name"] = msg.tool_name;
@@ -1338,16 +1339,164 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
     data.prompt = prompt;
     data.format = COMMON_CHAT_FORMAT_GPT_OSS;
 
-    // TODO: support tool calls in GPT-OSS?
+    // These special tokens are required to parse properly, so we include them
+    // even if parse_tool_calls is false.
+    data.preserved_tokens = {
+        "<|channel|>",
+        "<|constrain|>",
+        "<|message|>",
+        "<|start|>",
+        "<|end|>",
+    };
+
+    if (inputs.tools.is_array() && !inputs.tools.empty()) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            // tool calls can appear in commentary or analysis channels
+            auto channel = builder.add_rule("channel", "\"<|channel|>\" ( \"commentary\" | \"analysis\" )");
+
+            std::vector<std::string> tool_rules_recipient_in_role;
+            std::vector<std::string> tool_rules_recipient_in_channel;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+
+                tool_rules_recipient_in_role.push_back(
+                    builder.add_rule(name + "-call",
+                        "\"" + name + "\"" + channel + " \" <|constrain|>json\"? \"<|message|>\" " +
+                        builder.add_schema(name + "-args", parameters)
+                    )
+                );
+
+                tool_rules_recipient_in_channel.push_back(
+                    builder.add_rule(name + "-call",
+                        "\"" + name + "\"" + " \" <|constrain|>json\"? \"<|message|>\" " +
+                        builder.add_schema(name + "-args", parameters)
+                    )
+                );
+            });
+
+            auto recipient_in_role = builder.add_rule("recipient_in_role",
+                "\"<|start|>assistant\"? \" to=functions.\" ( " +
+                string_join(tool_rules_recipient_in_role, " | ") + " )"
+            );
+
+            auto recipient_in_channel = builder.add_rule("recipient_in_channel",
+                channel + " \" to=functions.\" ( " +
+                string_join(tool_rules_recipient_in_channel, " | ") + " )"
+            );
+
+            builder.add_rule("root", recipient_in_role + " | " + recipient_in_channel);
+
+            // Trigger on tool calls that appear in the commentary channel
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+                "<\\|channel\\|>(commentary|analysis) to"
+            });
+
+            // Trigger tool calls that appear in the role section, either at the
+            // start or in the middle.
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
+                "^ to"
+            });
+
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+                "<\\|start\\|>assistant to"
+            });
+        });
+    }
 
     return data;
 }
 static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
-    // TODO @ngxson : this won't work with --special enabled, we should fix that
-    builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|start|>assistant<|channel|>final<|message|>");
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
+    static const std::string constraint = "(?: (<\\|constrain\\|>)?([a-zA-Z0-9_-]+))";
+    static const std::string recipient("(?: to=functions\\.([^<\\s]+))");
+
+    static const common_regex start_regex("<\\|start\\|>assistant");
+    static const common_regex analysis_regex("<\\|channel\\|>analysis");
+    static const common_regex final_regex("<\\|channel\\|>final" + constraint + "?");
+    static const common_regex preamble_regex("<\\|channel\\|>commentary");
+    static const common_regex tool_call1_regex(recipient + "<\\|channel\\|>(analysis|commentary)" + constraint + "?");
+    static const common_regex tool_call2_regex("<\\|channel\\|>(analysis|commentary)" + recipient + constraint + "?");
+
+    auto consume_end = [&](bool include_end = false) {
+        if (auto res = builder.try_find_literal("<|end|>")) {
+            return res->prelude + (include_end ? builder.str(res->groups[0]) : "");
+        }
+        return builder.consume_rest();
+    };
+
+    auto handle_tool_call = [&](const std::string & name) {
+        if (auto args = builder.try_consume_json_with_dumped_args({{}})) {
+            if (builder.syntax().parse_tool_calls) {
+                if (!builder.add_tool_call(name, "", args->value) || args->is_partial) {
+                    throw common_chat_msg_partial_exception("incomplete tool call");
+                }
+            } else if (args->is_partial) {
+                throw common_chat_msg_partial_exception("incomplete tool call");
+            }
+        }
+    };
+
+    auto regex_match = [](const common_regex & regex, const std::string & input) -> std::optional<common_regex_match> {
+        auto match = regex.search(input, 0, true);
+        if (match.type == COMMON_REGEX_MATCH_TYPE_FULL) {
+            return match;
+        }
+        return std::nullopt;
+    };
+
+    do {
+        auto header_start_pos = builder.pos();
+        auto content_start = builder.try_find_literal("<|message|>");
+        if (!content_start) {
+            throw common_chat_msg_partial_exception("incomplete header");
+        }
+
+        auto header = content_start->prelude;
+
+        if (auto match = regex_match(tool_call1_regex, header)) {
+            auto group = match->groups[1];
+            auto name = header.substr(group.begin, group.end - group.begin);
+            handle_tool_call(name);
+            continue;
+        }
+
+        if (auto match = regex_match(tool_call2_regex, header)) {
+            auto group = match->groups[2];
+            auto name = header.substr(group.begin, group.end - group.begin);
+            handle_tool_call(name);
+            continue;
+        }
+
+        if (regex_match(analysis_regex, header)) {
+            builder.move_to(header_start_pos);
+            if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) {
+                builder.add_content(consume_end(true));
+            } else {
+                builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|end|>");
+            }
+            continue;
+        }
+
+        if(regex_match(final_regex, header) || regex_match(preamble_regex, header)) {
+            builder.add_content(consume_end());
+            continue;
+        }
+
+        // Possibly a malformed message, attempt to recover by rolling
+        // back to pick up the next <|start|>
+        LOG_DBG("%s: unknown header from message: %s\n", __func__, header.c_str());
+        builder.move_to(header_start_pos);
+    } while (builder.try_find_regex(start_regex, std::string::npos, false));
+
+    auto remaining = builder.consume_rest();
+    if (!remaining.empty()) {
+        LOG_DBG("%s: content after last message: %s\n", __func__, remaining.c_str());
     }
 }
 
diff --git a/models/templates/openai-gpt-oss-120b.jinja b/models/templates/openai-gpt-oss-120b.jinja
new file mode 100644
index 0000000000..dc7bb11927
--- /dev/null
+++ b/models/templates/openai-gpt-oss-120b.jinja
@@ -0,0 +1,331 @@
+{#-
+  In addition to the normal inputs of `messages` and `tools`, this template also accepts the
+  following kwargs:
+  - "builtin_tools": A list, can contain "browser" and/or "python".
+  - "model_identity": A string that optionally describes the model identity.
+  - "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
+ #}
+
+{#- Tool Definition Rendering ============================================== #}
+{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
+    {%- if param_spec.type == "array" -%}
+        {%- if param_spec['items'] -%}
+            {%- if param_spec['items']['type'] == "string" -%}
+                {{- "string[]" }}
+            {%- elif param_spec['items']['type'] == "number" -%}
+                {{- "number[]" }}
+            {%- elif param_spec['items']['type'] == "integer" -%}
+                {{- "number[]" }}
+            {%- elif param_spec['items']['type'] == "boolean" -%}
+                {{- "boolean[]" }}
+            {%- else -%}
+                {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
+                {%- if inner_type == "object | object" or inner_type|length > 50 -%}
+                    {{- "any[]" }}
+                {%- else -%}
+                    {{- inner_type + "[]" }}
+                {%- endif -%}
+            {%- endif -%}
+            {%- if param_spec.nullable -%}
+                {{- " | null" }}
+            {%- endif -%}
+        {%- else -%}
+            {{- "any[]" }}
+            {%- if param_spec.nullable -%}
+                {{- " | null" }}
+            {%- endif -%}
+        {%- endif -%}
+    {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
+        {#- Handle array of types like ["object", "object"] from Union[dict, list] #}
+        {%- if param_spec.type | length > 1 -%}
+            {{- param_spec.type | join(" | ") }}
+        {%- else -%}
+            {{- param_spec.type[0] }}
+        {%- endif -%}
+    {%- elif param_spec.oneOf -%}
+        {#- Handle oneOf schemas - check for complex unions and fallback to any #}
+        {%- set has_object_variants = false -%}
+        {%- for variant in param_spec.oneOf -%}
+            {%- if variant.type == "object" -%}
+                {%- set has_object_variants = true -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- if has_object_variants and param_spec.oneOf|length > 1 -%}
+            {{- "any" }}
+        {%- else -%}
+            {%- for variant in param_spec.oneOf -%}
+                {{- render_typescript_type(variant, required_params) -}}
+                {%- if variant.description %}
+                    {{- "// " + variant.description }}
+                {%- endif -%}
+                {%- if variant.default is defined %}
+                    {{ "// default: " + variant.default|tojson }}
+                {%- endif -%}
+                {%- if not loop.last %}
+                    {{- " | " }}
+                {% endif -%}
+            {%- endfor -%}
+        {%- endif -%}
+    {%- elif param_spec.type == "string" -%}
+        {%- if param_spec.enum -%}
+            {{- '"' + param_spec.enum|join('" | "') + '"' -}}
+        {%- else -%}
+            {{- "string" }}
+            {%- if param_spec.nullable %}
+                {{- " | null" }}
+            {%- endif -%}
+        {%- endif -%}
+    {%- elif param_spec.type == "number" -%}
+        {{- "number" }}
+    {%- elif param_spec.type == "integer" -%}
+        {{- "number" }}
+    {%- elif param_spec.type == "boolean" -%}
+        {{- "boolean" }}
+
+    {%- elif param_spec.type == "object" -%}
+        {%- if param_spec.properties -%}
+            {{- "{\n" }}
+            {%- for prop_name, prop_spec in param_spec.properties.items() -%}
+                {{- prop_name -}}
+                {%- if prop_name not in (param_spec.required or []) -%}
+                    {{- "?" }}
+                {%- endif -%}
+                {{- ": " }}
+                {{ render_typescript_type(prop_spec, param_spec.required or []) }}
+                {%- if not loop.last -%}
+                    {{-", " }}
+                {%- endif -%}
+            {%- endfor -%}
+            {{- "}" }}
+        {%- else -%}
+            {{- "object" }}
+        {%- endif -%}
+    {%- else -%}
+        {{- "any" }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_namespace(namespace_name, tools) -%}
+    {{- "## " + namespace_name + "\n\n" }}
+    {{- "namespace " + namespace_name + " {\n\n" }}
+    {%- for tool in tools %}
+        {%- set tool = tool.function %}
+        {{- "// " + tool.description + "\n" }}
+        {{- "type "+ tool.name + " = " }}
+        {%- if tool.parameters and tool.parameters.properties %}
+            {{- "(_: {\n" }}
+            {%- for param_name, param_spec in tool.parameters.properties.items() %}
+                {%- if param_spec.description %}
+                    {{- "// " + param_spec.description + "\n" }}
+                {%- endif %}
+                {{- param_name }}
+                {%- if param_name not in (tool.parameters.required or []) -%}
+                    {{- "?" }}
+                {%- endif -%}
+                {{- ": " }}
+                {{- render_typescript_type(param_spec, tool.parameters.required or []) }}
+                {%- if param_spec.default is defined -%}
+                    {%- if param_spec.enum %}
+                        {{- ", // default: " + param_spec.default }}
+                    {%- elif param_spec.oneOf %}
+                        {{- "// default: " + param_spec.default }}
+                    {%- else %}
+                        {{- ", // default: " + param_spec.default|tojson }}
+                    {%- endif -%}
+                {%- endif -%}
+                {%- if not loop.last %}
+                    {{- ",\n" }}
+                {%- else %}
+                    {{- ",\n" }}
+                {%- endif -%}
+            {%- endfor %}
+            {{- "}) => any;\n\n" }}
+        {%- else -%}
+            {{- "() => any;\n\n" }}
+        {%- endif -%}
+    {%- endfor %}
+    {{- "} // namespace " + namespace_name }}
+{%- endmacro -%}
+
+{%- macro render_builtin_tools(browser_tool, python_tool) -%}
+    {%- if browser_tool %}
+        {{- "## browser\n\n" }}
+        {{- "// Tool for browsing.\n" }}
+        {{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\n" }}
+        {{- "// Cite information from the tool using the following format:\n" }}
+        {{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.\n" }}
+        {{- "// Do not quote more than 10 words directly from the tool output.\n" }}
+        {{- "// sources=web (default: web)\n" }}
+        {{- "namespace browser {\n\n" }}
+        {{- "// Searches for information related to `query` and displays `topn` results.\n" }}
+        {{- "type search = (_: {\n" }}
+        {{- "query: string,\n" }}
+        {{- "topn?: number, // default: 10\n" }}
+        {{- "source?: string,\n" }}
+        {{- "}) => any;\n\n" }}
+        {{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\n" }}
+        {{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.\n" }}
+        {{- "// If `cursor` is not provided, the most recent page is implied.\n" }}
+        {{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\n" }}
+        {{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\n" }}
+        {{- "// Use this function without `id` to scroll to a new location of an opened page.\n" }}
+        {{- "type open = (_: {\n" }}
+        {{- "id?: number | string, // default: -1\n" }}
+        {{- "cursor?: number, // default: -1\n" }}
+        {{- "loc?: number, // default: -1\n" }}
+        {{- "num_lines?: number, // default: -1\n" }}
+        {{- "view_source?: boolean, // default: false\n" }}
+        {{- "source?: string,\n" }}
+        {{- "}) => any;\n\n" }}
+        {{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\n" }}
+        {{- "type find = (_: {\n" }}
+        {{- "pattern: string,\n" }}
+        {{- "cursor?: number, // default: -1\n" }}
+        {{- "}) => any;\n\n" }}
+        {{- "} // namespace browser\n\n" }}
+    {%- endif -%}
+
+    {%- if python_tool %}
+        {{- "## python\n\n" }}
+        {{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\n\n" }}
+        {{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\n\n" }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{#- System Message Construction ============================================ #}
+{%- macro build_system_message() -%}
+    {%- if model_identity is not defined %}
+        {%- set model_identity = "You are ChatGPT, a large language model trained by OpenAI." %}
+    {%- endif %}
+    {{- model_identity + "\n" }}
+    {{- "Knowledge cutoff: 2024-06\n" }}
+    {{- "Current date: " + strftime_now("%Y-%m-%d") + "\n\n" }}
+    {%- if reasoning_effort is not defined %}
+        {%- set reasoning_effort = "medium" %}
+    {%- endif %}
+    {{- "Reasoning: " + reasoning_effort + "\n\n" }}
+    {%- if builtin_tools %}
+        {{- "# Tools\n\n" }}
+        {%- set available_builtin_tools = namespace(browser=false, python=false) %}
+        {%- for tool in builtin_tools %}
+            {%- if tool == "browser" %}
+                {%- set available_builtin_tools.browser = true %}
+            {%- elif tool == "python" %}
+                {%- set available_builtin_tools.python = true %}
+            {%- endif %}
+        {%- endfor %}
+        {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
+    {%- endif -%}
+    {{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
+    {%- if tools -%}
+        {{- "\nCalls to these tools must go to the commentary channel: 'functions'." }}
+    {%- endif -%}
+{%- endmacro -%}
+
+{#- Main Template Logic ================================================= #}
+{#- Set defaults #}
+
+{#- Render system message #}
+{{- "<|start|>system<|message|>" }}
+{{- build_system_message() }}
+{{- "<|end|>" }}
+
+{#- Extract developer message #}
+{%- if messages[0].role == "developer" or messages[0].role == "system" %}
+    {%- set developer_message = messages[0].content %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set developer_message = "" %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{#- Render developer message #}
+{%- if developer_message or tools %}
+    {{- "<|start|>developer<|message|>" }}
+    {%- if developer_message %}
+        {{- "# Instructions\n\n" }}
+        {{- developer_message }}
+        {{- "\n\n" }}
+    {%- endif %}
+    {%- if tools -%}
+        {{- "# Tools\n\n" }}
+        {{- render_tool_namespace("functions", tools) }}
+    {%- endif -%}
+    {{- "<|end|>" }}
+{%- endif %}
+
+{#- Render messages #}
+{%- set last_tool_call = namespace(name=none) %}
+{%- for message in loop_messages -%}
+    {#- At this point only assistant/user/tool messages should remain #}
+    {%- if message.role == 'assistant' -%}
+        {#- Checks to ensure the messages are being passed in the format we expect #}
+        {%- if "content" in message %}
+            {%- if "<|channel|>analysis<|message|>" in message.content or "<|channel|>final<|message|>" in message.content %}
+                {{- raise_exception("You have passed a message containing <|channel|> tags in the content field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.") }}
+            {%- endif %}
+        {%- endif %}
+        {%- if "thinking" in message %}
+            {%- if "<|channel|>analysis<|message|>" in message.thinking or "<|channel|>final<|message|>" in message.thinking %}
+                {{- raise_exception("You have passed a message containing <|channel|> tags in the thinking field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.") }}
+            {%- endif %}
+        {%- endif %}
+        {%- if "tool_calls" in message %}
+            {#- We need very careful handling here - we want to drop the tool call analysis message if the model #}
+            {#- has output a later <|final|> message, but otherwise we want to retain it. This is the only case #}
+            {#- when we render CoT/analysis messages in inference. #}
+            {%- set future_final_message = namespace(found=false) %}
+            {%- for future_message in loop_messages[loop.index:] %}
+                {%- if future_message.role == 'assistant' and "tool_calls" not in future_message %}
+                    {%- set future_final_message.found = true %}
+                {%- endif %}
+            {%- endfor %}
+            {#- We assume max 1 tool call per message, and so we infer the tool call name #}
+            {#- in "tool" messages from the most recent assistant tool call name #}
+            {%- set tool_call = message.tool_calls[0] %}
+            {%- if tool_call.function %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {%- if message.content and message.thinking %}
+                {{- raise_exception("Cannot pass both content and thinking in an assistant message with tool calls! Put the analysis message in one or the other, but not both.") }}
+            {%- elif message.content and not future_final_message.found %}
+                {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
+            {%- elif message.thinking and not future_final_message.found %}
+                {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
+            {%- endif %}
+            {{- "<|start|>assistant to=" }}
+            {{- "functions." + tool_call.name + "<|channel|>commentary " }}
+            {{- (tool_call.content_type if tool_call.content_type is defined else "json") + "<|message|>" }}
+            {{- tool_call.arguments|tojson }}
+            {{- "<|call|>" }}
+            {%- set last_tool_call.name = tool_call.name %}
+        {%- elif loop.last and not add_generation_prompt %}
+            {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
+            {#- This is a situation that should only occur in training, never in inference. #}
+            {%- if "thinking" in message %}
+                {{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
+            {%- endif %}
+            {#- <|return|> indicates the end of generation, but <|end|> does not #}
+            {#- <|return|> should never be an input to the model, but we include it as the final token #}
+            {#- when training, so the model learns to emit it. #}
+            {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
+        {%- else %}
+            {#- CoT is dropped during all previous turns, so we never render it for inference #}
+            {{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
+            {%- set last_tool_call.name = none %}
+        {%- endif %}
+    {%- elif message.role == 'tool' -%}
+        {%- if last_tool_call.name is none %}
+            {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
+        {%- endif %}
+        {{- "<|start|>functions." + last_tool_call.name }}
+        {{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
+    {%- elif message.role == 'user' -%}
+        {{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- Generation prompt #}
+{%- if add_generation_prompt -%}
+<|start|>assistant
+{%- endif -%}
\ No newline at end of file
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index f7e03e702e..de5d1681df 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -2341,7 +2341,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
         // @ngxson : quick hack for gpt-oss, always render these tokens
         for (const auto & t : token_to_id) {
-            if (t.first == "<|channel|>" || t.first == "<|message|>" || t.first == "<|start|>") {
+            if (t.first == "<|channel|>" || t.first == "<|message|>" || t.first == "<|start|>" || t.first == "<|constrain|>") {
                 id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
             }
         }
@@ -2388,6 +2388,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             if (has_return && has_call && has_end) {
                 special_eog_ids.erase(end_id);
+                id_to_token[end_id].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
                 LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
             }
         }
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 99b4b4d5ba..ea4060c876 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -1439,6 +1439,188 @@ static void test_template_output_parsers() {
                       /* expect_grammar_triggered= */ false
         );
     }
+    {
+        auto tmpls = read_templates("models/templates/openai-gpt-oss-120b.jinja");
+        std::vector<std::string> end_tokens{ "<|return|>", "<|call|>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_GPT_OSS, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_GPT_OSS, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
+
+        assert_msg_equals(simple_assist_msg("", "I'm\nthink"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthink",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function<|message|>{\"arg1",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>analysis to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary<|message|>Hello, world!\nWhat's up?",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary<|message|>Hello, world!\nWhat's up?<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+
+        // Test parse_tool_calls == false
+        assert_msg_equals(
+            simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                    /* .reasoning_in_content = */ false,
+                    /* .thinking_forced_open = */ false,
+                    /* .parse_tool_calls = */ false,
+                }));
+        assert_msg_equals(
+            simple_assist_msg("", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function<|message|>{\"arg1",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                    /* .reasoning_in_content = */ false,
+                    /* .thinking_forced_open = */ false,
+                    /* .parse_tool_calls = */ false,
+                }));
+        assert_msg_equals(
+            simple_assist_msg("", "I'm\nthinking"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                    /* .reasoning_in_content = */ false,
+                    /* .thinking_forced_open = */ false,
+                    /* .parse_tool_calls = */ false,
+                }));
+
+        // Test reasoning formats
+        assert_msg_equals(
+            simple_assist_msg(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>Hello, world!\nWhat's up?"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE,
+                }));
+
+        assert_msg_equals(
+            simple_assist_msg(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>Hello, world!\nWhat's up?"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                    /* .reasoning_in_content = */ true,
+                }));
+
+        // Test tool calling in role header
+        assert_msg_equals(simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                " to=functions.special_function<|channel|>commentary <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                " to=functions.special_function<|channel|>analysis <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+        assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"),
+            common_chat_parse(
+                "<|channel|>analysis<|message|>I'm\nthinking<|end|>"
+                "<|start|>assistant to=functions.special_function<|channel|>analysis <|constrain|>json<|message|>{\"arg1\": 1}",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
+                }));
+    }
 }
 
 static void test_msg_diffs_compute() {
diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index eddf12f9697c2c61c61fc58c3795f547070f2fb3..fad36ed3390de112b91127387d323325180e2ea9 100644
GIT binary patch
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HcwR#Q7mb;w

diff --git a/tools/server/webui/src/components/ChatMessage.tsx b/tools/server/webui/src/components/ChatMessage.tsx
index 1c579f2659..a1a7d152f9 100644
--- a/tools/server/webui/src/components/ChatMessage.tsx
+++ b/tools/server/webui/src/components/ChatMessage.tsx
@@ -62,8 +62,7 @@ export default function ChatMessage({
       return { content: msg.content };
     }
     const REGEX_THINK_OPEN = /<think>|<\|channel\|>analysis<\|message\|>/;
-    const REGEX_THINK_CLOSE =
-      /<\/think>|<\|start\|>assistant<\|channel\|>final<\|message\|>/;
+    const REGEX_THINK_CLOSE = /<\/think>|<\|end\|>/;
     let actualContent = '';
     let thought = '';
     let isThinking = false;
diff --git a/tools/server/webui/src/utils/misc.ts b/tools/server/webui/src/utils/misc.ts
index 1aebccbeac..15182e4cab 100644
--- a/tools/server/webui/src/utils/misc.ts
+++ b/tools/server/webui/src/utils/misc.ts
@@ -131,9 +131,7 @@ export function filterThoughtFromMsgs(messages: APIMessage[]) {
       content:
         msg.role === 'assistant'
           ? contentStr
-              .split(
-                /<\/think>|<\|start\|>assistant<\|channel\|>final<\|message\|>/
-              )
+              .split(/<\/think>|<\|end\|>/)
               .at(-1)!
               .trim()
           : contentStr,

From 5ba36f61033d2819be27e2abb70e9a3bd20c0fde Mon Sep 17 00:00:00 2001
From: uvos <carl@uvos.xyz>
Date: Thu, 14 Aug 2025 16:23:56 +0200
Subject: [PATCH 068/287] HIP: Cleanup hipification header (#15285)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

add expicit conversion operator to support older versions of rocm
Switch over to hip_bf16 from legacy hip_bfloat16
Simplify RDNA3 define
Reduce swap over of new hipblas api to rocm 6.5 as this version is used for rocm 7.0 previews

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/convert.cu    |  6 +++---
 ggml/src/ggml-cuda/convert.cuh   | 13 +++++++++++++
 ggml/src/ggml-cuda/cpy-utils.cuh | 12 ++----------
 ggml/src/ggml-cuda/getrows.cu    |  7 ++++---
 ggml/src/ggml-cuda/mmvf.cu       |  5 +++--
 ggml/src/ggml-cuda/set-rows.cu   |  9 +--------
 ggml/src/ggml-cuda/vendors/hip.h | 13 ++++++-------
 7 files changed, 32 insertions(+), 33 deletions(-)

diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index e3beddbc1b..8f0efdcc12 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -31,8 +31,8 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
     dequantize_kernel(vx, ib, iqs, v);
 
     const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
-    y[iy0 + 0]        = float(v.x);
-    y[iy0 + y_offset] = float(v.y);
+    y[iy0 + 0]        = ggml_cuda_cast<dst_t>(v.x);
+    y[iy0 + y_offset] = ggml_cuda_cast<dst_t>(v.y);
 }
 
 template <bool need_check>
@@ -630,7 +630,7 @@ static __global__ void convert_unary(
 
     const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00;
     const int64_t iy = ((i03*ne02 + i02)*ne01 + i01)*ne00 + i00;
-    y[iy] = float(x[ix]);
+    y[iy] = ggml_cuda_cast<dst_t>(x[ix]);
 }
 
 template <typename src_t, typename dst_t>
diff --git a/ggml/src/ggml-cuda/convert.cuh b/ggml/src/ggml-cuda/convert.cuh
index f04214be17..c62e8a1b10 100644
--- a/ggml/src/ggml-cuda/convert.cuh
+++ b/ggml/src/ggml-cuda/convert.cuh
@@ -29,3 +29,16 @@ typedef to_t_nc_cuda_t<nv_bfloat16> to_bf16_nc_cuda_t;
 to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type);
 to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type);
 to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type);
+
+template<typename dst_t, typename src_t>
+ __host__ __device__ inline dst_t ggml_cuda_cast(src_t x) {
+    if constexpr (std::is_same_v<dst_t, src_t>) {
+        return x;
+    } else if constexpr(std::is_same_v<dst_t, nv_bfloat16>) {
+        return __float2bfloat16(float(x));
+    } else if constexpr(std::is_same_v<src_t, nv_bfloat16>) {
+        return __bfloat162float(x);
+    } else {
+        return float(x);
+    }
+}
diff --git a/ggml/src/ggml-cuda/cpy-utils.cuh b/ggml/src/ggml-cuda/cpy-utils.cuh
index 410c12b7ba..e621cb9811 100644
--- a/ggml/src/ggml-cuda/cpy-utils.cuh
+++ b/ggml/src/ggml-cuda/cpy-utils.cuh
@@ -1,15 +1,7 @@
 #pragma once
 
 #include "ggml-common.h"
-
-template<typename src_t, typename dst_t>
-static __device__ __forceinline__ void convert_flt(const src_t * src, dst_t * dst) {
-    if constexpr (std::is_same_v<src_t, dst_t>) {
-        *dst = *src;
-    } else {
-        *dst = float(*src);
-    }
-}
+#include "convert.cuh"
 
 static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
     if (x <= val[0]) return 0;
@@ -221,5 +213,5 @@ static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
 
 template<typename src_t, typename dst_t>
 static __device__ void cpy_1_flt(const char * cxi, char * cdsti) {
-    convert_flt((const src_t *)cxi, (dst_t *)cdsti);
+    *(dst_t *) cdsti = ggml_cuda_cast<dst_t>(*(const src_t *) cxi);
 }
diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index f77b2629a1..68d3254fbe 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -1,5 +1,6 @@
 #include "getrows.cuh"
 #include "dequantize.cuh"
+#include "convert.cuh"
 
 template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void k_get_rows(
@@ -34,8 +35,8 @@ static __global__ void k_get_rows(
     dfloat2 v;
     dequantize_kernel(src0_row, ib, iqs, v);
 
-    dst_row[iybs + iqs + 0]        = float(v.x);
-    dst_row[iybs + iqs + y_offset] = float(v.y);
+    dst_row[iybs + iqs + 0]        = ggml_cuda_cast<dst_t>(v.x);
+    dst_row[iybs + iqs + y_offset] = ggml_cuda_cast<dst_t>(v.y);
 }
 
 template<typename src0_t, typename dst_t>
@@ -62,7 +63,7 @@ static __global__ void k_get_rows_float(
     dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
     const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
 
-    dst_row[i00] = float(src0_row[i00]);
+    dst_row[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
 }
 
 template<typename grad_t, typename dst_t>
diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu
index 1ad4bc75ba..16100b6804 100644
--- a/ggml/src/ggml-cuda/mmvf.cu
+++ b/ggml/src/ggml-cuda/mmvf.cu
@@ -1,5 +1,6 @@
 #include "ggml.h"
 #include "common.cuh"
+#include "convert.cuh"
 #include "mmvf.cuh"
 
 template <typename T, typename type_acc, int ncols_dst, int block_size>
@@ -93,8 +94,8 @@ static __global__ void mul_mat_vec_f(
 #pragma unroll
             for (int j = 0; j < ncols_dst; ++j) {
                 const float2 tmpy = y2[j*stride_col_y2 + col2];
-                sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
-                sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
+                sumf[j] += ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
+                sumf[j] += ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
             }
         }
     } else {
diff --git a/ggml/src/ggml-cuda/set-rows.cu b/ggml/src/ggml-cuda/set-rows.cu
index 0798343645..b4115a43c2 100644
--- a/ggml/src/ggml-cuda/set-rows.cu
+++ b/ggml/src/ggml-cuda/set-rows.cu
@@ -3,11 +3,6 @@
 
 typedef void (*set_rows_kernel_t)(const char * src, char * dst);
 
-template<typename src_t, typename dst_t>
-__device__ __forceinline__ void set_rows_1(const src_t * src_f, dst_t * dst_f) {
-    convert_flt(src_f, dst_f);
-}
-
 // Generic quantized set_rows kernel template
 template<typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
 static __global__ void k_set_rows_quant(
@@ -117,9 +112,7 @@ static __global__ void k_set_rows(
     const src_t * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
     dst_t * dst_row_ptr    = dst + dst_row*s1 + i02*s2 + i03*s3;
 
-    const src_t* src_elem = src0_row + i00;
-    dst_t* dst_elem = dst_row_ptr + i00;
-    set_rows_1(src_elem, dst_elem);
+    dst_row_ptr[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
 
     GGML_UNUSED(ne10);
     GGML_UNUSED(ne13);
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index ec1b59caaf..6e9c67aca0 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -4,7 +4,7 @@
 #include <hip/hip_runtime.h>
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
-#include <hip/hip_bfloat16.h>
+#include <hip/hip_bf16.h>
 
 #define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
@@ -135,7 +135,7 @@
 #define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
 #define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
 
-#if HIP_VERSION >= 70000000
+#if HIP_VERSION >= 60500000
 #define CUBLAS_COMPUTE_16F HIPBLAS_COMPUTE_16F
 #define CUBLAS_COMPUTE_32F HIPBLAS_COMPUTE_32F
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_COMPUTE_32F_FAST_16F
@@ -147,7 +147,7 @@
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
 #define cublasComputeType_t hipblasDatatype_t
 #define cudaDataType_t hipblasDatatype_t
-#endif // HIP_VERSION >= 7000000
+#endif // HIP_VERSION >= 6050000
 
 #if !defined(__HIP_PLATFORM_AMD__)
 #error "The HIP backend supports only AMD targets"
@@ -179,8 +179,7 @@
 #define RDNA4
 #endif
 
-#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
-    defined(__gfx1150__) || defined(__gfx1151__)
+#if defined(__GFX11__)
 #define RDNA3
 #endif
 
@@ -197,8 +196,8 @@
     #define __has_builtin(x) 0
 #endif
 
-typedef hip_bfloat16 nv_bfloat16;
-typedef short2 nv_bfloat162; // FIXME there is no 2x BF16 type being defined in bfloat16.h, ad-hoc compilation fix
+typedef __hip_bfloat16 nv_bfloat16;
+typedef __hip_bfloat162 nv_bfloat162;
 
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));

From e4e915912cfd2ee15c5a4a0074813232134892f6 Mon Sep 17 00:00:00 2001
From: simevo <github@simevo.com>
Date: Thu, 14 Aug 2025 17:45:27 +0200
Subject: [PATCH 069/287] devops : fix compile bug when the
 BASE_CUDA_DEV_CONTAINER is based on Ubuntu 24.04 (#15005)

fixes #15004

Co-authored-by: Paolo Greppi <paolo.greppi@libpf.com>
---
 .devops/cuda.Dockerfile | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
index 94f1433972..f9dc7f3163 100644
--- a/.devops/cuda.Dockerfile
+++ b/.devops/cuda.Dockerfile
@@ -60,8 +60,7 @@ RUN apt-get update \
     git \
     python3 \
     python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
+    && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

From 7a0de960452f9a57de7f1d167e57b6f3ee5ac1b6 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 14 Aug 2025 17:56:26 +0200
Subject: [PATCH 070/287] llama : add 18-layer model type for Gemma 3-270m
 (#15319)

This commit adds support for the 18-layer model type in the Gemma3
series, which is the size of the Gemma3-270m model.

The motivation for this commit is was the only change required for
Gemma3-270m to be converted to GGUF format and used with llama.cpp.

Once the model has been converted and uploaded to Huggingface it can be
used like this:
```console
$ ./build/bin/llama-cli -hf ggml-org/gemma-3-270m-GGUF:Q8_0
```
---
 src/llama-model.cpp | 1 +
 src/llama-model.h   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 58ca7df707..23a26f0c64 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1095,6 +1095,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
+                    case 18: type = LLM_TYPE_537M; break;
                     case 26: type = LLM_TYPE_1B; break;
                     case 34: type = LLM_TYPE_4B; break;
                     case 48: type = LLM_TYPE_12B; break;
diff --git a/src/llama-model.h b/src/llama-model.h
index 6fcd74d57f..46f7d0480f 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -39,6 +39,7 @@ enum llm_type {
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
+    LLM_TYPE_537M,
     LLM_TYPE_700M,
     LLM_TYPE_770M,
     LLM_TYPE_780M,

From f75b8306472811ecc4e4457d5573a09201f02183 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Thu, 14 Aug 2025 10:28:29 -0700
Subject: [PATCH 071/287] chat : include kwargs in template example (#15309)

---
 common/chat.cpp         | 3 ++-
 common/chat.h           | 3 ++-
 tools/main/main.cpp     | 2 +-
 tools/mtmd/mtmd-cli.cpp | 2 +-
 tools/server/server.cpp | 4 ++--
 5 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index 1fedb0230a..00f30994b5 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -473,11 +473,12 @@ std::string common_chat_format_single(
     return ss.str();
 }
 
-std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) {
+std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja, const std::map<std::string, std::string> & chat_template_kwargs) {
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
     inputs.add_bos = tmpls->add_bos;
     inputs.add_eos = tmpls->add_eos;
+    inputs.chat_template_kwargs = chat_template_kwargs;
     auto add_simple_msg = [&](auto role, auto content) {
         common_chat_msg msg;
         msg.role = role;
diff --git a/common/chat.h b/common/chat.h
index c4d6b2e85e..d1e480c918 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -187,7 +187,8 @@ std::string common_chat_format_single(
 // Returns an example of formatted chat
 std::string common_chat_format_example(
     const struct common_chat_templates * tmpls,
-    bool use_jinja);
+    bool use_jinja,
+    const std::map<std::string, std::string> & chat_template_kwargs);
 
 const char*               common_chat_format_name(common_chat_format format);
 const char*               common_reasoning_format_name(common_reasoning_format format);
diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index eb36c68840..dc776f59e9 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -220,7 +220,7 @@ int main(int argc, char ** argv) {
                 LOG_WRN("*** User-specified prompt will pre-start conversation, did you mean to set --system-prompt (-sys) instead?\n");
             }
 
-            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja).c_str());
+            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja, params.default_template_kwargs).c_str());
         } else {
             LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
         }
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index 599e682e0f..5fde6ca0c3 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -108,7 +108,7 @@ struct mtmd_cli_context {
         }
 
         tmpls = common_chat_templates_init(model, params.chat_template);
-        LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(tmpls.get(), params.use_jinja).c_str());
+        LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(tmpls.get(), params.use_jinja, params.default_template_kwargs).c_str());
 
         init_vision_context(params);
 
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 8578d49e03..0b40f7bfa4 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -2053,7 +2053,7 @@ struct server_context {
 
         chat_templates = common_chat_templates_init(model, params_base.chat_template);
         try {
-            common_chat_format_example(chat_templates.get(), params.use_jinja);
+            common_chat_format_example(chat_templates.get(), params.use_jinja, params.default_template_kwargs);
         } catch (const std::exception & e) {
             SRV_WRN("%s: Chat template parsing error: %s\n", __func__, e.what());
             SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
@@ -5075,7 +5075,7 @@ int main(int argc, char ** argv) {
     // print sample chat example to make it clear which template is used
     LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
         common_chat_templates_source(ctx_server.chat_templates.get()),
-        common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params_base.use_jinja).c_str());
+        common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params_base.use_jinja, ctx_server.params_base.default_template_kwargs).c_str());
 
     ctx_server.queue_tasks.on_new_task([&ctx_server](server_task && task) {
         ctx_server.process_single_task(std::move(task));

From df36bce667bf14f8e538645547754386f9516326 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 14 Aug 2025 22:10:51 +0300
Subject: [PATCH 072/287] eval-callback : stop on first NaN (#15320)

* eval-callback : stop on first NaN

* cont : log error
---
 examples/eval-callback/eval-callback.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 4afd80eb45..61eefc7248 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -7,6 +7,7 @@
 #include <cstdio>
 #include <string>
 #include <vector>
+#include <numeric>
 
 /**
  * This the arbitrary data which will be passed to each callback.
@@ -77,6 +78,12 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
         LOG("                                     ]\n");
         LOG("                                     sum = %f\n", sum);
     }
+
+    // TODO: make this abort configurable/optional?
+    if (std::isnan(sum)) {
+        LOG_ERR("encountered NaN - aborting\n");
+        exit(0);
+    }
 }
 
 /**

From 4227c9be4268ac844921b90f31595f81236bd317 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 14 Aug 2025 23:21:24 +0200
Subject: [PATCH 073/287] CUDA: fix negative KV_max values in FA (#15321)

---
 ggml/src/ggml-cuda/fattn-common.cuh | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index e46f0e2081..d4ed938391 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -539,11 +539,15 @@ static __global__ void flash_attn_mask_to_KV_max(
         all_inf = warp_reduce_all(all_inf);
 
         if (!all_inf) {
-            KV_max_sj += FATTN_KQ_STRIDE;
             break;
         }
     }
 
+    // If the break in the loop was not triggered, KV_max_sj is now -FATTN_KQ_STRIDE.
+    // If the break was triggered it's the lower edge of the tile with the first non-masked values.
+    // In either case, walk back the decrementation by FATTN_KQ_STRIDE.
+    KV_max_sj += FATTN_KQ_STRIDE;
+
     if (threadIdx.x != 0) {
         return;
     }

From b07791aa1d4831a08ad54ca19aa206c0c5c0f34a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 15 Aug 2025 11:23:17 +0200
Subject: [PATCH 074/287] test-opt: fix backend support check (#15317)

* test-opt: fix backend support check

* Update tests/test-opt.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tests/test-opt.cpp | 55 +++++++++++++++++++++++++++++++++-------------
 1 file changed, 40 insertions(+), 15 deletions(-)

diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index dc36b29eab..f02b4cad8c 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -438,7 +438,6 @@ static std::pair<int, int> test_forward_backward(
         float weights;
         ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
         const bool subtest_ok = weights == -ndata * .5;
-        TEST_LOG("%s: ndata=%d weights=%f\n", __func__, (int) ndata, (double) weights);
         helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass);
     }
     {
@@ -821,11 +820,7 @@ static std::pair<int, int> test_regression(
         ggml_backend_tensor_get(b, &b_fit, 0, sizeof(float));
         float tol = adamw ? 1e-2 : 5e-2;
         const bool aok = almost_equal(a_fit, a_true, tol);
-        if (!aok)
-          TEST_LOG("%s: a_fit=%f a_true=%f\n", __func__, (double)a_fit, (double)a_true);
         const bool bok = almost_equal(b_fit, b_true, tol);
-        if (!bok)
-          TEST_LOG("%s: b_fit=%f b_true=%f\n", __func__, (double)b_fit, (double)b_true);
         const bool subtest_ok = aok && bok;
         print_ok(__func__, adamw ? subtest_ok : true, npass, ntest, "subtest=weights");
     }
@@ -934,19 +929,49 @@ int main(void) {
             printf("  Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024);
             printf("\n");
 
-            if (optim == GGML_OPT_OPTIMIZER_TYPE_SGD && !strcmp(devname, "Vulkan0"))
-              //TODO: even though backend returns false for currently
-              // unimplemented sgd op, we still need this
-              continue;
-            if (!strcmp(devname, "WebGPU"))
-              // GGML_OP_SUM implementation missing
-              continue;
-            std::pair<int, int> result = test_backend(backend_sched, backends[i], optim);
+            bool skip;
+            {
+                struct ggml_init_params params = {
+                    /*.mem_size   =*/ 6*ggml_tensor_overhead(),
+                    /*.mem_buffer =*/ nullptr,
+                    /*.no_alloc   =*/ true,
+                };
+                ggml_context * ctx = ggml_init(params);
+                ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+                ggml_set_param(a);
+                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+                ggml_tensor * c = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+                ggml_tensor * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
 
-            printf("  %d/%d tests passed\n", result.first, result.second);
+                ggml_tensor * t = nullptr;
+                switch (optim) {
+                    case GGML_OPT_OPTIMIZER_TYPE_ADAMW: {
+                        ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 7);
+                        t = ggml_opt_step_adamw(ctx, a, b, c, d, p);
+                    } break;
+                    case GGML_OPT_OPTIMIZER_TYPE_SGD: {
+                        ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 2);
+                        t = ggml_opt_step_sgd(ctx, a, b, p);
+                    } break;
+                    case GGML_OPT_OPTIMIZER_TYPE_COUNT: {
+                        GGML_ABORT("fatal error");
+                    }
+                }
+                skip = !ggml_backend_supports_op(backends[i], t);
+                ggml_free(ctx);
+            }
+
+            std::pair<int, int> result;
+            if (!skip) {
+                result = test_backend(backend_sched, backends[i], optim);
+                printf("  %d/%d tests passed\n", result.first, result.second);
+            }
 
             printf("  Backend %s %s: ", ggml_backend_name(backends[i]), ggml_opt_optimizer_name(optim));
-            if (result.first == result.second) {
+            if (skip) {
+                printf("\033[0;33mSKIPPED\033[0m\n");
+                n_ok++;
+            } else if (result.first == result.second) {
                 printf("\033[1;32mOK\033[0m\n");
                 n_ok++;
             } else {

From 7aeee88cfe9c0434eac722b0f7c21404f48758a5 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Fri, 15 Aug 2025 03:27:02 -0700
Subject: [PATCH 075/287] ci : move ccache action to ggml-org fork (#15328)

---
 .github/workflows/build.yml               | 44 +++++++++++------------
 .github/workflows/copilot-setup-steps.yml |  2 +-
 .github/workflows/release.yml             | 18 +++++-----
 3 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index d4ed3ce7e1..d5af79f240 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -64,7 +64,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-arm64
           evict-old-files: 1d
@@ -104,7 +104,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-x64
           evict-old-files: 1d
@@ -144,7 +144,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-arm64-webgpu
           evict-old-files: 1d
@@ -199,7 +199,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-cpu-cmake
           evict-old-files: 1d
@@ -251,7 +251,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
           evict-old-files: 1d
@@ -330,7 +330,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-latest-cmake-rpc
           evict-old-files: 1d
@@ -363,7 +363,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-vulkan
           evict-old-files: 1d
@@ -400,7 +400,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-webgpu
           evict-old-files: 1d
@@ -457,7 +457,7 @@ jobs:
           sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libcurl4-openssl-dev
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-hip
           evict-old-files: 1d
@@ -487,7 +487,7 @@ jobs:
           apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-musa
           evict-old-files: 1d
@@ -532,7 +532,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-sycl
           evict-old-files: 1d
@@ -580,7 +580,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-sycl-fp16
           evict-old-files: 1d
@@ -611,7 +611,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-ios
           evict-old-files: 1d
@@ -648,7 +648,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-tvos
           evict-old-files: 1d
@@ -720,7 +720,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-swift
           evict-old-files: 1d
@@ -766,7 +766,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-msys2
           variant: ccache
@@ -834,7 +834,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-${{ matrix.build }}
           variant: ccache
@@ -948,7 +948,7 @@ jobs:
               apt install -y cmake build-essential ninja-build libgomp1 git libcurl4-openssl-dev
 
         - name: ccache
-          uses: hendrikmuhs/ccache-action@v1.2.16
+          uses: ggml-org/ccache-action@v1.2.16
           with:
             key: ubuntu-latest-cmake-cuda
             evict-old-files: 1d
@@ -977,7 +977,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-cuda-${{ matrix.cuda }}
           variant: ccache
@@ -1033,7 +1033,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-sycl
           variant: ccache
@@ -1079,7 +1079,7 @@ jobs:
           & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
 
       - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ${{ github.job }}
           evict-old-files: 1d
@@ -1146,7 +1146,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: android-build
           evict-old-files: 1d
diff --git a/.github/workflows/copilot-setup-steps.yml b/.github/workflows/copilot-setup-steps.yml
index a6553187ef..2d61ce3035 100644
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -29,7 +29,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: copilot-setup-steps
           evict-old-files: 1d
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 4ed6126f48..4dbe51b7f0 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -32,7 +32,7 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-arm64
           evict-old-files: 1d
@@ -85,7 +85,7 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: macOS-latest-cmake-x64
           evict-old-files: 1d
@@ -147,7 +147,7 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-cpu-cmake
           evict-old-files: 1d
@@ -198,7 +198,7 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: ubuntu-22-cmake-vulkan
           evict-old-files: 1d
@@ -256,7 +256,7 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-cpu-${{ matrix.arch }}
           variant: ccache
@@ -328,7 +328,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }}
           variant: ccache
@@ -398,7 +398,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-cuda-${{ matrix.cuda }}
           variant: ccache
@@ -471,7 +471,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-sycl
           variant: ccache
@@ -545,7 +545,7 @@ jobs:
           git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
 
       - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-latest-cmake-hip-${{ matrix.name }}-x64
           evict-old-files: 1d

From d3248d9b6557c75d59954c594bb53cf517591e91 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Fri, 15 Aug 2025 14:02:39 +0200
Subject: [PATCH 076/287] ci : fix ios-xcode-build (#15324)

* fix ios-xcode-build

* use xcode-select with fixed version

* switch to macos-15 to get xcode 16.4
---
 .github/workflows/build.yml   | 5 +++++
 .github/workflows/release.yml | 6 +++++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index d5af79f240..83aaea9947 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1113,6 +1113,11 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
+      - name: Setup Xcode
+        uses: maxim-lobanov/setup-xcode@v1
+        with:
+          xcode-version: latest-stable
+
       - name: Build
         id: cmake_build
         run: |
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 4dbe51b7f0..9ebb0236fa 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -600,7 +600,7 @@ jobs:
           name: llama-bin-win-hip-${{ matrix.name }}-x64.zip
 
   ios-xcode-build:
-    runs-on: macos-latest
+    runs-on: macos-15
 
     steps:
       - name: Checkout code
@@ -608,6 +608,10 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Xcode
+        run: |
+          sudo xcode-select -s /Applications/Xcode_16.4.app
+
       - name: Build
         id: cmake_build
         run: |

From ff27f80a74bbe5303acd511a6781a1de6d619b3c Mon Sep 17 00:00:00 2001
From: Aaron Teo <aaron.teo1@ibm.com>
Date: Fri, 15 Aug 2025 21:11:22 +0800
Subject: [PATCH 077/287] ggml: initial IBM zDNN backend (#14975)

* ggml-zdnn: inital backend impl

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: temp change z17 to arch15

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: fix build bugs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: tensor->extra logging check

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: add layout name mapping, ztensor information

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: separate logging into its own line

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: add shape comparison

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: add ggml_tensor shape log

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: fix incorrect shape logging

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add output buffer check

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: run compute and store into tensor->extra

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add set_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add more loggers

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: update set_tensor logging to check only for matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: last working matmul version

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add comments to prevent accidentally deleting lines

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: support op out_prod

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: update op out_prod to use tensor->extra

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: rewrite the backend implementation

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: bugfix new impl

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix compiler warnings and bugfixes

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: test ztensor finding in init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: implement at least 1 op to test

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: assign tensor->extra to buffer

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add check for view tensors to prevent init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: rework init_tensor to create new buffers

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: switch to std vector instead of array

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: switch buffers back and set to arbitrary number

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: impl init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: update supports_op matmul matrix

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix incorrect ztensor shape, reduce memory padding

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: code clean up

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: impl matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix compiler error missing type

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix missing data transform call

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add bias init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: tighten memory usage, change string allocation

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add bias ztensor and data free

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add bias data transform

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add more debug info for extra buffer transform

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add logger to check if mat mul ops go through set_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: activate bias transform in matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: move weights transform into mulmat

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add more safeguards in matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix sequencing of transforms

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: bugfix transform ztensor vs origtensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: figure out why sigtrap is happening

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix sigsegv

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: move everything back to local declaration

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: move bias data to local also

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: bring back working matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: rewrite into mre

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix missing vector import

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix missing vector import in header

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt to fix sigsegv

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix missing load tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix invalid ztensor buffer release

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add logging to debug free buffer

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: remove free_buffer debug info

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add parmblkformat detections

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add nnpa installed detection

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add zdnn_init call for static libs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt at fixing invalid buffer

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: switch to using deque to fix pointer deref problem

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add weights logging to check

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt to use unique ptr

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add tensor to pre_tfm_desc logging

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add inputs logging

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: disable op_none initialisation for testing

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix missing return from init_tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: load ztensors in cgraph exec

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: work on moving output ztensor as well

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: disable logging and breakpoints for full test

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt at manually changing the layout

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt at using default nwhc format instead

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: disable global load ztensor for now

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix errorenous output load tensor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: add guards to prevent loading ztensor if transformed

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: code cleanup

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: bring load ztensor back to init routine

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: code clean up

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix ztensor deallocation abort

stabilise ggml <-> zdnn api

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: clean up matmul selection

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: clean up project structure

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: update documentation, prepare for upstream

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* chore: add codeowners

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: disable batched matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: attempt at fixing tensor views during matmul

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: deny all view tensors directly

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix pr comments

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* docs: update ops docs for zdnn

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: redo test-backend-ops for ops.md

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: fix typo in build-s390x.md

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* codeowners: remove taronaeo for now

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "codeowners: remove taronaeo for now"

This reverts commit 411ea4ed78d08778967bd0bd33a6538cfcbe082f.

* ggml-zdnn: remove unused ggml_zdnn macro

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

---------

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
---
 .../ISSUE_TEMPLATE/010-bug-compilation.yml    |    2 +-
 .github/ISSUE_TEMPLATE/011-bug-results.yml    |    2 +-
 .github/labeler.yml                           |    5 +
 CODEOWNERS                                    |    1 +
 docs/build-s390x.md                           |   40 +-
 docs/ops.md                                   |  177 +-
 docs/ops/zDNN.csv                             | 8134 +++++++++++++++++
 ggml/CMakeLists.txt                           |    1 +
 ggml/include/ggml-zdnn.h                      |   16 +
 ggml/src/CMakeLists.txt                       |    1 +
 ggml/src/ggml-backend-reg.cpp                 |    7 +
 ggml/src/ggml-cpu/CMakeLists.txt              |    2 +-
 ggml/src/ggml-zdnn/CMakeLists.txt             |   36 +
 ggml/src/ggml-zdnn/ggml-zdnn-impl.h           |   97 +
 ggml/src/ggml-zdnn/ggml-zdnn.cpp              |  846 ++
 15 files changed, 9265 insertions(+), 102 deletions(-)
 create mode 100644 docs/ops/zDNN.csv
 create mode 100644 ggml/include/ggml-zdnn.h
 create mode 100644 ggml/src/ggml-zdnn/CMakeLists.txt
 create mode 100644 ggml/src/ggml-zdnn/ggml-zdnn-impl.h
 create mode 100644 ggml/src/ggml-zdnn/ggml-zdnn.cpp

diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
index 95a0b5cc75..feb0d51205 100644
--- a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
+++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
@@ -40,7 +40,7 @@ body:
     attributes:
         label: GGML backends
         description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL]
+        options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL, zDNN]
         multiple: true
     validations:
       required: true
diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml
index d1034bbb69..c42a14ff83 100644
--- a/.github/ISSUE_TEMPLATE/011-bug-results.yml
+++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml
@@ -42,7 +42,7 @@ body:
     attributes:
         label: GGML backends
         description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL]
+        options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL, zDNN]
         multiple: true
     validations:
       required: true
diff --git a/.github/labeler.yml b/.github/labeler.yml
index df6a7a40ed..c4da4ab4e1 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -22,6 +22,11 @@ Vulkan:
         - any-glob-to-any-file:
             - ggml/include/ggml-vulkan.h
             - ggml/src/ggml-vulkan/**
+IBM zDNN:
+    - changed-files:
+        - any-glob-to-any-file:
+            - ggml/include/ggml-zdnn.h
+            - ggml/src/ggml-zdnn/**
 documentation:
     - changed-files:
         - any-glob-to-any-file:
diff --git a/CODEOWNERS b/CODEOWNERS
index 4c0dd4b725..b698b1f4cb 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -10,3 +10,4 @@
 /ggml/src/ggml-opt.cpp @JohannesGaessler
 /ggml/src/gguf.cpp @JohannesGaessler
 /ggml/src/ggml-vulkan/ @0cc4m
+/ggml/src/ggml-zdnn/ @taronaeo
diff --git a/docs/build-s390x.md b/docs/build-s390x.md
index 4d5857753a..b36a199814 100644
--- a/docs/build-s390x.md
+++ b/docs/build-s390x.md
@@ -76,6 +76,23 @@ cmake --build build --config Release -j $(nproc)
     cmake --build build --config Release -j $(nproc)
     ```
 
+## IBM zDNN Accelerator
+
+This provides acceleration using the IBM zAIU co-processor located in the Telum I and Telum II processors. Make sure to have the [IBM zDNN library](https://github.com/IBM/zDNN) installed.
+
+#### Compile from source from IBM
+
+You may find the official build instructions here: [Building and Installing zDNN](https://github.com/IBM/zDNN?tab=readme-ov-file#building-and-installing-zdnn)
+
+### Compilation
+
+```bash
+cmake -S . -B build             \
+    -DCMAKE_BUILD_TYPE=Release  \
+    -DGGML_ZDNN=ON
+cmake --build build --config Release -j$(nproc)
+```
+
 ## Getting GGUF Models
 
 All models need to be converted to Big-Endian. You can achieve this in three cases:
@@ -145,15 +162,15 @@ All models need to be converted to Big-Endian. You can achieve this in three cas
 
 ### 1. SIMD Acceleration
 
-Only available in IBM z15 or later system with the `-DGGML_VXE=ON` (turned on by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z14/arch12. In such systems, the APIs can still run but will use a scalar implementation.
+Only available in IBM z15/LinuxONE 3 or later system with the `-DGGML_VXE=ON` (turned on by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z14/arch12. In such systems, the APIs can still run but will use a scalar implementation.
 
 ### 2. NNPA Vector Intrinsics Acceleration
 
-Only available in IBM z16 or later system with the `-DGGML_NNPA=ON` (turned off by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation.
+Only available in IBM z16/LinuxONE 4 or later system with the `-DGGML_NNPA=ON` (turned off by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation.
 
-### 3. zDNN Accelerator
+### 3. zDNN Accelerator (WIP)
 
-_Only available in IBM z16 / LinuxONE 4 or later system. No support currently available._
+Only available in IBM z17/LinuxONE 5 or later system with the `-DGGML_ZDNN=ON` compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs will default back to CPU routines.
 
 ### 4. Spyre Accelerator
 
@@ -229,11 +246,12 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 
 ## Appendix A: Hardware Support Matrix
 
-|         | Support | Minimum Compiler Version |
-| ------- | ------- | ------------------------ |
-| IBM z15 | ✅      |                          |
-| IBM z16 | ✅      |                          |
-| IBM z17 | ✅      | GCC 15.1.0               |
+|          | Support | Minimum Compiler Version |
+| -------- | ------- | ------------------------ |
+| IBM z15  | ✅      |                          |
+| IBM z16  | ✅      |                          |
+| IBM z17  | ✅      | GCC 15.1.0               |
+| IBM zDNN | ✅      |                          |
 
 -   ✅ - supported and verified to run as intended
 -   🚫 - unsupported, we are unlikely able to provide support
@@ -242,7 +260,7 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 
 |            | VX/VXE/VXE2 | NNPA | zDNN | Spyre |
 | ---------- | ----------- | ---- | ---- | ----- |
-| FP32       | ✅          | ✅   | ❓   | ❓    |
+| FP32       | ✅          | ✅   | ✅   | ❓    |
 | FP16       | ✅          | ✅   | ❓   | ❓    |
 | BF16       | 🚫          | 🚫   | ❓   | ❓    |
 | Q4_0       | ✅          | ✅   | ❓   | ❓    |
@@ -273,4 +291,4 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself
 
-Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on July 25, 2025.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on July 31, 2025.
diff --git a/docs/ops.md b/docs/ops.md
index 1a474d9047..9a81ca0a97 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -12,91 +12,92 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan |
-|-----------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 |
-|                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
-|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
-|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
-|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ |
-|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ |
-|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ |
+| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | zDNN |
+|-----------|------|------|------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
+|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
+|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
+|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ |
+|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ |
diff --git a/docs/ops/zDNN.csv b/docs/ops/zDNN.csv
new file mode 100644
index 0000000000..bf633844f5
--- /dev/null
+++ b/docs/ops/zDNN.csv
@@ -0,0 +1,8134 @@
+"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name"
+"zDNN","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","RELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","RELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","SIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","SIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","GELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","GELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","GELU_QUICK","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","SILU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","SILU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","HARDSIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","SGN","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","NEG","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","NEG","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","STEP","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","STEP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","TANH","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","TANH","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","ELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","ELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","RELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","RELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","SIGMOID","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","SIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","GELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","GELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","GELU_QUICK","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","SILU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","SILU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","HARDSIGMOID","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","EXP","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","zDNN"
+"zDNN","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","zDNN"
+"zDNN","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","NEG","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","NEG","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","STEP","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","STEP","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","TANH","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
+"zDNN","TANH","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","zDNN"
+"zDNN","ELU","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","zDNN"
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+"zDNN","CONV_TRANSPOSE_1D","ne_input=[2,7,1,1],ne_kernel=[1337,9,7,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[2,7,1,1],ne_kernel=[1337,9,7,1],s0=2,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[2,7,1,1],ne_kernel=[1337,9,7,1],s0=3,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[13,7,1,1],ne_kernel=[1337,9,7,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[13,7,1,1],ne_kernel=[1337,9,7,1],s0=2,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[13,7,1,1],ne_kernel=[1337,9,7,1],s0=3,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[197,32,1,1],ne_kernel=[16,32,32,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[2,3,2,1],s0=3,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[2,3,2,1],s0=2,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[2,3,2,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=2,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","zDNN"
+"zDNN","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","zDNN"
+"zDNN","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","zDNN"
+"zDNN","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[100,10,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[1024,10,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[1024,12,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[2000,10,1,1]","support","0","no","zDNN"
+"zDNN","ARGMAX","type=f32,ne=[5438,3,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","zDNN"
+"zDNN","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","zDNN"
+"zDNN","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,2,1],v=0","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=0","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=1","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=1","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=1","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,2,1],v=1","support","0","no","zDNN"
+"zDNN","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=1","support","0","no","zDNN"
+"zDNN","DUP","type=f32,ne=[10,10,20,1]","support","0","no","zDNN"
+"zDNN","DUP","type=f16,ne=[10,10,20,1]","support","0","no","zDNN"
+"zDNN","DUP","type=i32,ne=[10,10,20,1]","support","0","no","zDNN"
+"zDNN","DUP","type=i16,ne=[10,10,20,1]","support","0","no","zDNN"
+"zDNN","DUP","type=f32,ne=[10,10,5,1],permute=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","DUP","type=f16,ne=[10,10,5,1],permute=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","DUP","type=f32,ne=[10,10,5,1],permute=[1,0,2,3]","support","0","no","zDNN"
+"zDNN","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","0","no","zDNN"
+"zDNN","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","0","no","zDNN"
+"zDNN","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","0","no","zDNN"
+"zDNN","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","0","no","zDNN"
+"zDNN","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","0","no","zDNN"
+"zDNN","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1","support","0","no","zDNN"
+"zDNN","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2","support","0","no","zDNN"
+"zDNN","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=bf16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q3_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q4_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q5_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=q6_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xxs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_xs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq2_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_xxs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq1_m,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_nl,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq3_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=iq4_xs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","zDNN"
+"zDNN","CONT","type=f32,ne=[10,10,10,1]","support","0","no","zDNN"
+"zDNN","CONT","type=f32,ne=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","CONT","type=f32,ne=[2,1,3,5]","support","0","no","zDNN"
+"zDNN","CONT","type=f32,ne=[2,3,5,7]","support","0","no","zDNN"
+"zDNN","CONT","type=f16,ne=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","CONT","type=f16,ne=[2,1,3,5]","support","0","no","zDNN"
+"zDNN","CONT","type=f16,ne=[2,3,5,7]","support","0","no","zDNN"
+"zDNN","CONT","type=bf16,ne=[2,1,1,1]","support","0","no","zDNN"
+"zDNN","CONT","type=bf16,ne=[2,1,3,5]","support","0","no","zDNN"
+"zDNN","CONT","type=bf16,ne=[2,3,5,7]","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
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+"zDNN","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","zDNN"
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+"zDNN","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
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+"zDNN","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
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+"zDNN","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
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+"zDNN","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
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+"zDNN","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","zDNN"
+"zDNN","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
+"zDNN","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
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+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=128,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=45,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=45,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=193,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","zDNN"
+"zDNN","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=0,m=32,n=1024,k=16","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=1,m=32,n=1024,k=16","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","zDNN"
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+"zDNN","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","zDNN"
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+"zDNN","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","0","no","zDNN"
+"zDNN","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","0","no","zDNN"
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+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","zDNN"
+"zDNN","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","zDNN"
+"zDNN","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","zDNN"
+"zDNN","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","zDNN"
+"zDNN","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","zDNN"
+"zDNN","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","zDNN"
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 1fb7abeaf0..90e274ccdb 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -188,6 +188,7 @@ option(GGML_VULKAN_VALIDATE                 "ggml: enable Vulkan validation"
 option(GGML_VULKAN_RUN_TESTS                "ggml: run Vulkan tests"                          OFF)
 option(GGML_WEBGPU                          "ggml: use WebGPU"                                OFF)
 option(GGML_WEBGPU_DEBUG                    "ggml: enable WebGPU debug output"                OFF)
+option(GGML_ZDNN                            "ggml: use zDNN"                                  OFF)
 option(GGML_METAL                           "ggml: use Metal"                                 ${GGML_METAL_DEFAULT})
 option(GGML_METAL_USE_BF16                  "ggml: use bfloat if available"                   OFF)
 option(GGML_METAL_NDEBUG                    "ggml: disable Metal debugging"                   OFF)
diff --git a/ggml/include/ggml-zdnn.h b/ggml/include/ggml-zdnn.h
new file mode 100644
index 0000000000..c2c30c977c
--- /dev/null
+++ b/ggml/include/ggml-zdnn.h
@@ -0,0 +1,16 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+GGML_BACKEND_API ggml_backend_t ggml_backend_zdnn_init(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 177fb28213..2b5b8169d7 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -382,6 +382,7 @@ ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
 ggml_add_backend(WebGPU)
+ggml_add_backend(zDNN)
 ggml_add_backend(OpenCL)
 
 foreach (target ggml-base ggml)
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 6c31513750..5f02a710a1 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -49,6 +49,10 @@
 #include "ggml-webgpu.h"
 #endif
 
+#ifdef GGML_USE_ZDNN
+#include "ggml-zdnn.h"
+#endif
+
 #ifdef GGML_USE_OPENCL
 #include "ggml-opencl.h"
 #endif
@@ -180,6 +184,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_WEBGPU
         register_backend(ggml_backend_webgpu_reg());
 #endif
+#ifdef GGML_USE_ZDNN
+        register_backend(ggml_backend_zdnn_reg());
+#endif
 #ifdef GGML_USE_OPENCL
         register_backend(ggml_backend_opencl_reg());
 #endif
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index f188d1638d..ce0a3e1285 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -460,7 +460,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version.
             #       binutils must also be updated to the latest for the -march=z17 flag to work. Otherwise, use -march=arch15.
             message(STATUS "z17 target")
-            list(APPEND ARCH_FLAGS -march=z17)
+            list(APPEND ARCH_FLAGS -march=arch15)
         else()
             message(STATUS "Unknown target")
             message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.")
diff --git a/ggml/src/ggml-zdnn/CMakeLists.txt b/ggml/src/ggml-zdnn/CMakeLists.txt
new file mode 100644
index 0000000000..0a723ce4de
--- /dev/null
+++ b/ggml/src/ggml-zdnn/CMakeLists.txt
@@ -0,0 +1,36 @@
+if (DEFINED ZDNN_ROOT)
+    message(STATUS "zdnn: using ZDNN_ROOT override: ${ZDNN_ROOT}")
+    set(ZDNN_HINT "${ZDNN_ROOT}")
+else()
+    set(ZDNN_HINT "")
+endif()
+
+find_path(ZDNN_INCLUDE
+            NAMES zdnn.h
+            HINTS ${ZDNN_HINT} /usr /usr/local
+            PATH_SUFFIXES include)
+if (ZDNN_INCLUDE)
+    message(STATUS "zdnn: found include: ${ZDNN_INCLUDE}")
+else()
+    message(FATAL_ERROR "zdnn: include directory not found, please set ZDNN_ROOT to the proper path if necessary")
+endif()
+
+find_library(ZDNN_LIB
+                NAMES zdnn
+                HINTS ${ZDNN_HINT} /usr /usr/local
+                PATH_SUFFIXES lib lib64)
+if (ZDNN_LIB)
+    message(STATUS "zdnn: found library: ${ZDNN_LIB}")
+else()
+    message(FATAL_ERROR "zdnn: library not found, please set ZDNN_ROOT to the proper path if necessary")
+endif()
+
+file(GLOB GGML_SOURCES_ZDNN "*.c" "*.cpp")
+file(GLOB GGML_HEADERS_ZDNN "*.h" "*.hpp")
+
+ggml_add_backend_library(ggml-zdnn ${GGML_HEADERS_ZDNN} ${GGML_SOURCES_ZDNN})
+target_link_libraries(ggml-zdnn PRIVATE ${ZDNN_LIB})
+target_include_directories(ggml-zdnn PRIVATE ${ZDNN_INCLUDE})
+target_link_directories(ggml-zdnn PRIVATE ${ZDNN_LIB})
+
+target_compile_definitions(ggml-zdnn PRIVATE GGML_USE_ZDNN)
diff --git a/ggml/src/ggml-zdnn/ggml-zdnn-impl.h b/ggml/src/ggml-zdnn/ggml-zdnn-impl.h
new file mode 100644
index 0000000000..9dcb040fa8
--- /dev/null
+++ b/ggml/src/ggml-zdnn/ggml-zdnn-impl.h
@@ -0,0 +1,97 @@
+#ifndef GGML_ZDNN_IMPL
+#define GGML_ZDNN_IMPL
+
+#include "zdnn.h"
+#include "ggml.h"
+#include "ggml-zdnn.h"
+
+#include <vector>
+#include <memory>
+#include <vecintrin.h>
+
+#define GGML_ZDNN_NAME    "zDNN"
+#define GGML_ZDNN_VERSION ZDNN_VERNUM
+
+#define vec_neg(a)    (-(a))                // Vector Negate
+#define vec_add(a, b) ((a) + (b))           // Vector Add
+#define vec_sub(a, b) ((a) - (b))           // Vector Subtract
+#define vec_mul(a, b) ((a) * (b))           // Vector Multiply
+#define vec_div(a, b) ((a) / (b))           // Vector Divide
+#define vec_sl(a, b)  ((a) << (b))          // Vector Shift Left
+#define vec_sra(a, b) ((a) >> (b))          // Vector Shift Right
+#define vec_sr(a, b)  ((a) >> (b))          // Vector Shift Right Algebraic
+#define vec_slo(a, b) vec_slb(a, (b) << 64) // Vector Shift Left by Octet
+#define vec_sro(a, b) vec_srb(a, (b) << 64) // Vector Shift Right by Octet
+
+#ifndef vec_and
+#define vec_and(a, b) ((a) & (b)) // Vector AND
+#endif
+
+#ifndef vec_or
+#define vec_or(a, b)  ((a) | (b)) // Vector OR
+#endif
+
+#ifndef vec_xor
+#define vec_xor(a, b) ((a) ^ (b)) // Vector XOR
+#endif
+
+typedef   signed char char8x16_t  __attribute__((vector_size(16)));
+typedef unsigned char uchar8x16_t __attribute__((vector_size(16)));
+
+typedef int8_t   int8x16_t  __attribute__((vector_size(16)));
+typedef int16_t  int16x8_t  __attribute__((vector_size(16)));
+typedef int32_t  int32x4_t  __attribute__((vector_size(16)));
+typedef uint8_t  uint8x16_t __attribute__((vector_size(16)));
+typedef uint16_t uint16x8_t __attribute__((vector_size(16)));
+typedef uint32_t uint32x4_t __attribute__((vector_size(16)));
+
+typedef float float32x4_t   __attribute__((vector_size(16)));
+typedef double double64x2_t __attribute__((vector_size(16)));
+
+typedef   signed long long long64x2_t  __attribute__((vector_size(16)));
+typedef unsigned long long ulong64x2_t __attribute__((vector_size(16)));
+
+#define ZDNN_CHECK(stmt)                \
+    do {                                \
+        zdnn_status status = (stmt);    \
+        GGML_ASSERT(status == ZDNN_OK); \
+    } while (0);
+
+struct ggml_backend_zdnn_device_context {
+    int zdnn_device;
+    int zdnn_device_ref_count;
+
+    bool has_parmblkformat_0;
+    bool has_parmblkformat_1;
+
+    size_t max_size;
+
+    char name[128];
+};
+
+struct ggml_backend_zdnn_context {
+    int device;
+    ggml_cgraph * gf;
+};
+
+struct ggml_backend_zdnn_buffer {
+    void * data;
+    size_t size;
+
+    zdnn_tensor_desc pre_tfm_desc;
+    zdnn_tensor_desc tfm_desc;
+    zdnn_ztensor     ztensor;
+
+    char name[GGML_MAX_NAME];
+};
+
+struct ggml_backend_zdnn_buffer_context {
+    void * all_data;
+    size_t all_size;
+    bool owned;
+
+    int n_buffers;
+    std::vector<std::unique_ptr<ggml_backend_zdnn_buffer>> buffers;
+};
+
+#endif  // GGML_ZDNN_IMPL
diff --git a/ggml/src/ggml-zdnn/ggml-zdnn.cpp b/ggml/src/ggml-zdnn/ggml-zdnn.cpp
new file mode 100644
index 0000000000..7507a52aea
--- /dev/null
+++ b/ggml/src/ggml-zdnn/ggml-zdnn.cpp
@@ -0,0 +1,846 @@
+#include "zdnn.h"
+#include "ggml-zdnn.h"
+#include "ggml-zdnn-impl.h"
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include <vector>
+#include <memory>
+#include <csignal>
+#include <unistd.h>
+
+inline zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return FP32;
+        case GGML_TYPE_F16:
+            return FP16;
+        case GGML_TYPE_BF16:
+            return BFLOAT;
+        case GGML_TYPE_I8:
+            return INT8;
+        case GGML_TYPE_I32:
+            return INT32;
+        case GGML_TYPE_Q8_0:
+            return INT8;
+        default:
+            GGML_ABORT("%s: fatal: unable to determine zTensor data type",
+                       __func__);
+            break;
+    }
+}
+
+inline void ggml_zdnn_create_tensor(zdnn_tensor_desc  & pre_tfm_desc,
+                                    zdnn_tensor_desc  & tfm_desc,
+                                    zdnn_ztensor      & ztensor,
+                              const ggml_tensor       * src,
+                              const int64_t           * ne,
+                              const zdnn_data_layouts   layout) {
+    zdnn_init_pre_transformed_desc(
+        layout,
+        ggml_zdnn_type_mapping(src->type),
+        &pre_tfm_desc,
+        ne[3], ne[2], ne[1], ne[0]
+    );
+
+    ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
+    ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
+}
+
+inline void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor,
+                                          void * buffer) {
+    ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
+}
+
+inline void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
+    switch (tensor->op) {
+        case GGML_OP_MUL_MAT:
+            {
+                zdnn_init_pre_transformed_desc(
+                    ZDNN_2D,
+                    ggml_zdnn_type_mapping(tensor->type),
+                    &buffer->pre_tfm_desc,
+                    tensor->ne[1], tensor->ne[0]
+                );
+            } break;
+
+        default:
+            {
+                // For 4D tensors, GGML uses NCHW layout. However, because zDNN
+                // automatically transforms everything to NHWC, we will use it
+                // directly to avoid the performance penalty changing the
+                // layout and reshaping the tensor.
+                zdnn_init_pre_transformed_desc(
+                    ZDNN_NHWC,
+                    ggml_zdnn_type_mapping(tensor->type),
+                    &buffer->pre_tfm_desc,
+                    tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
+                );
+
+                // TODO: Consider adding a ggml check.
+                // TODO: If tensor = 4D, use ZDNN_NCHW by default.
+                // TODO: If tensor = 2D, use ZDNN_NHWC by default.
+            } break;
+    }
+
+    ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
+    ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
+}
+
+static void ggml_zdnn_mul_mat_op(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const enum ggml_type type = src0->type;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    const ggml_tensor * weights = src0;
+    const ggml_tensor * inputs  = src1;
+          ggml_tensor * output  = dst;
+
+    ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
+    ggml_backend_zdnn_buffer * inputs_extra  = (ggml_backend_zdnn_buffer *)inputs->extra;
+    ggml_backend_zdnn_buffer * output_extra  = (ggml_backend_zdnn_buffer *)output->extra;
+
+    zdnn_tensor_desc ptd_bias, td_bias;
+    zdnn_ztensor zt_bias;
+
+    const int64_t weights_rows = ne01;
+    const int64_t weights_cols = ne00;
+    const int64_t inputs_rows  = ne11;
+    const int64_t inputs_cols  = ne10;
+
+    assert(inputs_cols == weights_cols);
+
+    const int64_t output_rows = ne1;
+    const int64_t output_cols = ne0;
+
+    const int64_t bias_dim  [GGML_MAX_DIMS]  = { 1, 1, 1, output_cols };
+    ggml_zdnn_create_tensor(ptd_bias, td_bias, zt_bias, output, bias_dim, ZDNN_1D);
+
+    void * bias_data = (void *)calloc(ne0, ggml_element_size(output));
+    if (weights_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(weights_extra->ztensor, weights->data);
+    if (inputs_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(inputs_extra->ztensor, inputs->data);
+    ggml_zdnn_load_tensor(zt_bias, bias_data);
+
+    // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+    //               __func__, weights_extra->name,
+    //               weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
+    //               weights_extra->pre_tfm_desc.dim1,
+    //               weights_extra->pre_tfm_desc.dim2,
+    //               weights_extra->pre_tfm_desc.dim3,
+    //               weights_extra->pre_tfm_desc.dim4);
+
+    // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+    //               __func__, inputs_extra->name,
+    //               inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
+    //               inputs_extra->pre_tfm_desc.dim1,
+    //               inputs_extra->pre_tfm_desc.dim2,
+    //               inputs_extra->pre_tfm_desc.dim3,
+    //               inputs_extra->pre_tfm_desc.dim4);
+
+    GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
+    GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
+    GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1  == inputs->ne[0]  && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
+    GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2  == inputs->ne[1]  && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
+
+    ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &zt_bias,
+                                        false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
+    // TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
+    ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
+
+    ZDNN_CHECK(zdnn_free_ztensor_buffer(&zt_bias));
+    free(bias_data);
+}
+
+static void ggml_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    bool use_mul_mat_vec =
+        (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F16)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
+
+    bool use_mul_mat_vec_q =
+        ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    bool use_mul_mat_q =
+        ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    // debug helpers
+    // GGML_LOG_INFO("%s: use_mul_mat_vec   = %d\n", __func__, use_mul_mat_vec);
+    // GGML_LOG_INFO("%s: use_mul_mat_vec_q = %d\n", __func__, use_mul_mat_vec_q);
+    // GGML_LOG_INFO("%s: use_mul_mat_q     = %d\n", __func__, use_mul_mat_q);
+    // GGML_LOG_INFO("%s: src0: %8d %8d %8d %8d\n", __func__, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
+    // GGML_LOG_INFO("%s:       %8d %8d %8d %8d\n", __func__, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
+    // GGML_LOG_INFO("%s: src1: %8d %8d %8d %8d\n", __func__, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
+    // GGML_LOG_INFO("%s:       %8d %8d %8d %8d\n", __func__, src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
+    // GGML_LOG_INFO("%s: src0 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
+    // GGML_LOG_INFO("%s: src1 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
+
+    if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16
+        && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)
+        && src1->ne[2] * src1->ne[3] > 1) {
+        // general KQ + KQV multi-batch
+        GGML_LOG_INFO("%s: using zdnn_mul_mat_batched for KQ + KQV multi-batch\n", __func__);
+        // ggml_zdnn_mul_mat_batched(ctx, src0, src1, dst);
+    } else if (use_mul_mat_vec) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec for vector multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec, nullptr);
+    } else if (use_mul_mat_vec_q) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec_q for quantized vector multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec_q, ggml_zdnn_quantize_row_q8_1);
+    } else if (use_mul_mat_q) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_q for quantized matrix multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_q, ggml_zdnn_quantize_mmq_q8_1);
+    } else {
+        // GGML_LOG_INFO("%s: using zdnn_op_mul_mat for general matrix multiplication\n", __func__);
+        ggml_zdnn_mul_mat_op(ctx, src0, src1, dst);
+    }
+}
+
+static bool ggml_zdnn_compute_forward(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) {
+    switch (dst->op) {
+        case GGML_OP_MUL_MAT:
+            ggml_zdnn_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], dst);
+            break;
+
+        default:
+            return false;
+    }
+
+    return true;
+}
+
+static enum ggml_status ggml_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * gf) {
+    ggml_backend_zdnn_context        * ctx     = (       ggml_backend_zdnn_context *)backend->context;
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)backend->device->context;
+
+    ctx->gf = gf;
+    for (int i = 0; i < gf->n_nodes; i++) {
+        ggml_tensor * node = gf->nodes[i];
+
+        if (ggml_is_empty(node)
+            || node->op == GGML_OP_NONE
+            || node->op == GGML_OP_RESHAPE
+            || node->op == GGML_OP_VIEW
+            || node->op == GGML_OP_PERMUTE
+            || node->op == GGML_OP_TRANSPOSE) {
+            continue;
+        }
+
+        bool ok = ggml_zdnn_compute_forward(ctx, node);
+        if (!ok) {
+            GGML_LOG_ERROR("%s: unsupported op %s (%s)\n",
+                           __func__, node->name, ggml_op_name(node->op));
+        }
+
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+static bool ggml_zdnn_supports_op(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_PERMUTE:
+            return true;
+
+        case GGML_OP_MUL_MAT:
+            {
+                const ggml_tensor * src0 = op->src[0];
+                const ggml_tensor * src1 = op->src[1];
+
+                const int64_t ne10 = src1->ne[0];
+                const int64_t ne0 = op->ne[0];
+                const int64_t ne1 = op->ne[1];
+
+                const int64_t max_batch = ctx_dev->max_size;
+
+                return ggml_is_matrix(src0) &&
+                       ggml_is_matrix(src1) &&
+                       ggml_is_contiguous(src0) &&
+                       ggml_is_contiguous(src1) &&
+                       src0->view_src == nullptr && src1->view_src == nullptr &&
+                       src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 &&
+                       (ne0 <= max_batch && ne1 <= max_batch && ne10 <= max_batch);
+            } break;
+
+        default:
+            return false;
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+//
+// globals
+//
+
+// initialised in ggml_backend_zdnn_reg
+static ggml_backend_reg    g_ggml_backend_zdnn_reg;
+static ggml_backend_device g_ggml_backend_zdnn_device;
+
+static ggml_backend_zdnn_device_context g_ggml_ctx_dev_main = {
+    /* .zdnn_device           = */ 0,
+    /* .zdnn_device_ref_count = */ 0,
+    /* .has_parmblkformat_0   = */ false,
+    /* .has_parmblkformat_1   = */ false,
+    /* .max_size              = */ 0,
+    /* .name                  = */ "",
+};
+
+static int ggml_backend_zdnn_device_acq(ggml_backend_zdnn_device_context * ctx) {
+    assert(ctx != NULL);
+
+    if (ctx->zdnn_device == 0) {
+        ctx->zdnn_device = 1;
+    }
+
+    if (ctx->zdnn_device >= 1) {
+        ctx->has_parmblkformat_0 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0);
+        ctx->has_parmblkformat_1 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1);
+        ctx->max_size = zdnn_get_nnpa_max_dim_idx_size();
+        strncpy(ctx->name, GGML_ZDNN_NAME, sizeof(ctx->name) - 1);
+    }
+
+    ctx->zdnn_device_ref_count++;
+    return ctx->zdnn_device;
+}
+
+static void ggml_backend_zdnn_device_rel(ggml_backend_zdnn_device_context * ctx) {
+    assert(ctx != NULL);
+    assert(ctx->zdnn_device_ref_count > 0);
+
+    ctx->zdnn_device_ref_count--;
+    if (ctx->zdnn_device_ref_count == 0) {
+        if (ctx->zdnn_device >= 0) {
+            ctx->zdnn_device = 0;
+        }
+    }
+}
+
+static ggml_backend_zdnn_context * ggml_zdnn_init(ggml_backend_dev_t dev) {
+    GGML_LOG_INFO("%s: allocating\n", __func__);
+    GGML_LOG_INFO("%s: found 1 device\n", __func__);
+
+    #ifdef STATIC_LIB
+    zdnn_init();
+    #endif
+
+    ggml_backend_zdnn_context * ctx = new ggml_backend_zdnn_context();
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context;
+
+    int device = 1;
+    GGML_LOG_INFO("%s: picking default device: %s\n", __func__, ctx_dev->name);
+
+    ctx->device = device;
+    GGML_LOG_INFO("%s: NNPA name: %s\n", __func__, ctx_dev->name);
+    GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_0 = %s\n", __func__, ctx_dev->has_parmblkformat_0 ? "true" : "false");
+    GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_1 = %s\n", __func__, ctx_dev->has_parmblkformat_1 ? "true" : "false");
+
+    ctx->gf = nullptr;
+
+    return ctx;
+}
+
+static void ggml_zdnn_free(ggml_backend_zdnn_context * ctx) {
+    GGML_LOG_INFO("%s: deallocating\n", __func__);
+    delete ctx;
+}
+
+//
+// backend interface
+//
+
+static void ggml_backend_zdnn_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    for (int i = 0; i < ctx->n_buffers; i++) {
+        if (ctx->buffers[i]->ztensor.buffer != NULL && ctx->buffers[i]->ztensor.is_transformed) {
+            ZDNN_CHECK(zdnn_free_ztensor_buffer(&ctx->buffers[i]->ztensor));
+        }
+    }
+
+    delete ctx;
+}
+
+static void * ggml_backend_zdnn_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+    return ctx->all_data;
+}
+
+static enum ggml_status ggml_backend_zdnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    if (tensor->view_src != NULL) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
+        return GGML_STATUS_SUCCESS;
+    }
+
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    const int64_t tsize = ggml_nbytes(tensor);
+    int buffer_idx = ctx->n_buffers;
+
+    std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+    zdnn_buffer->data = tensor->data;
+    zdnn_buffer->size = tsize;
+    strncpy(zdnn_buffer->name, tensor->name, GGML_MAX_NAME - 1);
+
+    ggml_zdnn_init_tensor(zdnn_buffer.get(), tensor);
+    tensor->extra = zdnn_buffer.get();
+
+    ctx->buffers.push_back(std::move(zdnn_buffer));
+    ctx->n_buffers++;
+
+    // GGML_LOG_INFO("%s: initialised tensor '%s' in buffer %d, size = %8.2f MiB\n",
+    //               __func__, tensor->name, buffer_idx, tsize);
+
+    return GGML_STATUS_SUCCESS;
+}
+
+static void ggml_backend_zdnn_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    memset((char *)tensor->data + offset, value, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    memcpy((char *)tensor->data + offset, data, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    memcpy(data, (const char *)tensor->data + offset, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    memset(ctx->all_data, value, ctx->all_size);
+}
+
+static ggml_backend_buffer_i ggml_backend_zdnn_buffer_i = {
+    /* .free_buffer   = */ ggml_backend_zdnn_buffer_free_buffer,
+    /* .get_base      = */ ggml_backend_zdnn_buffer_get_base,
+    /* .init_tensor   = */ ggml_backend_zdnn_buffer_init_tensor,
+    /* .memset_tensor = */ ggml_backend_zdnn_buffer_memset_tensor,
+    /* .set_tensor    = */ ggml_backend_zdnn_buffer_set_tensor,
+    /* .get_tensor    = */ ggml_backend_zdnn_buffer_get_tensor,
+    /* .cpy_tensor    = */ NULL,
+    /* .clear         = */ ggml_backend_zdnn_buffer_clear,
+    /* .reset         = */ NULL,
+};
+
+//
+// default buffer type
+//
+
+static const char * ggml_backend_zdnn_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_zdnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context();
+
+    const size_t size_page = sysconf(_SC_PAGESIZE);
+
+    size_t size_aligned = size;
+    if ((size_aligned % size_page) != 0) {
+        size_aligned += size_page - (size_aligned % size_page);
+    }
+
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)buft->device->context;
+
+    GGML_ASSERT(ctx_dev->zdnn_device >= 0);
+    int device = ctx_dev->zdnn_device; GGML_UNUSED(device);
+
+    ctx->all_data  = ggml_aligned_malloc(size_aligned);
+    ctx->all_size  = size_aligned;
+    ctx->owned     = true;
+    ctx->n_buffers = 1;
+
+    if (ctx->all_data != NULL) {
+        std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+        zdnn_buffer->data = ctx->all_data;
+        zdnn_buffer->size = size_aligned;
+        ctx->buffers.push_back(std::move(zdnn_buffer));
+    }
+
+    if (size_aligned > 0 && (ctx->all_data == NULL)) {
+        GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f\n",
+                       __func__, size_aligned / 1024.0 / 1024.0);
+        delete ctx;
+        return NULL;
+    }
+
+    return ggml_backend_buffer_init(buft, ggml_backend_zdnn_buffer_i, ctx, size);
+}
+
+static size_t ggml_backend_zdnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 256;
+
+    GGML_UNUSED(buft);
+}
+
+static bool ggml_backend_zdnn_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return true;
+
+    GGML_UNUSED(buft);
+}
+
+ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_type_zdnn = {
+        /* .iface   = */ {
+            /* .get_name       = */ ggml_backend_zdnn_buffer_type_get_name,
+            /* .alloc_buffer   = */ ggml_backend_zdnn_buffer_type_alloc_buffer,
+            /* .get_alignment  = */ ggml_backend_zdnn_buffer_type_get_alignment,
+            /* .get_max_size   = */ NULL,
+            /* .get_alloc_size = */ NULL,  // defaults to ggml_nbytes
+            /* .is_host        = */ ggml_backend_zdnn_buffer_type_is_host,
+        },
+        /* .device  = */ &g_ggml_backend_zdnn_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_type_zdnn;
+}
+
+static const char * ggml_backend_zdnn_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
+    return GGML_ZDNN_NAME "_Mapped";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_from_ptr_type(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_zdnn = {
+        /* .iface = */ {
+            /* .get_name       = */ ggml_backend_zdnn_buffer_from_ptr_type_get_name,
+            /* .alloc_buffer   = */ ggml_backend_zdnn_buffer_type_alloc_buffer,
+            /* .get_alignment  = */ ggml_backend_zdnn_buffer_type_get_alignment,
+            /* .get_max_size   = */ NULL,
+            /* .get_alloc_size = */ NULL,  // defaults to ggml_nbytes
+            /* .is_host        = */ ggml_backend_zdnn_buffer_type_is_host,
+        },
+        /* .device  = */ &g_ggml_backend_zdnn_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_from_ptr_type_zdnn;
+}
+
+//
+// backend
+//
+
+static const char * ggml_backend_zdnn_name(ggml_backend_t backend) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_zdnn_free(ggml_backend_t backend) {
+    ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend->context;
+
+    ggml_zdnn_free(ctx);
+    free(backend);
+}
+
+static enum ggml_status ggml_backend_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    return ggml_zdnn_graph_compute(backend, cgraph);
+}
+
+static ggml_backend_i ggml_backend_zdnn_i = {
+    /* .get_name           = */ ggml_backend_zdnn_name,
+    /* .free               = */ ggml_backend_zdnn_free,
+    /* .set_tensor_async   = */ NULL,
+    /* .get_tensor_async   = */ NULL,
+    /* .cpy_tensor_async   = */ NULL,
+    /* .synchronize        = */ NULL,
+    /* .graph_plan_create  = */ NULL,
+    /* .graph_plan_free    = */ NULL,
+    /* .graph_plan_update  = */ NULL,
+    /* .graph_plan_compute = */ NULL,
+    /* .graph_compute      = */ ggml_backend_zdnn_graph_compute,
+    /* .event_record       = */ NULL,
+    /* .event_wait         = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_zdnn_guid(void) {
+    static const char * guid_str = "IBM-ZDNN-ACCELER";
+    return reinterpret_cast<ggml_guid_t>((void *)guid_str);
+}
+
+// TODO: remove in the future
+ggml_backend_t ggml_backend_zdnn_init(void) {
+    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_zdnn_reg(), 0);
+
+    ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend_t)malloc(sizeof(ggml_backend));
+    *backend = (ggml_backend) {
+        /* .guid       = */ ggml_backend_zdnn_guid(),
+        /* .iface      = */ ggml_backend_zdnn_i,
+        /* .device     = */ dev,
+        /* .context    = */ ctx,
+    };
+
+    return backend;
+}
+
+bool ggml_backend_is_zdnn(ggml_backend_t backend) {
+    return backend != NULL &&
+           ggml_guid_matches(backend->guid, ggml_backend_zdnn_guid());
+
+    GGML_UNUSED(backend);
+}
+
+//
+// backend device
+//
+
+static const char * ggml_backend_zdnn_device_get_name(ggml_backend_dev_t dev) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(dev);
+}
+
+static const char * ggml_backend_zdnn_device_get_description(ggml_backend_dev_t dev) {
+    return "IBM Z Neural Network Processing Assist (NNPA)";
+}
+
+static void ggml_backend_zdnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    *free  = 0;
+    *total = 0;
+}
+
+static enum ggml_backend_dev_type ggml_backend_zdnn_device_get_type(ggml_backend_dev_t dev) {
+    return GGML_BACKEND_DEVICE_TYPE_ACCEL;
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zdnn_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_zdnn_device_get_name(dev);
+    props->description = ggml_backend_zdnn_device_get_description(dev);
+    props->type        = ggml_backend_zdnn_device_get_type(dev);
+    ggml_backend_zdnn_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = (ggml_backend_dev_caps) {
+        /* .async                = */ false,
+        /* .host_buffer          = */ false,
+        /* .buffer_from_host_ptr = */ true,
+        /* .events               = */ false,
+    };
+}
+
+static ggml_backend_t ggml_backend_zdnn_device_init(ggml_backend_dev_t dev, const char * params) {
+    ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend *)malloc(sizeof(ggml_backend));
+    *backend = (ggml_backend) {
+        /* .guid       = */ ggml_backend_zdnn_guid(),
+        /* .iface      = */ ggml_backend_zdnn_i,
+        /* .device     = */ dev,
+        /* .context    = */ ctx,
+    };
+
+    return backend;
+
+    GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zdnn_device_get_buffer_type(ggml_backend_dev_t dev) {
+    return ggml_backend_zdnn_buffer_type();
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_zdnn_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+    ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context();
+
+    ctx->all_data  = ptr;
+    ctx->all_size  = size;
+    ctx->owned     = false;
+    ctx->n_buffers = 0;
+
+    const size_t size_page = sysconf(_SC_PAGESIZE);
+
+    // page-align the data ptr
+    {
+        const uintptr_t offs = (uintptr_t) ptr % size_page;
+        ptr  = (void *)((char *)ptr - offs);
+        size += offs;
+    }
+
+    size_t size_aligned = size;
+    if ((size_aligned % size_page) != 0) {
+        size_aligned += size_page - (size_aligned % size_page);
+    }
+
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context;
+
+    GGML_ASSERT(ctx_dev->zdnn_device >= 0);
+    int device = ctx_dev->zdnn_device; GGML_UNUSED(device);
+
+    std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+    zdnn_buffer->data = ptr;
+    zdnn_buffer->size = size;
+    ctx->buffers.push_back(std::move(zdnn_buffer));
+
+    GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB\n",
+                  __func__, size_aligned / 1024.0 / 1024.0);
+
+    ++ctx->n_buffers;
+
+    return ggml_backend_buffer_init(ggml_backend_zdnn_buffer_from_ptr_type(), ggml_backend_zdnn_buffer_i, ctx, size);
+}
+
+static bool ggml_backend_zdnn_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *) dev->context;
+
+    return ggml_zdnn_supports_op(ctx_dev, op);
+}
+
+static bool ggml_backend_zdnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    return
+        buft->iface.get_name == ggml_backend_zdnn_buffer_type_get_name ||
+        buft->iface.get_name == ggml_backend_zdnn_buffer_from_ptr_type_get_name;
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_device_i ggml_backend_zdnn_device_i = {
+    /* .get_name             = */ ggml_backend_zdnn_device_get_name,
+    /* .get_description      = */ ggml_backend_zdnn_device_get_description,
+    /* .get_memory           = */ ggml_backend_zdnn_device_get_memory,
+    /* .get_type             = */ ggml_backend_zdnn_device_get_type,
+    /* .get_props            = */ ggml_backend_zdnn_device_get_props,
+    /* .init_backend         = */ ggml_backend_zdnn_device_init,
+    /* .get_buffer_type      = */ ggml_backend_zdnn_device_get_buffer_type,
+    /* .get_host_buffer_type = */ NULL,
+    /* .buffer_from_host_ptr = */ ggml_backend_zdnn_device_buffer_from_ptr,
+    /* .supports_op          = */ ggml_backend_zdnn_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_zdnn_device_supports_buft,
+    /* .offload_op           = */ NULL,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
+};
+
+//
+// backend registry
+//
+
+static const char * ggml_backend_zdnn_reg_get_name(ggml_backend_reg_t reg) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_zdnn_reg_device_count(ggml_backend_reg_t reg) {
+    if (!zdnn_is_nnpa_installed()) {
+        return 0;
+    }
+    return 1;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_zdnn_reg_device_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+
+    return &g_ggml_backend_zdnn_device;
+
+    GGML_UNUSED(reg);
+    GGML_UNUSED(index);
+}
+
+static ggml_backend_feature g_ggml_backend_zdnn_features[] = {
+    { "NNPA", zdnn_is_nnpa_installed() ? "1" : "0" },
+    { "NNPA_PARMBLKFORMAT_0", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0) ? "1" : "0" },
+    { "NNPA_PARMBLKFORMAT_1", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1) ? "1" : "0" },
+    { NULL, NULL },
+};
+
+static ggml_backend_feature * ggml_backend_zdnn_get_features(ggml_backend_reg_t reg) {
+    return g_ggml_backend_zdnn_features;
+
+    GGML_UNUSED(reg);
+}
+
+static void * ggml_backend_zdnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    if (strcmp(name, "ggml_backend_get_features") == 0) {
+        return (void *) ggml_backend_zdnn_get_features;
+    }
+
+    return NULL;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_reg_i ggml_backend_zdnn_reg_i = {
+    /* .get_name         = */ ggml_backend_zdnn_reg_get_name,
+    /* .get_device_count = */ ggml_backend_zdnn_reg_device_count,
+    /* .get_device       = */ ggml_backend_zdnn_reg_device_get,
+    /* .get_proc_address = */ ggml_backend_zdnn_get_proc_address,
+};
+
+static void ggml_zdnn_cleanup(void) {
+    ggml_backend_zdnn_device_rel(&g_ggml_ctx_dev_main);
+}
+
+// TODO: make thread-safe
+ggml_backend_reg_t ggml_backend_zdnn_reg(void) {
+    ggml_backend_zdnn_device_acq(&g_ggml_ctx_dev_main);
+
+    // register cleanup callback
+    atexit(ggml_zdnn_cleanup);
+
+    {
+        g_ggml_backend_zdnn_reg = (ggml_backend_reg) {
+            /* .api_version = */ GGML_ZDNN_VERSION,
+            /* .iface       = */ ggml_backend_zdnn_reg_i,
+            /* .context     = */ NULL,
+        };
+
+        g_ggml_backend_zdnn_device = (ggml_backend_device) {
+            /* .iface       = */ ggml_backend_zdnn_device_i,
+            /* .reg         = */ &g_ggml_backend_zdnn_reg,
+            /* .context     = */ &g_ggml_ctx_dev_main,
+        };
+
+        return &g_ggml_backend_zdnn_reg;
+    }
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_zdnn_reg)

From db3010bd23980bad5f5d93ec3e6757ec531a413b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 15 Aug 2025 16:28:28 +0300
Subject: [PATCH 078/287] vulkan : fix compile warnings on macos (#15340)

ggml-ci
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index b26801826f..c5354293aa 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -8360,7 +8360,7 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, cons
         (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
         freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale,
         src2 != nullptr, (uint32_t)src0->ne[2], s1, s2,
-        sections[0], sections[1], sections[2], sections[3], backprop
+        { sections[0], sections[1], sections[2], sections[3] }, backprop
     }, dryrun);
 }
 
@@ -9627,7 +9627,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     default:
         std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
         GGML_ABORT("fatal error");
-        return false;
     }
 
     vk_context compute_ctx;
@@ -10912,7 +10911,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 default:
                     return false;
             }
-            break;
         case GGML_OP_GLU:
             switch (ggml_get_glu_op(op)) {
                 case GGML_GLU_OP_GEGLU:
@@ -10928,7 +10926,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 default:
                     return false;
             }
-            break;
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             {
@@ -10992,7 +10989,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 }
 
                 return true;
-            } break;
+            }
         case GGML_OP_FLASH_ATTN_EXT:
             {
                 ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
@@ -11082,7 +11079,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     default:
                         return false;
                 }
-            } break;
+            }
         case GGML_OP_SET_ROWS:
             {
                 switch (op->type) {
@@ -11099,7 +11096,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     default:
                         return false;
                 }
-            } break;
+            }
         case GGML_OP_CONT:
         case GGML_OP_CPY:
         case GGML_OP_DUP:
@@ -11151,7 +11148,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     return true;
                 }
                 return false;
-            } break;
+            }
         case GGML_OP_REPEAT:
             return ggml_type_size(op->type) == sizeof(float) && ggml_type_size(op->src[0]->type) == sizeof(float);
         case GGML_OP_REPEAT_BACK:

From 5edf1592fdb9131d01321aeef4241c6a34969e27 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 15 Aug 2025 17:16:36 +0300
Subject: [PATCH 079/287] vulkan : fix out-of-bounds access in argmax kernel
 (#15342)

ggml-ci
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp            |  2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp | 17 +++++++++++++----
 tests/test-backend-ops.cpp                      |  1 +
 3 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c5354293aa..76a0cfa4f0 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -8392,7 +8392,7 @@ static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx,
 }
 
 static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGMAX, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGMAX, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }, dryrun);
 }
 
 static void ggml_vk_count_equal(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp b/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp
index eaf4da341e..a1d4c240dd 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp
@@ -5,6 +5,8 @@
 
 #extension GL_EXT_control_flow_attributes : enable
 
+#define FLT_MAX 3.402823466e+38F
+
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
@@ -19,19 +21,26 @@ void main() {
     const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
     const uint col = gl_LocalInvocationID.x;
 
-    if (col >= p.KX) {
+    if (row >= p.KY) {
         return;
     }
-    A_TYPE amax = data_a[row*p.KX + col];
-    tmp[col] = col;
+
+    A_TYPE amax = -FLT_MAX;
+    uint acol = col;
+
+    if (col < p.KX) {
+        amax = data_a[row*p.KX + col];
+    }
 
     for (uint i = col + BLOCK_SIZE; i < p.KX; i += BLOCK_SIZE) {
         A_TYPE val = data_a[row*p.KX + i];
         if (val > amax) {
             amax = val;
-            tmp[col] = i;
+            acol = i;
         }
     }
+
+    tmp[col] = acol;
     tmpmax[col] = amax;
 
     barrier();
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index cc9c3a0d57..f4565f9b71 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5528,6 +5528,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
 
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32,    1, 1, 1}));
+    test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32,  513, 1, 1}));
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {100,  10, 1, 1}));
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));
     test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 12, 1, 1}));

From e2c1bfff5305c661ac53e9d57cb732ff626a2242 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Sat, 16 Aug 2025 00:52:14 +0800
Subject: [PATCH 080/287] opencl: add initial mxfp4 support via mv (#15270)

* opencl: add reference `mul_mv_mxfp4_f32`

* opencl: add reference `mul_mv_id` for mxfp4

* Q4_0 tranpose fix for Adreno

---------

Co-authored-by: shawngu-quic <shawngu@qti.qualcomm.com>
---
 ggml/src/ggml-opencl/CMakeLists.txt           |   2 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 147 +++++++++++++-
 .../kernels/mul_mv_id_mxfp4_f32.cl            | 189 ++++++++++++++++++
 .../ggml-opencl/kernels/mul_mv_mxfp4_f32.cl   | 144 +++++++++++++
 ggml/src/ggml-opencl/kernels/transpose.cl     |  20 ++
 5 files changed, 496 insertions(+), 6 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_id_mxfp4_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index d8290faa46..015821f34f 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -82,7 +82,9 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_1d_8x_flat
     mul_mv_q4_0_f32_1d_16x_flat
     mul_mv_q6_k
+    mul_mv_mxfp4_f32
     mul_mv_id_q4_0_f32_8x_flat
+    mul_mv_id_mxfp4_f32
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
     mul
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index fc838684ac..b8cbf6cf0d 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -365,6 +365,7 @@ struct ggml_backend_opencl_context {
     cl_program program_mul_mv_q4_0_f32_1d_8x_flat;
     cl_program program_mul_mv_q4_0_f32_1d_16x_flat;
     cl_program program_mul_mv_q6_K;
+    cl_program program_mul_mv_mxfp4_f32;
     cl_program program_mul_mv_f16_f16;
     cl_program program_mul_mv_f16_f32_1row;
     cl_program program_mul_mv_f16_f32_l4;
@@ -398,6 +399,7 @@ struct ggml_backend_opencl_context {
     cl_program program_conv_2d_f16_f32;
     cl_program program_tsembd;
     cl_program program_mul_mv_id_q4_0_f32_8x_flat;
+    cl_program program_mul_mv_id_mxfp4_f32;
     cl_program program_mul_mm_f32_f32_l4_lm;
     cl_program program_mul_mm_f16_f32_l4_lm;
 
@@ -439,6 +441,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_convert_block_q4_0_noshuffle;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
     cl_kernel kernel_mul_mv_q6_K_f32;
+    cl_kernel kernel_mul_mv_mxfp4_f32;
     cl_kernel kernel_im2col_f32, kernel_im2col_f16;
     cl_kernel kernel_argsort_f32_i32;
     cl_kernel kernel_sum_rows_f32;
@@ -455,6 +458,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_conv_2d_f16_f32;
     cl_kernel kernel_timestep_embedding;
     cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
+    cl_kernel kernel_mul_mv_id_mxfp4_f32;
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
 
@@ -577,6 +581,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_transpose_32;
     cl_kernel kernel_transpose_32_16;
     cl_kernel kernel_transpose_16;
+    cl_kernel kernel_transpose_16_4x1;
 
     cl_mem A_s_d_max;            // max scale buffer size for transpose
     cl_mem A_q_d_max;            // max weight buffer size for transpose
@@ -971,6 +976,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_mxfp4_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_mxfp4_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_mxfp4_f32.cl");
+#endif
+        backend_ctx->program_mul_mv_mxfp4_f32 =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32, "kernel_mul_mv_mxfp4_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_f16_f16
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1611,6 +1632,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_id_mxfp4_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_id_mxfp4_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32.cl");
+#endif
+        backend_ctx->program_mul_mv_id_mxfp4_f32 =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32, "kernel_mul_mv_id_mxfp4_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // Adreno kernels
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     // transpose
@@ -1628,6 +1665,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err));
         CL_CHECK((backend_ctx->kernel_transpose_32    = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err));
         CL_CHECK((backend_ctx->kernel_transpose_16    = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16", &err), err));
+        CL_CHECK((backend_ctx->kernel_transpose_16_4x1    = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_4x1", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -2552,13 +2590,14 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return true;
             } else if (op->src[0]->type == GGML_TYPE_F32) {
                 return op->src[1]->type == GGML_TYPE_F32;
-            } else if (op->src[0]->type == GGML_TYPE_Q4_0 ||
+            } else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_MXFP4 ||
                        op->src[0]->type == GGML_TYPE_Q6_K) {
                 return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
             }
             return false;
         case GGML_OP_MUL_MAT_ID:
-            if (op->src[0]->type == GGML_TYPE_Q4_0) {
+            if (op->src[0]->type == GGML_TYPE_Q4_0 ||
+                op->src[0]->type == GGML_TYPE_MXFP4) {
                 if (op->src[1]->type == GGML_TYPE_F32) {
                     return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
                 }
@@ -2944,7 +2983,10 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         // cl_mem qT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, q_size_bytes, NULL, &err);
         CL_CHECK(err);
 
-        // size_t d_size_bytes = M * (K / 32) / 2 * sizeof(float);
+        bool K_tile_trans = true;
+        if ((K / 32) % 4 != 0){
+            K_tile_trans =false;
+        }
         size_t d_size_bytes = M * (K / 32) * 2;
         region.origin = 0;
         region.size = d_size_bytes;
@@ -2985,10 +3027,15 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         qT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
         CL_CHECK(err);
 
-        img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
         memset(&img_desc_1d, 0, sizeof(img_desc_1d));
+        if (K_tile_trans) {
+            img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
+            img_desc_1d.image_width = M * K / 32 / 4;
+        } else {
+            img_fmt_1d = { CL_R, CL_HALF_FLOAT };
+            img_desc_1d.image_width = M * K / 32;
+        }
         img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.image_width = M * K / 32 / 4;
         img_desc_1d.buffer = extra->d;
         d_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
         CL_CHECK(err);
@@ -3024,6 +3071,10 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         int width_s = K / 32 / 4;
 
         kernel = backend_ctx->kernel_transpose_16;
+        if (!K_tile_trans) {
+            kernel = backend_ctx->kernel_transpose_16_4x1;
+            width_s = K / 32;
+        }
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &d_d_image1D));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &dT_d_image1D));
         CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_s));
@@ -6254,11 +6305,47 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
             CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
             break;
+        case GGML_TYPE_MXFP4: {
+            kernel = backend_ctx->kernel_mul_mv_mxfp4_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 2;
+                ndst = nth1*2;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 2;
+                ndst = nth1*2;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r3));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(float)*nth0,nullptr));
+            break;
+        }
         default:
             GGML_ASSERT(false && "not implemented");
     }
 
-    if (src0t == GGML_TYPE_Q4_0 ||
+    if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_MXFP4 ||
         src0t == GGML_TYPE_Q4_1 ||
         src0t == GGML_TYPE_Q8_0 ||
         src0t == GGML_TYPE_Q2_K) {
@@ -6307,10 +6394,12 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
     ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
     ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
     ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
 
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
     cl_ulong offset1 = extra1->offset + src1->view_offs;
     cl_ulong offset2 = extra2->offset + src2->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
@@ -6325,7 +6414,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
     const int ne03 = src0->ne[3];
 
     const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
     const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
 
     const int ne10 = src1->ne[0];
     const int ne11 = src1->ne[1];
@@ -6334,6 +6425,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
     const cl_ulong nb11 = src1->nb[1];
     const cl_ulong nb12 = src1->nb[2];
+    const cl_ulong nb13 = src1->nb[3];
 
     const int ne20 = src2->ne[0];
     const int ne21 = src2->ne[1];
@@ -6401,6 +6493,49 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
             break;
         }
+        case GGML_TYPE_MXFP4: {
+            kernel = backend_ctx->kernel_mul_mv_id_mxfp4_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                sgs  = 16;
+                nsg  = 2;
+                ndst = 2;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                sgs  = 64;
+                nsg  = 2;
+                ndst = 2;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne20));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne21));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb21));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
+            CL_CHECK(clSetKernelArg(kernel, 24, sizeof(float)*sgs,nullptr));
+
+            break;
+        }
         default:
             GGML_ASSERT(false && "not implemented");;
     }
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_id_mxfp4_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_id_mxfp4_f32.cl
new file mode 100644
index 0000000000..d50bd1fc42
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_id_mxfp4_f32.cl
@@ -0,0 +1,189 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK_MXFP4 32
+typedef struct {
+    uchar e; // E8M0
+    uchar qs[QK_MXFP4/2];
+} block_mxfp4;
+
+constant static float kvalues_mxfp4_f[16] = {
+    0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
+};
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint) x << 23;
+    }
+
+    return as_float(bits);
+}
+
+#ifdef INTEL_GPU
+#define N_R0_MXFP4 2 // number of rows each subgroup works on
+#define N_SG_MXFP4 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_MXFP4 2
+#define N_SG_MXFP4 2
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_mv_mxfp4_f32(
+    global char * src0,
+    global char * src1,
+    global char * dst,
+    int ne00,
+    ulong nb01,
+    ulong nb02,
+    ulong nb03,
+    int ne12,
+    ulong nb11,
+    ulong nb12,
+    ulong nb13,
+    int ne0,
+    int ne1,
+    int r2,
+    int r3,
+    local  char * shmem
+) {
+    local float * shmem_f32 = (local float *) shmem;
+    int nb = ne00/QK_MXFP4;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = 0;
+
+    int first_row = (r0 * N_SG_MXFP4 + get_sub_group_id()) * N_R0_MXFP4;
+
+    uint i12 = im%ne12;
+    uint i13 = im/ne12;
+
+    ulong offset_src0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    ulong offset_src1 =        r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global block_mxfp4 * x = (global block_mxfp4 *) (src0 + offset_src0);
+    global float       * y = (global float       *) (src1 + offset_src1);
+
+    const short ix = get_sub_group_local_id()/2;  // 0...15
+    const short it = get_sub_group_local_id()%2;  // 0 or 1
+
+    shmem_f32[get_sub_group_local_id()] = kvalues_mxfp4_f[get_sub_group_local_id()%16];
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    float4 yl[4];
+    float sumf[N_R0_MXFP4] = {0.f};
+
+    global float * yb = y + ix * QK_MXFP4 + it * 8;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        global float4 * y4 = (global float4 *)yb;
+        yl[0] = y4[0];
+        yl[1] = y4[4];
+        yl[2] = y4[1];
+        yl[3] = y4[5];
+
+        for (short row = 0; row < N_R0_MXFP4; row++) {
+            global block_mxfp4 * xb = x + row*nb + ib;
+            global uchar       * q2 = (global uchar *)(xb->qs + 8*it);
+
+            float4 acc1 = yl[0]*(float4)(shmem_f32[q2[0] &  0x0F], shmem_f32[q2[1] &  0x0F], shmem_f32[q2[2] &  0x0F], shmem_f32[q2[3] &  0x0F]);
+            float4 acc2 = yl[1]*(float4)(shmem_f32[q2[0] >> 4   ], shmem_f32[q2[1] >> 4   ], shmem_f32[q2[2] >> 4   ], shmem_f32[q2[3] >> 4   ]);
+            float4 acc3 = yl[2]*(float4)(shmem_f32[q2[4] &  0x0F], shmem_f32[q2[5] &  0x0F], shmem_f32[q2[6] &  0x0F], shmem_f32[q2[7] &  0x0F]);
+            float4 acc4 = yl[3]*(float4)(shmem_f32[q2[4] >> 4   ], shmem_f32[q2[5] >> 4   ], shmem_f32[q2[6] >> 4   ], shmem_f32[q2[7] >> 4   ]);
+
+            acc1 = (acc1 + acc3) + (acc2 + acc4);
+
+            sumf[row] += e8m0_to_fp32(xb->e) * ((acc1.s0 + acc1.s1) + (acc1.s2 + acc1.s3));
+        }
+
+        yb += (N_SIMDWIDTH/2) * QK_MXFP4;
+    }
+
+    global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
+
+    for (int row = 0; row < N_R0_MXFP4 && first_row + row < ne0; ++row) {
+        float sum_all = sub_group_reduce_add(sumf[row]);
+        if (get_sub_group_local_id() == 0) {
+            dst_f32[first_row + row] = sum_all;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_id_mxfp4_f32(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * src2,
+    ulong         offset2,
+    global char * dst,
+    ulong         offsetd,
+    int           ne00,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb03,
+    int           ne11,
+    int           ne12,
+    ulong         nb11,
+    ulong         nb12,
+    ulong         nb13,
+    int           ne20,
+    int           ne21,
+    ulong         nb21,
+    int           ne0,
+    int           ne1,
+    int           r2,
+    int           r3,
+    local  char * shmem
+) {
+    src0 = (global char *)((global char *)src0 + offset0);
+    src1 = (global char *)((global char *)src1 + offset1);
+    src2 = (global char *)((global char *)src2 + offset2);
+    dst  = (global char *)((global char *)dst  + offsetd);
+
+    const int iid1 = get_group_id(2)/ne20;
+    const int idx  = get_group_id(2)%ne20;
+
+    int i02 = ((global int *) (src2 + iid1*nb21))[idx];
+
+    int i11 = idx % ne11;
+    int i12 = iid1;
+
+    int i1 = idx;
+    int i2 = i12;
+
+    global char * src0_cur = src0 + i02*nb02;
+    global char * src1_cur = src1 + i11*nb11 + i12*nb12;
+
+    global char * dst_cur = dst + (i1*ne0 + i2*ne1*ne0)*sizeof(float);
+
+    mul_mv_mxfp4_f32(src0_cur, src1_cur, dst_cur,
+        ne00, nb01, nb02, nb03, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shmem);
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32.cl
new file mode 100644
index 0000000000..9a4d4b9bad
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32.cl
@@ -0,0 +1,144 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK_MXFP4 32
+typedef struct {
+    uchar e; // E8M0
+    uchar qs[QK_MXFP4/2];
+} block_mxfp4;
+
+constant static float kvalues_mxfp4_f[16] = {
+    0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
+};
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint) x << 23;
+    }
+
+    return as_float(bits);
+}
+
+#ifdef INTEL_GPU
+#define N_R0_MXFP4 2 // number of rows each subgroup works on
+#define N_SG_MXFP4 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_MXFP4 2
+#define N_SG_MXFP4 2
+#define N_SIMDWIDTH 64
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_mxfp4_f32(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * dst,
+    ulong         offsetd,
+    int ne00,
+    ulong nb01,
+    ulong nb02,
+    ulong nb03,
+    int ne12,
+    ulong nb11,
+    ulong nb12,
+    ulong nb13,
+    int ne0,
+    int ne1,
+    int r2,
+    int r3,
+    local  char * shmem
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    local float * shmem_f32 = (local float *) shmem;
+    int nb = ne00/QK_MXFP4;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SG_MXFP4 + get_sub_group_id()) * N_R0_MXFP4;
+
+    uint i12 = im%ne12;
+    uint i13 = im/ne12;
+
+    ulong offset_src0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    ulong offset_src1 =        r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global block_mxfp4 * x = (global block_mxfp4 *) (src0 + offset_src0);
+    global float       * y = (global float       *) (src1 + offset_src1);
+
+    const short ix = get_sub_group_local_id()/2;  // 0...15
+    const short it = get_sub_group_local_id()%2;  // 0 or 1
+
+    shmem_f32[get_sub_group_local_id()] = kvalues_mxfp4_f[get_sub_group_local_id()%16];
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    float4 yl[4];
+    float sumf[N_R0_MXFP4] = {0.f};
+
+    global float * yb = y + ix * QK_MXFP4 + it * 8;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        global float4 * y4 = (global float4 *)yb;
+        yl[0] = y4[0];
+        yl[1] = y4[4];
+        yl[2] = y4[1];
+        yl[3] = y4[5];
+
+        for (short row = 0; row < N_R0_MXFP4; row++) {
+            global block_mxfp4 * xb = x + row*nb + ib;
+            global uchar       * q2 = (global uchar *)(xb->qs + 8*it);
+
+            float4 acc1 = yl[0]*(float4)(shmem_f32[q2[0] &  0x0F], shmem_f32[q2[1] &  0x0F], shmem_f32[q2[2] &  0x0F], shmem_f32[q2[3] &  0x0F]);
+            float4 acc2 = yl[1]*(float4)(shmem_f32[q2[0] >> 4   ], shmem_f32[q2[1] >> 4   ], shmem_f32[q2[2] >> 4   ], shmem_f32[q2[3] >> 4   ]);
+            float4 acc3 = yl[2]*(float4)(shmem_f32[q2[4] &  0x0F], shmem_f32[q2[5] &  0x0F], shmem_f32[q2[6] &  0x0F], shmem_f32[q2[7] &  0x0F]);
+            float4 acc4 = yl[3]*(float4)(shmem_f32[q2[4] >> 4   ], shmem_f32[q2[5] >> 4   ], shmem_f32[q2[6] >> 4   ], shmem_f32[q2[7] >> 4   ]);
+
+            acc1 = (acc1 + acc3) + (acc2 + acc4);
+
+            sumf[row] += e8m0_to_fp32(xb->e) * ((acc1.s0 + acc1.s1) + (acc1.s2 + acc1.s3));
+        }
+
+        yb += (N_SIMDWIDTH/2) * QK_MXFP4;
+    }
+
+    global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
+
+    for (int row = 0; row < N_R0_MXFP4 && first_row + row < ne0; ++row) {
+        float sum_all = sub_group_reduce_add(sumf[row]);
+        if (get_sub_group_local_id() == 0) {
+            dst_f32[first_row + row] = sum_all;
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/transpose.cl b/ggml/src/ggml-opencl/kernels/transpose.cl
index a11490b304..536dd560a9 100644
--- a/ggml/src/ggml-opencl/kernels/transpose.cl
+++ b/ggml/src/ggml-opencl/kernels/transpose.cl
@@ -24,6 +24,26 @@ kernel void kernel_transpose_16(
     write_imageh(output, (i_2+3)*rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
 }
 
+// Padded kernel for irregular shape
+kernel void kernel_transpose_16_4x1(
+    __read_only image1d_buffer_t input,
+    __write_only image1d_buffer_t output,
+    const uint rows,
+    const uint cols
+) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int j_2 = j << 2;
+
+    half temp0 = read_imageh(input, (j_2 + 0) * cols + i).x;
+    half temp1 = read_imageh(input, (j_2 + 1) * cols + i).x;
+    half temp2 = read_imageh(input, (j_2 + 2) * cols + i).x;
+    half temp3 = read_imageh(input, (j_2 + 3) * cols + i).x;
+
+    write_imageh(output, i * rows + j, (half4)(temp0, temp1, temp2, temp3));
+}
+
 // 32-bit transpose, loading/storing a 4x4 tile of elements
 kernel void kernel_transpose_32(
     __read_only image1d_buffer_t input,

From 5e6229a8409ac786e62cb133d09f1679a9aec13e Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 15 Aug 2025 19:50:52 +0200
Subject: [PATCH 081/287] common : fix double bos, use common_chat_templates
 for add_bos and add_eos (#15326)

This commit updates common_chat_templates_apply_jinja to use the
the add_bos and add_eos parameters from the chat template instead of
the inputs.

The motivation for this is that currently if the `add_bos` and `add_eos`
from the input parameters are used it is possible to there will be a
missmatch between the model and the chat template which can lead to the
the removal of duplicate BOS/EOS tokens in chat.cpp `apply` to not
happen leading to two BOS tokens being added to the template.
---
 common/chat.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index 00f30994b5..23d3828f9c 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -2061,8 +2061,8 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.enable_thinking = inputs.enable_thinking;
     params.grammar = inputs.grammar;
     params.now = inputs.now;
-    params.add_bos = inputs.add_bos;
-    params.add_eos = inputs.add_eos;
+    params.add_bos = tmpls->add_bos;
+    params.add_eos = tmpls->add_eos;
 
     params.extra_context = json::object();
     for (auto el : inputs.chat_template_kwargs) {

From 912ff8c119f01ae029543c7fdf7a84f91a0437a3 Mon Sep 17 00:00:00 2001
From: rmatif <kingrealriadh@gmail.com>
Date: Sat, 16 Aug 2025 10:05:55 +0200
Subject: [PATCH 082/287] OpenCL: add initial FA support (#14987)

* add F16/F16 fa support

* fix kernel init

* use mad instead of fma

* use inline function

* mark FA with sinks as unsupported for now

* add pragma unroll to loops
---
 ggml/src/ggml-opencl/CMakeLists.txt           |   3 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 248 +++++++++++++
 .../src/ggml-opencl/kernels/flash_attn_f16.cl | 343 +++++++++++++++++
 .../src/ggml-opencl/kernels/flash_attn_f32.cl | 343 +++++++++++++++++
 .../ggml-opencl/kernels/flash_attn_f32_f16.cl | 346 ++++++++++++++++++
 5 files changed, 1283 insertions(+)
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 015821f34f..9a7ccbcff0 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -112,6 +112,9 @@ set(GGML_OPENCL_KERNELS
     mul_mat_f16_f32
     conv2d
     conv2d_f16_f32
+    flash_attn_f32_f16
+    flash_attn_f16
+    flash_attn_f32
 )
 
 foreach (K ${GGML_OPENCL_KERNELS})
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index b8cbf6cf0d..8a2ac7e377 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -25,6 +25,7 @@
 #include <vector>
 #include <string>
 #include <cmath>
+#include <map>
 #include <memory>
 #include <charconv>
 #include <mutex>
@@ -424,6 +425,14 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
     cl_kernel kernel_soft_max, kernel_soft_max_4;
     cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16_q1;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_q1;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16;
+    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16_q1;
+    std::map<std::pair<int, int>, int>       kernels_flash_attn_bm;
+    std::map<std::pair<int, int>, int>       kernels_flash_attn_bn;
     cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
     cl_kernel kernel_set_rows_f32, kernel_set_rows_f16;
     cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
@@ -1308,6 +1317,73 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // flash_attn
+    {
+        #ifdef GGML_OPENCL_EMBED_KERNELS
+                const std::string kernel_src_f16 {
+                    #include "flash_attn_f16.cl.h"
+                };
+                const std::string kernel_src_f32 {
+                    #include "flash_attn_f32.cl.h"
+                };
+                const std::string kernel_src_f32_f16 {
+                    #include "flash_attn_f32_f16.cl.h"
+                };
+        #else
+                const std::string kernel_src_f16 = read_file("flash_attn_f16.cl");
+                const std::string kernel_src_f32 = read_file("flash_attn_f32.cl");
+                const std::string kernel_src_f32_f16 = read_file("flash_attn_f32_f16.cl");
+        #endif
+
+        if (!kernel_src_f16.empty() && !kernel_src_f32.empty() && !kernel_src_f32_f16.empty()) {
+            const struct { int dk; int dv; int bm; int bn; } fa_dims[] = {
+                { 64,  64, 64, 64}, { 80,  80, 64, 32}, { 96,  96, 64, 32},
+                {112, 112, 32, 32}, {128, 128, 32, 32}, {192, 128, 16, 16},
+                {192, 192, 16, 16}, {256, 256, 16, 16},
+            };
+
+            for (size_t i = 0; i < sizeof(fa_dims)/sizeof(fa_dims[0]); ++i) {
+                const int dk = fa_dims[i].dk;
+                const int dv = fa_dims[i].dv;
+                const int bm = fa_dims[i].bm;
+                const int bn = fa_dims[i].bn;
+                std::string OPTS = compile_opts +
+                    " -D DK=" + std::to_string(dk) +
+                    " -D DV=" + std::to_string(dv) +
+                    " -D BLOCK_M=" + std::to_string(bm) +
+                    " -D BLOCK_N=" + std::to_string(bn);
+
+                cl_program prog_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16.c_str(), OPTS);
+                cl_kernel k_f16, k_f16_q1;
+                CL_CHECK((k_f16 = clCreateKernel(prog_f16, "flash_attn_f16", &err), err));
+                CL_CHECK((k_f16_q1 = clCreateKernel(prog_f16, "flash_attn_f16_q1", &err), err));
+                backend_ctx->kernels_flash_attn_f16[{dk, dv}] = k_f16;
+                backend_ctx->kernels_flash_attn_f16_q1[{dk, dv}] = k_f16_q1;
+                CL_CHECK(clReleaseProgram(prog_f16));
+
+                cl_program prog_f32 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32.c_str(), OPTS);
+                cl_kernel k_f32, k_f32_q1;
+                CL_CHECK((k_f32 = clCreateKernel(prog_f32, "flash_attn_f32", &err), err));
+                CL_CHECK((k_f32_q1 = clCreateKernel(prog_f32, "flash_attn_f32_q1", &err), err));
+                backend_ctx->kernels_flash_attn_f32[{dk, dv}] = k_f32;
+                backend_ctx->kernels_flash_attn_f32_q1[{dk, dv}] = k_f32_q1;
+                CL_CHECK(clReleaseProgram(prog_f32));
+
+                cl_program prog_f32_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32_f16.c_str(), OPTS);
+                cl_kernel k_f32_f16, k_f32_f16_q1;
+                CL_CHECK((k_f32_f16 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16", &err), err));
+                CL_CHECK((k_f32_f16_q1 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16_q1", &err), err));
+                backend_ctx->kernels_flash_attn_f32_f16[{dk, dv}] = k_f32_f16;
+                backend_ctx->kernels_flash_attn_f32_f16_q1[{dk, dv}] = k_f32_f16_q1;
+                CL_CHECK(clReleaseProgram(prog_f32_f16));
+
+                backend_ctx->kernels_flash_attn_bm[{dk, dv}] = bm;
+                backend_ctx->kernels_flash_attn_bn[{dk, dv}] = bn;
+            }
+            GGML_LOG_CONT(".");
+        }
+    }
+
     // argsort
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2636,6 +2712,45 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SUM_ROWS:
             return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
+        case GGML_OP_FLASH_ATTN_EXT:
+            {
+                if (op->src[4]) {
+                    return false;
+                }
+
+                const ggml_tensor * q = op->src[0];
+                const ggml_tensor * k = op->src[1];
+                const ggml_tensor * v = op->src[2];
+
+                const int dk = q->ne[0];
+                const int dv = v->ne[0];
+
+                const struct { int dk; int dv; } supported_dims[] = {
+                    { 64,  64}, { 80,  80}, { 96,  96},
+                    {112, 112}, {128, 128}, {192, 128},
+                    {192, 192}, {256, 256},
+                };
+
+                bool dims_supported = false;
+                for (size_t i = 0; i < sizeof(supported_dims)/sizeof(supported_dims[0]); ++i) {
+                    if (supported_dims[i].dk == dk && supported_dims[i].dv == dv) {
+                        dims_supported = true;
+                        break;
+                    }
+                }
+                if (!dims_supported) {
+                    return false;
+                }
+
+                const bool is_f32_f32 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F32 &&
+                                        v->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+                const bool is_f16_f16 = q->type == GGML_TYPE_F16 && k->type == GGML_TYPE_F16 &&
+                                        v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16;
+                const bool is_f32_f16 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 &&
+                                        v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F32;
+
+                return is_f32_f32 || is_f16_f16 || is_f32_f16;
+            }
         default:
             return false;
     }
@@ -5451,6 +5566,133 @@ static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst);
 }
 
+static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, const ggml_tensor * k, ggml_tensor * dst) {
+    const ggml_tensor * v = dst->src[2];
+    const ggml_tensor * mask = dst->src[3];
+    GGML_ASSERT(q->extra);
+    GGML_ASSERT(k->extra);
+    GGML_ASSERT(v->extra);
+    GGML_ASSERT(dst->extra);
+    if (mask) {
+        GGML_ASSERT(mask->extra);
+    }
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    const int n_q = q->ne[1];
+    const int n_kv = k->ne[1];
+    const int d_head_q = q->ne[0];
+    const int d_head_v = v->ne[0];
+    const int n_head = q->ne[2];
+    const int n_head_kv = k->ne[2];
+    const int n_batch = q->ne[3];
+
+    cl_kernel kernel = NULL;
+
+    const bool is_f16 = q->type == GGML_TYPE_F16;
+    const bool is_mixed = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16;
+    const std::pair<int, int> dk_dv = {d_head_q, d_head_v};
+
+    if (n_q == 1) {
+        if (is_mixed) {
+            kernel = backend_ctx->kernels_flash_attn_f32_f16_q1.at(dk_dv);
+        } else if (is_f16) {
+            kernel = backend_ctx->kernels_flash_attn_f16_q1.at(dk_dv);
+        } else {
+            kernel = backend_ctx->kernels_flash_attn_f32_q1.at(dk_dv);
+        }
+    } else {
+        if (is_mixed) {
+            kernel = backend_ctx->kernels_flash_attn_f32_f16.at(dk_dv);
+        } else if (is_f16) {
+            kernel = backend_ctx->kernels_flash_attn_f16.at(dk_dv);
+        } else {
+            kernel = backend_ctx->kernels_flash_attn_f32.at(dk_dv);
+        }
+    }
+    GGML_ASSERT(kernel != NULL);
+
+    ggml_tensor_extra_cl * extra_q = (ggml_tensor_extra_cl *)q->extra;
+    ggml_tensor_extra_cl * extra_k = (ggml_tensor_extra_cl *)k->extra;
+    ggml_tensor_extra_cl * extra_v = (ggml_tensor_extra_cl *)v->extra;
+    ggml_tensor_extra_cl * extra_o = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl * extra_mask = mask ? (ggml_tensor_extra_cl *)mask->extra : NULL;
+
+    cl_ulong offset_q = extra_q->offset + q->view_offs;
+    cl_ulong offset_k = extra_k->offset + k->view_offs;
+    cl_ulong offset_v = extra_v->offset + v->view_offs;
+    cl_ulong offset_o = extra_o->offset + dst->view_offs;
+    cl_mem   mask_buffer = extra_mask ? extra_mask->data_device : NULL;
+    cl_ulong offset_mask = extra_mask ? extra_mask->offset + mask->view_offs : 0;
+
+    const cl_ulong q_nb1 = q->nb[1], q_nb2 = q->nb[2], q_nb3 = q->nb[3];
+    const cl_ulong k_nb1 = k->nb[1], k_nb2 = k->nb[2], k_nb3 = k->nb[3];
+    const cl_ulong v_nb1 = v->nb[1], v_nb2 = v->nb[2], v_nb3 = v->nb[3];
+    const cl_ulong o_nb1 = dst->nb[1], o_nb2 = dst->nb[2], o_nb3 = dst->nb[3];
+    const cl_ulong mask_nb1 = mask ? mask->nb[1] : 0;
+    const cl_ulong mask_nb2 = mask ? mask->nb[2] : 0;
+    const cl_ulong mask_nb3 = mask ? mask->nb[3] : 0;
+    const int mask_ne2 = mask ? mask->ne[2] : 0;
+    const int mask_ne3 = mask ? mask->ne[3] : 0;
+
+    float scale, max_bias, logit_softcap;
+    const float * params = (const float *)dst->op_params;
+    scale         = params[0];
+    max_bias      = params[1];
+    logit_softcap = params[2];
+
+    const int is_causal = (mask == NULL && n_q > 1 && n_q == n_kv);
+
+    const int n_head_log2_val = n_head > 0 ? 1u << (int)floorf(log2f((float)n_head)) : 0;
+    const float n_head_log2_f = n_head_log2_val > 0 ? (float)n_head_log2_val : 1.0f;
+    const float m0 = powf(2.0f, -(max_bias) / n_head_log2_f);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2_f);
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra_q->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset_q));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra_k->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset_k));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extra_v->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset_v));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem),   &extra_o->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offset_o));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(float),    &scale));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int),      &n_q));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),     &n_kv));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),     &is_causal));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),     &n_head));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &q_nb1)); CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &q_nb2)); CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &q_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &k_nb1)); CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &k_nb2)); CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &k_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &v_nb1)); CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &v_nb2)); CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &v_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &o_nb1)); CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &o_nb2)); CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &o_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 25, sizeof(float),    &max_bias));
+    CL_CHECK(clSetKernelArg(kernel, 26, sizeof(float),    &m0));
+    CL_CHECK(clSetKernelArg(kernel, 27, sizeof(float),    &m1));
+    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(int),      &n_head_log2_val));
+    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(float),    &logit_softcap));
+    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(int),      &n_head_kv));
+    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(cl_mem),   &mask_buffer));
+    CL_CHECK(clSetKernelArg(kernel, 32, sizeof(cl_ulong), &offset_mask));
+    CL_CHECK(clSetKernelArg(kernel, 33, sizeof(cl_ulong), &mask_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 34, sizeof(cl_ulong), &mask_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 35, sizeof(cl_ulong), &mask_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 36, sizeof(int),      &mask_ne2));
+    CL_CHECK(clSetKernelArg(kernel, 37, sizeof(int),      &mask_ne3));
+
+    if (n_q == 1) {
+        const size_t wg_size = 64;
+        size_t local_work_size[] = { wg_size, 1 };
+        size_t global_work_size[] = { wg_size, (size_t)(n_head * n_batch) };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
+    } else {
+        const int block_m = backend_ctx->kernels_flash_attn_bm.at(dk_dv);
+        const size_t wg_size = block_m;
+        size_t local_work_size[] = { wg_size, 1 };
+        size_t global_work_size[] = { (size_t)((n_q + block_m - 1) / block_m) * wg_size, (size_t)(n_head * n_batch) };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
+    }
+}
+
 static void ggml_cl_mul_mat_f16_f32_tiled(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
@@ -7607,6 +7849,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             }
             func = ggml_cl_sum_rows;
             break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            if (!any_on_device) {
+                return false;
+            }
+            ggml_cl_flash_attn(backend, tensor->src[0], tensor->src[1], tensor);
+            return true;
         default:
             return false;
     }
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
new file mode 100644
index 0000000000..fea06867e1
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
@@ -0,0 +1,343 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define ACC_TYPE float
+#define ACC_TYPE4 float4
+#define DATA_TYPE half
+#define DATA_TYPE4 half4
+#define CONVERT_ACC4(x) convert_float4(x)
+#define CONVERT_DATA4(x) convert_half4(x)
+
+#define DK_VEC (DK/4)
+#define DV_VEC (DV/4)
+#define WG_SIZE (BLOCK_M)
+#define Q1_WG_SIZE 64
+
+inline float get_alibi_slope(
+    const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
+) {
+    if (max_bias <= 0.0f) {
+        return 1.0f;
+    }
+    const float base = h < n_head_log2 ? m0 : m1;
+    const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+    return pow(base, exph);
+}
+__kernel void flash_attn_f16(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int block_q_idx = get_group_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int my_query_row = block_q_idx * BLOCK_M + tid;
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    if (my_query_row < n_q) {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
+        const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
+        #pragma unroll
+        for (int i = 0; i < DK_VEC; ++i) {
+            q_priv[i] = CONVERT_ACC4(q_ptr[i]);
+        }
+    }
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) {
+        o_acc[i] = (ACC_TYPE4)(0.0f);
+    }
+    ACC_TYPE m_i = -INFINITY;
+    ACC_TYPE l_i = 0.0f;
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    __local DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
+    __local DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
+
+    for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
+            const int row = i / DK_VEC;
+            const int col = i % DK_VEC;
+            const int k_row_idx = k_start + row;
+            if (k_row_idx < n_kv) {
+                const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                l_k[row][col] = ((__global DATA_TYPE4*)(k_base + k_row_offset))[col];
+            }
+        }
+        for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
+            const int row = i / DV_VEC;
+            const int col = i % DV_VEC;
+            const int v_row_idx = k_start + row;
+            if (v_row_idx < n_kv) {
+                const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                l_v[row][col] = ((__global DATA_TYPE4*)(v_base + v_row_offset))[col];
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (my_query_row >= n_q) {
+            continue;
+        }
+
+        for (int j = 0; j < BLOCK_N; j += 2) {
+            const int k_row0 = k_start + j;
+            const int k_row1 = k_start + j + 1;
+
+            ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+            ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+            #pragma unroll
+            for (int k = 0; k < DK_VEC; k++) {
+                dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
+                dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+            }
+            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+
+            if (is_causal) {
+                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
+                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
+            }
+
+            if (k_row0 >= n_kv) score0 = -INFINITY;
+            if (k_row1 >= n_kv) score1 = -INFINITY;
+
+            if (mask_base != NULL) {
+                const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+            }
+
+            if (logit_softcap > 0.0f) {
+                score0 = logit_softcap * tanh(score0 / logit_softcap);
+                score1 = logit_softcap * tanh(score1 / logit_softcap);
+            }
+
+            const ACC_TYPE m_new = max(m_i, max(score0, score1));
+            const ACC_TYPE p0 = exp(score0 - m_new);
+            const ACC_TYPE p1 = exp(score1 - m_new);
+            const ACC_TYPE scale_prev = exp(m_i - m_new);
+
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
+            }
+            l_i = l_i * scale_prev + p0 + p1;
+            m_i = m_new;
+        }
+    }
+
+    if (my_query_row < n_q) {
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
+        if (l_i > 0.0f) {
+            const ACC_TYPE l_inv = 1.0f / l_i;
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
+            }
+        } else {
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = (DATA_TYPE4)(0.0f);
+            }
+        }
+    }
+}
+
+__kernel void flash_attn_f16_q1(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+    const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_ACC4(q_ptr[i]);
+    }
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    ACC_TYPE m_i = -INFINITY;
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        m_i = max(m_i, score);
+    }
+
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_final = local_m[0];
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+    ACC_TYPE l_i = 0.0f;
+
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
+        const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        const ACC_TYPE p = exp(score - m_final);
+        l_i += p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; i++) {
+            o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
+        }
+    }
+
+    __local ACC_TYPE local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+    global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
+    const ACC_TYPE l_final = local_l[0];
+
+    if (l_final > 0.0f) {
+        const ACC_TYPE l_inv = 1.0f / l_final;
+        for (int i = 0; i < DV_VEC; i++) {
+            local_o_comp[tid] = o_acc[i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #pragma unroll
+            for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+                if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+            if (tid == 0) {
+                o_row[i] = CONVERT_DATA4(local_o_comp[0] * l_inv);
+            }
+        }
+    } else if (tid == 0) {
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
new file mode 100644
index 0000000000..2d657327d6
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
@@ -0,0 +1,343 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define ACC_TYPE float
+#define ACC_TYPE4 float4
+#define DATA_TYPE float
+#define DATA_TYPE4 float4
+#define CONVERT_ACC4(x) (x)
+#define CONVERT_DATA4(x) (x)
+
+#define DK_VEC (DK/4)
+#define DV_VEC (DV/4)
+#define WG_SIZE (BLOCK_M)
+#define Q1_WG_SIZE 64
+
+inline float get_alibi_slope(
+    const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
+) {
+    if (max_bias <= 0.0f) {
+        return 1.0f;
+    }
+    const float base = h < n_head_log2 ? m0 : m1;
+    const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+    return pow(base, exph);
+}
+__kernel void flash_attn_f32(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int block_q_idx = get_group_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int my_query_row = block_q_idx * BLOCK_M + tid;
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    if (my_query_row < n_q) {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
+        const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
+        #pragma unroll
+        for (int i = 0; i < DK_VEC; ++i) {
+            q_priv[i] = CONVERT_ACC4(q_ptr[i]);
+        }
+    }
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) {
+        o_acc[i] = (ACC_TYPE4)(0.0f);
+    }
+    ACC_TYPE m_i = -INFINITY;
+    ACC_TYPE l_i = 0.0f;
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    __local DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
+    __local DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
+
+    for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
+            const int row = i / DK_VEC;
+            const int col = i % DK_VEC;
+            const int k_row_idx = k_start + row;
+            if (k_row_idx < n_kv) {
+                const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                l_k[row][col] = ((__global DATA_TYPE4*)(k_base + k_row_offset))[col];
+            }
+        }
+        for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
+            const int row = i / DV_VEC;
+            const int col = i % DV_VEC;
+            const int v_row_idx = k_start + row;
+            if (v_row_idx < n_kv) {
+                const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                l_v[row][col] = ((__global DATA_TYPE4*)(v_base + v_row_offset))[col];
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (my_query_row >= n_q) {
+            continue;
+        }
+
+        for (int j = 0; j < BLOCK_N; j += 2) {
+            const int k_row0 = k_start + j;
+            const int k_row1 = k_start + j + 1;
+
+            ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+            ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+            #pragma unroll
+            for (int k = 0; k < DK_VEC; k++) {
+                dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
+                dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+            }
+            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+
+            if (is_causal) {
+                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
+                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
+            }
+
+            if (k_row0 >= n_kv) score0 = -INFINITY;
+            if (k_row1 >= n_kv) score1 = -INFINITY;
+
+            if (mask_base != NULL) {
+                const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+            }
+
+            if (logit_softcap > 0.0f) {
+                score0 = logit_softcap * tanh(score0 / logit_softcap);
+                score1 = logit_softcap * tanh(score1 / logit_softcap);
+            }
+
+            const ACC_TYPE m_new = max(m_i, max(score0, score1));
+            const ACC_TYPE p0 = exp(score0 - m_new);
+            const ACC_TYPE p1 = exp(score1 - m_new);
+            const ACC_TYPE scale_prev = exp(m_i - m_new);
+
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
+            }
+            l_i = l_i * scale_prev + p0 + p1;
+            m_i = m_new;
+        }
+    }
+
+    if (my_query_row < n_q) {
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
+        if (l_i > 0.0f) {
+            const ACC_TYPE l_inv = 1.0f / l_i;
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
+            }
+        } else {
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = (DATA_TYPE4)(0.0f);
+            }
+        }
+    }
+}
+
+__kernel void flash_attn_f32_q1(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+    const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_ACC4(q_ptr[i]);
+    }
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    ACC_TYPE m_i = -INFINITY;
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        m_i = max(m_i, score);
+    }
+
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_final = local_m[0];
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+    ACC_TYPE l_i = 0.0f;
+
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
+        const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        const ACC_TYPE p = exp(score - m_final);
+        l_i += p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; i++) {
+            o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
+        }
+    }
+
+    __local ACC_TYPE local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+    global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
+    const ACC_TYPE l_final = local_l[0];
+
+    if (l_final > 0.0f) {
+        const ACC_TYPE l_inv = 1.0f / l_final;
+        for (int i = 0; i < DV_VEC; i++) {
+            local_o_comp[tid] = o_acc[i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #pragma unroll
+            for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+                if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+            if (tid == 0) {
+                o_row[i] = CONVERT_DATA4(local_o_comp[0] * l_inv);
+            }
+        }
+    } else if (tid == 0) {
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
new file mode 100644
index 0000000000..7067bd2591
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
@@ -0,0 +1,346 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define ACC_TYPE float
+#define ACC_TYPE4 float4
+#define Q_DATA_TYPE4 float4
+#define KV_DATA_TYPE4 half4
+#define O_DATA_TYPE4 float4
+#define MASK_DATA_TYPE half
+#define CONVERT_Q_ACC4(x) (x)
+#define CONVERT_KV_ACC4(x) convert_float4(x)
+#define CONVERT_O_DATA4(x) (x)
+
+#define DK_VEC (DK/4)
+#define DV_VEC (DV/4)
+#define WG_SIZE (BLOCK_M)
+#define Q1_WG_SIZE 64
+
+inline float get_alibi_slope(
+    const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
+) {
+    if (max_bias <= 0.0f) {
+        return 1.0f;
+    }
+    const float base = h < n_head_log2 ? m0 : m1;
+    const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+    return pow(base, exph);
+}
+__kernel void flash_attn_f32_f16(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int block_q_idx = get_group_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int my_query_row = block_q_idx * BLOCK_M + tid;
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    if (my_query_row < n_q) {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
+        const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
+        #pragma unroll
+        for (int i = 0; i < DK_VEC; ++i) {
+            q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+        }
+    }
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) {
+        o_acc[i] = (ACC_TYPE4)(0.0f);
+    }
+    ACC_TYPE m_i = -INFINITY;
+    ACC_TYPE l_i = 0.0f;
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    __local KV_DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
+    __local KV_DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
+
+    for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
+            const int row = i / DK_VEC;
+            const int col = i % DK_VEC;
+            const int k_row_idx = k_start + row;
+            if (k_row_idx < n_kv) {
+                const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_base + k_row_offset))[col];
+            }
+        }
+        for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
+            const int row = i / DV_VEC;
+            const int col = i % DV_VEC;
+            const int v_row_idx = k_start + row;
+            if (v_row_idx < n_kv) {
+                const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                l_v[row][col] = ((__global KV_DATA_TYPE4*)(v_base + v_row_offset))[col];
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        if (my_query_row >= n_q) {
+            continue;
+        }
+
+        for (int j = 0; j < BLOCK_N; j += 2) {
+            const int k_row0 = k_start + j;
+            const int k_row1 = k_start + j + 1;
+
+            ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+            ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+            #pragma unroll
+            for (int k = 0; k < DK_VEC; k++) {
+                dot_acc0 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j][k]), dot_acc0);
+                dot_acc1 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j+1][k]), dot_acc1);
+            }
+            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+
+            if (is_causal) {
+                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
+                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
+            }
+
+            if (k_row0 >= n_kv) score0 = -INFINITY;
+            if (k_row1 >= n_kv) score1 = -INFINITY;
+
+            if (mask_base != NULL) {
+                const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+            }
+
+            if (logit_softcap > 0.0f) {
+                score0 = logit_softcap * tanh(score0 / logit_softcap);
+                score1 = logit_softcap * tanh(score1 / logit_softcap);
+            }
+
+            const ACC_TYPE m_new = max(m_i, max(score0, score1));
+            const ACC_TYPE p0 = exp(score0 - m_new);
+            const ACC_TYPE p1 = exp(score1 - m_new);
+            const ACC_TYPE scale_prev = exp(m_i - m_new);
+
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_KV_ACC4(l_v[j][i]) + p1 * CONVERT_KV_ACC4(l_v[j+1][i]);
+            }
+            l_i = l_i * scale_prev + p0 + p1;
+            m_i = m_new;
+        }
+    }
+
+    if (my_query_row < n_q) {
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+        if (l_i > 0.0f) {
+            const ACC_TYPE l_inv = 1.0f / l_i;
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = CONVERT_O_DATA4(o_acc[i] * l_inv);
+            }
+        } else {
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_row[i] = (O_DATA_TYPE4)(0.0f);
+            }
+        }
+    }
+}
+
+__kernel void flash_attn_f32_f16_q1(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int tid = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+    const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    ACC_TYPE m_i = -INFINITY;
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        m_i = max(m_i, score);
+    }
+
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_final = local_m[0];
+
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+    ACC_TYPE l_i = 0.0f;
+
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
+        const global KV_DATA_TYPE4* v_ptr = (const global KV_DATA_TYPE4*)(v_base + v_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; k++) {
+            dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        const ACC_TYPE p = exp(score - m_final);
+        l_i += p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; i++) {
+            o_acc[i] = mad(p, CONVERT_KV_ACC4(v_ptr[i]), o_acc[i]);
+        }
+    }
+
+    __local ACC_TYPE local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+    global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+    const ACC_TYPE l_final = local_l[0];
+
+    if (l_final > 0.0f) {
+        const ACC_TYPE l_inv = 1.0f / l_final;
+        for (int i = 0; i < DV_VEC; i++) {
+            local_o_comp[tid] = o_acc[i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #pragma unroll
+            for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+                if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+            if (tid == 0) {
+                o_row[i] = CONVERT_O_DATA4(local_o_comp[0] * l_inv);
+            }
+        }
+    } else if (tid == 0) {
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_row[i] = (O_DATA_TYPE4)(0.0f);
+    }
+}

From 2e2b22ba6607414a5d619ac6d2f034b5b02214e5 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 16 Aug 2025 03:58:38 -0500
Subject: [PATCH 083/287] vulkan: Add missing bounds checking to
 scalar/coopmat1 mul_mat_id (#15334)

---
 ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp | 10 +++++++---
 tests/test-backend-ops.cpp                      |  2 ++
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index 8c5114a79d..a61a464c7b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -801,7 +801,7 @@ void main() {
             }
 #else
             const uint row_i = ic * BN + loadc_b + l;
-            if (row_i < _ne1) {
+            if (row_i < _ne1 && block + loadr_b < end_k) {
                 const u16vec2 row_idx = row_ids[row_i];
                 buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]);
             } else {
@@ -875,7 +875,9 @@ void main() {
 
                 const u16vec2 row_idx = row_ids[row_i];
 
-                data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]);
+                if (dr + cm_row * TM + store_r < p.M) {
+                    data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]);
+                }
             }
         }
     }
@@ -925,7 +927,9 @@ void main() {
 #endif // MUL_MAT_ID
                 [[unroll]] for (uint cr = 0; cr < TM; cr++) {
 #ifdef MUL_MAT_ID
-                    data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+                    if (dr_warp + cr < p.M) {
+                        data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+                    }
 #else
                     if (dr_warp + cr < p.M && dc_warp + cc < p.N) {
                         data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index f4565f9b71..39547f0649 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5824,6 +5824,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, b, 32, 1024, 16));
     }
 
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));
+
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
             for (int n_mats : {4, 8}) {

From de2192794f4e8e04f2e8167ef2424905145e88fc Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 16 Aug 2025 04:18:31 -0500
Subject: [PATCH 084/287] vulkan: Support mul_mat_id with f32 accumulators
 (#15337)

* vulkan: Add missing bounds checking to scalar/coopmat1 mul_mat_id

* vulkan: Support mul_mat_id with f32 accumulators, but they are not hooked up

- There's no explicit way to request f32 precision for mul_mat_id, but there
probably should be, and this gets the code in place for that.
- A couple fixes to check_results.
- Remove casts to fp16 in coopmat1 FA shader (found by inspection).
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 160 ++++++++----------
 .../vulkan-shaders/flash_attn_cm1.comp        |   8 +-
 2 files changed, 79 insertions(+), 89 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 76a0cfa4f0..6315584996 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2387,26 +2387,26 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
         }
 #endif
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f16acc,   matmul_id_iq1_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f16acc,   matmul_id_iq1_m_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f16, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
     } else
@@ -2502,51 +2502,27 @@ static void ggml_vk_load_shaders(vk_device& device) {
         }
 #endif
 
-        if (device->coopmat_acc_f16_support) {
-            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f16acc,   matmul_id_iq1_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f16acc,   matmul_id_iq1_m_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        } else {
-            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-
-            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f16acc,   matmul_id_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f16acc,   matmul_id_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        }
+        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MM
     } else
@@ -2631,27 +2607,27 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
         CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id);
 
-        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f16acc, matmul_id_q5_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f16acc, matmul_id_q5_1_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f16acc, matmul_id_q8_0_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 
-        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f16acc, matmul_id_q2_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f16acc, matmul_id_q3_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f16acc, matmul_id_q4_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f16acc, matmul_id_q5_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f16acc, matmul_id_q6_k_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f16acc,   matmul_id_iq1_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f16acc,   matmul_id_iq1_m_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f16acc, matmul_id_iq2_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f16acc,  matmul_id_iq2_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f16acc,   matmul_id_iq2_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f16acc, matmul_id_iq3_xxs_f32, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MMQ
 #undef CREATE_MM
@@ -4470,7 +4446,17 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
             return nullptr;
     }
 
-    return ctx->device->fp16 ? ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f32acc;
+    // XXX TODO 'prec' is not actually allowed in mul_mat_id.
+    bool prefer_fp16acc = ctx->device->fp16 /*&& prec == GGML_PREC_DEFAULT*/;
+    bool support_fp16acc = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f16acc != nullptr;
+    bool support_fp32acc = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f32acc != nullptr;
+
+    if (support_fp16acc && (prefer_fp16acc || !support_fp32acc)) {
+        return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f16acc;
+    } else {
+        GGML_ASSERT(support_fp32acc);
+        return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f32acc;
+    }
 }
 
 static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
@@ -11723,6 +11709,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         } else {
             tensor_clone = ggml_glu_split(ggml_ctx, src_clone[0], src_clone[1], (ggml_glu_op) tensor->op_params[0]);
         }
+        ggml_set_op_params_i32(tensor_clone, 2, ggml_get_op_params_i32(tensor, 2));
+        ggml_set_op_params_i32(tensor_clone, 3, ggml_get_op_params_i32(tensor, 3));
     } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
         if (src1 == nullptr) {
             tensor_clone = ggml_dup(ggml_ctx, src_clone[0]);
@@ -11807,6 +11795,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         src_clone[0]->flags = src0->flags;
         tensor_clone = ggml_opt_step_sgd(ggml_ctx, src_clone[0], src_clone[1],
         src_clone[2]);
+    } else if (tensor->op == GGML_OP_ADD_ID) {
+        tensor_clone = ggml_add_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]);
     }
     else {
         std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 230e815f22..81cc3f81fc 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -210,7 +210,7 @@ void main() {
 
         [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
             [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
-                Of[r][d] = float16_t(eMf[r]) * Of[r][d];
+                Of[r][d] = ACC_TYPE(eMf[r]) * Of[r][d];
             }
         }
         [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
@@ -233,7 +233,7 @@ void main() {
                 vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]);
 #endif
                 [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
-                    Of[r][d] += float16_t(Pf[r]) * ACC_TYPEV4(Vf);
+                    Of[r][d] += ACC_TYPE(Pf[r]) * ACC_TYPEV4(Vf);
                 }
             }
         }
@@ -288,7 +288,7 @@ void main() {
     [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
         [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
 
-            Of[r][d] = float16_t(eMf[r]) * Of[r][d];
+            Of[r][d] = ACC_TYPE(eMf[r]) * Of[r][d];
             tmpshv4[tid] = Of[r][d];
 
             barrier();
@@ -357,7 +357,7 @@ void main() {
 
     [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
         [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
-            Of[r][d] *= float16_t(Lfrcp[r]);
+            Of[r][d] *= ACC_TYPE(Lfrcp[r]);
         }
     }
 

From 1fe00296f587dfca0957e006d146f5875b61e43d Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 16 Aug 2025 11:48:22 -0500
Subject: [PATCH 085/287] vulkan: fuse adds (#15252)

* vulkan: fuse adds

Fuse adds that have the same shape, which are common in MoE models.
It will currently fuse up to 6 adds, because we assume no more than
8 descriptors per dispatch. But this could be changed.

* check runtimeDescriptorArray feature

* disable multi_add for Intel due to likely driver bug
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 207 +++++++++++++++++-
 .../vulkan-shaders/generic_binary_head.comp   |  19 +-
 .../ggml-vulkan/vulkan-shaders/multi_add.comp |  68 ++++++
 .../src/ggml-vulkan/vulkan-shaders/utils.comp |  25 +++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   2 +
 tests/test-backend-ops.cpp                    |   5 +-
 6 files changed, 301 insertions(+), 25 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/utils.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 6315584996..20674bee58 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -103,6 +103,8 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 struct ggml_backend_vk_context;
 
 #define MAX_PARAMETER_COUNT 8
+// Max number of adds that can be fused without exceeding MAX_PARAMETER_COUNT.
+#define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 2)
 
 struct vk_pipeline_struct {
     std::string name;
@@ -368,6 +370,7 @@ struct vk_device_struct {
     bool float_controls_rte_fp16;
     bool subgroup_add;
     bool subgroup_shuffle;
+    bool multi_add;
 
     bool integer_dot_product;
 
@@ -449,6 +452,9 @@ struct vk_device_struct {
     vk_pipeline pipeline_div[2][2][2];
     vk_pipeline pipeline_div_norepeat[2][2][2];
 
+    // indexed by num_additional_fused_ops == num_adds - 1
+    vk_pipeline pipeline_multi_add[MAX_FUSED_ADDS];
+
     vk_pipeline pipeline_add_id_f32;
 
     vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32;
@@ -801,6 +807,14 @@ struct vk_op_binary_push_constants {
     float param1; float param2; int32_t param3;
 };
 
+struct vk_op_multi_add_push_constants {
+    // shape for dst
+    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;
+
+    // strides for srcs+dst
+    uint32_t nb[8][4];
+};
+
 struct vk_op_add_id_push_constants {
     uint32_t ne0;
     uint32_t ne1;
@@ -2994,6 +3008,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     CREATE_BINARY(div, _norepeat, {1})
 #undef CREATE_BINARY
 
+    if (device->multi_add) {
+        for (uint32_t i = 0; i < MAX_FUSED_ADDS; ++i) {
+            ggml_vk_create_pipeline(device, device->pipeline_multi_add[i], "multi_add_f32_" + std::to_string(i+1), multi_add_f32_len, multi_add_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
+        }
+    }
+
     ggml_vk_create_pipeline(device, device->pipeline_add_id_f32, "add_id_f32", add_id_f32_len, add_id_f32_data, "main", 4, sizeof(vk_op_add_id_push_constants), {1, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
@@ -3533,6 +3553,12 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->pipeline_robustness = pl_robustness_features.pipelineRobustness;
 
+        device->multi_add = vk12_props.shaderRoundingModeRTEFloat16 &&
+                            device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_multi_add_push_constants) &&
+                            vk12_features.runtimeDescriptorArray &&
+                            device->vendor_id != VK_VENDOR_ID_INTEL &&
+                            getenv("GGML_VK_DISABLE_MULTI_ADD") == nullptr;
+
         if (device->subgroup_size_control) {
             device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
             device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
@@ -6887,6 +6913,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         switch (op) {
         case GGML_OP_ADD:
         {
+            if (ctx->num_additional_fused_ops > 0) {
+                return ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
+            }
             auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add;
             return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
         }
@@ -7743,6 +7772,107 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }, dryrun);
 }
 
+static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    const ggml_tensor *first_node = cgraph->nodes[node_idx];
+    const ggml_tensor *dst = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
+
+    // Make a list of all the tensors used by the op.
+    // Last element of the list is the dest tensor.
+    const ggml_tensor *tensors[MAX_PARAMETER_COUNT];
+    uint32_t num_srcs = ctx->num_additional_fused_ops + 2;
+    uint32_t num_tensors = num_srcs + 1;
+    GGML_ASSERT(num_tensors <= MAX_PARAMETER_COUNT);
+
+    tensors[0] = first_node->src[0];
+    tensors[1] = first_node->src[1];
+    for (int32_t i = 0; i < ctx->num_additional_fused_ops; ++i) {
+        // check whether the previous result is src[0] or src[1]
+        if (cgraph->nodes[node_idx + i] == cgraph->nodes[node_idx + i + 1]->src[0]) {
+            tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[1];
+        } else {
+            tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[0];
+        }
+    }
+    tensors[num_srcs] = dst;
+
+    vk_op_multi_add_push_constants pc;
+    pc.ne20 = (uint32_t)dst->ne[0];
+    pc.ne21 = (uint32_t)dst->ne[1];
+    pc.ne22 = (uint32_t)dst->ne[2];
+    pc.ne23 = (uint32_t)dst->ne[3];
+
+    for (uint32_t i = 0; i < num_tensors; ++i) {
+        const ggml_tensor *t = tensors[i];
+        pc.nb[i][0] = (uint32_t)t->nb[0] / sizeof(float);
+        pc.nb[i][1] = (uint32_t)t->nb[1] / sizeof(float);
+        pc.nb[i][2] = (uint32_t)t->nb[2] / sizeof(float);
+        pc.nb[i][3] = (uint32_t)t->nb[3] / sizeof(float);
+    }
+
+    vk_pipeline pipeline = ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
+
+    if (pipeline == nullptr) {
+        std::cerr << "ggml_vulkan: Error: Missing multi_add";
+        GGML_ABORT("fatal error");
+    }
+
+    if (dryrun) {
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
+        return;
+    }
+
+    ggml_backend_vk_buffer_context * buf_ctx[MAX_PARAMETER_COUNT];
+    vk_buffer buf[MAX_PARAMETER_COUNT];
+    size_t offset[MAX_PARAMETER_COUNT];
+    bool uma[MAX_PARAMETER_COUNT];
+
+    for (uint32_t i = 0; i < num_tensors; ++i) {
+        buf_ctx[i] = (ggml_backend_vk_buffer_context *)tensors[i]->buffer->context;
+        buf[i] = nullptr;
+        offset[i] = 0;
+        uma[i] = false;
+
+        if (ctx->device->uma) {
+            ggml_vk_host_get(ctx->device, tensors[i]->data, buf[i], offset[i]);
+            uma[i] = buf[i] != nullptr;
+        }
+        if (!uma[i]) {
+            buf[i] = buf_ctx[i]->dev_buffer;
+            offset[i] = vk_tensor_offset(tensors[i]) + tensors[i]->view_offs;
+        }
+        GGML_ASSERT(buf[i] != nullptr);
+    }
+    // If any remaining descriptors are unused, just point them at src[0]
+    for (uint32_t i = num_tensors; i < MAX_PARAMETER_COUNT; ++i) {
+        buf[i] = buf[0];
+        offset[i] = 0;
+    }
+
+    std::array<uint32_t, 3> elements;
+
+    uint32_t ne = ggml_nelements(dst);
+    if (ne > 262144) {
+        elements = { 512, 512, CEIL_DIV(ne, 262144) };
+    } else if (ne > 512) {
+        elements = { 512, CEIL_DIV(ne, 512), 1 };
+    } else {
+        elements = { ne, 1, 1 };
+    }
+
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
+        {
+            vk_subbuffer{ buf[0], offset[0], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[1], offset[1], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[2], offset[2], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[3], offset[3], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[4], offset[4], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[5], offset[5], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[6], offset[6], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[7], offset[7], VK_WHOLE_SIZE },
+        }, pc, elements);
+}
+
 static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     const uint32_t src0_type_size = ggml_type_size(src0->type);
     const uint32_t src1_type_size = ggml_type_size(src1->type);
@@ -9703,8 +9833,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
 
         break;
     case GGML_OP_ADD:
-        ggml_vk_add(ctx, compute_ctx, src0, src1, node, dryrun);
-
+        if (ctx->num_additional_fused_ops) {
+            ggml_vk_multi_add(ctx, compute_ctx, cgraph, node_idx, dryrun);
+        } else {
+            ggml_vk_add(ctx, compute_ctx, src0, src1, node, dryrun);
+        }
         break;
     case GGML_OP_SUB:
         ggml_vk_sub(ctx, compute_ctx, src0, src1, node, dryrun);
@@ -10586,6 +10719,58 @@ static bool ggml_vk_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, st
     return true;
 }
 
+static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) {
+
+    const ggml_tensor *first_node = cgraph->nodes[node_idx];
+    if (first_node->op != GGML_OP_ADD) {
+        return 0;
+    }
+
+    if (!ctx->device->multi_add) {
+        return 0;
+    }
+
+    int32_t num_adds = 1;
+    while (node_idx + num_adds < cgraph->n_nodes &&
+           cgraph->nodes[node_idx + num_adds]->op == GGML_OP_ADD &&
+           num_adds < MAX_FUSED_ADDS) {
+        num_adds++;
+    }
+
+    // The shader currently requires same shapes (but different strides are allowed),
+    // everything f32, and no misalignment
+    for (int32_t i = 0; i < num_adds; ++i) {
+        const ggml_tensor *next_node = cgraph->nodes[node_idx + i];
+        if (!ggml_are_same_shape(first_node, next_node->src[0]) ||
+            !ggml_are_same_shape(first_node, next_node->src[1]) ||
+            next_node->type != GGML_TYPE_F32 ||
+            next_node->src[0]->type != GGML_TYPE_F32 ||
+            next_node->src[1]->type != GGML_TYPE_F32 ||
+            get_misalign_bytes(ctx, next_node) ||
+            get_misalign_bytes(ctx, next_node->src[0]) ||
+            get_misalign_bytes(ctx, next_node->src[1])) {
+            num_adds = i;
+        }
+    }
+
+    // Verify we can fuse these
+    ggml_op adds[MAX_FUSED_ADDS];
+    for (int32_t i = 0; i < num_adds; ++i) {
+        adds[i] = GGML_OP_ADD;
+    }
+
+    // decrease num_adds if they can't all be fused
+    while (num_adds > 1 && !ggml_can_fuse(cgraph, node_idx, adds, num_adds)) {
+        num_adds--;
+    }
+
+    // a single add is not "fused", so just return zero
+    if (num_adds == 1) {
+        return 0;
+    }
+    return num_adds;
+}
+
 static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
     VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
@@ -10599,8 +10784,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
     uint64_t total_mat_mul_bytes = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
-        if (!ctx->device->disable_fusion && ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
-            ctx->num_additional_fused_ops = 1;
+        if (!ctx->device->disable_fusion) {
+            uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
+            if (num_adds) {
+                ctx->num_additional_fused_ops = num_adds - 1;
+            } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                ctx->num_additional_fused_ops = 1;
+            }
         }
         ggml_vk_build_graph(ctx, cgraph, i, nullptr, 0, true, false, false, false);
         if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) {
@@ -10675,8 +10865,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
             mul_mat_bytes += ggml_nbytes(cgraph->nodes[i]->src[0]);
         }
 
-        if (!ctx->device->disable_fusion && ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
-            ctx->num_additional_fused_ops = 1;
+        if (!ctx->device->disable_fusion) {
+            uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
+            if (num_adds) {
+                ctx->num_additional_fused_ops = num_adds - 1;
+            } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                ctx->num_additional_fused_ops = 1;
+            }
         }
 
         // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
index 4b4316cf3d..750e785753 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp
@@ -2,6 +2,7 @@
 #extension GL_EXT_control_flow_attributes : require
 
 #include "rte.comp"
+#include "utils.comp"
 
 layout (push_constant) uniform parameter
 {
@@ -28,25 +29,9 @@ uint get_aoffset() { return p.misalign_offsets >> 16; }
 uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; }
 uint get_doffset() { return p.misalign_offsets & 0xFF; }
 
-// mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1
-uint fastmod(uint a, uint b) {
-    if ((b & (b-1)) == 0) {
-        return a & (b-1);
-    }
-    return a % b;
-}
-
-uint fastdiv(uint a, uint b) {
-    return (a < b) ? 0 : (a / b);
-}
 
 void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03) {
-    i03 = fastdiv(idx, (p.ne02*p.ne01*p.ne00));
-    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    i02 = fastdiv((idx - i03_offset), (p.ne01*p.ne00));
-    const uint i02_offset = i02*p.ne01*p.ne00;
-    i01 = (idx - i03_offset - i02_offset) / p.ne00;
-    i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    get_indices(idx, i00, i01, i02, i03, p.ne00, p.ne01, p.ne02, p.ne03);
 }
 
 uint src0_idx(uint i00, uint i01, uint i02, uint i03) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
new file mode 100644
index 0000000000..0c7acb7060
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
@@ -0,0 +1,68 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_nonuniform_qualifier : enable
+#extension GL_EXT_control_flow_attributes : require
+
+#include "rte.comp"
+#include "types.comp"
+#include "utils.comp"
+
+layout (push_constant) uniform parameter2
+{
+    // shape for dst
+    uint ne20; uint ne21; uint ne22; uint ne23;
+
+    // strides for srcs+dst
+    uint nb[8][4];
+} p;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];} a[];
+layout (binding = 0) writeonly buffer D {D_TYPE data_d[];} d[];
+
+layout(constant_id = 0) const uint num_srcs = 2;
+
+uint src_idx(uint s, uint i00, uint i01, uint i02, uint i03) {
+    return i03*p.nb[s][3] + i02*p.nb[s][2] + i01*p.nb[s][1] + i00*p.nb[s][0];
+}
+
+uint dst_idx(uint i00, uint i01, uint i02, uint i03) {
+    uint nb20 = p.nb[num_srcs][0];
+    uint nb21 = p.nb[num_srcs][1];
+    uint nb22 = p.nb[num_srcs][2];
+    uint nb23 = p.nb[num_srcs][3];
+    return i03*nb23 + i02*nb22 + i01*nb21 + i00*nb20;
+}
+
+uint get_idx() {
+    return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
+const uint num_threads = 256;
+
+layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    uint idx = get_idx();
+
+    uint ne = p.ne20 * p.ne21 * p.ne22 * p.ne23;
+
+    // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation
+    const uint num_iter = 2;
+
+    [[unroll]] for (uint i = 0; i < num_iter; ++i) {
+        if (idx >= ne) {
+            continue;
+        }
+        uint i00, i01, i02, i03;
+        get_indices(idx, i00, i01, i02, i03, p.ne20, p.ne21, p.ne22, p.ne23);
+
+        FLOAT_TYPE sum = FLOAT_TYPE(0);
+        [[unroll]] for (uint s = 0; s < num_srcs; ++s) {
+            sum += FLOAT_TYPE(a[s].data_a[src_idx(s, i00, i01, i02, i03)]);
+        }
+        d[num_srcs].data_d[dst_idx(i00, i01, i02, i03)] = D_TYPE(sum);
+
+        idx += num_threads;
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/utils.comp b/ggml/src/ggml-vulkan/vulkan-shaders/utils.comp
new file mode 100644
index 0000000000..dc4a1e6d96
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/utils.comp
@@ -0,0 +1,25 @@
+#ifndef UTILS_COMP
+#define UTILS_COMP
+
+// mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1
+uint fastmod(uint a, uint b) {
+    if ((b & (b-1)) == 0) {
+        return a & (b-1);
+    }
+    return a % b;
+}
+
+uint fastdiv(uint a, uint b) {
+    return (a < b) ? 0 : (a / b);
+}
+
+void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03, uint ne00, uint ne01, uint ne02, uint ne03) {
+    i03 = fastdiv(idx, (ne02*ne01*ne00));
+    const uint i03_offset = i03 * ne02*ne01*ne00;
+    i02 = fastdiv((idx - i03_offset), (ne01*ne00));
+    const uint i02_offset = i02*ne01*ne00;
+    i01 = (idx - i03_offset - i02_offset) / ne00;
+    i00 = idx - i03_offset - i02_offset - i01*ne00;
+}
+
+#endif // UTILS_COMP
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 68933d19f2..67a9ee422b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -677,6 +677,8 @@ void process_shaders() {
 
     string_to_spv("add_id_f32", "add_id.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
 
+    string_to_spv("multi_add_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}});
+
     for (auto &c : compiles) {
         c.wait();
     }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 39547f0649..0df5ebb565 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2491,12 +2491,12 @@ struct test_bin_bcast : public test_case {
         : op(op), type(type), ne(ne), nr(nr), nf(nf) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        GGML_ASSERT(nf <= 8);
+        GGML_ASSERT(nf <= 16);
 
         ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
         ggml_set_name(a, "a");
 
-        ggml_tensor * b[8];
+        ggml_tensor * b[16];
         for (int i = 0; i < nf; ++i) {
             b[i] = ggml_new_tensor(ctx, type, 4, ne.data());
             ggml_set_name(b[i], (std::string("b") + std::to_string(i)).c_str());
@@ -5658,6 +5658,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 2}, 6));
     test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 2, 2}, 7));
     test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {16, 5, 4, 3}, {2, 2, 2, 2}, 8));
+    test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {16, 5, 4, 3}, {1, 1, 1, 1}, 16));
 
     test_cases.emplace_back(new test_add1());
     test_cases.emplace_back(new test_scale());

From 65349f26f2299e06477ec8e85e46243046801358 Mon Sep 17 00:00:00 2001
From: Tarek Dakhran <t.dakhran@gmail.com>
Date: Sat, 16 Aug 2025 23:33:54 +0200
Subject: [PATCH 086/287] model : support vision LiquidAI LFM2-VL family
 (#15347)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* wip lfm2 vision model

* Fix conv weight

* Implement dynamic resolution

* Fix cuda

* support LFM2-VL-450M

* happy CI

* Remove extra `ggml_conv` and put others into the right place

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 convert_hf_to_gguf.py          |  45 +++++++++++-
 gguf-py/gguf/constants.py      |   1 +
 gguf-py/gguf/tensor_mapping.py |   1 +
 tools/mtmd/clip-impl.h         |   3 +
 tools/mtmd/clip.cpp            | 124 ++++++++++++++++++++++++++++++++-
 5 files changed, 171 insertions(+), 3 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 444e2cbdfb..bd21e55f4a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -8251,8 +8251,7 @@ class GptOssModel(TextModel):
         self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
 
 
-@ModelBase.register("Lfm2ForCausalLM")
-@ModelBase.register("LFM2ForCausalLM")
+@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
 class LFM2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.LFM2
 
@@ -8287,6 +8286,13 @@ class LFM2Model(TextModel):
         self._add_feed_forward_length()
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+        if is_vision_tensor:
+            # skip vision tensors
+            return []
+
+        name = name.replace("language_model.", "")
+
         # conv op requires 2d tensor
         if 'conv.conv' in name:
             data_torch = data_torch.squeeze(1)
@@ -8294,6 +8300,41 @@ class LFM2Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("Lfm2VlForConditionalGeneration")
+class LFM2VLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        # TODO(tarek): for dynamic resolution image_size is not specified, setting here for compatibility
+        self.hparams_vision["image_size"] = 256
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["layer_norm_eps"]))
+        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("downsample_factor", 2))
+        self.gguf_writer.add_vision_use_gelu(True)
+        # python notation, e.g. for vision_feature_layer == -1, we pick last layer -> vision_feature_layers_to_drop = 0
+        vision_feature_layers_to_drop = -(self.global_config.get("vision_feature_layer", -1) + 1)
+        self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys) - vision_feature_layers_to_drop)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+
+        if is_vision_tensor:
+            # remove "model." prefix
+            name = name.replace("model.vision_tower.", "vision_tower.")
+            name = name.replace("model.multi_modal_projector.", "multi_modal_projector.")
+
+            if "patch_embedding.weight" in name:
+                data_torch = data_torch.view(data_torch.shape[0], 16, 16, 3).permute(0, 3, 1, 2)
+
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return [] # skip other tensors
+
+
 @ModelBase.register("SmallThinkerForCausalLM")
 class SmallThinkerModel(TextModel):
     model_arch = gguf.MODEL_ARCH.SMALLTHINKER
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 911eea504a..41804f3a2b 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -2832,6 +2832,7 @@ class VisionProjectorType:
     QWEN2A = "qwen2a" # audio
     QWEN25O = "qwen2.5o" # omni
     VOXTRAL = "voxtral"
+    LFM2 = "lfm2"
 
 
 # Items here are (block size, type size)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index c5c2798090..87edaa3232 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1272,6 +1272,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MM_INP_NORM: (
             "multi_modal_projector.norm",
+            "multi_modal_projector.layer_norm",
             "pre_mm_projector_norm",
         ),
 
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index f1eb633369..706ed2e3b5 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -82,6 +82,7 @@
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
 #define TN_MM_INP_NORM     "mm.input_norm.weight"
+#define TN_MM_INP_NORM_B   "mm.input_norm.bias"
 #define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
 #define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3
 #define TN_MM_PROJECTOR    "mm.model.fc.weight"         // idefics3
@@ -133,6 +134,7 @@ enum projector_type {
     PROJECTOR_TYPE_QWEN2A,
     PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
     PROJECTOR_TYPE_VOXTRAL,
+    PROJECTOR_TYPE_LFM2,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -153,6 +155,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_QWEN2A,    "qwen2a"},
     { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
     { PROJECTOR_TYPE_VOXTRAL,   "voxtral"},
+    { PROJECTOR_TYPE_LFM2,      "lfm2"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index fdaf9738e8..c27f8ebbd9 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -265,6 +265,7 @@ struct clip_model {
 
     // LLaVA projection
     ggml_tensor * mm_input_norm_w = nullptr;
+    ggml_tensor * mm_input_norm_b = nullptr;
     ggml_tensor * mm_0_w = nullptr;
     ggml_tensor * mm_0_b = nullptr;
     ggml_tensor * mm_2_w = nullptr;
@@ -488,11 +489,17 @@ struct clip_graph {
 
     ggml_cgraph * build_siglip() {
         ggml_tensor * inp = build_inp();
+
+        ggml_tensor * learned_pos_embd = model.position_embeddings;
+        if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
+            learned_pos_embd = resize_position_embeddings();
+        }
+
         ggml_tensor * cur = build_vit(
                                 inp, n_patches,
                                 NORM_TYPE_NORMAL,
                                 hparams.ffn_op,
-                                model.position_embeddings,
+                                learned_pos_embd,
                                 nullptr);
 
         if (ctx->proj_type() == PROJECTOR_TYPE_GEMMA3) {
@@ -542,6 +549,45 @@ struct clip_graph {
                 bsz);
 
             cur = ggml_mul_mat(ctx0, model.projection, cur);
+        } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
+            // pixel unshuffle block
+            const int scale_factor = model.hparams.proj_scale_factor;
+            GGML_ASSERT(scale_factor > 1);
+
+            const int n_embd = cur->ne[0];
+            int width  = img.nx / patch_size;
+            int height = img.ny / patch_size;
+
+            // pad width and height to factor
+            const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width;
+            const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
+            cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
+            if (pad_width || pad_height) {
+                cur     = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
+                width  += pad_width;
+                height += pad_height;
+            }
+
+            // unshuffle h
+            cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
+            cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
+
+            // unshuffle w
+            cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
+            cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
+
+            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+
+            // projection
+            cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
+            cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
+            cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
+
+            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
+            cur = ggml_add(ctx0, cur, model.mm_1_b);
+            cur = ggml_gelu(ctx0, cur);
+            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
+            cur = ggml_add(ctx0, cur, model.mm_2_b);
         } else {
             GGML_ABORT("SigLIP: Unsupported projector type");
         }
@@ -1560,6 +1606,27 @@ private:
         }
     }
 
+    // siglip2 naflex
+    ggml_tensor * resize_position_embeddings() {
+        ggml_tensor * pos_embd = model.position_embeddings;
+        const int height       = img.ny / patch_size;
+        const int width        = img.nx / patch_size;
+
+        if (!pos_embd || height * width == pos_embd->ne[1]) {
+            return pos_embd;
+        }
+
+        const int n_pos_embd = std::sqrt(pos_embd->ne[1]);
+        pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_pos_embd, n_pos_embd);  // -> (n_embd, n_pos_embd, n_pos_embd)
+        pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3);                         // -> (n_pos_embd, n_pos_embd, n_embd)
+        pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, 1);    // -> (width, height, n_embd)
+        pos_embd = ggml_reshape_2d(ctx0, pos_embd, height * width, n_embd);          // -> (height * width, n_embd)
+        pos_embd = ggml_transpose(ctx0, pos_embd);                                   // -> (n_embd, height * width)
+        pos_embd = ggml_cont(ctx0, pos_embd);
+
+        return pos_embd;
+    }
+
     // build vision transformer (ViT) cgraph
     // this function should cover most of the models
     // if your model has specific features, you should probably duplicate this function
@@ -1966,6 +2033,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     switch (ctx->proj_type()) {
         case PROJECTOR_TYPE_GEMMA3:
         case PROJECTOR_TYPE_IDEFICS3:
+        case PROJECTOR_TYPE_LFM2:
             {
                 res = graph.build_siglip();
             } break;
@@ -2230,6 +2298,7 @@ struct clip_model_loader {
                         }
                     } break;
                 case PROJECTOR_TYPE_IDEFICS3:
+                case PROJECTOR_TYPE_LFM2:
                 case PROJECTOR_TYPE_INTERNVL:
                     {
                         get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
@@ -2533,6 +2602,15 @@ struct clip_model_loader {
                 {
                     model.projection = get_tensor(TN_MM_PROJECTOR);
                 } break;
+            case PROJECTOR_TYPE_LFM2:
+                {
+                    model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
+                    model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B);
+                    model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
+                    model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"));
+                    model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
+                    model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
+                } break;
             case PROJECTOR_TYPE_PIXTRAL:
                 {
                     model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
@@ -3428,6 +3506,43 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         res_imgs->grid_y = inst.grid_size.height;
         return true;
 
+    } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
+        GGML_ASSERT(params.proj_scale_factor);
+
+        // smart resize
+        const int width = img->nx;
+        const int height = img->ny;
+        const int total_factor = params.patch_size * params.proj_scale_factor;
+        constexpr int min_image_tokens = 64;
+        constexpr int max_image_tokens = 256;
+        const float min_pixels = min_image_tokens * total_factor * total_factor;
+        const float max_pixels = max_image_tokens * total_factor * total_factor;
+
+        auto round_by_factor = [f = total_factor](float x) { return static_cast<int>(std::nearbyintf(x / static_cast<float>(f))) * f; };
+        auto ceil_by_factor  = [f = total_factor](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
+        auto floor_by_factor = [f = total_factor](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
+
+        int h_bar = std::max(total_factor, round_by_factor(height));
+        int w_bar = std::max(total_factor, round_by_factor(width));
+
+        if (h_bar * w_bar > max_pixels) {
+            const auto beta = std::sqrt((height * width) / max_pixels);
+            h_bar = std::max(total_factor, floor_by_factor(height / beta));
+            w_bar = std::max(total_factor, floor_by_factor(width / beta));
+        } else if (h_bar * w_bar < min_pixels) {
+            const auto beta = std::sqrt(min_pixels / (height * width));
+            h_bar = ceil_by_factor(height * beta);
+            w_bar = ceil_by_factor(width * beta);
+        }
+
+        const std::array<uint8_t, 3> pad_color = {122, 116, 104};
+
+        clip_image_u8 resized_img;
+        image_manipulation::resize_and_pad_image(*img, resized_img, clip_image_size{w_bar, h_bar}, pad_color);
+        clip_image_f32_ptr res(clip_image_f32_init());
+        normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
+        res_imgs->entries.push_back(std::move(res));
+        return true;
     }
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
@@ -3630,6 +3745,10 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                     n_patches_sq /= 2;
                 }
             } break;
+        case PROJECTOR_TYPE_LFM2:
+            {
+                n_patches_sq = (img->nx / (params.patch_size * params.proj_scale_factor)) * (img->ny / (params.patch_size * params.proj_scale_factor));
+            } break;
         default:
             GGML_ABORT("unsupported projector type");
     }
@@ -4034,6 +4153,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         case PROJECTOR_TYPE_INTERNVL:
         case PROJECTOR_TYPE_QWEN2A:
         case PROJECTOR_TYPE_ULTRAVOX:
+        case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_VOXTRAL:
             {
                 // do nothing
@@ -4135,6 +4255,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_model_proj->ne[1];
         case PROJECTOR_TYPE_QWEN2A:
             return ctx->model.mm_fc_w->ne[1];
+        case PROJECTOR_TYPE_LFM2:
+            return ctx->model.mm_2_w->ne[1];
         default:
             GGML_ABORT("Unknown projector type");
     }

From de5627910df74298c998e6bb36ee3217375a5719 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 17 Aug 2025 03:41:45 -0500
Subject: [PATCH 087/287] vulkan: Optimize argsort (#15354)

- Launch an appropriate number of invocations (next larger power of two).
32 invocations is common and the barrier is much cheaper there.
- Specialize for "needs bounds checking" vs not.
- Make the code less branchy and [[unroll]] the loops. In the final code,
I see no branches inside the main loop (only predicated stores) when
needs_bounds_check is false.
- Always sort ascending, then apply the ascending vs descending option when
doing the final stores to memory.
- Copy the values into shared memory, makes them slightly cheaper to access.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 24 +++----
 .../ggml-vulkan/vulkan-shaders/argsort.comp   | 68 +++++++++++--------
 tests/test-backend-ops.cpp                    |  1 +
 3 files changed, 51 insertions(+), 42 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 20674bee58..4ecfb82566 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -345,6 +345,9 @@ enum vk_conv_shapes {
     CONV_SHAPE_COUNT,
 };
 
+static constexpr uint32_t num_argsort_pipelines = 11;
+static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
+
 struct vk_device_struct {
     std::recursive_mutex mutex;
 
@@ -505,7 +508,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
     vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16;
     vk_pipeline pipeline_rope_vision_f32, pipeline_rope_vision_f16;
-    vk_pipeline pipeline_argsort_f32;
+    vk_pipeline pipeline_argsort_f32[num_argsort_pipelines];
     vk_pipeline pipeline_sum_rows_f32;
     vk_pipeline pipeline_argmax_f32;
     vk_pipeline pipeline_count_equal_i32;
@@ -870,7 +873,6 @@ struct vk_op_soft_max_push_constants {
 
 struct vk_op_argsort_push_constants {
     uint32_t ncols;
-    uint32_t ncols_pad;
     int32_t order;
 };
 
@@ -3099,7 +3101,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f16, "rope_vision_f16", rope_vision_f16_len, rope_vision_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
     }
 
-    ggml_vk_create_pipeline(device, device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
+    for (uint32_t i = 0; i < num_argsort_pipelines; ++i) {
+        ggml_vk_create_pipeline(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1u<<i, 1, 1}, {1u<<i, i}, 1, true);
+    }
 
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
@@ -7160,7 +7164,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
     case GGML_OP_ARGSORT:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
-            return ctx->device->pipeline_argsort_f32;
+            uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
+            return ctx->device->pipeline_argsort_f32[idx];
         }
         return nullptr;
     case GGML_OP_SUM:
@@ -8485,16 +8490,8 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, c
 
     uint32_t ncols = src0->ne[0];
 
-    uint32_t ncols_pad = 1;
-    while (ncols_pad < ncols) {
-        ncols_pad *= 2;
-    }
-
-    GGML_ASSERT(ncols_pad <= 1024);
-
     ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, {
         ncols,
-        ncols_pad,
         op_params[0],
     }, dryrun);
 }
@@ -11367,6 +11364,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_ARGSORT:
+            return op->ne[0] <= max_argsort_cols;
         case GGML_OP_UPSCALE:
         case GGML_OP_ACC:
         case GGML_OP_CONCAT:
@@ -11376,7 +11375,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_SOFT_MAX_BACK:
-        case GGML_OP_ARGSORT:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGMAX:
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp b/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp
index d4fa45b1e1..dc53a401e0 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp
@@ -1,22 +1,24 @@
 #version 450
+#extension GL_EXT_control_flow_attributes : enable
 
 #include "types.comp"
 
-#define BLOCK_SIZE 1024
+layout(constant_id = 0) const int BLOCK_SIZE = 1024;
+layout(constant_id = 1) const int BLOCK_SIZE_LOG2 = 10;
 #define ASC 0
 
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1)          buffer D {int data_d[];};
 
 layout (push_constant) uniform parameter {
     uint ncols;
-    uint ncols_pad;
     uint order;
 } p;
 
 shared int dst_row[BLOCK_SIZE];
+shared A_TYPE a_sh[BLOCK_SIZE];
 
 void swap(uint idx0, uint idx1) {
     int tmp = dst_row[idx0];
@@ -24,7 +26,7 @@ void swap(uint idx0, uint idx1) {
     dst_row[idx1] = tmp;
 }
 
-void main() {
+void argsort(bool needs_bounds_check) {
     // bitonic sort
     const int col = int(gl_LocalInvocationID.x);
     const uint row = gl_WorkGroupID.y;
@@ -32,38 +34,46 @@ void main() {
     const uint row_offset = row * p.ncols;
 
     // initialize indices
-    if (col < p.ncols_pad) {
-        dst_row[col] = col;
-    }
+    dst_row[col] = col;
+    a_sh[col] = data_a[row_offset + col];
     barrier();
 
-    for (uint k = 2; k <= p.ncols_pad; k *= 2) {
-        for (uint j = k / 2; j > 0; j /= 2) {
-            const uint ixj = col ^ j;
-            if (col < p.ncols_pad && ixj > col) {
-                if ((col & k) == 0) {
-                    if (dst_row[col] >= p.ncols ||
-                        (dst_row[ixj] < p.ncols && (p.order == ASC ?
-                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] :
-                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]]))
-                    ) {
-                        swap(col, ixj);
-                    }
-                } else {
-                    if (dst_row[ixj] >= p.ncols ||
-                        (dst_row[col] < p.ncols && (p.order == ASC ?
-                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] :
-                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]]))
-                    ) {
-                        swap(col, ixj);
-                    }
-                }
+    uint num_outer_loop_iters = BLOCK_SIZE_LOG2;
+    [[unroll]] for (uint k = 2, outer_idx = 0; outer_idx < num_outer_loop_iters; k *= 2, outer_idx++) {
+        uint num_inner_loop_iters = outer_idx + 1;
+        [[unroll]] for (uint j = k / 2, inner_idx = 0; inner_idx < num_inner_loop_iters; j /= 2, inner_idx++) {
+            const int ixj = int(col ^ j);
+
+            int idx_0 = (col & k) == 0 ? col : ixj;
+            int idx_1 = (col & k) == 0 ? ixj : col;
+
+            int sh_idx_0 = dst_row[idx_0];
+            int sh_idx_1 = dst_row[idx_1];
+            bool idx_0_oob = needs_bounds_check ? sh_idx_0 >= p.ncols : false;
+            bool idx_1_oob = needs_bounds_check ? sh_idx_1 >= p.ncols : false;
+
+            if ((idx_0_oob ||
+                (!idx_1_oob && a_sh[sh_idx_0] > a_sh[sh_idx_1])) && (ixj > col)) {
+                swap(idx_0, idx_1);
             }
+
             barrier();
         }
     }
 
     if (col < p.ncols) {
-        data_d[row_offset + col] = dst_row[col];
+        if (p.order == ASC) {
+            data_d[row_offset + col] = dst_row[col];
+        } else {
+            data_d[row_offset + p.ncols - col - 1] = dst_row[col];
+        }
+    }
+}
+
+void main() {
+    if (p.ncols == BLOCK_SIZE) {
+        argsort(false);
+    } else {
+        argsort(true);
     }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 0df5ebb565..4623605f66 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6028,6 +6028,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1024, 1, 1, 1}, order));
     }
 
     for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR}) {

From b143fbc87af0324aa49e16cd91faf3ba8bb22231 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 17 Aug 2025 13:30:23 +0200
Subject: [PATCH 088/287] ci : fix hang in windows-hip build/release (#15365)

* fix hang in windows-latest-cmake-hip

* apply fix to release as well
---
 .github/workflows/build.yml   | 3 ++-
 .github/workflows/release.yml | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 83aaea9947..43553ac13b 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1070,7 +1070,8 @@ jobs:
           write-host "Downloading AMD HIP SDK Installer"
           Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9ebb0236fa..5367637e42 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -557,7 +557,8 @@ jobs:
           write-host "Downloading AMD HIP SDK Installer"
           Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm

From 4d196981d4db79e0105b939eaa7ecd40385b721c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 17 Aug 2025 14:47:42 +0200
Subject: [PATCH 089/287] convert : force patch_embd weights to F16 or F32 to
 avoid broken GGUFs (#15367)

* force patch_embd weights to f32

* use MmprojModel base tensor_force_quant instead
---
 convert_hf_to_gguf.py | 28 ++++++++++++----------------
 1 file changed, 12 insertions(+), 16 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index bd21e55f4a..b45c8f1d7f 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1334,6 +1334,12 @@ class MmprojModel(ModelBase):
             return None
         raise KeyError(f"could not find any of: {keys}")
 
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, name, n_dims  # unused
+        if ".patch_embd.weight" in new_name:
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return False
+
 
 @ModelBase.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(TextModel):
@@ -2305,10 +2311,9 @@ class SmolVLMModel(MmprojModel):
         self.gguf_writer.add_vision_use_gelu(True)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".embeddings." in name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -3296,12 +3301,9 @@ class Qwen2VLVisionModel(MmprojModel):
         self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
-        if ".patch_embd." in new_name:
-            return gguf.GGMLQuantizationType.F16
         if ".position_embd." in new_name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -3374,10 +3376,9 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
         yield ("audio_tower.embed_positions.weight", pos_embd)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".conv" in name and ".weight" in name:
             return gguf.GGMLQuantizationType.F16
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if name.startswith("thinker."):
@@ -3423,12 +3424,9 @@ class InternVisionModel(MmprojModel):
         self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
-        if ".patch_embd." in new_name:
-            return gguf.GGMLQuantizationType.F16
         if ".position_embd." in new_name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def _mapping_interns1_name(self, name):
         names_map = {
@@ -5062,13 +5060,12 @@ class Gemma3VisionModel(MmprojModel):
             self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         # related to https://github.com/ggml-org/llama.cpp/issues/13025
         if "input_projection" in name:
             return gguf.GGMLQuantizationType.F16
         if ".embeddings." in name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -7727,10 +7724,9 @@ class WhisperEncoderModel(MmprojModel):
         self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".conv" in name and ".weight" in name:
             return gguf.GGMLQuantizationType.F16
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused

From 19f4decae0ead52debe56095ba8d693b4f14e4df Mon Sep 17 00:00:00 2001
From: Dong Won Kim <63934649+ddwkim@users.noreply.github.com>
Date: Sun, 17 Aug 2025 23:03:09 +0900
Subject: [PATCH 090/287] vulkan: support sqrt (#15370)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 23 +++++++++++++++++++
 ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp | 17 ++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  2 ++
 3 files changed, 42 insertions(+)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4ecfb82566..bc227ffb9f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -464,6 +464,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bilinear_ac_f32;
     vk_pipeline pipeline_scale_f32;
     vk_pipeline pipeline_sqr_f32;
+    vk_pipeline pipeline_sqrt_f32;
     vk_pipeline pipeline_sin_f32;
     vk_pipeline pipeline_cos_f32;
     vk_pipeline pipeline_clamp_f32;
@@ -3031,6 +3032,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_sqrt_f32, "sqrt_f32", sqrt_f32_len, sqrt_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
@@ -6981,6 +6983,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_sqr_f32;
         }
         return nullptr;
+    case GGML_OP_SQRT:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_sqrt_f32;
+        }
+        return nullptr;
     case GGML_OP_SIN:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_sin_f32;
@@ -7290,6 +7297,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     case GGML_OP_CONCAT:
     case GGML_OP_UPSCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -7595,6 +7603,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_MUL:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -8242,6 +8251,10 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, vk_op_unary_push_constants_init(src0, dst), dryrun);
 }
 
+static void ggml_vk_sqrt(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQRT, vk_op_unary_push_constants_init(src0, dst), dryrun);
+}
+
 static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SIN, vk_op_unary_push_constants_init(src0, dst), dryrun);
 }
@@ -9697,6 +9710,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_UPSCALE:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -9766,6 +9780,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_OP_UPSCALE:
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
+        case GGML_OP_SQRT:
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
@@ -9867,6 +9882,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_SQR:
         ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun);
 
+        break;
+    case GGML_OP_SQRT:
+        ggml_vk_sqrt(ctx, compute_ctx, src0, node, dryrun);
+
         break;
     case GGML_OP_SIN:
         ggml_vk_sin(ctx, compute_ctx, src0, node, dryrun);
@@ -10118,6 +10137,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     case GGML_OP_UPSCALE:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -11357,6 +11377,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_SILU_BACK:
         case GGML_OP_RMS_NORM_BACK:
         case GGML_OP_SQR:
+        case GGML_OP_SQRT:
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
@@ -11801,6 +11822,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         tensor_clone = ggml_scale_bias(ggml_ctx, src_clone[0], params[0], params[1]);
     } else if (tensor->op == GGML_OP_SQR) {
         tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]);
+    } else if (tensor->op == GGML_OP_SQRT) {
+        tensor_clone = ggml_sqrt(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_SIN) {
         tensor_clone = ggml_sin(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_COS) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp
new file mode 100644
index 0000000000..4bc697b9b9
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp
@@ -0,0 +1,17 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
+    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(sqrt(val));
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 67a9ee422b..8c6627dee4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -566,6 +566,8 @@ void process_shaders() {
 
     string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
 
+    string_to_spv("sqrt_f32", "sqrt.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+
     string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
 
     string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});

From 21c17b5befc5f6be5992bc87fc1ba99d388561df Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 17 Aug 2025 11:08:57 -0500
Subject: [PATCH 091/287] vulkan: Use larger workgroups for mul_mat_vec when M
 is small (#15355)

* vulkan: Use larger workgroups for mul_mat_vec when M is small

Also use subgroup instructions for (part of) the reduction when supported.
Without this, the more expensive reductions would eat into the benefits of
the larger workgroups.

* update heuristic for amd/intel

Co-authored-by: 0cc4m <picard12@live.de>

---------

Co-authored-by: 0cc4m <picard12@live.de>
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 134 +++++++++++-------
 .../vulkan-shaders/mul_mat_vec_base.comp      |  38 ++++-
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  15 ++
 3 files changed, 134 insertions(+), 53 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index bc227ffb9f..7ef938066b 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -345,6 +345,12 @@ enum vk_conv_shapes {
     CONV_SHAPE_COUNT,
 };
 
+enum dmmv_wg_sizes {
+    DMMV_WG_SIZE_SUBGROUP,
+    DMMV_WG_SIZE_LARGE,
+    DMMV_WG_SIZE_COUNT,
+};
+
 static constexpr uint32_t num_argsort_pipelines = 11;
 static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
 
@@ -435,8 +441,8 @@ struct vk_device_struct {
     vk_pipeline pipeline_quantize_q8_1;
 
     vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
     vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_mul_mat_vec_p021_f16_f32[p021_max_gqa_ratio];
@@ -2773,54 +2779,61 @@ static void ggml_vk_load_shaders(vk_device& device) {
         rm_stdq = 2;
     uint32_t rm_iq = 2 * rm_kq;
 
-    for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32_"+std::to_string(i+1), mul_mat_vec_bf16_f32_f32_len, mul_mat_vec_bf16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq1_s_f32_f32_len,   mul_mat_vec_iq1_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq1_m_f32_f32_len,   mul_mat_vec_iq1_m_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq2_xxs_f32_f32_len, mul_mat_vec_iq2_xxs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq2_xs_f32_f32_len,  mul_mat_vec_iq2_xs_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f32_f32_len,   mul_mat_vec_iq2_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f32_f32_len, mul_mat_vec_iq3_xxs_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f32_f32_len,   mul_mat_vec_iq3_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f32_f32_len,  mul_mat_vec_iq4_xs_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f32_f32_len,  mul_mat_vec_iq4_nl_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f32_f32_len,   mul_mat_vec_mxfp4_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
+    for (uint32_t w = 0; w < DMMV_WG_SIZE_COUNT; ++w) {
+        uint32_t wg_size_subgroup16 = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_16 : (subgroup_size_16 * 4);
+        uint32_t wg_size_subgroup   = (w == DMMV_WG_SIZE_SUBGROUP) ? device->subgroup_size : (device->subgroup_size * 4);
 
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32_"+std::to_string(i+1), mul_mat_vec_bf16_f16_f32_len, mul_mat_vec_bf16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq1_s_f16_f32_len,   mul_mat_vec_iq1_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq1_m_f16_f32_len,   mul_mat_vec_iq1_m_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq2_xxs_f16_f32_len, mul_mat_vec_iq2_xxs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq2_xs_f16_f32_len,  mul_mat_vec_iq2_xs_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq2_s_f16_f32_len,   mul_mat_vec_iq2_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq3_xxs_f16_f32_len, mul_mat_vec_iq3_xxs_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f16_f32_len,   mul_mat_vec_iq3_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f16_f32_len,  mul_mat_vec_iq4_xs_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f16_f32_len,  mul_mat_vec_iq4_nl_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
-        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f16_f32_len,   mul_mat_vec_mxfp4_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
+        const bool s = device->subgroup_add && device->architecture != vk_device_architecture::AMD_GCN;
+
+        for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f32_f32_f32_len[s],  arr_dmmv_f32_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f16_f32_f32_len[s],  arr_dmmv_f16_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_bf16_f32_f32_len[s], arr_dmmv_bf16_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_0_f32_f32_len[s], arr_dmmv_q4_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_1_f32_f32_len[s], arr_dmmv_q4_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_0_f32_f32_len[s], arr_dmmv_q5_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_1_f32_f32_len[s], arr_dmmv_q5_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q8_0_f32_f32_len[s], arr_dmmv_q8_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q2_k_f32_f32_len[s], arr_dmmv_q2_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q3_k_f32_f32_len[s], arr_dmmv_q3_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_k_f32_f32_len[s], arr_dmmv_q4_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_k_f32_f32_len[s], arr_dmmv_q5_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q6_k_f32_f32_len[s], arr_dmmv_q6_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_s_f32_f32_len[s],   arr_dmmv_iq1_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_m_f32_f32_len[s],   arr_dmmv_iq1_m_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq2_xxs_f32_f32_len[s], arr_dmmv_iq2_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq2_xs_f32_f32_len[s],  arr_dmmv_iq2_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq2_s_f32_f32_len[s],   arr_dmmv_iq2_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq3_xxs_f32_f32_len[s], arr_dmmv_iq3_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq3_s_f32_f32_len[s],   arr_dmmv_iq3_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_xs_f32_f32_len[s],  arr_dmmv_iq4_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_nl_f32_f32_len[s],  arr_dmmv_iq4_nl_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_mxfp4_f32_f32_len[s],   arr_dmmv_mxfp4_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f32_f16_f32_len[s],  arr_dmmv_f32_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f16_f16_f32_len[s],  arr_dmmv_f16_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_bf16_f16_f32_len[s], arr_dmmv_bf16_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_0_f16_f32_len[s], arr_dmmv_q4_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_1_f16_f32_len[s], arr_dmmv_q4_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_0_f16_f32_len[s], arr_dmmv_q5_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_1_f16_f32_len[s], arr_dmmv_q5_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q8_0_f16_f32_len[s], arr_dmmv_q8_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q2_k_f16_f32_len[s], arr_dmmv_q2_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q3_k_f16_f32_len[s], arr_dmmv_q3_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_k_f16_f32_len[s], arr_dmmv_q4_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_k_f16_f32_len[s], arr_dmmv_q5_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q6_k_f16_f32_len[s], arr_dmmv_q6_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_s_f16_f32_len[s],   arr_dmmv_iq1_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_m_f16_f32_len[s],   arr_dmmv_iq1_m_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq2_xxs_f16_f32_len[s], arr_dmmv_iq2_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq2_xs_f16_f32_len[s],  arr_dmmv_iq2_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq2_s_f16_f32_len[s],   arr_dmmv_iq2_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq3_xxs_f16_f32_len[s], arr_dmmv_iq3_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq3_s_f16_f32_len[s],   arr_dmmv_iq3_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_xs_f16_f32_len[s],  arr_dmmv_iq4_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_nl_f16_f32_len[s],  arr_dmmv_iq4_nl_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_mxfp4_f16_f32_len[s],   arr_dmmv_mxfp4_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+        }
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -4389,7 +4402,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
     return (ctx->device->fp16 && prec == GGML_PREC_DEFAULT) ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc;
 }
 
-static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols) {
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols, uint32_t m, uint32_t k) {
     VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
     GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
     GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols);
@@ -4423,7 +4436,24 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
             return nullptr;
     }
 
-    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type][num_cols-1];
+    // heuristic to choose workgroup size
+    uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
+    if (ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
+        // Prefer larger workgroups when M is small, to spread the work out more
+        // and keep more SMs busy.
+        // q6_k seems to prefer small workgroup size even for "medium" values of M.
+        if (a_type == GGML_TYPE_Q6_K) {
+            if (m < 4096 && k >= 1024) {
+                dmmv_wg = DMMV_WG_SIZE_LARGE;
+            }
+        } else {
+            if (m <= 8192 && k >= 1024) {
+                dmmv_wg = DMMV_WG_SIZE_LARGE;
+            }
+        }
+    }
+
+    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[dmmv_wg][a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[dmmv_wg][a_type][num_cols-1];
 }
 
 static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) {
@@ -5730,7 +5760,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     } else {
         to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
     }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11);
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11, ne20, ne00);
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
     GGML_ASSERT(dmmv != nullptr);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
index 903753c7e2..b93e9948f7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
@@ -1,6 +1,10 @@
 #extension GL_EXT_control_flow_attributes : enable
 #extension GL_EXT_shader_16bit_storage : require
 #extension GL_EXT_shader_8bit_storage : require
+#if USE_SUBGROUP_ADD
+#extension GL_KHR_shader_subgroup_basic : require
+#extension GL_KHR_shader_subgroup_arithmetic : require
+#endif
 
 #ifdef MUL_MAT_ID
 #define EXPERT_COUNT 8
@@ -90,7 +94,38 @@ layout (constant_id = 2) const uint NUM_COLS = 1;
 
 shared FLOAT_TYPE tmpsh[NUM_COLS][NUM_ROWS][BLOCK_SIZE];
 
-void reduce_result(const in FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) {
+void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) {
+    // subgroupAdd is probably faster on devices that support it,
+    // particularly when the workgroup has more than one subgroup
+#if USE_SUBGROUP_ADD
+    // sum up partial sums within a subgroup
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            temp[j][n] = subgroupAdd(temp[j][n]);
+        }
+    }
+
+    // Go through shared memory to sum partials across subgroups
+    if (gl_SubgroupInvocationID == 0) {
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                tmpsh[j][n][gl_SubgroupID] = temp[j][n];
+            }
+        }
+    }
+    barrier();
+    if (tid == 0) {
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                temp[j][n] = FLOAT_TYPE(0);
+                [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
+                    temp[j][n] += tmpsh[j][n][s];
+                }
+                data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
+            }
+        }
+    }
+#else
     // sum up partial sums and write back result
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
@@ -115,4 +150,5 @@ void reduce_result(const in FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32
             }
         }
     }
+#endif
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 8c6627dee4..6cc474b90a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -472,6 +472,9 @@ void process_shaders() {
         string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
         string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}}));
 
+        string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
+        string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
+
         string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
 
         // Dequant shaders
@@ -789,6 +792,18 @@ void write_output_files() {
         fputs(data.c_str(), src);
         fputs(len.c_str(), src);
     }
+
+    for (const std::string& btype : {"f16", "f32"}) {
+    for (const auto& tname : type_names) {
+        fprintf(hdr, "extern unsigned char *arr_dmmv_%s_%s_f32_data[2];\n", tname.c_str(), btype.c_str());
+        fprintf(hdr, "extern uint64_t arr_dmmv_%s_%s_f32_len[2];\n", tname.c_str(), btype.c_str());
+        std::string data = "unsigned char *arr_dmmv_" + tname + "_" + btype + "_f32_data[2] = {mul_mat_vec_" + tname + "_" + btype + "_f32_data, mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_data};\n";
+        std::string len =  "uint64_t arr_dmmv_"       + tname + "_" + btype + "_f32_len[2] =  {mul_mat_vec_" + tname + "_" + btype + "_f32_len,  mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_len};\n";
+        fputs(data.c_str(), src);
+        fputs(len.c_str(), src);
+    }
+    }
+
     fclose(hdr);
     fclose(src);
 }

From e5155e698645242d4f019267ecc40ea9bad81b09 Mon Sep 17 00:00:00 2001
From: Oleksandr Kuvshynov <661042+okuvshynov@users.noreply.github.com>
Date: Sun, 17 Aug 2025 18:28:58 -0400
Subject: [PATCH 092/287] server : export max observed n_past value (#15361)

Add tracking for high watermark cache usage and make it available in /metrics endpoint.

Use-case: Tracking largest needed cache usage under realistic workload
to better understand memory requirements and be able to adjust
cache size/quantization for model/cache accordingly.
---
 tools/server/server.cpp | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 0b40f7bfa4..24bfae7891 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -1201,6 +1201,8 @@ struct server_task_result_metrics : server_task_result {
     uint64_t n_tokens_predicted_total        = 0;
     uint64_t t_tokens_generation_total       = 0;
 
+    uint64_t n_past_max = 0;
+
     uint64_t n_prompt_tokens_processed = 0;
     uint64_t t_prompt_processing       = 0;
 
@@ -1226,6 +1228,8 @@ struct server_task_result_metrics : server_task_result {
             { "n_tokens_predicted_total",        n_tokens_predicted_total },
             { "t_prompt_processing_total",       t_prompt_processing_total },
 
+            { "n_past_max",                      n_past_max },
+
             { "n_prompt_tokens_processed",       n_prompt_tokens_processed },
             { "t_prompt_processing",             t_prompt_processing },
             { "n_tokens_predicted",              n_tokens_predicted },
@@ -1587,6 +1591,8 @@ struct server_metrics {
     uint64_t n_tokens_predicted_total        = 0;
     uint64_t t_tokens_generation_total       = 0;
 
+    uint64_t n_past_max = 0;
+
     uint64_t n_prompt_tokens_processed = 0;
     uint64_t t_prompt_processing       = 0;
 
@@ -1605,6 +1611,10 @@ struct server_metrics {
         n_prompt_tokens_processed       += slot.n_prompt_tokens_processed;
         t_prompt_processing             += slot.t_prompt_processing;
         t_prompt_processing_total       += slot.t_prompt_processing;
+
+        if (slot.n_past > 0) {
+            n_past_max = std::max(n_past_max, (uint64_t) slot.n_past);
+        }
     }
 
     void on_prediction(const server_slot & slot) {
@@ -1620,6 +1630,9 @@ struct server_metrics {
             if (slot.is_processing()) {
                 n_busy_slots_total++;
             }
+            if (slot.n_past > 0) {
+                n_past_max = std::max(n_past_max, (uint64_t) slot.n_past);
+            }
         }
     }
 
@@ -2875,6 +2888,8 @@ struct server_context {
                     res->n_tokens_predicted_total        = metrics.n_tokens_predicted_total;
                     res->t_tokens_generation_total       = metrics.t_tokens_generation_total;
 
+                    res->n_past_max = metrics.n_past_max;
+
                     res->n_prompt_tokens_processed = metrics.n_prompt_tokens_processed;
                     res->t_prompt_processing       = metrics.t_prompt_processing;
                     res->n_tokens_predicted        = metrics.n_tokens_predicted;
@@ -4077,6 +4092,10 @@ int main(int argc, char ** argv) {
                     {"name",  "n_decode_total"},
                     {"help",  "Total number of llama_decode() calls"},
                     {"value",  res_metrics->n_decode_total}
+            }, {
+                    {"name",  "n_past_max"},
+                    {"help",  "Largest observed n_past."},
+                    {"value",  res_metrics->n_past_max}
             }, {
                     {"name",  "n_busy_slots_per_decode"},
                     {"help",  "Average number of busy slots per llama_decode() call"},

From ae532eac2c1df1d8edc3d2719145895b966de1bf Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 18 Aug 2025 00:56:29 -0500
Subject: [PATCH 093/287] vulkan: disable spirv-opt for bfloat16 shaders
 (#15352)

---
 ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 6cc474b90a..75c572d6fc 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -223,7 +223,8 @@ void string_to_spv_func(const std::string& _name, const std::string& in_fname, c
     std::string target_env = (name.find("_cm2") != std::string::npos) ? "--target-env=vulkan1.3" : "--target-env=vulkan1.2";
 
     // disable spirv-opt for coopmat shaders for https://github.com/ggerganov/llama.cpp/issues/10734
-    std::string opt_level = coopmat ? "" : "-O";
+    // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344
+    std::string opt_level = (coopmat || name.find("bf16") != std::string::npos) ? "" : "-O";
 
     #ifdef _WIN32
         std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, opt_level, "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""};

From f44f7931729022c57319a0124931120a169e0da9 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Mon, 18 Aug 2025 03:23:56 -0400
Subject: [PATCH 094/287] ggml-quants : fix make_qp_quants NANs and IQ1
 assertion errors (#15379)

* ggml-quants : fix make_qp_quants NANs and IQ1 assertion errors

* ggml-quants : avoid division by zero in make_q3_quants
---
 ggml/src/ggml-quants.c | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 94f6405ca1..727932123e 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -566,7 +566,7 @@ static float make_q3_quants(int n, int nmax, const float * GGML_RESTRICT x, int8
         for (int i = 0; i < n; ++i) {
             L[i] += nmax;
         }
-        return sumlx / suml2;
+        return suml2 > 0.0f ? sumlx / suml2 : 0.0f;
     }
     for (int i = 0; i < n; ++i) {
         int l = nearest_int(iscale * x[i]);
@@ -901,7 +901,7 @@ static float make_qp_quants(int n, int nmax, const float * GGML_RESTRICT x, uint
     for (int i = 0; i < n; ++i) {
         max = MAX(max, x[i]);
     }
-    if (!max) { // all zero
+    if (max < GROUP_MAX_EPS) { // all zero
         for (int i = 0; i < n; ++i) { L[i] = 0; }
         return 0.f;
     }
@@ -966,7 +966,7 @@ static float make_qp_quants(int n, int nmax, const float * GGML_RESTRICT x, uint
             break;
         }
     }
-    return sumlx/suml2;
+    return suml2 > 0.0f ? sumlx / suml2 : 0.0f;
 }
 
 static void quantize_row_q2_K_impl(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k, const float * GGML_RESTRICT quant_weights) {
@@ -4266,7 +4266,7 @@ static void quantize_row_iq1_s_impl(const float * GGML_RESTRICT x, void * GGML_R
                     sumw[j+1] = sumw[j] + weight[i];
                 }
             }
-            float best_score = -FLT_MIN, scale = max;
+            float best_score = -FLT_MAX, scale = max;
             int besti1 = -1, besti2 = -1, best_shift = 0;
             for (int i1 = 0; i1 <= block_size; ++i1) {
                 for (int i2 = i1; i2 <= block_size; ++i2) {
@@ -4442,7 +4442,7 @@ static void quantize_row_iq1_m_impl(const float * GGML_RESTRICT x, void * GGML_R
                 idx[2*j] = j;
             }
             qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
-            float best_score = -FLT_MIN, scale = max;
+            float best_score = -FLT_MAX, scale = max;
             int besti1 = -1, besti2 = -1, best_k = -1;
             // 0: +, +
             // 1: +, -

From 618575c5825d7d4f170e686e772178d2aae148ae Mon Sep 17 00:00:00 2001
From: Dobri Danchev <12420863+danchev@users.noreply.github.com>
Date: Mon, 18 Aug 2025 05:50:48 -0500
Subject: [PATCH 095/287] Fix broken build: require updated pip to support
 --break-system-packages (#15357)

* Revert "devops : fix compile bug when the BASE_CUDA_DEV_CONTAINER is based on Ubuntu 24.04 (#15005)"

This reverts commit e4e915912cfd2ee15c5a4a0074813232134892f6.

* devops: Allow pip to modify externally-managed python environment (system installation)

- Updated pip install commands to include the --break-system-packages
  flag, ensuring compatibility when working with system-managed Python
  environments (PEP 668).

- Note: The --break-system-packages option was introduced in 2023.
  Ensure pip is updated to a recent version before using this flag.

fixes [#15004](https://github.com/danchev/llama.cpp/issues/15004)
---
 .devops/cuda.Dockerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
index f9dc7f3163..4b708ae278 100644
--- a/.devops/cuda.Dockerfile
+++ b/.devops/cuda.Dockerfile
@@ -60,6 +60,7 @@ RUN apt-get update \
     git \
     python3 \
     python3-pip \
+    && pip install --upgrade pip setuptools wheel \
     && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \
     && apt clean -y \

From d1d82416006e7ff41780cb0e9b5f28d30a267497 Mon Sep 17 00:00:00 2001
From: davidef <davidef1986@gmail.com>
Date: Mon, 18 Aug 2025 16:51:42 +0200
Subject: [PATCH 096/287] server : fix incoming tasks not process in order
 (#15395)

---
 tools/server/server.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 24bfae7891..ab88f3d26c 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -1729,7 +1729,7 @@ struct server_queue {
     void pop_deferred_task() {
         std::unique_lock<std::mutex> lock(mutex_tasks);
         if (!queue_tasks_deferred.empty()) {
-            queue_tasks.emplace_back(std::move(queue_tasks_deferred.front()));
+            queue_tasks.emplace_front(std::move(queue_tasks_deferred.front()));
             queue_tasks_deferred.pop_front();
         }
         condition_tasks.notify_one();

From 3007baf201e7ffcda17dbdb0335997fa50a6595b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 18 Aug 2025 18:11:44 +0300
Subject: [PATCH 097/287] readme : update hot topics (#15397)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 11d9290786..8446756384 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,7 @@ LLM inference in C/C++
 
 ## Hot topics
 
+- **[guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)**
 - **[[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)**
 - Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)

From baa9255a45105d2d3b4ec432af13b7a6eda3ff35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Mon, 18 Aug 2025 19:30:17 +0200
Subject: [PATCH 098/287] llama : merge conts and reshapes and remove
 unnecessary cont (#15380)

* remove unnecessary conts and merge reshapes

* restore necessary conts

* merge more conts and reshapes

* merge even more conts and reshapes
---
 src/llama-model.cpp | 143 +++++++++++++++++++++++---------------------
 tools/mtmd/clip.cpp |  45 +++++++-------
 2 files changed, 96 insertions(+), 92 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 23a26f0c64..0c149cd478 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6743,9 +6743,9 @@ struct llm_build_falcon : public llm_graph_context {
 
                 ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_ext(
@@ -7023,9 +7023,9 @@ struct llm_build_dbrx : public llm_graph_context {
 
                 Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
@@ -7145,13 +7145,13 @@ struct llm_build_starcoder : public llm_graph_context {
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                 cb(cur, "bqkv", il);
 
-                ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
+                ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -7367,13 +7367,15 @@ struct llm_build_bert : public llm_graph_context {
                         cb(cur, "bqkv", il);
                     }
 
-                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                    Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
+                    Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
+                    Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
+                    Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
                     Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
                     Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
                     Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 }
 
                 if (model.layers[il].attn_q_norm) {
@@ -7381,6 +7383,10 @@ struct llm_build_bert : public llm_graph_context {
                             model.layers[il].attn_q_norm,
                             model.layers[il].attn_q_norm_b,
                             LLM_NORM, il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                } else {
+                    Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 }
 
                 if (model.layers[il].attn_k_norm) {
@@ -7388,11 +7394,11 @@ struct llm_build_bert : public llm_graph_context {
                             model.layers[il].attn_k_norm,
                             model.layers[il].attn_k_norm_b,
                             LLM_NORM, il);
-                }
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                } else {
+                    Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                }
 
                 // RoPE
                 if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
@@ -7537,9 +7543,9 @@ struct llm_build_neo_bert : public llm_graph_context {
 
                 Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 // RoPE
                 Qcur = ggml_rope_ext(
@@ -7646,13 +7652,13 @@ struct llm_build_bloom : public llm_graph_context {
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                 cb(cur, "bqkv", il);
 
-                ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
+                ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -7770,7 +7776,7 @@ struct llm_build_mpt : public llm_graph_context {
 
                 ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
                 ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -7789,17 +7795,18 @@ struct llm_build_mpt : public llm_graph_context {
                             model.layers[il].attn_k_norm_b,
                             LLM_NORM, il);
                     cb(Kcur, "Kcur", il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
-                    Qcur = ggml_cont(ctx0, Qcur);
+                    Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     cb(Qcur, "Qcur", il);
 
-                    Kcur = ggml_cont(ctx0, Kcur);
+                    Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                     cb(Kcur, "Kcur", il);
                 }
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -8051,9 +8058,9 @@ struct llm_build_qwen : public llm_graph_context {
 
                 ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,   n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_ext(
@@ -9026,21 +9033,21 @@ struct llm_build_phi2 : public llm_graph_context {
 
                     Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                     Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                    Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
+                    Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
                     Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                     Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                     Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 }
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -9164,21 +9171,21 @@ struct llm_build_phi3 : public llm_graph_context {
 
                     Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));
                     Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd));
-                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
+                    Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
+                    Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
                     Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                     Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                     Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 }
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, rope_factors,
                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -9428,17 +9435,17 @@ struct llm_build_gpt2 : public llm_graph_context {
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                 cb(cur, "bqkv", il);
 
-                ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
+                ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
@@ -9534,9 +9541,9 @@ struct llm_build_codeshell : public llm_graph_context {
 
                 ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
@@ -10864,8 +10871,8 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
         ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_correct_coef, modalities); // [n_altup, n_tokens]
         all_coefs = ggml_scale_bias(ctx0, all_coefs, 1.0f, 1.0f); // + 1.0
         cb(all_coefs, "all_coefs", il);
-        all_coefs = ggml_cont(ctx0, ggml_transpose(ctx0, all_coefs)); // [n_tokens, n_altup]
-        all_coefs = ggml_reshape_3d(ctx0, all_coefs, 1, n_tokens, n_altup); // [1, n_tokens, n_altup]
+        all_coefs = ggml_transpose(ctx0, all_coefs); // [n_tokens, n_altup]
+        all_coefs = ggml_cont_3d(ctx0, all_coefs, 1, n_tokens, n_altup); // [1, n_tokens, n_altup]
 
         innovation = ggml_repeat_4d(ctx0, innovation, n_embd, n_tokens, n_altup, 1);
         ggml_tensor * corrected = ggml_mul(ctx0, innovation, all_coefs); // [n_embd, n_tokens, n_altup]
@@ -12278,9 +12285,9 @@ struct llm_build_gptneox : public llm_graph_context {
 
                 ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                 ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
@@ -13413,17 +13420,17 @@ struct llm_build_jais : public llm_graph_context {
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                 cb(cur, "bqkv", il);
 
-                ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
-                ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));
+                ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd));
+                ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd));
+                ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa));
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_cont_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
@@ -13526,6 +13533,7 @@ struct llm_build_chatglm : public llm_graph_context {
                     }
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
                     cur = build_lora_mm(model.layers[il].wqkv, cur);
                     cb(cur, "wqkv", il);
@@ -13535,11 +13543,10 @@ struct llm_build_chatglm : public llm_graph_context {
                     }
                     Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                     Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                    Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
+                    Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 }
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
                 //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
@@ -13660,6 +13667,7 @@ struct llm_build_glm4 : public llm_graph_context {
                     }
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 } else {
                     cur = build_lora_mm(model.layers[il].wqkv, cur);
                     cb(cur, "wqkv", il);
@@ -13669,11 +13677,10 @@ struct llm_build_glm4 : public llm_graph_context {
                     }
                     Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
                     Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
-                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                    Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
+                    Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
                 }
 
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -16840,13 +16847,13 @@ private:
 
             ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head, n_tokens, n_embd_head_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
             ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv, n_tokens, n_embd_head_k * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
-            ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, qkv, n_embd_head_v * n_head_kv, n_tokens, qkv->nb[1], v_offset * ggml_element_size(qkv)));
+            ggml_tensor * Vcur = ggml_view_2d(ctx0, qkv, n_embd_head_v * n_head_kv, n_tokens, qkv->nb[1], v_offset * ggml_element_size(qkv));
 
             cb(Qcur, "Qcur", il);
             cb(Kcur, "Kcur", il);
             cb(Vcur, "Vcur", il);
 
-            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv, n_tokens);
+            Vcur = ggml_cont_3d(ctx0, Vcur, n_embd_head_v, n_head_kv, n_tokens);
 
             Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
             cb(Qcur, "Qcur_normed", il);
@@ -16913,15 +16920,13 @@ private:
         cb(zx, "mamba_in_proj", il);
         // {8192, 5, 1, 1} -> {8192, 1, 5, 1}
         zx = ggml_permute(ctx0, zx, 0, 2, 1, 3);
-        zx = ggml_cont(ctx0, zx);
-        zx = ggml_reshape_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
+        zx = ggml_cont_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
         cb(zx, "mamba_in_proj_out", il);
 
         // split into z and x
         // => {head_dim * n_heads, n_seq_tokens, n_seqs}
         ggml_tensor * x = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], head_dim*ggml_element_size(zx));
-        x = ggml_cont(ctx0, x);
-        x = ggml_reshape_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
+        x = ggml_cont_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
         // x = ggml_permute(ctx0, x, 0, 2, 1, 3);
         cb(x, "mamba_x_split", il);
 
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index c27f8ebbd9..099e282370 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -508,8 +508,8 @@ struct clip_graph {
             const int patches_per_image = n_patches_x;
             const int kernel_size = hparams.proj_scale_factor;
 
-            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-            cur = ggml_reshape_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size);
+            cur = ggml_transpose(ctx0, cur);
+            cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size);
 
             // doing a pool2d to reduce the number of output tokens
             cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
@@ -537,13 +537,13 @@ struct clip_graph {
             GGML_ASSERT(scale_factor != 0);
             cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height, bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_4d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 height / scale_factor,
                 width / scale_factor,
                 bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_reshape_3d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_3d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 seq / (scale_factor * scale_factor),
                 bsz);
@@ -570,13 +570,13 @@ struct clip_graph {
 
             // unshuffle h
             cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
-            cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
+            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
 
             // unshuffle w
-            cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
-            cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
+            cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
+            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
 
-            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+            cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
 
             // projection
             cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
@@ -715,15 +715,15 @@ struct clip_graph {
             auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
             inp = ggml_add(ctx0, inp, inp_1);
 
-            inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
-            inp = ggml_reshape_4d(
+            inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
+            inp = ggml_cont_4d(
                 ctx0, inp,
                 n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
             inp = ggml_reshape_4d(
                 ctx0, inp,
                 n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
-            inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
-            inp = ggml_reshape_3d(
+            inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+            inp = ggml_cont_3d(
                 ctx0, inp,
                 n_embd, n_patches_x * n_patches_y, batch_size);
         }
@@ -988,14 +988,14 @@ struct clip_graph {
             GGML_ASSERT(scale_factor > 0);
             cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_4d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 height / scale_factor,
                 width / scale_factor,
                 bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
             // flatten to 2D
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_2d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 cur->ne[1] * cur->ne[2]);
         }
@@ -1081,14 +1081,14 @@ struct clip_graph {
                 n_patches_y,
                 bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_4d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 n_patches_x / scale_factor,
                 n_patches_y / scale_factor,
                 bsz);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
             // flatten to 2D
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, cur),
+            cur = ggml_cont_2d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
                 n_patches / scale_factor / scale_factor);
             cb(cur, "pixel_shuffle", -1);
@@ -1321,8 +1321,8 @@ struct clip_graph {
                 ggml_tensor * block_1 = nullptr;
                 {
                     // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
-                    mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
-                    mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
+                    mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3);
+                    mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
                     // stride = 1, padding = 1, bias is nullptr
                     block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
 
@@ -1427,9 +1427,9 @@ struct clip_graph {
                 mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
                 // mlp_2 ne = [2048, 576, 1, 1]
                 // // AVG Pool Layer 2*2, strides = 2
-                mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
+                mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3);
                 // mlp_2 ne = [576, 2048, 1, 1]
-                mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
+                mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
                 // mlp_2 ne [24, 24, 2048, 1]
                 mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
                 // weight ne = [3, 3, 2048, 1]
@@ -1449,8 +1449,8 @@ struct clip_graph {
         // glm projector
         else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) {
             size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
-            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
-            embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
+            embeddings = ggml_permute(ctx0,embeddings,1,0,2,3);
+            embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
             embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
             embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
             embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
@@ -2005,7 +2005,6 @@ private:
                 ggml_row_size(cur->type, n_dim),
                 ggml_row_size(cur->type, n_dim*n_head),
                 n_dim/2 * ggml_element_size(cur));
-            second = ggml_cont(ctx0, second); // copy, because ggml_rope don't play well with non-contiguous tensors
             second = ggml_rope_ext(
                 ctx0,
                 second,

From f0c541d315e97b297b3421c52ebde53340ee66b3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 18 Aug 2025 20:35:47 +0300
Subject: [PATCH 099/287] scripts : update sync scripts

---
 scripts/sync-ggml-am.sh | 47 +++--------------------------------------
 scripts/sync-ggml.sh    | 16 +-------------
 2 files changed, 4 insertions(+), 59 deletions(-)

diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 29d30e0a18..826c560cd8 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -74,21 +74,7 @@ while read c; do
         cmake/common.cmake \
         cmake/ggml-config.cmake.in \
         src/ggml-cpu/cmake/FindSIMD.cmake \
-        src/ggml*.h \
-        src/ggml*.c \
-        src/ggml*.cpp \
-        src/gguf*.cpp \
-        src/ggml-blas/* \
-        src/ggml-cann/* \
-        src/ggml-cpu/* \
-        src/ggml-cuda/* \
-        src/ggml-hip/* \
-        src/ggml-metal/* \
-        src/ggml-musa/* \
-        src/ggml-opencl/* \
-        src/ggml-rpc/* \
-        src/ggml-sycl/* \
-        src/ggml-vulkan/* \
+        src/ggml* \
         include/ggml*.h \
         include/gguf*.h \
         tests/test-opt.cpp \
@@ -131,21 +117,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # cmake/ggml-config.cmake.in        -> ggml/cmake/ggml-config.cmake.in
     # src/ggml-cpu/cmake/FindSIMD.cmake -> ggml/src/ggml-cpu/cmake/FindSIMD.cmake
     #
-    # src/ggml*.c          -> ggml/src/ggml*.c
-    # src/ggml*.cpp        -> ggml/src/ggml*.cpp
-    # src/ggml*.h          -> ggml/src/ggml*.h
-    # src/gguf*.cpp        -> ggml/src/gguf*.cpp
-    # src/ggml-blas/*      -> ggml/src/ggml-blas/*
-    # src/ggml-cann/*      -> ggml/src/ggml-cann/*
-    # src/ggml-cpu/*       -> ggml/src/ggml-cpu/*
-    # src/ggml-cuda/*      -> ggml/src/ggml-cuda/*
-    # src/ggml-hip/*       -> ggml/src/ggml-hip/*
-    # src/ggml-metal/*     -> ggml/src/ggml-metal/*
-    # src/ggml-musa/*      -> ggml/src/ggml-musa/*
-    # src/ggml-opencl/*    -> ggml/src/ggml-opencl/*
-    # src/ggml-rpc/*       -> ggml/src/ggml-rpc/*
-    # src/ggml-sycl/*      -> ggml/src/ggml-sycl/*
-    # src/ggml-vulkan/*    -> ggml/src/ggml-vulkan/*
+    # src/ggml* -> ggml/src/ggml*
     #
     # include/ggml*.h -> ggml/include/ggml*.h
     # include/gguf*.h -> ggml/include/gguf*.h
@@ -163,20 +135,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/([[:space:]]| [ab]\/)cmake\/common.cmake/\1ggml\/cmake\/common.cmake/g' \
         -e 's/([[:space:]]| [ab]\/)cmake\/ggml-config.cmake.in/\1ggml\/cmake\/ggml-config.cmake.in/g' \
         -e 's/([[:space:]]| [ab]\/)src\/ggml-cpu\/cmake\/FindSIMD.cmake/\1ggml\/src\/ggml-cpu\/cmake\/FindSIMD.cmake/g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml(.*)\.c/\1ggml\/src\/ggml\2.c/g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml(.*)\.cpp/\1ggml\/src\/ggml\2.cpp/g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml(.*)\.h/\1ggml\/src\/ggml\2.h/g' \
-        -e 's/([[:space:]]| [ab]\/)src\/gguf(.*)\.cpp/\1ggml\/src\/gguf\2.cpp/g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-blas\//\1ggml\/src\/ggml-blas\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-cpu\//\1ggml\/src\/ggml-cpu\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-cuda\//\1ggml\/src\/ggml-cuda\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-hip\//\1ggml\/src\/ggml-hip\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-metal\//\1ggml\/src\/ggml-metal\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-opencl\//\1ggml\/src\/ggml-opencl\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-rpc\//\1ggml\/src\/ggml-rpc\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \
-        -e 's/([[:space:]]| [ab]\/)src\/ggml-vulkan\//\1ggml\/src\/ggml-vulkan\//g' \
+        -e 's/([[:space:]]| [ab]\/)src\/ggml(.*)/\1ggml\/src\/ggml\2/g' \
         -e 's/([[:space:]]| [ab]\/)include\/ggml(.*)\.h/\1ggml\/include\/ggml\2.h/g' \
         -e 's/([[:space:]]| [ab]\/)include\/gguf(.*)\.h/\1ggml\/include\/gguf\2.h/g' \
         -e 's/([[:space:]]| [ab]\/)tests\/(.*)\.cpp/\1tests\/\2.cpp/g' \
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index 9b98329e09..2da9b57891 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -6,21 +6,7 @@ cp -rpv ../ggml/src/CMakeLists.txt   ./ggml/src/CMakeLists.txt
 cp -rpv ../ggml/cmake/* ./ggml/cmake/
 cp -rpv ../ggml/src/ggml-cpu/cmake/* ./ggml/src/ggml-cpu/cmake/
 
-cp -rpv ../ggml/src/ggml*.c        ./ggml/src/
-cp -rpv ../ggml/src/ggml*.cpp      ./ggml/src/
-cp -rpv ../ggml/src/ggml*.h        ./ggml/src/
-cp -rpv ../ggml/src/gguf*.cpp      ./ggml/src/
-cp -rpv ../ggml/src/ggml-blas/*    ./ggml/src/ggml-blas/
-cp -rpv ../ggml/src/ggml-cann/*    ./ggml/src/ggml-cann/
-cp -rpv ../ggml/src/ggml-cpu/*     ./ggml/src/ggml-cpu/
-cp -rpv ../ggml/src/ggml-cuda/*    ./ggml/src/ggml-cuda/
-cp -rpv ../ggml/src/ggml-hip/*     ./ggml/src/ggml-hip/
-cp -rpv ../ggml/src/ggml-metal/*   ./ggml/src/ggml-metal/
-cp -rpv ../ggml/src/ggml-musa/*    ./ggml/src/ggml-musa/
-cp -rpv ../ggml/src/ggml-opencl/*  ./ggml/src/ggml-opencl/
-cp -rpv ../ggml/src/ggml-rpc/*     ./ggml/src/ggml-rpc/
-cp -rpv ../ggml/src/ggml-sycl/*    ./ggml/src/ggml-sycl/
-cp -rpv ../ggml/src/ggml-vulkan/*  ./ggml/src/ggml-vulkan/
+cp -rpv ../ggml/src/ggml* ./ggml/src/
 
 cp -rpv ../ggml/include/ggml*.h ./ggml/include/
 cp -rpv ../ggml/include/gguf*.h ./ggml/include/

From 60212f1ead2dce9bf1ac69633a7069258ae604d8 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 18 Aug 2025 22:02:11 +0300
Subject: [PATCH 100/287] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 7e2875f870..22348782d1 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-b141fc226b68e4af383101c39da90b54ede98850
+323951f1bdcdfbd5b5ff3a9a7c3770e63b1a560e

From 6d7f1117e3e3285d0c5c11b5ebb0439e27920082 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 18 Aug 2025 22:02:50 +0300
Subject: [PATCH 101/287] codeowners : remove mmv.*

---
 CODEOWNERS | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index b698b1f4cb..18564a08b1 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -5,7 +5,6 @@
 /tools/server/ @ngxson
 /ggml/src/ggml-cuda/fattn* @JohannesGaessler
 /ggml/src/ggml-cuda/mmq.* @JohannesGaessler
-/ggml/src/ggml-cuda/mmv.* @JohannesGaessler
 /ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
 /ggml/src/ggml-opt.cpp @JohannesGaessler
 /ggml/src/gguf.cpp @JohannesGaessler

From f08c4c0d8d0cb6caaf8b7ad316039232b9fa059c Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 18 Aug 2025 22:53:52 +0200
Subject: [PATCH 102/287] mtmd : clean up clip_n_output_tokens (#15391)

---
 tools/mtmd/clip.cpp | 60 +++++++++++++++++++--------------------------
 tools/mtmd/clip.h   |  5 ----
 tools/mtmd/tests.sh |  1 +
 3 files changed, 26 insertions(+), 40 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 099e282370..a32fe84fa7 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -3648,8 +3648,9 @@ int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 *
 int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
 
-    // only for models using fixed size square images
-    int n_patches_sq = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
+    // for models with fixed size image, the input image is already pre-processed and resized to square
+    int patch_size = params.patch_size;
+    int n_patches = (img->nx / patch_size) * (img->ny / patch_size);
 
     projector_type proj = ctx->proj_type();
 
@@ -3663,27 +3664,27 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_LDPV2:
         case PROJECTOR_TYPE_GLM_EDGE:
             {
-                n_patches_sq /= 4;
+                n_patches /= 4;
                 if (ctx->model.mm_glm_tok_boi) {
-                    n_patches_sq += 2; // for BOI and EOI token embeddings
+                    n_patches += 2; // for BOI and EOI token embeddings
                 }
             } break;
         case PROJECTOR_TYPE_MINICPMV:
             {
                 // Use actual config value if available, otherwise fall back to hardcoded values
                 if (params.minicpmv_query_num > 0) {
-                    n_patches_sq = params.minicpmv_query_num;
+                    n_patches = params.minicpmv_query_num;
                 } else {
                     // Fallback to hardcoded values for legacy models
                     if (params.minicpmv_version == 2) {
-                        n_patches_sq = 96;
+                        n_patches = 96;
                     } else if (params.minicpmv_version == 3) {
-                        n_patches_sq = 64;
+                        n_patches = 64;
                     } else if (params.minicpmv_version == 4) {
-                        n_patches_sq = 64;
+                        n_patches = 64;
                     } else if (params.minicpmv_version == 5) {
                         // MiniCPM-V 4.0
-                        n_patches_sq = 64;
+                        n_patches = 64;
                     } else {
                         GGML_ABORT("Unknown minicpmv version");
                     }
@@ -3692,67 +3693,56 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
             {
-                // dynamic size
+                // dynamic size (2 conv, so double patch size)
                 int patch_size = params.patch_size * 2;
                 int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
                 int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
-                n_patches_sq = x_patch * y_patch;
+                n_patches = x_patch * y_patch;
             } break;
         case PROJECTOR_TYPE_GEMMA3:
-            {
-                int n_per_side = params.image_size / params.patch_size;
-                int n_per_side_2d_pool = n_per_side / params.proj_scale_factor;
-                n_patches_sq = n_per_side_2d_pool * n_per_side_2d_pool;
-            } break;
         case PROJECTOR_TYPE_IDEFICS3:
         case PROJECTOR_TYPE_INTERNVL:
+        case PROJECTOR_TYPE_LLAMA4:
+        case PROJECTOR_TYPE_LFM2:
             {
                 // both W and H are divided by proj_scale_factor
-                n_patches_sq /= (params.proj_scale_factor * params.proj_scale_factor);
+                int scale_factor = ctx->model.hparams.proj_scale_factor;
+                n_patches /= (scale_factor * scale_factor);
             } break;
         case PROJECTOR_TYPE_PIXTRAL:
             {
                 // dynamic size
                 int n_merge = params.spatial_merge_size;
-                int n_patches_x = img->nx / params.patch_size / (n_merge > 0 ? n_merge : 1);
-                int n_patches_y = img->ny / params.patch_size / (n_merge > 0 ? n_merge : 1);
-                n_patches_sq = n_patches_y * n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row
-            } break;
-        case PROJECTOR_TYPE_LLAMA4:
-            {
-                int scale_factor = ctx->model.hparams.proj_scale_factor;
-                n_patches_sq /= (scale_factor * scale_factor);
+                int n_patches_x = img->nx / patch_size / (n_merge > 0 ? n_merge : 1);
+                int n_patches_y = img->ny / patch_size / (n_merge > 0 ? n_merge : 1);
+                n_patches = n_patches_y * n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row
             } break;
         case PROJECTOR_TYPE_VOXTRAL:
         case PROJECTOR_TYPE_ULTRAVOX:
         case PROJECTOR_TYPE_QWEN2A:
             {
-                n_patches_sq = img->nx;
+                n_patches = img->nx;
 
                 const int proj_stack_factor = ctx->model.hparams.proj_stack_factor;
                 if (ctx->model.audio_has_stack_frames()) {
                     GGML_ASSERT(proj_stack_factor > 0);
-                    const int n_len = CLIP_ALIGN(n_patches_sq, proj_stack_factor);
-                    n_patches_sq = n_len / proj_stack_factor;
+                    const int n_len = CLIP_ALIGN(n_patches, proj_stack_factor);
+                    n_patches = n_len / proj_stack_factor;
                 }
 
                 // whisper downscales input token by half after conv1d
-                n_patches_sq /= 2;
+                n_patches /= 2;
 
                 if (ctx->model.audio_has_avgpool()) {
                     // divide by 2 because of nn.AvgPool1d(2, stride=2)
-                    n_patches_sq /= 2;
+                    n_patches /= 2;
                 }
             } break;
-        case PROJECTOR_TYPE_LFM2:
-            {
-                n_patches_sq = (img->nx / (params.patch_size * params.proj_scale_factor)) * (img->ny / (params.patch_size * params.proj_scale_factor));
-            } break;
         default:
             GGML_ABORT("unsupported projector type");
     }
 
-    return n_patches_sq;
+    return n_patches;
 }
 
 static std::vector<std::vector<std::vector<float>>> get_1d_sincos_pos_embed_from_grid_new(int embed_dim, const std::vector<std::vector<float>> & pos) {
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index 08f3efb7b1..3387cdbd36 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -82,11 +82,6 @@ struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch
  */
 void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
 
-bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
-
-/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
-bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
-
 /** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
 bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
 
diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh
index e73cf96af2..6f8a5f86ac 100755
--- a/tools/mtmd/tests.sh
+++ b/tools/mtmd/tests.sh
@@ -68,6 +68,7 @@ add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
 add_test_vision "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
 add_test_vision "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
 add_test_vision "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
+add_test_vision "ggml-org/LFM2-VL-450M-GGUF:Q8_0"
 
 add_test_audio  "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
 add_test_audio  "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"

From f0d3c7405c323784a60f14ddddfbac3f7404d417 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 19 Aug 2025 08:45:12 +0300
Subject: [PATCH 103/287] batched-bench : use rand tokens (#15398)

---
 tools/batched-bench/batched-bench.cpp | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp
index 03628f74b2..c6c601add3 100644
--- a/tools/batched-bench/batched-bench.cpp
+++ b/tools/batched-bench/batched-bench.cpp
@@ -57,6 +57,13 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const llama_vocab * vocab   = llama_model_get_vocab(model);
+    const int32_t       n_vocab = llama_vocab_n_tokens(vocab);
+
+    const auto get_token_rand = [n_vocab]() -> llama_token {
+        return std::rand() % n_vocab;
+    };
+
     auto * mem = llama_get_memory(ctx);
 
     const int32_t n_kv_max = llama_n_ctx(ctx);
@@ -93,7 +100,7 @@ int main(int argc, char ** argv) {
     // warm up
     {
         for (int i = 0; i < 16; ++i) {
-            common_batch_add(batch, 0, i, { 0 }, false);
+            common_batch_add(batch, get_token_rand(), i, { 0 }, false);
         }
 
         if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
@@ -127,7 +134,7 @@ int main(int argc, char ** argv) {
 
                 for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
                     for (int i = 0; i < pp; ++i) {
-                        common_batch_add(batch, 0, i, { j }, i == pp - 1);
+                        common_batch_add(batch, get_token_rand(), i, { j }, i == pp - 1);
                     }
                 }
 
@@ -154,7 +161,7 @@ int main(int argc, char ** argv) {
                     common_batch_clear(batch);
 
                     for (int j = 0; j < pl; ++j) {
-                        common_batch_add(batch, 0, pp + i, { j }, true);
+                        common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
                     }
 
                     if (!decode_helper(ctx, batch, ctx_params.n_batch)) {

From 9d262f4bad0d37838100133537aaf0a83835ed12 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 19 Aug 2025 08:45:26 +0300
Subject: [PATCH 104/287] server : remove swa_full warning (#15399)

---
 src/llama-context.cpp | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 7d7abad5d4..1ebfc88ab6 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -145,11 +145,6 @@ llama_context::llama_context(
                 __func__, n_ctx_per_seq, hparams.n_ctx_train);
     }
 
-    if (!params.swa_full && cparams.n_seq_max > 1 && hparams.is_swa_any()) {
-        LLAMA_LOG_WARN("%s: requested n_seq_max (%u) > 1, but swa_full is not enabled -- performance may be degraded: %s\n",
-                __func__, cparams.n_seq_max, "https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573");
-    }
-
     if (!hparams.vocab_only) {
         // GPU backends
         for (auto * dev : model.devices) {

From e9288e886970884f288533cd597b3798995b4099 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 19 Aug 2025 10:29:36 +0200
Subject: [PATCH 105/287] chat : clarify the meaning of reasoning_format
 (#15408)

* chat : clarify the meaning of reasoning_format

* add link to this PR
---
 common/chat.cpp     | 1 -
 common/common.h     | 7 +++++--
 tests/test-chat.cpp | 2 +-
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index 23d3828f9c..5fe5643d38 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -632,7 +632,6 @@ const char * common_reasoning_format_name(common_reasoning_format format) {
         case COMMON_REASONING_FORMAT_AUTO:     return "auto";
         case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
         case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
-        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
         default:
             throw std::runtime_error("Unknown reasoning format");
     }
diff --git a/common/common.h b/common/common.h
index 75596e6b32..dfb63461bc 100644
--- a/common/common.h
+++ b/common/common.h
@@ -239,12 +239,15 @@ struct common_params_diffusion {
     bool    add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0
 };
 
+// reasoning API response format (not to be confused as chat template's reasoning format)
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
-    COMMON_REASONING_FORMAT_AUTO,
+    COMMON_REASONING_FORMAT_AUTO,            // Same as deepseek, using `message.reasoning_content`
     COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
     COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
-    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
+    // do not extend this enum unless you absolutely have to
+    // in most cases, use COMMON_REASONING_FORMAT_AUTO
+    // see: https://github.com/ggml-org/llama.cpp/pull/15408
 };
 
 
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index ea4060c876..a6daa93a82 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -1408,7 +1408,7 @@ static void test_template_output_parsers() {
                 /* is_partial= */ false,
                 {
                     /* .format = */ COMMON_CHAT_FORMAT_GRANITE,
-                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_GRANITE,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
                 }));
 
         // Test parsing tool calls

From 6424594c56f4dbd0573455d89a0d89a0ac093d13 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marvin=20Gie=C3=9Fing?= <marvin.giessing@gmail.com>
Date: Tue, 19 Aug 2025 10:54:31 +0200
Subject: [PATCH 106/287] ggml-cpu: add mxfp4 VSX intrinsics for Power9+
 (ppc64le) hardware (#15385)

* Added VSX intrinsics for Power9+ systems

Signed-off-by: mgiessing <marvin.giessing@gmail.com>

* Manual unrolling for minor perf improvement

Signed-off-by: mgiessing <marvin.giessing@gmail.com>

* Update ggml/src/ggml-cpu/arch/powerpc/quants.c

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Signed-off-by: mgiessing <marvin.giessing@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-cpu/arch-fallback.h       |  1 -
 ggml/src/ggml-cpu/arch/powerpc/quants.c | 66 +++++++++++++++++++++++++
 2 files changed, 66 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index f476127995..0bfb92df17 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -73,7 +73,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
diff --git a/ggml/src/ggml-cpu/arch/powerpc/quants.c b/ggml/src/ggml-cpu/arch/powerpc/quants.c
index 49aae7a23b..d3dfd049ea 100644
--- a/ggml/src/ggml-cpu/arch/powerpc/quants.c
+++ b/ggml/src/ggml-cpu/arch/powerpc/quants.c
@@ -278,6 +278,72 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined(__POWER9_VECTOR__)
+    const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector unsigned char vshift4 = vec_splats((unsigned char)4);
+    vector float vsumf0 = vec_splats(0.0f);
+
+    vector signed char kv = vec_xl(0, (const signed char *)kvalues_mxfp4);
+
+#pragma GCC unroll 8
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
+
+        vector float vyd = vec_splats(GGML_CPU_FP16_TO_FP32(y[ib].d) *
+                                      GGML_E8M0_TO_FP32_HALF(x[ib].e));
+
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
+
+        vector signed char qxs = (vector signed char)vec_xl(0, x[ib].qs);
+
+        vector unsigned char lo_nibbles = (vector unsigned char)vec_and(qxs, lowMask);
+        vector unsigned char hi_nibbles = (vector unsigned char)vec_sr(qxs, vshift4);
+
+        vector signed char q4x0 = vec_perm(kv, kv, lo_nibbles);
+        vector signed char q4x1 = vec_perm(kv, kv, hi_nibbles);
+
+        vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0));
+        vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1));
+
+        vector signed int vsumi0 = vec_splats((int32_t)0);
+        vsumi0 = vec_sum4s(qv0, vsumi0);
+        vsumi0 = vec_sum4s(qv1, vsumi0);
+
+        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vyd, vsumf0);
+    }
+
+    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
+    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
+    sumf = vec_extract(vsumf0, 0);
+    *s = sumf;
+#else
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_mxfp4_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
+}
+
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

From 67f09a3a27db443f9870aac87e163dba0d08131e Mon Sep 17 00:00:00 2001
From: R0CKSTAR <yeahdongcn@gmail.com>
Date: Tue, 19 Aug 2025 18:33:47 +0800
Subject: [PATCH 107/287] musa: handle __hgt2_mask, available starting from
 MUSA SDK rc4.3.0 (#15413)

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---
 ggml/src/ggml-cuda/common.cuh | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 2b14b30ac9..76ace816ff 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -78,6 +78,8 @@
 #define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
 
 // Moore Threads
+#define MUSART_HMASK 40300 // MUSA rc4.3, min. ver. for half2 -> uint mask comparisons
+
 #define GGML_CUDA_CC_QY1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000
 #define GGML_CUDA_CC_QY2 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000
 #define GGML_CUDA_CC_NG  (GGML_CUDA_CC_OFFSET_MTHREADS + 0x310) // TBD
@@ -490,13 +492,14 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || defined(GGML_USE_HIP)
 }
 
-#if CUDART_VERSION < CUDART_HMASK
+#if (defined(CUDART_VERSION) && CUDART_VERSION < CUDART_HMASK) || defined(GGML_USE_HIP) || \
+    (defined(MUSART_VERSION) && MUSART_VERSION < MUSART_HMASK)
 static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
     const uint32_t mask_low  = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
     const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
     return mask_low | mask_high;
 }
-#endif // CUDART_VERSION < CUDART_HMASK
+#endif // (defined(CUDART_VERSION) && CUDART_VERSION < CUDART_HMASK) || defined(GGML_USE_HIP) || (defined(MUSART_VERSION) && MUSART_VERSION < MUSART_HMASK)
 
 static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
 #if defined(GGML_USE_HIP)

From a6d3cfe7fa6ea1fb0e1ba8243b731db22ddc0b49 Mon Sep 17 00:00:00 2001
From: SHUAI YANG <shuaiyang047@163.com>
Date: Tue, 19 Aug 2025 21:28:22 +0800
Subject: [PATCH 108/287] CANN: optimize rope operator (#15335)

* optimize rope ops

* amendment

* delete trailing whitespace

* change the variable name
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 166 +++++++++++++++++++------------
 ggml/src/ggml-cann/common.h      |  13 +++
 2 files changed, 117 insertions(+), 62 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 259a2928b1..2a5cb8abfa 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -2154,86 +2154,129 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
-    // theta_scale arange, [0,1,...,ne00/2 - 1]
     int64_t theta_scale_length = ne00 / 2;
-    ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(),
-                                          theta_scale_length * sizeof(float_t));
-    void* theta_scale_buffer = theta_scale_allocator.get();
     int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1};
     size_t theta_scale_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
                           theta_scale_length * sizeof(float_t)};
 
-    aclTensor* acl_theta_scale_tensor =
-        ggml_cann_create_tensor(theta_scale_buffer, ACL_FLOAT, sizeof(float_t),
-                                theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-    float start = 0;
-    float step = 1;
-    float stop = ne00 / 2;
-    float n_elements = ne00 / 2;
-    aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
-
-    // power
-    aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor,
-                            acl_theta_scale_tensor);
-
-    // freq_scale
-    if (freq_scale != 1) {
-        aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true);
-    }
-
-    // freq_factors
-    if (src2) {
-        aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
-            src2->data, ggml_cann_type_mapping(src2->type),
-            ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-        aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor);
-        ggml_cann_release_resources(ctx, acl_freq_factors_tensor);
-    }
-
-    // position
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
     int64_t position_length = src1->ne[0];
     int64_t position_ne[] = {1, 1, position_length, 1};
     size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t), sizeof(int32_t),
                             sizeof(int32_t) * position_length};
-    aclTensor* acl_position_tensor = ggml_cann_create_tensor(
-        src1->data, ggml_cann_type_mapping(src1->type),
-        ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
 
-    // power * position
-    int64_t theta_length = theta_scale_length * position_length;
-    ggml_cann_pool_alloc theta_allocator(ctx.pool(),
-                                         theta_length * sizeof(float_t));
-    void* theta_buffer = theta_allocator.get();
     int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1};
     size_t theta_nb[GGML_MAX_DIMS];
     theta_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
     }
-    aclTensor* acl_theta_tensor =
-        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
-                                theta_ne, theta_nb, GGML_MAX_DIMS);
-    aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
-              acl_theta_tensor);
 
-    // sin/cos
-    ggml_cann_pool_alloc sin_allocator(ctx.pool(),
-                                       theta_length * sizeof(float_t));
-    void* sin_buffer = sin_allocator.get();
+    bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
+    bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);
+
+    // used for accuracy testing
+    bool is_attention = is_q || is_k;
+
+    if(ctx.init_ptr == nullptr || !is_attention) {
+        // theta_scale arange, [0,1,...,ne00/2 - 1]
+        if(ctx.init_ptr != nullptr){
+            ACL_CHECK(aclrtFree(ctx.init_ptr));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+
+        aclTensor* acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
+                                    theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+        float start = 0;
+        float step = 1;
+        float stop = ne00 / 2;
+        float n_elements = ne00 / 2;
+        aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
+
+        // power
+        aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor,
+                                acl_theta_scale_tensor);
+
+        // freq_scale
+        if (freq_scale != 1) {
+            aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true);
+        }
+
+        // freq_factors
+        if (src2) {
+            aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
+                src2->data, ggml_cann_type_mapping(src2->type),
+                ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+            aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor);
+            ggml_cann_release_resources(ctx, acl_freq_factors_tensor);
+        }
+        // release
+        ggml_cann_release_resources(ctx, acl_theta_scale_tensor,acl_theta_scale);
+    }
+
+    if(ctx.sin_ptr == nullptr) {
+        int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
+        ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+    }
+    if(position_length > ctx.max_prompt_length) {
+        ctx.max_prompt_length = position_length;
+        int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
+        ACL_CHECK(aclrtFree(ctx.sin_ptr));
+        ACL_CHECK(aclrtFree(ctx.cos_ptr));
+        ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+    }
+
+    bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
+
+    if(is_fisrt_layer || !is_attention) {
+
+        aclTensor* acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
+                                    theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+
+        // position
+        aclTensor* acl_position_tensor = ggml_cann_create_tensor(
+            src1->data, ggml_cann_type_mapping(src1->type),
+            ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
+
+        // power * position
+        int64_t theta_length = theta_scale_length * position_length;
+        ggml_cann_pool_alloc theta_allocator(ctx.pool(),
+                                            theta_length * sizeof(float_t));
+        void* theta_buffer = theta_allocator.get();
+
+        aclTensor* acl_theta_tensor =
+            ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
+                                    theta_ne, theta_nb, GGML_MAX_DIMS);
+        aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
+                acl_theta_tensor);
+
+        // sin/cos
+        aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
+            ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+            GGML_MAX_DIMS, ACL_FORMAT_ND);
+        aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
+
+        aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
+            ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+            GGML_MAX_DIMS, ACL_FORMAT_ND);
+        aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
+
+        // release
+        ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
+            acl_theta_tensor, acl_sin_tensor, acl_cos_tensor);
+    }
+
     aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-        sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-        GGML_MAX_DIMS, ACL_FORMAT_ND);
-    aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
-
-    ggml_cann_pool_alloc cos_allocator(ctx.pool(),
-                                       theta_length * sizeof(float_t));
-    void* cos_buffer = cos_allocator.get();
+            ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+            GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-        cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-        GGML_MAX_DIMS, ACL_FORMAT_ND);
-    aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
+            ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+            GGML_MAX_DIMS, ACL_FORMAT_ND);
 
     // attn_factor
     if (attn_factor != 1) {
@@ -2257,8 +2300,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     }
 
     // release
-    ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
-        acl_theta_tensor, acl_sin_tensor, acl_cos_tensor, acl_theta_scale);
+    ggml_cann_release_resources(ctx, acl_sin_tensor, acl_cos_tensor);
 }
 
 #ifdef __cplusplus
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 9d294f72b6..2c2033bfba 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -368,6 +368,10 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+    void* init_ptr = nullptr;
+    void* sin_ptr = nullptr;
+    void* cos_ptr = nullptr;
+    int64_t max_prompt_length = 65536;
 #ifdef USE_ACL_GRAPH
     /// Cached CANN ACL graph used for executing the current ggml computation graph.
     std::unique_ptr<ggml_cann_graph> cann_graph;
@@ -414,6 +418,15 @@ struct ggml_backend_cann_context {
                 ACL_CHECK(aclrtDestroyStream(streams[i]));
             }
         }
+        if(init_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(init_ptr));
+        }
+        if(sin_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(sin_ptr));
+        }
+        if(cos_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(cos_ptr));
+        }
     }
 
     /**

From d2fcd91cf96b46f4485ce46b4e3a32bf0df37715 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 19 Aug 2025 16:46:37 +0300
Subject: [PATCH 109/287] server : disable context shift by default (#15416)

* server : disable context shift by default

ggml-ci

* server : make scopr of test parameters local
---
 common/arg.cpp                              | 7 +++++++
 common/common.h                             | 2 +-
 tools/server/tests/unit/test_basic.py       | 2 +-
 tools/server/tests/unit/test_completion.py  | 4 ++--
 tools/server/tests/unit/test_ctx_shift.py   | 6 ++----
 tools/server/tests/unit/test_embedding.py   | 2 +-
 tools/server/tests/unit/test_infill.py      | 2 +-
 tools/server/tests/unit/test_lora.py        | 2 +-
 tools/server/tests/unit/test_rerank.py      | 2 +-
 tools/server/tests/unit/test_security.py    | 2 +-
 tools/server/tests/unit/test_slot_save.py   | 2 +-
 tools/server/tests/unit/test_speculative.py | 3 ++-
 tools/server/tests/unit/test_tokenize.py    | 2 +-
 tools/server/tests/unit/test_tool_call.py   | 2 ++
 tools/server/tests/utils.py                 | 6 +++---
 tools/tts/tts.cpp                           | 1 -
 16 files changed, 27 insertions(+), 20 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 98baac4c14..d3868018ef 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1530,6 +1530,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.ctx_shift = false;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    add_opt(common_arg(
+        {"--context-shift"},
+        string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
+        [](common_params & params) {
+            params.ctx_shift = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
         string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
diff --git a/common/common.h b/common/common.h
index dfb63461bc..920de7b50a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -375,7 +375,7 @@ struct common_params {
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool flash_attn        = false; // flash attention
     bool no_perf           = false; // disable performance metrics
-    bool ctx_shift         = true;  // context shift on inifinite text generation
+    bool ctx_shift         = false;  // context shift on inifinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
     bool kv_unified        = false; // enable unified KV cache
 
diff --git a/tools/server/tests/unit/test_basic.py b/tools/server/tests/unit/test_basic.py
index 1485de8ceb..c7b3af0489 100644
--- a/tools/server/tests/unit/test_basic.py
+++ b/tools/server/tests/unit/test_basic.py
@@ -5,7 +5,7 @@ from utils import *
 server = ServerPreset.tinyllama2()
 
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py
index be3a0052c6..adb6f27864 100644
--- a/tools/server/tests/unit/test_completion.py
+++ b/tools/server/tests/unit/test_completion.py
@@ -7,7 +7,7 @@ from utils import *
 server = ServerPreset.tinyllama2()
 
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
@@ -229,7 +229,7 @@ def test_nocache_long_input_prompt():
         "temperature": 1.0,
         "cache_prompt": False,
     })
-    assert res.status_code == 200
+    assert res.status_code == 400
 
 
 def test_completion_with_tokens_input():
diff --git a/tools/server/tests/unit/test_ctx_shift.py b/tools/server/tests/unit/test_ctx_shift.py
index 2431ac7088..8f51bc301a 100644
--- a/tools/server/tests/unit/test_ctx_shift.py
+++ b/tools/server/tests/unit/test_ctx_shift.py
@@ -11,7 +11,7 @@ Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu
 Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
 """.strip()
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
@@ -25,6 +25,7 @@ def test_ctx_shift_enabled():
     # the prompt is truncated to keep the last 109 tokens
     # 64 tokens are generated thanks to shifting the context when it gets full
     global server
+    server.enable_ctx_shift = True
     server.start()
     res = server.make_request("POST", "/completion", data={
         "n_predict": 64,
@@ -42,7 +43,6 @@ def test_ctx_shift_enabled():
 ])
 def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool):
     global server
-    server.disable_ctx_shift = True
     server.n_predict = -1
     server.start()
     res = server.make_request("POST", "/completion", data={
@@ -56,7 +56,6 @@ def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, tr
 
 def test_ctx_shift_disabled_long_prompt():
     global server
-    server.disable_ctx_shift = True
     server.start()
     res = server.make_request("POST", "/completion", data={
         "n_predict": 64,
@@ -68,7 +67,6 @@ def test_ctx_shift_disabled_long_prompt():
 
 def test_ctx_shift_disabled_stream():
     global server
-    server.disable_ctx_shift = True
     server.start()
     res = server.make_stream_request("POST", "/v1/completions", data={
         "n_predict": 256,
diff --git a/tools/server/tests/unit/test_embedding.py b/tools/server/tests/unit/test_embedding.py
index 0feb452ccf..50601b8396 100644
--- a/tools/server/tests/unit/test_embedding.py
+++ b/tools/server/tests/unit/test_embedding.py
@@ -8,7 +8,7 @@ server = ServerPreset.bert_bge_small()
 
 EPSILON = 1e-3
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.bert_bge_small()
diff --git a/tools/server/tests/unit/test_infill.py b/tools/server/tests/unit/test_infill.py
index 10554db0f6..73dacdae81 100644
--- a/tools/server/tests/unit/test_infill.py
+++ b/tools/server/tests/unit/test_infill.py
@@ -3,7 +3,7 @@ from utils import *
 
 server = ServerPreset.tinyllama_infill()
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama_infill()
diff --git a/tools/server/tests/unit/test_lora.py b/tools/server/tests/unit/test_lora.py
index c1aa8be70e..00b2f245f6 100644
--- a/tools/server/tests/unit/test_lora.py
+++ b/tools/server/tests/unit/test_lora.py
@@ -5,7 +5,7 @@ server = ServerPreset.stories15m_moe()
 
 LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf"
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.stories15m_moe()
diff --git a/tools/server/tests/unit/test_rerank.py b/tools/server/tests/unit/test_rerank.py
index f4f570ad5e..0b63c7821e 100644
--- a/tools/server/tests/unit/test_rerank.py
+++ b/tools/server/tests/unit/test_rerank.py
@@ -4,7 +4,7 @@ from utils import *
 server = ServerPreset.jina_reranker_tiny()
 
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.jina_reranker_tiny()
diff --git a/tools/server/tests/unit/test_security.py b/tools/server/tests/unit/test_security.py
index 620b25376b..0e11580553 100644
--- a/tools/server/tests/unit/test_security.py
+++ b/tools/server/tests/unit/test_security.py
@@ -6,7 +6,7 @@ server = ServerPreset.tinyllama2()
 
 TEST_API_KEY = "sk-this-is-the-secret-key"
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
diff --git a/tools/server/tests/unit/test_slot_save.py b/tools/server/tests/unit/test_slot_save.py
index 38704f5ece..1b428cc2a8 100644
--- a/tools/server/tests/unit/test_slot_save.py
+++ b/tools/server/tests/unit/test_slot_save.py
@@ -3,7 +3,7 @@ from utils import *
 
 server = ServerPreset.tinyllama2()
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py
index 54db38cf3b..38ca4325ba 100644
--- a/tools/server/tests/unit/test_speculative.py
+++ b/tools/server/tests/unit/test_speculative.py
@@ -16,7 +16,7 @@ def create_server():
     server.draft_max = 8
 
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def fixture_create_server():
     return create_server()
 
@@ -91,6 +91,7 @@ def test_slot_ctx_not_exceeded():
 def test_with_ctx_shift():
     global server
     server.n_ctx = 64
+    server.enable_ctx_shift = True
     server.start()
     res = server.make_request("POST", "/completion", data={
         "prompt": "Hello " * 56,
diff --git a/tools/server/tests/unit/test_tokenize.py b/tools/server/tests/unit/test_tokenize.py
index 382457c9d6..424cac5f3d 100644
--- a/tools/server/tests/unit/test_tokenize.py
+++ b/tools/server/tests/unit/test_tokenize.py
@@ -4,7 +4,7 @@ from utils import *
 server = ServerPreset.tinyllama2()
 
 
-@pytest.fixture(scope="module", autouse=True)
+@pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
diff --git a/tools/server/tests/unit/test_tool_call.py b/tools/server/tests/unit/test_tool_call.py
index 20f048c6f6..a3c3ccdf58 100755
--- a/tools/server/tests/unit/test_tool_call.py
+++ b/tools/server/tests/unit/test_tool_call.py
@@ -22,6 +22,8 @@ def create_server():
     server.model_alias = "tinyllama-2-tool-call"
     server.server_port = 8081
     server.n_slots = 1
+    server.n_ctx = 8192
+    server.n_batch = 2048
 
 class CompletionMode(Enum):
     NORMAL = "normal"
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index bc547ca03b..49277e6000 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -79,7 +79,7 @@ class ServerProcess:
     draft: int | None = None
     api_key: str | None = None
     lora_files: List[str] | None = None
-    disable_ctx_shift: int | None = False
+    enable_ctx_shift: int | None = False
     draft_min: int | None = None
     draft_max: int | None = None
     no_webui: bool | None = None
@@ -178,8 +178,8 @@ class ServerProcess:
         if self.lora_files:
             for lora_file in self.lora_files:
                 server_args.extend(["--lora", lora_file])
-        if self.disable_ctx_shift:
-            server_args.extend(["--no-context-shift"])
+        if self.enable_ctx_shift:
+            server_args.append("--context-shift")
         if self.api_key:
             server_args.extend(["--api-key", self.api_key])
         if self.draft_max:
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index a71e9bf5b5..18f01a9946 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -581,7 +581,6 @@ int main(int argc, char ** argv) {
 
     params.model = params.vocoder.model;
     params.embedding = true;
-    params.ctx_shift = false; // silence warning
     params.n_ubatch = params.n_batch;
 
     common_init_result llama_init_cts = common_init_from_params(params);

From 1e19f5d462b6df490a13103ca555d851f47e5fa5 Mon Sep 17 00:00:00 2001
From: Gian-Carlo Pascutto <gcp@sjeng.org>
Date: Tue, 19 Aug 2025 18:58:14 +0200
Subject: [PATCH 110/287] common : Add top-nsigma sampler to help globally
 (#15428)

Fixes #15423.
---
 common/arg.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index d3868018ef..d9f6ec96d0 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1830,7 +1830,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.sampling.top_n_sigma = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}).set_sparam());
+    ).set_sparam());
     add_opt(common_arg(
         {"--xtc-probability"}, "N",
         string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability),

From 9ef6b0b835450ee10cd7be934ad8aef681dc1f43 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 19 Aug 2025 19:58:28 +0300
Subject: [PATCH 111/287] model : add gpt-oss type strings (#15424)

---
 src/llama-model.cpp | 7 ++++++-
 src/llama-model.h   | 1 +
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 0c149cd478..431102edea 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -86,6 +86,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_40B:           return "40B";
         case LLM_TYPE_65B:           return "65B";
         case LLM_TYPE_70B:           return "70B";
+        case LLM_TYPE_120B:          return "120B";
         case LLM_TYPE_142B:          return "142B";
         case LLM_TYPE_236B:          return "236B";
         case LLM_TYPE_290B:          return "290B";
@@ -1834,7 +1835,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
                 hparams.set_swa_pattern(2);
 
-                // TODO: switch (hparams.n_layer)
+                switch (hparams.n_layer) {
+                    case 24: type = LLM_TYPE_20B; break;
+                    case 36: type = LLM_TYPE_120B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
             } break;
         case LLM_ARCH_LFM2:
             {
diff --git a/src/llama-model.h b/src/llama-model.h
index 46f7d0480f..f639fa1398 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -79,6 +79,7 @@ enum llm_type {
     LLM_TYPE_40B,
     LLM_TYPE_65B,
     LLM_TYPE_70B,
+    LLM_TYPE_120B,
     LLM_TYPE_142B,
     LLM_TYPE_236B,
     LLM_TYPE_290B,

From fb22dd07a639e81c7415e30b146f545f1a2f2caf Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Wed, 20 Aug 2025 02:25:51 +0800
Subject: [PATCH 112/287] opencl: mark `argsort` unsupported if cols exceed
 workgroup limit (#15375)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 20 +++++++++++++++++---
 1 file changed, 17 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 8a2ac7e377..df27501361 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -333,6 +333,7 @@ struct ggml_backend_opencl_context {
 
     cl_int alignment;
     size_t max_alloc_size;
+    size_t max_workgroup_size;
     bool fp16_support;
     bool has_vector_subgroup_broadcast;
     bool disable_fusion;
@@ -2218,6 +2219,9 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
     clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &backend_ctx->max_alloc_size, NULL);
     GGML_LOG_INFO("ggml_opencl: max mem alloc size: %zu MB\n", backend_ctx->max_alloc_size/1024/1024);
 
+    clGetDeviceInfo(device, CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(size_t), &backend_ctx->max_workgroup_size, NULL);
+    GGML_LOG_INFO("ggml_opencl: device max workgroup size: %lu\n", backend_ctx->max_workgroup_size);
+
     // Check SVM.
     cl_device_svm_capabilities svm_caps;
     CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_SVM_CAPABILITIES, sizeof(cl_device_svm_capabilities), &svm_caps, 0));
@@ -2533,7 +2537,8 @@ static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggm
 }
 
 static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
-    GGML_UNUSED(dev);
+    ggml_backend_opencl_device_context * dev_ctx     = (ggml_backend_opencl_device_context *)dev->context;
+    ggml_backend_opencl_context *        backend_ctx = dev_ctx->backend_ctx;
 
     switch (op->op) {
         case GGML_OP_NONE:
@@ -2708,8 +2713,17 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
         }
         case GGML_OP_IM2COL:
             return true;
-        case GGML_OP_ARGSORT:
-            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_ARGSORT: {
+            cl_kernel kernel = backend_ctx->kernel_argsort_f32_i32;
+            int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel);
+
+            int cols = 1;
+            while (cols < op->ne[0]) {
+                cols *= 2;
+            }
+
+            return cols <= max_workgroup_size && op->src[0]->type == GGML_TYPE_F32;
+        }
         case GGML_OP_SUM_ROWS:
             return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
         case GGML_OP_FLASH_ATTN_EXT:

From a094f381432d92c4bf92d2d6167284316ba73a62 Mon Sep 17 00:00:00 2001
From: R0CKSTAR <yeahdongcn@gmail.com>
Date: Wed, 20 Aug 2025 10:17:37 +0800
Subject: [PATCH 113/287] musa: fix build warnings (#15258)

* musa: fix build warnings

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* fix warning: comparison of integers of different signs: 'const int' and 'unsigned int' [-Wsign-compare]

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

---------

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---
 ggml/src/ggml-cuda/add-id.cu         |  6 +++---
 ggml/src/ggml-cuda/fattn-mma-f16.cuh | 12 +++++++-----
 ggml/src/ggml-cuda/fattn-tile-f16.cu | 20 ++++++++++----------
 ggml/src/ggml-cuda/fattn-tile-f32.cu | 13 +------------
 ggml/src/ggml-cuda/fattn-vec-f16.cuh |  4 ++--
 ggml/src/ggml-cuda/fattn-vec-f32.cuh | 15 ++-------------
 ggml/src/ggml-cuda/fattn-wmma-f16.cu |  6 +++---
 ggml/src/ggml-cuda/mmf.cu            |  4 ----
 ggml/src/ggml-cuda/mmq.cuh           |  4 +++-
 ggml/src/ggml-cuda/reduce_rows.cuh   |  2 +-
 10 files changed, 32 insertions(+), 54 deletions(-)

diff --git a/ggml/src/ggml-cuda/add-id.cu b/ggml/src/ggml-cuda/add-id.cu
index 8bed62ac9d..8d9cf692b4 100644
--- a/ggml/src/ggml-cuda/add-id.cu
+++ b/ggml/src/ggml-cuda/add-id.cu
@@ -11,14 +11,14 @@ static __global__ void add_id_kernel(
     const int64_t i1 = blockIdx.x;
     const int64_t i2 = blockIdx.y;
 
-    const int i11 = *(int32_t *) ((char *) src2 + i1*sizeof(int32_t) + i2*nb21);
+    const int i11 = *(const int32_t *) ((const char *) src2 + i1*sizeof(int32_t) + i2*nb21);
 
     const size_t nb1 = ne0 * sizeof(float);
     const size_t nb2 = ne1 * nb1;
 
     float * dst_row = (float *)((char *)dst + i1*nb1 + i2*nb2);
-    const float * src0_row = (const float *)((char *)src0 +  i1*nb01 + i2*nb02);
-    const float * src1_row = (const float *)((char *)src1 + i11*nb11);
+    const float * src0_row = (const float *)((const char *)src0 +  i1*nb01 + i2*nb02);
+    const float * src1_row = (const float *)((const char *)src1 + i11*nb11);
 
     for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
         dst_row[i0] = src0_row[i0] + src1_row[i0];
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 39731baaeb..1d7e0b037e 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -1237,10 +1237,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     }
 #else
     GGML_UNUSED(Q_f2); GGML_UNUSED(K_h2); GGML_UNUSED(V_h2);
-    GGML_UNUSED(mask_h2); GGML_UNUSED(dstk); GGML_UNUSED(dstk_fixup);
+    GGML_UNUSED(mask_h2); GGML_UNUSED(sinks_f);
+    GGML_UNUSED(dstk); GGML_UNUSED(dstk_fixup);
     GGML_UNUSED(scale); GGML_UNUSED(slope); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(stride_Q1);
-    GGML_UNUSED(stride_Q2); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
+    GGML_UNUSED(ne01); GGML_UNUSED(ne02);
+    GGML_UNUSED(stride_Q1); GGML_UNUSED(stride_Q2);
+    GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
     GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
     NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
@@ -1395,8 +1397,8 @@ static __global__ void flash_attn_ext_f16(
         (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
index 1e23f8f79c..4111bcc04f 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -299,17 +299,17 @@ static __global__ void flash_attn_tile_ext_f16(
         }
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
-    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02);
-    GGML_UNUSED(ne03); GGML_UNUSED(ne10); GGML_UNUSED(ne11);
-    GGML_UNUSED(ne12); GGML_UNUSED(ne13); GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33); GGML_UNUSED(nb01); GGML_UNUSED(nb02);
-    GGML_UNUSED(nb03); GGML_UNUSED(nb11); GGML_UNUSED(nb12);
-    GGML_UNUSED(nb13); GGML_UNUSED(nb21); GGML_UNUSED(nb22);
-    GGML_UNUSED(nb23);
+    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
+    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
+    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
+    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
+    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
+    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
+    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && defined(FP16_AVAILABLE)
 }
diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu
index c58194937d..1c1dc725d2 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -38,17 +38,6 @@ static __global__ void flash_attn_tile_ext_f32(
     return;
 #endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
-        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
-        GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-        GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-        GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-        GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-        GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-        GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-        GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-        GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
-        GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-        GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
         NO_DEVICE_CODE;
         return;
     }
@@ -313,7 +302,7 @@ static __global__ void flash_attn_tile_ext_f32(
     }
 #else
     GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
index b05f682cd3..2131b5feef 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -349,8 +349,8 @@ static __global__ void flash_attn_vec_ext_f16(
         dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
index d6d0bfb744..a06fba6cde 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -37,17 +37,6 @@ static __global__ void flash_attn_vec_ext_f32(
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(D == 128 || D == 256)) {
-        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-        GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
-        GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-        GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-        GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02);
-        GGML_UNUSED(ne03); GGML_UNUSED(ne10); GGML_UNUSED(ne11);
-        GGML_UNUSED(ne12); GGML_UNUSED(ne13); GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-        GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33); GGML_UNUSED(nb01); GGML_UNUSED(nb02);
-        GGML_UNUSED(nb03); GGML_UNUSED(nb11); GGML_UNUSED(nb12);
-        GGML_UNUSED(nb13); GGML_UNUSED(nb21); GGML_UNUSED(nb22);
-        GGML_UNUSED(nb23);
         NO_DEVICE_CODE;
         return;
     }
@@ -346,8 +335,8 @@ static __global__ void flash_attn_vec_ext_f32(
     }
 #else
     GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
-    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
     GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index 6bc7943ccd..2e2de8a093 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -471,9 +471,9 @@ static __global__ void flash_attn_ext_f16(
         dst_meta[j_dst_unrolled] = dst_meta_val;
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
-    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
-    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
+    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
     GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
     GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
index 1437367e87..5c66fe5bb1 100644
--- a/ggml/src/ggml-cuda/mmf.cu
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -151,7 +151,6 @@ static void mul_mat_f_cuda(
         cudaStream_t stream) {
     typedef tile<16, 8, T>     tile_A;
     typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
 
     GGML_ASSERT(!ids && "mul_mat_id not implemented");
 
@@ -352,9 +351,6 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr
     GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
     GGML_ASSERT(        nb0        == ts_dst);
 
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
-
     const float   * src1_d =       (const float   *) src1->data;
     const int32_t *  ids_d = ids ? (const int32_t *)  ids->data : nullptr;
     float         *  dst_d =       (float         *)  dst->data;
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 96129bd831..c22907d404 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -2855,12 +2855,14 @@ static __device__ __forceinline__ void mmq_write_back_mma(
 #else
     typedef tile<16, 8, int> tile_C;
     constexpr int rows_per_warp = 2 * granularity;
-#endif
+#endif // defined(AMD_MFMA_AVAILABLE)
     constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp.
 
     const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I);
 #if defined(TURING_MMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
     static_assert(nwarps*tile_C::I == mmq_y, "nwarps*tile_C::I != mmq_y");
+#else
+    GGML_UNUSED(nwarps);
 #endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)
 
 #pragma unroll
diff --git a/ggml/src/ggml-cuda/reduce_rows.cuh b/ggml/src/ggml-cuda/reduce_rows.cuh
index 6bee204136..6bcae9e52f 100644
--- a/ggml/src/ggml-cuda/reduce_rows.cuh
+++ b/ggml/src/ggml-cuda/reduce_rows.cuh
@@ -39,7 +39,7 @@ static __global__ void reduce_rows_f32(const float * __restrict__ x, float * __r
         }
         __syncthreads();
         sum = 0.0f;
-        if (lane_id < (blockDim.x / WARP_SIZE)) {
+        if (lane_id < (static_cast<int>(blockDim.x) / WARP_SIZE)) {
             sum = s_sum[lane_id];
         }
         sum = warp_reduce_sum(sum);

From 2f37014073f4c6ddc8f241c927db87337c71aa52 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 20 Aug 2025 13:30:46 +0300
Subject: [PATCH 114/287] lookahead : add sample command to readme (#15447)

* lookahead : add sample command to readme

* cont : build-agnostic command
---
 examples/lookahead/README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/examples/lookahead/README.md b/examples/lookahead/README.md
index aab3cd0ca4..c82de2a5a9 100644
--- a/examples/lookahead/README.md
+++ b/examples/lookahead/README.md
@@ -5,3 +5,9 @@ Demonstration of lookahead decoding technique:
 https://lmsys.org/blog/2023-11-21-lookahead-decoding/
 
 More info: https://github.com/ggml-org/llama.cpp/pull/4207
+
+Sample command:
+
+```bash
+llama-lookahead -hf ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF -p "// network server implemented in C\n// author: Peter Hacker\n\n#include" -e -ngl 99 -t 4 -n 512 -c 4096 -kvu
+```

From 37f10f955f70e0158d50343d0b9a3f92d194daae Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 20 Aug 2025 12:31:16 +0200
Subject: [PATCH 115/287] make : remove make in favor of CMake (#15449)

This commit removes the content from the Makefile and updates the
current deprecation message to information that `make` has been
replaced by CMake instead.

The message when `make` is invoked will now be the following:
```console
$ make
Makefile:6: *** Build system changed:
 The Makefile build has been replaced by CMake.

 For build instructions see:
 https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md

.  Stop.
```

The motivation for this is that many, if not all targets fail to build
now, after changes to the system, and `make` has also been deprected for
some time now.
---
 Makefile | 1611 +-----------------------------------------------------
 1 file changed, 6 insertions(+), 1605 deletions(-)

diff --git a/Makefile b/Makefile
index ac442aec09..bcbc770205 100644
--- a/Makefile
+++ b/Makefile
@@ -1,1608 +1,9 @@
-ifndef LLAMA_MAKEFILE
-$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
-endif
+define newline
 
-# Define the default target now so that it is always the first target
-BUILD_TARGETS = \
-	libllava.a \
-	llama-batched \
-	llama-batched-bench \
-	llama-bench \
-	llama-cli \
-	llama-convert-llama2c-to-ggml \
-	llama-embedding \
-	llama-eval-callback \
-	llama-export-lora \
-	llama-gbnf-validator \
-	llama-gguf \
-	llama-gguf-hash \
-	llama-gguf-split \
-	llama-gritlm \
-	llama-imatrix \
-	llama-infill \
-	llama-llava-cli \
-	llama-minicpmv-cli\
-	llama-qwen2vl-cli\
-	llama-lookahead \
-	llama-lookup \
-	llama-lookup-create \
-	llama-lookup-merge \
-	llama-lookup-stats \
-	llama-parallel \
-	llama-passkey \
-	llama-perplexity \
-	llama-q8dot \
-	llama-quantize \
-	llama-quantize-stats \
-	llama-retrieval \
-	llama-save-load-state \
-	llama-server \
-	llama-simple \
-	llama-simple-chat \
-	llama-run \
-	llama-speculative \
-	llama-tokenize \
-	llama-vdot \
-	llama-cvector-generator \
-	llama-gen-docs \
-	tests/test-c.o
 
-# Binaries only useful for tests
-TEST_TARGETS = \
-	tests/test-arg-parser \
-	tests/test-autorelease \
-	tests/test-backend-ops \
-	tests/test-chat \
-	tests/test-chat-template \
-	tests/test-double-float \
-	tests/test-grammar-integration \
-	tests/test-grammar-parser \
-	tests/test-json-schema-to-grammar \
-	tests/test-llama-grammar \
-	tests/test-log \
-	tests/test-model-load-cancel \
-	tests/test-quantize-fns \
-	tests/test-quantize-perf \
-	tests/test-rope \
-	tests/test-sampling \
-	tests/test-tokenizer-0 \
-	tests/test-tokenizer-1-bpe \
-	tests/test-tokenizer-1-spm
-#	tests/test-opt \
+endef
 
-# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
-LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \
-	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
-	retrieval speculative infill tokenize parallel export-lora lookahead lookup passkey gritlm
-
-# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them.
-#  We don't want to clutter things too much, so we only build replacements for the most commonly used binaries.
-LEGACY_TARGETS_BUILD = main quantize perplexity embedding server
-
-# Deprecation aliases
-ifdef LLAMA_CUBLAS
-$(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.)
-endif
-
-ifdef LLAMA_CUDA
-GGML_CUDA := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_KOMPUTE
-GGML_KOMPUTE := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_METAL
-GGML_METAL := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_RPC
-GGML_RPC := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_SYCL
-GGML_SYCL := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_SYCL_F16
-GGML_SYCL_F16 := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_OPENBLAS
-GGML_OPENBLAS := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_OPENBLAS64
-GGML_OPENBLAS64 := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_BLIS
-GGML_BLIS := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_NO_LLAMAFILE
-GGML_NO_LLAMAFILE := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_NO_ACCELERATE
-GGML_NO_ACCELERATE := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_NO_OPENMP
-GGML_NO_OPENMP := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_NO_METAL
-GGML_NO_METAL := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifdef LLAMA_DISABLE_LOGS
-REMOVE_WARNING := 1
-endif
-
-ifdef LLAMA_SERVER_VERBOSE
-REMOVE_WARNING := 1
-endif
-
-ifndef UNAME_S
-UNAME_S := $(shell uname -s)
-endif
-
-ifndef UNAME_P
-UNAME_P := $(shell uname -p)
-endif
-
-ifndef UNAME_M
-UNAME_M := $(shell uname -m)
-endif
-
-# In GNU make default CXX is g++ instead of c++.  Let's fix that so that users
-# of non-gcc compilers don't have to provide g++ alias or wrapper.
-DEFCC  := cc
-DEFCXX := c++
-ifeq ($(origin CC),default)
-CC  := $(DEFCC)
-endif
-ifeq ($(origin CXX),default)
-CXX := $(DEFCXX)
-endif
-
-# Mac OS + Arm can report x86_64
-# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
-ifeq ($(UNAME_S),Darwin)
-	ifndef GGML_NO_METAL
-		GGML_METAL := 1
-	endif
-
-	GGML_NO_OPENMP := 1
-
-	ifneq ($(UNAME_P),arm)
-		SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
-		ifeq ($(SYSCTL_M),1)
-			# UNAME_P := arm
-			# UNAME_M := arm64
-			warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
-		endif
-	endif
-endif
-
-ifdef GGML_METAL
-	GGML_METAL_EMBED_LIBRARY := 1
-endif
-
-ifdef GGML_RPC
-	BUILD_TARGETS += rpc-server
-endif
-
-ifdef GGML_VULKAN
-	BUILD_TARGETS += vulkan-shaders-gen
-endif
-
-default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD)
-
-test: $(TEST_TARGETS)
-	@failures=0; \
-	for test_target in $(TEST_TARGETS); do \
-		if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \
-			./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \
-		elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
-			continue; \
-		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
-			continue; \
-		else \
-			echo "Running test $$test_target..."; \
-			./$$test_target; \
-		fi; \
-		if [ $$? -ne 0 ]; then \
-			printf 'Test %s FAILED!\n\n' $$test_target; \
-			failures=$$(( failures + 1 )); \
-		else \
-			printf 'Test %s passed.\n\n' $$test_target; \
-		fi; \
-	done; \
-	if [ $$failures -gt 0 ]; then \
-		printf '\n%s tests failed.\n' $$failures; \
-		exit 1; \
-	fi
-	@echo 'All tests passed.'
-
-all: $(BUILD_TARGETS) $(TEST_TARGETS) $(LEGACY_TARGETS_BUILD)
-
-ifdef RISCV_CROSS_COMPILE
-CC	:= riscv64-unknown-linux-gnu-gcc
-CXX	:= riscv64-unknown-linux-gnu-g++
-endif
-
-#
-# Compile flags
-#
-
-# keep standard at C11 and C++17
-MK_CPPFLAGS  = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU
-MK_CFLAGS    = -std=c11   -fPIC
-MK_CXXFLAGS  = -std=c++17 -fPIC
-MK_NVCCFLAGS = -std=c++17
-
-ifdef LLAMA_NO_CCACHE
-GGML_NO_CCACHE := 1
-DEPRECATE_WARNING := 1
-endif
-
-ifndef GGML_NO_CCACHE
-CCACHE := $(shell which ccache)
-ifdef CCACHE
-export CCACHE_SLOPPINESS = time_macros
-$(info I ccache found, compilation results will be cached. Disable with GGML_NO_CCACHE.)
-CC    := $(CCACHE) $(CC)
-CXX   := $(CCACHE) $(CXX)
-else
-$(info I ccache not found. Consider installing it for faster compilation.)
-endif # CCACHE
-endif # GGML_NO_CCACHE
-
-# clock_gettime came in POSIX.1b (1993)
-# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
-# posix_memalign came in POSIX.1-2001 / SUSv3
-# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
-MK_CPPFLAGS += -D_XOPEN_SOURCE=600
-
-# Somehow in OpenBSD whenever POSIX conformance is specified
-# some string functions rely on locale_t availability,
-# which was introduced in POSIX.1-2008, forcing us to go higher
-ifeq ($(UNAME_S),OpenBSD)
-	MK_CPPFLAGS += -U_XOPEN_SOURCE -D_XOPEN_SOURCE=700
-endif
-
-# Data types, macros and functions related to controlling CPU affinity and
-# some memory allocation are available on Linux through GNU extensions in libc
-ifeq ($(UNAME_S),Linux)
-	MK_CPPFLAGS += -D_GNU_SOURCE
-	MK_LDFLAGS  += -ldl
-endif
-
-# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
-# and on macOS its availability depends on enabling Darwin extensions
-# similarly on DragonFly, enabling BSD extensions is necessary
-ifeq ($(UNAME_S),Darwin)
-	MK_CPPFLAGS += -D_DARWIN_C_SOURCE
-endif
-ifeq ($(UNAME_S),DragonFly)
-	MK_CPPFLAGS += -D__BSD_VISIBLE
-endif
-
-# alloca is a non-standard interface that is not visible on BSDs when
-# POSIX conformance is specified, but not all of them provide a clean way
-# to enable it in such cases
-ifeq ($(UNAME_S),FreeBSD)
-	MK_CPPFLAGS += -D__BSD_VISIBLE
-endif
-ifeq ($(UNAME_S),NetBSD)
-	MK_CPPFLAGS += -D_NETBSD_SOURCE
-endif
-ifeq ($(UNAME_S),OpenBSD)
-	MK_CPPFLAGS += -D_BSD_SOURCE
-endif
-
-ifdef GGML_SCHED_MAX_COPIES
-	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(GGML_SCHED_MAX_COPIES)
-endif
-
-ifdef LLAMA_DEBUG
-	MK_CFLAGS    += -O0 -g
-	MK_CXXFLAGS  += -O0 -g
-	MK_LDFLAGS   += -g
-	MK_NVCCFLAGS += -O0 -g
-
-	ifeq ($(UNAME_S),Linux)
-		MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS
-	endif
-else
-	MK_CPPFLAGS   += -DNDEBUG
-	MK_CFLAGS     += -O3 -g
-	MK_CXXFLAGS   += -O3 -g
-	MK_NVCCFLAGS  += -O3 -g
-endif
-
-ifdef LLAMA_SANITIZE_THREAD
-	MK_CFLAGS   += -fsanitize=thread -g
-	MK_CXXFLAGS += -fsanitize=thread -g
-	MK_LDFLAGS  += -fsanitize=thread -g
-endif
-
-ifdef LLAMA_SANITIZE_ADDRESS
-	MK_CFLAGS   += -fsanitize=address -fno-omit-frame-pointer -g
-	MK_CXXFLAGS += -fsanitize=address -fno-omit-frame-pointer -g
-	MK_LDFLAGS  += -fsanitize=address -fno-omit-frame-pointer -g
-endif
-
-ifdef LLAMA_SANITIZE_UNDEFINED
-	MK_CFLAGS   += -fsanitize=undefined -g
-	MK_CXXFLAGS += -fsanitize=undefined -g
-	MK_LDFLAGS  += -fsanitize=undefined -g
-endif
-
-ifdef LLAMA_SERVER_SSL
-	MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT
-	MK_LDFLAGS += -lssl -lcrypto
-endif
-
-ifndef GGML_NO_CPU_AARCH64
-	MK_CPPFLAGS += -DGGML_USE_CPU_REPACK
-endif
-
-# warnings
-WARN_FLAGS = \
-	-Wall \
-	-Wextra \
-	-Wpedantic \
-	-Wcast-qual \
-	-Wno-unused-function
-
-MK_CFLAGS += \
-	$(WARN_FLAGS) \
-	-Wshadow \
-	-Wstrict-prototypes \
-	-Wpointer-arith \
-	-Wmissing-prototypes \
-	-Werror=implicit-int \
-	-Werror=implicit-function-declaration
-
-MK_CXXFLAGS += \
-	$(WARN_FLAGS) \
-	-Wmissing-declarations \
-	-Wmissing-noreturn
-
-ifeq ($(LLAMA_FATAL_WARNINGS),1)
-	MK_CFLAGS   += -Werror
-	MK_CXXFLAGS += -Werror
-endif
-
-# this version of Apple ld64 is buggy
-ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))'
-	MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER
-endif
-
-# OS specific
-# TODO: support Windows
-ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)'
-	MK_CFLAGS   += -pthread
-	MK_CXXFLAGS += -pthread
-endif
-
-# detect Windows
-ifneq ($(findstring _NT,$(UNAME_S)),)
-	_WIN32 := 1
-endif
-
-# library name prefix
-ifneq ($(_WIN32),1)
-	LIB_PRE := lib
-endif
-
-# Dynamic Shared Object extension
-ifneq ($(_WIN32),1)
-	DSO_EXT := .so
-else
-	DSO_EXT := .dll
-endif
-
-# Windows Sockets 2 (Winsock) for network-capable apps
-ifeq ($(_WIN32),1)
-	LWINSOCK2 := -lws2_32
-endif
-
-ifdef LLAMA_GPROF
-	MK_CFLAGS   += -pg
-	MK_CXXFLAGS += -pg
-endif
-
-# Architecture specific
-# TODO: probably these flags need to be tweaked on some architectures
-#       feel free to update the Makefile for your architecture and send a pull request or issue
-
-ifndef RISCV_CROSS_COMPILE
-
-ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
-	# Use all CPU extensions that are available:
-	MK_CFLAGS     += -march=native -mtune=native
-	HOST_CXXFLAGS += -march=native -mtune=native
-
-	# Usage AMX build test
-	#MK_CFLAGS     += -march=graniterapids -mtune=graniterapids
-	#HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids
-
-	# Usage AVX-only
-	#MK_CFLAGS   += -mfma -mf16c -mavx
-	#MK_CXXFLAGS += -mfma -mf16c -mavx
-
-	# Usage SSSE3-only (Not is SSE3!)
-	#MK_CFLAGS   += -mssse3
-	#MK_CXXFLAGS += -mssse3
-endif
-
-ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))'
-	# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
-	# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
-	# https://github.com/ggml-org/llama.cpp/issues/2922
-	MK_CFLAGS   += -Xassembler -muse-unaligned-vector-move
-	MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move
-
-	# Target Windows 8 for PrefetchVirtualMemory
-	MK_CPPFLAGS += -D_WIN32_WINNT=0x602
-endif
-
-ifneq ($(filter aarch64%,$(UNAME_M)),)
-	# Apple M1, M2, etc.
-	# Raspberry Pi 3, 4, Zero 2 (64-bit)
-	# Nvidia Jetson
-	MK_CFLAGS   += -mcpu=native
-	MK_CXXFLAGS += -mcpu=native
-	JETSON_RELEASE_INFO = $(shell jetson_release)
-	ifdef JETSON_RELEASE_INFO
-		ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),)
-			JETSON_EOL_MODULE_DETECT = 1
-			CC = aarch64-unknown-linux-gnu-gcc
-			cxx = aarch64-unknown-linux-gnu-g++
-		endif
-	endif
-endif
-
-ifneq ($(filter armv6%,$(UNAME_M)),)
-	# Raspberry Pi 1, Zero
-	MK_CFLAGS   += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
-	MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
-endif
-
-ifneq ($(filter armv7%,$(UNAME_M)),)
-	# Raspberry Pi 2
-	MK_CFLAGS   += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
-	MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
-endif
-
-ifneq ($(filter armv8%,$(UNAME_M)),)
-	# Raspberry Pi 3, 4, Zero 2 (32-bit)
-	MK_CFLAGS   += -mfp16-format=ieee -mno-unaligned-access
-	MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access
-endif
-
-ifneq ($(filter ppc64%,$(UNAME_M)),)
-	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
-	ifneq (,$(findstring POWER9,$(POWER9_M)))
-		MK_CFLAGS   += -mcpu=power9
-		MK_CXXFLAGS += -mcpu=power9
-	endif
-endif
-
-ifneq ($(filter ppc64le%,$(UNAME_M)),)
-	MK_CFLAGS   += -mcpu=powerpc64le
-	MK_CXXFLAGS += -mcpu=powerpc64le
-	CUDA_POWER_ARCH = 1
-endif
-
-ifneq ($(filter loongarch64%,$(UNAME_M)),)
-	MK_CFLAGS   += -mlasx
-	MK_CXXFLAGS += -mlasx
-endif
-
-ifneq ($(filter riscv64%,$(UNAME_M)),)
-	MK_CFLAGS   += -march=rv64gcv -mabi=lp64d
-	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
-endif
-
-else # RISC-V CROSS COMPILATION
-	MK_CFLAGS   += -march=rv64gcv -mabi=lp64d
-	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
-endif
-
-ifndef GGML_NO_ACCELERATE
-	# Mac OS - include Accelerate framework.
-	# `-framework Accelerate` works both with Apple Silicon and Mac Intel
-	ifeq ($(UNAME_S),Darwin)
-		MK_CPPFLAGS  += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE
-		MK_CPPFLAGS  += -DACCELERATE_NEW_LAPACK
-		MK_CPPFLAGS  += -DACCELERATE_LAPACK_ILP64
-		MK_LDFLAGS   += -framework Accelerate
-		OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
-	endif
-endif # GGML_NO_ACCELERATE
-
-ifndef GGML_NO_OPENMP
-	MK_CPPFLAGS += -DGGML_USE_OPENMP
-	MK_CFLAGS   += -fopenmp
-	MK_CXXFLAGS += -fopenmp
-endif # GGML_NO_OPENMP
-
-ifdef GGML_OPENBLAS
-	MK_CPPFLAGS  += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas)
-	MK_CFLAGS    += $(shell pkg-config --cflags-only-other openblas)
-	MK_LDFLAGS   += $(shell pkg-config --libs openblas)
-	OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
-endif # GGML_OPENBLAS
-
-ifdef GGML_OPENBLAS64
-	MK_CPPFLAGS  += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64)
-	MK_CFLAGS    += $(shell pkg-config --cflags-only-other openblas64)
-	MK_LDFLAGS   += $(shell pkg-config --libs openblas64)
-	OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
-endif # GGML_OPENBLAS64
-
-ifdef GGML_BLIS
-	MK_CPPFLAGS  += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
-	MK_LDFLAGS   += -lblis -L/usr/local/lib
-	OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
-endif # GGML_BLIS
-
-ifdef GGML_NVPL
-	MK_CPPFLAGS  += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
-	MK_LDFLAGS   += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
-	OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
-endif # GGML_NVPL
-
-ifndef GGML_NO_LLAMAFILE
-	MK_CPPFLAGS  += -DGGML_USE_LLAMAFILE
-	OBJ_GGML_EXT += ggml/src/ggml-cpu/llamafile/sgemm.o
-endif
-
-ifndef GGML_NO_AMX
-	MK_CPPFLAGS += -DGGML_USE_AMX
-	OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o
-endif
-
-# only necessary for the CPU backend files
-MK_CPPFLAGS += -Iggml/src/ggml-cpu
-
-ifdef GGML_RPC
-	MK_CPPFLAGS  += -DGGML_USE_RPC
-	OBJ_GGML_EXT += ggml/src/ggml-rpc.o
-endif # GGML_RPC
-
-OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
-OBJ_CUDA_TMPL     += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
-
-ifdef GGML_CUDA_FA_ALL_QUANTS
-	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*.cu))
-else
-	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu))
-	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu))
-	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*f16-f16.cu))
-endif # GGML_CUDA_FA_ALL_QUANTS
-
-ifdef GGML_CUDA
-	ifneq ('', '$(wildcard /opt/cuda)')
-		CUDA_PATH ?= /opt/cuda
-	else
-		CUDA_PATH ?= /usr/local/cuda
-	endif
-
-	MK_CPPFLAGS  += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
-	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib
-	MK_NVCCFLAGS += -use_fast_math
-
-	OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
-	OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
-	OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
-
-ifdef LLAMA_FATAL_WARNINGS
-	MK_NVCCFLAGS += -Werror all-warnings
-endif # LLAMA_FATAL_WARNINGS
-
-ifndef JETSON_EOL_MODULE_DETECT
-	MK_NVCCFLAGS += --forward-unknown-to-host-compiler
-endif # JETSON_EOL_MODULE_DETECT
-
-ifdef LLAMA_DEBUG
-	MK_NVCCFLAGS += -lineinfo
-endif # LLAMA_DEBUG
-
-ifdef GGML_CUDA_DEBUG
-	MK_NVCCFLAGS += --device-debug
-endif # GGML_CUDA_DEBUG
-
-ifdef GGML_CUDA_NVCC
-	NVCC = $(CCACHE) $(GGML_CUDA_NVCC)
-else
-	NVCC = $(CCACHE) nvcc
-endif # GGML_CUDA_NVCC
-
-ifdef CUDA_DOCKER_ARCH
-	MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
-else ifndef CUDA_POWER_ARCH
-	MK_NVCCFLAGS += -arch=native
-endif # CUDA_DOCKER_ARCH
-
-ifdef GGML_CUDA_FORCE_MMQ
-	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
-endif # GGML_CUDA_FORCE_MMQ
-
-ifdef GGML_CUDA_FORCE_CUBLAS
-	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS
-endif # GGML_CUDA_FORCE_CUBLAS
-
-ifdef GGML_CUDA_F16
-	MK_NVCCFLAGS += -DGGML_CUDA_F16
-endif # GGML_CUDA_F16
-
-ifdef GGML_CUDA_DMMV_F16
-	MK_NVCCFLAGS += -DGGML_CUDA_F16
-endif # GGML_CUDA_DMMV_F16
-
-ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE
-	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE)
-else
-	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
-endif # GGML_CUDA_PEER_MAX_BATCH_SIZE
-
-ifdef GGML_CUDA_NO_PEER_COPY
-	MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY
-endif # GGML_CUDA_NO_PEER_COPY
-
-ifdef GGML_CUDA_CCBIN
-	MK_NVCCFLAGS += -ccbin $(GGML_CUDA_CCBIN)
-endif # GGML_CUDA_CCBIN
-
-ifdef GGML_CUDA_NO_FA
-	MK_NVCCFLAGS += -DGGML_CUDA_NO_FA
-endif # GGML_CUDA_NO_FA
-
-ifdef GGML_CUDA_FA_ALL_QUANTS
-	MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
-endif # GGML_CUDA_FA_ALL_QUANTS
-
-ifdef JETSON_EOL_MODULE_DETECT
-define NVCC_COMPILE
-	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
-endef # NVCC_COMPILE
-else
-define NVCC_COMPILE
-	$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
-endef # NVCC_COMPILE
-endif # JETSON_EOL_MODULE_DETECT
-
-ggml/src/ggml-cuda/%.o: \
-	ggml/src/ggml-cuda/%.cu \
-	ggml/include/ggml.h \
-	ggml/src/ggml-common.h \
-	ggml/src/ggml-cuda/common.cuh
-	$(NVCC_COMPILE)
-
-ggml/src/ggml-cuda/ggml-cuda.o: \
-	ggml/src/ggml-cuda/ggml-cuda.cu \
-	ggml/include/ggml-cuda.h \
-	ggml/include/ggml.h \
-	ggml/include/ggml-backend.h \
-	ggml/src/ggml-backend-impl.h \
-	ggml/src/ggml-common.h \
-	$(wildcard ggml/src/ggml-cuda/*.cuh)
-	$(NVCC_COMPILE)
-endif # GGML_CUDA
-
-ifdef GGML_VULKAN
-	MK_CPPFLAGS  += -DGGML_USE_VULKAN
-	MK_LDFLAGS   += $(shell pkg-config --libs vulkan)
-	OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
-
-ifdef GGML_VULKAN_CHECK_RESULTS
-	MK_CPPFLAGS  += -DGGML_VULKAN_CHECK_RESULTS
-endif
-
-ifdef GGML_VULKAN_DEBUG
-	MK_CPPFLAGS  += -DGGML_VULKAN_DEBUG
-endif
-
-ifdef GGML_VULKAN_MEMORY_DEBUG
-	MK_CPPFLAGS  += -DGGML_VULKAN_MEMORY_DEBUG
-endif
-
-ifdef GGML_VULKAN_PERF
-	MK_CPPFLAGS  += -DGGML_VULKAN_PERF
-endif
-
-ifdef GGML_VULKAN_VALIDATE
-	MK_CPPFLAGS  += -DGGML_VULKAN_VALIDATE
-endif
-
-ifdef GGML_VULKAN_RUN_TESTS
-	MK_CPPFLAGS  += -DGGML_VULKAN_RUN_TESTS
-endif
-
-GLSLC_CMD  = glslc
-_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen
-_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp
-_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp
-_ggml_vk_input_dir = ggml/src/ggml-vulkan/vulkan-shaders
-_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp)
-
-ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
-	$(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@
-
-$(_ggml_vk_header): $(_ggml_vk_source)
-
-$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen
-	$(_ggml_vk_genshaders_cmd) \
-		--glslc      $(GLSLC_CMD) \
-		--input-dir  $(_ggml_vk_input_dir) \
-		--target-hpp $(_ggml_vk_header) \
-		--target-cpp $(_ggml_vk_source)
-
-vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
-	$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
-
-endif # GGML_VULKAN
-
-ifdef GGML_HIP
-	ifeq ($(wildcard /opt/rocm),)
-		ROCM_PATH      ?= /usr
-		AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
-	else
-		ROCM_PATH	?= /opt/rocm
-		AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
-	endif
-
-	MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA
-
-	MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
-	MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
-	MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas
-
-	HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
-
-	HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
-
-ifdef GGML_CUDA_FORCE_MMQ
-	HIPFLAGS += -DGGML_CUDA_FORCE_MMQ
-endif # GGML_CUDA_FORCE_MMQ
-
-ifdef GGML_CUDA_FORCE_CUBLAS
-	HIPFLAGS += -DGGML_CUDA_FORCE_CUBLAS
-endif # GGML_CUDA_FORCE_CUBLAS
-
-ifdef GGML_CUDA_NO_PEER_COPY
-	HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY
-endif # GGML_CUDA_NO_PEER_COPY
-
-ifdef GGML_CUDA_NO_FA
-	HIPFLAGS += -DGGML_CUDA_NO_FA
-endif # GGML_CUDA_NO_FA
-
-	OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
-	OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
-	OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
-
-ggml/src/ggml-cuda/ggml-cuda.o: \
-	ggml/src/ggml-cuda/ggml-cuda.cu \
-	ggml/include/ggml-cuda.h \
-	ggml/include/ggml.h \
-	ggml/include/ggml-backend.h \
-	ggml/src/ggml-backend-impl.h \
-	ggml/src/ggml-common.h \
-	$(wildcard ggml/src/ggml-cuda/*.cuh)
-	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-
-ggml/src/ggml-cuda/%.o: \
-	ggml/src/ggml-cuda/%.cu \
-	ggml/include/ggml.h \
-	ggml/src/ggml-common.h \
-	ggml/src/ggml-cuda/common.cuh
-	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-endif # GGML_HIP
-
-ifdef GGML_MUSA
-	ifeq ($(wildcard /opt/musa),)
-		MUSA_PATH ?= /usr/local/musa
-	else
-		MUSA_PATH ?= /opt/musa
-	endif
-	MUSA_ARCHITECTURES ?= 21;22;31
-
-	MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA
-	MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib
-	MK_LDFLAGS += -lmusa -lmusart -lmublas
-
-	ifndef GGML_NO_OPENMP
-		# For Ubuntu Focal
-		MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp
-		MK_LDFLAGS  += -L/usr/lib/llvm-10/lib
-		# For Ubuntu Jammy
-		MK_CPPFLAGS += -I/usr/lib/llvm-14/lib/clang/14.0.0/include
-		MK_LDFLAGS  += -L/usr/lib/llvm-14/lib
-	endif # GGML_NO_OPENMP
-
-	CC  := $(MUSA_PATH)/bin/clang
-	CXX := $(MUSA_PATH)/bin/clang++
-	MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc
-
-	MUSAFLAGS  = -fsigned-char -x musa -mtgpu
-	MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch))
-
-ifdef GGML_CUDA_FORCE_MMQ
-	MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ
-endif # GGML_CUDA_FORCE_MMQ
-
-ifdef GGML_CUDA_FORCE_CUBLAS
-	MUSAFLAGS += -DGGML_CUDA_FORCE_CUBLAS
-endif # GGML_CUDA_FORCE_CUBLAS
-
-ifdef GGML_CUDA_F16
-	MUSAFLAGS += -DGGML_CUDA_F16
-endif # GGML_CUDA_F16
-
-ifdef GGML_CUDA_DMMV_F16
-	MUSAFLAGS += -DGGML_CUDA_F16
-endif # GGML_CUDA_DMMV_F16
-
-ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE
-	MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE)
-else
-	MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
-endif # GGML_CUDA_PEER_MAX_BATCH_SIZE
-
-ifdef GGML_CUDA_NO_PEER_COPY
-	MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY
-endif # GGML_CUDA_NO_PEER_COPY
-
-ifdef GGML_CUDA_NO_FA
-	MUSAFLAGS += -DGGML_CUDA_NO_FA
-endif # GGML_CUDA_NO_FA
-
-ifdef GGML_CUDA_FA_ALL_QUANTS
-	MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
-endif # GGML_CUDA_FA_ALL_QUANTS
-
-	OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
-	OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
-	OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
-
-ggml/src/ggml-cuda/ggml-cuda.o: \
-	ggml/src/ggml-cuda/ggml-cuda.cu \
-	ggml/include/ggml-cuda.h \
-	ggml/include/ggml.h \
-	ggml/include/ggml-backend.h \
-	ggml/src/ggml-backend-impl.h \
-	ggml/src/ggml-common.h \
-	$(wildcard ggml/src/ggml-cuda/*.cuh)
-	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $<
-
-ggml/src/ggml-cuda/%.o: \
-	ggml/src/ggml-cuda/%.cu \
-	ggml/include/ggml.h \
-	ggml/src/ggml-common.h \
-	ggml/src/ggml-cuda/common.cuh
-	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $<
-endif # GGML_MUSA
-
-ifdef GGML_METAL
-	MK_CPPFLAGS  += -DGGML_USE_METAL
-	MK_LDFLAGS   += -framework Foundation -framework Metal -framework MetalKit
-	OBJ_GGML_EXT += ggml/src/ggml-metal/ggml-metal.o
-
-ifdef GGML_METAL_USE_BF16
-	MK_CPPFLAGS += -DGGML_METAL_USE_BF16
-endif # GGML_METAL_USE_BF16
-ifdef GGML_METAL_NDEBUG
-	MK_CPPFLAGS += -DGGML_METAL_NDEBUG
-endif
-ifdef GGML_METAL_EMBED_LIBRARY
-	MK_CPPFLAGS  += -DGGML_METAL_EMBED_LIBRARY
-	OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o
-endif
-endif # GGML_METAL
-
-ifdef GGML_METAL
-ggml/src/ggml-metal/ggml-metal.o: \
-	ggml/src/ggml-metal/ggml-metal.m \
-	ggml/src/ggml-metal/ggml-metal-impl.h \
-	ggml/include/ggml-metal.h \
-	ggml/include/ggml.h
-	$(CC) $(CFLAGS) -c $< -o $@
-
-ifdef GGML_METAL_EMBED_LIBRARY
-ggml/src/ggml-metal-embed.o: \
-	ggml/src/ggml-metal/ggml-metal.metal \
-	ggml/src/ggml-metal/ggml-metal-impl.h \
-	ggml/src/ggml-common.h
-	@echo "Embedding Metal library"
-	@sed -e '/__embed_ggml-common.h__/r      ggml/src/ggml-common.h'                -e '/__embed_ggml-common.h__/d'      < ggml/src/ggml-metal/ggml-metal.metal           > ggml/src/ggml-metal/ggml-metal-embed.metal.tmp
-	@sed -e '/#include "ggml-metal-impl.h"/r ggml/src/ggml-metal/ggml-metal-impl.h' -e '/#include "ggml-metal-impl.h"/d' < ggml/src/ggml-metal/ggml-metal-embed.metal.tmp > ggml/src/ggml-metal/ggml-metal-embed.metal
-	$(eval TEMP_ASSEMBLY=$(shell mktemp -d))
-	@echo ".section __DATA, __ggml_metallib"                       >  $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	@echo ".globl _ggml_metallib_start"                            >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	@echo "_ggml_metallib_start:"                                  >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	@echo ".incbin \"ggml/src/ggml-metal/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	@echo ".globl _ggml_metallib_end"                              >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	@echo "_ggml_metallib_end:"                                    >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
-	$(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@
-	@rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s
-	@rmdir ${TEMP_ASSEMBLY}
-endif
-endif # GGML_METAL
-
-DIR_GGML = ggml
-DIR_LLAMA = src
-DIR_COMMON = common
-
-OBJ_GGML = \
-	$(DIR_GGML)/src/ggml.o \
-	$(DIR_GGML)/src/ggml-alloc.o \
-	$(DIR_GGML)/src/ggml-backend.o \
-	$(DIR_GGML)/src/ggml-backend-reg.o \
-	$(DIR_GGML)/src/ggml-opt.o \
-	$(DIR_GGML)/src/ggml-quants.o \
-	$(DIR_GGML)/src/ggml-threading.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \
-	$(DIR_GGML)/src/ggml-cpu/repack.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \
-	$(OBJ_GGML_EXT)
-
-OBJ_LLAMA = \
-	$(DIR_LLAMA)/llama.o \
-	$(DIR_LLAMA)/llama-vocab.o \
-	$(DIR_LLAMA)/llama-grammar.o \
-	$(DIR_LLAMA)/llama-sampling.o \
-	$(DIR_LLAMA)/unicode.o \
-	$(DIR_LLAMA)/unicode-data.o
-
-OBJ_COMMON = \
-	$(DIR_COMMON)/common.o \
-	$(DIR_COMMON)/arg.o \
-	$(DIR_COMMON)/log.o \
-	$(DIR_COMMON)/console.o \
-	$(DIR_COMMON)/ngram-cache.o \
-	$(DIR_COMMON)/sampling.o \
-	$(DIR_COMMON)/speculative.o \
-	$(DIR_COMMON)/chat.o \
-	$(DIR_COMMON)/build-info.o \
-	$(DIR_COMMON)/json-schema-to-grammar.o
-
-OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON)
-
-LIB_GGML   = $(LIB_PRE)ggml$(DSO_EXT)
-LIB_GGML_S = $(LIB_PRE)ggml.a
-
-LIB_LLAMA   = $(LIB_PRE)llama$(DSO_EXT)
-LIB_LLAMA_S = $(LIB_PRE)llama.a
-
-LIB_COMMON   = $(LIB_PRE)common$(DSO_EXT)
-LIB_COMMON_S = $(LIB_PRE)common.a
-
-LIB_ALL   = $(LIB_GGML)   $(LIB_LLAMA)   $(LIB_COMMON)
-LIB_ALL_S = $(LIB_GGML_S) $(LIB_LLAMA_S) $(LIB_COMMON_S)
-
-GF_CC := $(CC)
-include scripts/get-flags.mk
-
-# combine build flags with cmdline overrides
-override CPPFLAGS  := $(MK_CPPFLAGS) $(CPPFLAGS)
-override CFLAGS    := $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS)
-BASE_CXXFLAGS      := $(MK_CXXFLAGS) $(CXXFLAGS)
-override CXXFLAGS  := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS) $(CPPFLAGS)
-override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
-override LDFLAGS   := $(MK_LDFLAGS) $(LDFLAGS)
-
-# identify CUDA host compiler
-ifdef GGML_CUDA
-GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
-include scripts/get-flags.mk
-CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
-endif
-
-ifdef LLAMA_CURL
-override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL
-override LDFLAGS  := $(LDFLAGS) -lcurl
-endif
-
-#
-# Print build information
-#
-
-$(info I llama.cpp build info: )
-$(info I UNAME_S:   $(UNAME_S))
-$(info I UNAME_P:   $(UNAME_P))
-$(info I UNAME_M:   $(UNAME_M))
-$(info I CFLAGS:    $(CFLAGS))
-$(info I CXXFLAGS:  $(CXXFLAGS))
-$(info I NVCCFLAGS: $(NVCCFLAGS))
-$(info I LDFLAGS:   $(LDFLAGS))
-$(info I CC:        $(shell $(CC)   --version | head -n 1))
-$(info I CXX:       $(shell $(CXX)  --version | head -n 1))
-ifdef GGML_CUDA
-$(info I NVCC:      $(shell $(NVCC) --version | tail -n 1))
-CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
-ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
-
-ifndef CUDA_DOCKER_ARCH
-ifndef CUDA_POWER_ARCH
-$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus )
-endif # CUDA_POWER_ARCH
-endif # CUDA_DOCKER_ARCH
-
-endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
-endif # GGML_CUDA
-$(info )
-
-ifdef DEPRECATE_WARNING
-$(info !!! DEPRECATION WARNING !!!)
-$(info The following LLAMA_ options are deprecated and will be removed in the future. Use the GGML_ prefix instead)
-$(info   - LLAMA_CUDA)
-$(info   - LLAMA_METAL)
-$(info   - LLAMA_METAL_EMBED_LIBRARY)
-$(info   - LLAMA_OPENMP)
-$(info   - LLAMA_RPC)
-$(info   - LLAMA_SYCL)
-$(info   - LLAMA_SYCL_F16)
-$(info   - LLAMA_OPENBLAS)
-$(info   - LLAMA_OPENBLAS64)
-$(info   - LLAMA_BLIS)
-$(info   - LLAMA_NO_LLAMAFILE)
-$(info   - LLAMA_NO_ACCELERATE)
-$(info   - LLAMA_NO_OPENMP)
-$(info   - LLAMA_NO_METAL)
-$(info   - LLAMA_NO_CCACHE)
-$(info )
-endif
-
-ifdef REMOVE_WARNING
-$(info !!! REMOVAL WARNING !!!)
-$(info The following LLAMA_ options have been removed and are no longer supported)
-$(info   - LLAMA_DISABLE_LOGS   (https://github.com/ggml-org/llama.cpp/pull/9418))
-$(info   - LLAMA_SERVER_VERBOSE (https://github.com/ggml-org/llama.cpp/pull/9418))
-$(info )
-endif
-
-#
-# Build libraries
-#
-
-# Libraries
-LIB_GGML   = libggml.so
-LIB_GGML_S = libggml.a
-
-LIB_LLAMA   = libllama.so
-LIB_LLAMA_S = libllama.a
-
-LIB_COMMON   = libcommon.so
-LIB_COMMON_S = libcommon.a
-
-# Targets
-BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S)
-
-# Dependency files
-DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d)
-
-# Default target
-all: $(BUILD_TARGETS)
-
-# force c++ build for source file that have same name as c file
-# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files
-$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp
-	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
-
-# Rules for building object files
-$(DIR_GGML)/%.o: $(DIR_GGML)/%.c
-	$(CC) $(CFLAGS) -MMD -c $< -o $@
-
-$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp
-	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
-
-$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp
-	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
-
-$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp
-	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
-
-# Rules for building libraries
-$(LIB_GGML): $(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
-
-$(LIB_GGML_S): $(OBJ_GGML)
-	ar rcs $(LIB_GGML_S) $^
-
-$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML)
-	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
-
-$(LIB_LLAMA_S): $(OBJ_LLAMA)
-	ar rcs $(LIB_LLAMA_S) $^
-
-$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML)
-	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
-
-$(LIB_COMMON_S): $(OBJ_COMMON)
-	ar rcs $(LIB_COMMON_S) $^
-
-# Include dependency files
--include $(DEP_FILES)
-
-# Clean generated server assets
-clean-server-assets:
-	find tools/server -type f -name "*.js.hpp"   -delete
-	find tools/server -type f -name "*.mjs.hpp"  -delete
-	find tools/server -type f -name "*.css.hpp"  -delete
-	find tools/server -type f -name "*.html.hpp" -delete
-
-# Clean rule
-clean: clean-server-assets
-	rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS)
-	rm -rvf *.a *.dll *.so *.dot
-	find ggml src common tests examples pocs -type f -name "*.o" -delete
-	find ggml src common tests examples pocs -type f -name "*.d" -delete
-
-#
-# Examples
-#
-
-# $< is the first prerequisite, i.e. the source file.
-# Explicitly compile this to an object file so that it can be cached with ccache.
-# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead.
-
-# Helper function that replaces .c, .cpp, and .cu file endings with .o:
-GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
-
-llama-cli: tools/main/main.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	@echo
-	@echo '====  Run ./llama-cli -h for help.  ===='
-	@echo
-
-llama-run: tools/run/run.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-simple: examples/simple/simple.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-simple-chat: examples/simple-chat/simple-chat.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-tokenize: tools/tokenize/tokenize.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-batched: examples/batched/batched.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-batched-bench: tools/batched-bench/batched-bench.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-quantize: tools/quantize/quantize.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-quantize-stats: tools/quantize-stats/quantize-stats.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-perplexity: tools/perplexity/perplexity.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-imatrix: tools/imatrix/imatrix.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-embedding: examples/embedding/embedding.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-gritlm: examples/gritlm/gritlm.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-save-load-state: examples/save-load-state/save-load-state.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-gguf: examples/gguf/gguf.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-examples/gguf-hash/deps/sha1/sha1.o: \
-	examples/gguf-hash/deps/sha1/sha1.c
-	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
-
-examples/gguf-hash/deps/xxhash/xxhash.o: \
-	examples/gguf-hash/deps/xxhash/xxhash.c
-	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
-
-examples/gguf-hash/deps/sha256/sha256.o: \
-	examples/gguf-hash/deps/sha256/sha256.c
-	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
-
-llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/sha1.o examples/gguf-hash/deps/xxhash/xxhash.o examples/gguf-hash/deps/sha256/sha256.o\
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-gguf-split: tools/gguf-split/gguf-split.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-eval-callback: examples/eval-callback/eval-callback.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-cvector-generator: tools/cvector-generator/cvector-generator.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-bench: tools/llama-bench/llama-bench.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-export-lora: tools/export-lora/export-lora.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-retrieval: examples/retrieval/retrieval.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-speculative: examples/speculative/speculative.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-parallel: examples/parallel/parallel.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-lookahead: examples/lookahead/lookahead.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-lookup: examples/lookup/lookup.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-lookup-create: examples/lookup/lookup-create.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-lookup-merge: examples/lookup/lookup-merge.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-lookup-stats: examples/lookup/lookup-stats.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-passkey: examples/passkey/passkey.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-ifdef GGML_RPC
-rpc-server: tools/rpc/rpc-server.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-endif # GGML_RPC
-
-llama-server: \
-	tools/server/server.cpp \
-	tools/server/utils.hpp \
-	tools/server/httplib.h \
-	tools/server/index.html.hpp \
-	tools/server/loading.html.hpp \
-	common/chat.cpp \
-	common/chat.h \
-	common/chat-template.hpp \
-	common/json.hpp \
-	common/minja.hpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Itools/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
-
-# Portable equivalent of `cd tools/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-tools/server/%.hpp: tools/server/public/% FORCE Makefile
-	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
-		echo "unsigned char $${NAME}[] = {" && \
-		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
-		echo "};" && \
-		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
-	) > $@
-
-llama-gen-docs: examples/gen-docs/gen-docs.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-libllava.a: tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
-	common/stb_image.h \
-	common/base64.hpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
-
-llama-llava-cli: tools/mtmd/llava-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
-
-llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
-
-llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
-
-ifeq ($(UNAME_S),Darwin)
-swift: examples/batched.swift
-	(cd examples/batched.swift; make build)
-endif
-
-common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
-	@sh scripts/build-info.sh "$(CC)" > $@.tmp
-	@if ! cmp -s $@.tmp $@; then \
-		mv $@.tmp $@; \
-	else \
-		rm $@.tmp; \
-	fi
-
-common/build-info.o: common/build-info.cpp
-	$(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@
-
-#
-# Tests
-#
-
-tests: $(TEST_TARGETS)
-
-tests/test-arg-parser: tests/test-arg-parser.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-llama-grammar: tests/test-llama-grammar.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-log: tests/test-log.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-grammar-parser: tests/test-grammar-parser.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-grammar-integration: tests/test-grammar-integration.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-double-float: tests/test-double-float.cpp
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-chat: tests/test-chat.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-opt: tests/test-opt.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-quantize-fns: tests/test-quantize-fns.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-quantize-perf: tests/test-quantize-perf.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-sampling: tests/test-sampling.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-tokenizer-0: tests/test-tokenizer-0.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-rope: tests/test-rope.cpp ggml/src/ggml.o \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-c.o: tests/test-c.c include/llama.h
-	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
-
-tests/test-backend-ops: tests/test-backend-ops.cpp \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-model-load-cancel: tests/test-model-load-cancel.cpp tests/get-model.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-autorelease: tests/test-autorelease.cpp tests/get-model.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-chat-template: tests/test-chat-template.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-#
-# PoCs
-#
-
-llama-vdot: pocs/vdot/vdot.cpp ggml/src/ggml.o \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
-	$(OBJ_GGML)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-#
-# Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
-#
-# Mark legacy binary targets as .PHONY so that they are always checked.
-.PHONY: FORCE main quantize perplexity embedding server
-
-# Define the object file target
-examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-
-# NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate.
-#  Eventually we will want to remove these target from building all the time.
-main: examples/deprecation-warning/deprecation-warning.o
-	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
-	@echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead."
-
-server: examples/deprecation-warning/deprecation-warning.o
-	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
-	@echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead."
-
-quantize: examples/deprecation-warning/deprecation-warning.o
-ifneq (,$(wildcard quantize))
-	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead."
-	@echo "  Remove the 'quantize' binary to remove this warning."
-	@echo "#########"
-endif
-
-perplexity: examples/deprecation-warning/deprecation-warning.o
-ifneq (,$(wildcard perplexity))
-	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead."
-	@echo "  Remove the 'perplexity' binary to remove this warning."
-	@echo "#########"
-endif
-
-embedding: examples/deprecation-warning/deprecation-warning.o
-ifneq (,$(wildcard embedding))
-	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead."
-	@echo "  Remove the 'embedding' binary to remove this warning."
-	@echo "#########"
-endif
+$(error Build system changed:$(newline)\
+The Makefile build has been replaced by CMake.$(newline)$(newline)\
+For build instructions see:$(newline)\
+https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md$(newline)${newline})

From 1a99c2d948209d9ea5eac8b6dc0a297107244540 Mon Sep 17 00:00:00 2001
From: xiaobing318 <71554036+xiaobing318@users.noreply.github.com>
Date: Wed, 20 Aug 2025 18:32:05 +0800
Subject: [PATCH 116/287] cmake : fix target include directories (#15450)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update docker.yml

修改docker.yml文件中的内容使其停止周期性的运行该workflow，如果想要运行该workflow可以手动启动

* feat:Modify the header file include path

1. There's no llava directory in the tools directory.
2. Because the command `target_include_directories(mtmd PUBLIC .)` is used in the `mtmd` CMakeLists.txt file, other targets that link against `mtmd` automatically include the `mtmd` directory as a search path for header files. Therefore, you can remove `target_include_directories(${TARGET} PRIVATE ../llava`` or use `target_include_directories(${TARGET} PRIVATE ../mtmd`` to explicitly require the `llama-server` target to use header files from `mtmd`.

* Restore the docker.yml file
---
 tools/server/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index c2a56aaa7f..83b608c32a 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -33,7 +33,7 @@ endforeach()
 add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 
-target_include_directories(${TARGET} PRIVATE ../llava)
+target_include_directories(${TARGET} PRIVATE ../mtmd)
 target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
 target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
 

From ec5ab1a36c11dd3efcf4ec8d1ac89a13a8117bc3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <fujie_email@sina.com>
Date: Wed, 20 Aug 2025 18:33:30 +0800
Subject: [PATCH 117/287] common : fix context shift help message (#15448)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 common/arg.cpp  | 2 +-
 common/common.h | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index d9f6ec96d0..eeb6c94af4 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1532,7 +1532,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--context-shift"},
-        string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
+        string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
         [](common_params & params) {
             params.ctx_shift = true;
         }
diff --git a/common/common.h b/common/common.h
index 920de7b50a..614e41a246 100644
--- a/common/common.h
+++ b/common/common.h
@@ -375,7 +375,7 @@ struct common_params {
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool flash_attn        = false; // flash attention
     bool no_perf           = false; // disable performance metrics
-    bool ctx_shift         = false;  // context shift on inifinite text generation
+    bool ctx_shift         = false;  // context shift on infinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
     bool kv_unified        = false; // enable unified KV cache
 

From 657b8a77bd01854f99d37a47318fa24f2e7e298f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 20 Aug 2025 14:26:01 +0200
Subject: [PATCH 118/287] chat: handle gpt-oss return/end token inconsistency
 (#15421)

This commit addresses an inconsistency during inference by adding a new
member to the `templates_params` struct to indicate whether the chat is
in inference mode. This allows the gpt-oss specific function
`common_chat_params_init_gpt_oss` to check this flag and the
`add_generation_prompt` flag to determine if it should replace the
`<|return|>` token with the `<|end|>` token in the prompt.

The motivation for this change is to ensure that the formatted prompt of
past messages in `common_chat_format_single` matches the output of the
formatted new message. The issue is that the gpt-oss template returns
different end tags: `<|return|>` when `add_generation_prompt` is false,
and `<|end|>` when `add_generation_prompt` is true. This causes the
substring function to start at an incorrect position, resulting in
tokenization starting with 'tart|>' instead of '<|start|>'.

Resolves: https://github.com/ggml-org/llama.cpp/issues/15417
---
 common/chat.cpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/common/chat.cpp b/common/chat.cpp
index 5fe5643d38..7f6809a4ed 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -147,6 +147,7 @@ struct templates_params {
     json extra_context;
     bool add_bos;
     bool add_eos;
+    bool is_inference = true;
 };
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -1336,6 +1337,17 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
     common_chat_params data;
     auto prompt = apply(tmpl, inputs);
 
+    // Check if we need to replace the return token with end token during
+    // inference and without generation prompt. For more details see:
+    // https://github.com/ggml-org/llama.cpp/issues/15417
+    if (inputs.is_inference && !inputs.add_generation_prompt) {
+        static constexpr std::string_view return_token = "<|return|>";
+        static constexpr std::string_view end_token    = "<|end|>";
+        if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) {
+            prompt.replace(pos, return_token.length(), end_token);
+        }
+    }
+
     data.prompt = prompt;
     data.format = COMMON_CHAT_FORMAT_GPT_OSS;
 

From fec9519802ae1567048abb126cdd5ea160a22d0f Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Wed, 20 Aug 2025 09:33:14 -0500
Subject: [PATCH 119/287] vulkan: shorten pipeline name strings (#15431)

These detailed strings were causing increased build time on gcc.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 92 ++++++++++++++--------------
 1 file changed, 46 insertions(+), 46 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 7ef938066b..c59a588b97 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2786,53 +2786,53 @@ static void ggml_vk_load_shaders(vk_device& device) {
         const bool s = device->subgroup_add && device->architecture != vk_device_architecture::AMD_GCN;
 
         for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f32_f32_f32_len[s],  arr_dmmv_f32_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f16_f32_f32_len[s],  arr_dmmv_f16_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_bf16_f32_f32_len[s], arr_dmmv_bf16_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_0_f32_f32_len[s], arr_dmmv_q4_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_1_f32_f32_len[s], arr_dmmv_q4_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_0_f32_f32_len[s], arr_dmmv_q5_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_1_f32_f32_len[s], arr_dmmv_q5_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q8_0_f32_f32_len[s], arr_dmmv_q8_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q2_k_f32_f32_len[s], arr_dmmv_q2_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q3_k_f32_f32_len[s], arr_dmmv_q3_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_k_f32_f32_len[s], arr_dmmv_q4_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_k_f32_f32_len[s], arr_dmmv_q5_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q6_k_f32_f32_len[s], arr_dmmv_q6_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_s_f32_f32_len[s],   arr_dmmv_iq1_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_m_f32_f32_len[s],   arr_dmmv_iq1_m_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq2_xxs_f32_f32_len[s], arr_dmmv_iq2_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq2_xs_f32_f32_len[s],  arr_dmmv_iq2_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq2_s_f32_f32_len[s],   arr_dmmv_iq2_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq3_xxs_f32_f32_len[s], arr_dmmv_iq3_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq3_s_f32_f32_len[s],   arr_dmmv_iq3_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_xs_f32_f32_len[s],  arr_dmmv_iq4_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_nl_f32_f32_len[s],  arr_dmmv_iq4_nl_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_mxfp4_f32_f32_len[s],   arr_dmmv_mxfp4_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[s],  arr_dmmv_f32_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[s],  arr_dmmv_f16_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[s], arr_dmmv_bf16_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[s], arr_dmmv_q4_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[s], arr_dmmv_q4_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[s], arr_dmmv_q5_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[s], arr_dmmv_q5_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[s], arr_dmmv_q8_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[s], arr_dmmv_q2_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[s], arr_dmmv_q3_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[s], arr_dmmv_q4_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[s], arr_dmmv_q5_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[s], arr_dmmv_q6_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[s],   arr_dmmv_iq1_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[s],   arr_dmmv_iq1_m_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[s], arr_dmmv_iq2_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[s],  arr_dmmv_iq2_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[s],   arr_dmmv_iq2_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[s], arr_dmmv_iq3_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[s],   arr_dmmv_iq3_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[s],  arr_dmmv_iq4_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[s],  arr_dmmv_iq4_nl_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[s],   arr_dmmv_mxfp4_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
 
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f32_f16_f32_len[s],  arr_dmmv_f32_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_f16_f16_f32_len[s],  arr_dmmv_f16_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_bf16_f16_f32_len[s], arr_dmmv_bf16_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_0_f16_f32_len[s], arr_dmmv_q4_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_1_f16_f32_len[s], arr_dmmv_q4_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_0_f16_f32_len[s], arr_dmmv_q5_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_1_f16_f32_len[s], arr_dmmv_q5_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q8_0_f16_f32_len[s], arr_dmmv_q8_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q2_k_f16_f32_len[s], arr_dmmv_q2_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q3_k_f16_f32_len[s], arr_dmmv_q3_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q4_k_f16_f32_len[s], arr_dmmv_q4_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q5_k_f16_f32_len[s], arr_dmmv_q5_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_q6_k_f16_f32_len[s], arr_dmmv_q6_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_s_f16_f32_len[s],   arr_dmmv_iq1_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq1_m_f16_f32_len[s],   arr_dmmv_iq1_m_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq2_xxs_f16_f32_len[s], arr_dmmv_iq2_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq2_xs_f16_f32_len[s],  arr_dmmv_iq2_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq2_s_f16_f32_len[s],   arr_dmmv_iq2_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1), arr_dmmv_iq3_xxs_f16_f32_len[s], arr_dmmv_iq3_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_iq3_s_f16_f32_len[s],   arr_dmmv_iq3_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_xs_f16_f32_len[s],  arr_dmmv_iq4_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),  arr_dmmv_iq4_nl_f16_f32_len[s],  arr_dmmv_iq4_nl_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32_"+std::to_string(w)+"_"+std::to_string(i+1),   arr_dmmv_mxfp4_f16_f32_len[s],   arr_dmmv_mxfp4_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[s],  arr_dmmv_f32_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[s],  arr_dmmv_f16_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[s], arr_dmmv_bf16_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[s], arr_dmmv_q4_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[s], arr_dmmv_q4_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[s], arr_dmmv_q5_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[s], arr_dmmv_q5_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[s], arr_dmmv_q8_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[s], arr_dmmv_q2_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[s], arr_dmmv_q3_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[s], arr_dmmv_q4_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[s], arr_dmmv_q5_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[s], arr_dmmv_q6_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[s],   arr_dmmv_iq1_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[s],   arr_dmmv_iq1_m_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[s], arr_dmmv_iq2_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[s],  arr_dmmv_iq2_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[s],   arr_dmmv_iq2_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[s], arr_dmmv_iq3_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[s],   arr_dmmv_iq3_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[s],  arr_dmmv_iq4_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[s],  arr_dmmv_iq4_nl_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[s],   arr_dmmv_mxfp4_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
         }
     }
 

From 7a6e91ad26160dd6dfb33d29ac441617422f28e7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 20 Aug 2025 16:58:49 +0200
Subject: [PATCH 120/287] CUDA: replace GGML_CUDA_F16 with CUDA arch checks
 (#15433)

---
 docs/build.md                     | 13 ++++----
 docs/multimodal/MobileVLM.md      |  2 +-
 ggml/CMakeLists.txt               |  1 -
 ggml/src/ggml-cuda/CMakeLists.txt | 10 ------
 ggml/src/ggml-cuda/common.cuh     | 10 +-----
 ggml/src/ggml-cuda/convert.cu     |  2 +-
 ggml/src/ggml-cuda/cpy.cu         |  4 +--
 ggml/src/ggml-cuda/dequantize.cuh | 54 ++++++++-----------------------
 ggml/src/ggml-cuda/getrows.cu     |  2 +-
 ggml/src/ggml-cuda/ggml-cuda.cu   |  4 ---
 ggml/src/ggml-cuda/vecdotq.cuh    | 12 +++----
 ggml/src/ggml-musa/CMakeLists.txt |  4 ---
 12 files changed, 32 insertions(+), 86 deletions(-)

diff --git a/docs/build.md b/docs/build.md
index dd486fe293..b35a898ba9 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -197,13 +197,12 @@ The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enab
 
 The following compilation options are also available to tweak performance:
 
-| Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
-|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower.                       |
-| GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models                                                                                                                                                                                       |
-| GGML_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
-| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
-| GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                  |
+| Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                                                                                                                      |
+|-------------------------------|------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
+| GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for CDNA and RDNA4) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000).                                            |
+| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                                                                                                                  |
+| GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                                                                                                           |
 
 ## MUSA
 
diff --git a/docs/multimodal/MobileVLM.md b/docs/multimodal/MobileVLM.md
index 4f5eca6190..3bfab9f3d2 100644
--- a/docs/multimodal/MobileVLM.md
+++ b/docs/multimodal/MobileVLM.md
@@ -194,7 +194,7 @@ llama_print_timings:       total time =   44411.01 ms /   377 tokens
 ## Orin compile and run
 ### compile
 ```sh
-make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32
+make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 -j 32
 ```
 ### run on Orin
 ### case 1
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 90e274ccdb..2ead001e2c 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -158,7 +158,6 @@ option(GGML_CUDA                            "ggml: use CUDA"
 option(GGML_MUSA                            "ggml: use MUSA"                                  OFF)
 option(GGML_CUDA_FORCE_MMQ                  "ggml: use mmq kernels instead of cuBLAS"         OFF)
 option(GGML_CUDA_FORCE_CUBLAS               "ggml: always use cuBLAS instead of mmq kernels"  OFF)
-option(GGML_CUDA_F16                        "ggml: use 16 bit floats for some calculations"   OFF)
 set   (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                             "ggml: max. batch size for using peer access")
 option(GGML_CUDA_NO_PEER_COPY               "ggml: do not use peer to peer copies"            OFF)
diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index bce07ac362..ea824965aa 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -24,12 +24,6 @@ if (CUDAToolkit_FOUND)
         #     for best performance and to also build real architectures for the most commonly used GPUs.
         if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24")
             set(CMAKE_CUDA_ARCHITECTURES "native")
-        elseif(GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16)
-            if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8")
-                set(CMAKE_CUDA_ARCHITECTURES "60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real")
-            else()
-                set(CMAKE_CUDA_ARCHITECTURES "60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real")
-            endif()
         else()
             if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8")
                 set(CMAKE_CUDA_ARCHITECTURES "50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real")
@@ -91,10 +85,6 @@ if (CUDAToolkit_FOUND)
         add_compile_definitions(GGML_CUDA_NO_FA)
     endif()
 
-    if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16)
-        add_compile_definitions(GGML_CUDA_F16)
-    endif()
-
     if (GGML_CUDA_NO_PEER_COPY)
         add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
     endif()
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 76ace816ff..767ad83f60 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -206,14 +206,6 @@ static const char * cu_get_error_str(CUresult err) {
 #define GGML_CUDA_ASSUME(x)
 #endif // CUDART_VERSION >= 11010
 
-#ifdef GGML_CUDA_F16
-typedef half dfloat; // dequantize float
-typedef half2 dfloat2;
-#else
-typedef float dfloat; // dequantize float
-typedef float2 dfloat2;
-#endif // GGML_CUDA_F16
-
 #if (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
 #define GGML_USE_VMM
 #endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
@@ -559,7 +551,7 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 #endif // CUDART_VERSION >= 12050
 }
 
-typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
+typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v);
 
 static __device__ __forceinline__ float get_alibi_slope(
     const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1
diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index 8f0efdcc12..7a8b6fdf5f 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -27,7 +27,7 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
     const int64_t y_offset = qr == 1 ? 1 : qk/2;
 
     // dequantize
-    dfloat2 v;
+    float2 v;
     dequantize_kernel(vx, ib, iqs, v);
 
     const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index f9bb025643..0380784ab4 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -42,7 +42,7 @@ static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
 
 #pragma unroll
     for (int j = 0; j < QK8_0; j += 2) {
-        dfloat2 dq;
+        float2 dq;
         dequantize_q8_0(cxi, 0, j, dq);
         *(cdstf + j) = dq.x;
         *(cdstf + j + 1) = dq.y;
@@ -55,7 +55,7 @@ static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) {
 
 #pragma unroll
     for (int j = 0; j < qk/2; j++) {
-        dfloat2 dq;
+        float2 dq;
         dequant(cxi, 0, j, dq);
         *(cdstf + j) = dq.x;
         *(cdstf + j + qk/2) = dq.y;
diff --git a/ggml/src/ggml-cuda/dequantize.cuh b/ggml/src/ggml-cuda/dequantize.cuh
index bd3c2d9db9..e060fb29fd 100644
--- a/ggml/src/ggml-cuda/dequantize.cuh
+++ b/ggml/src/ggml-cuda/dequantize.cuh
@@ -1,48 +1,37 @@
 #include "common.cuh"
 
-static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q4_0 * x = (const block_q4_0 *) vx;
 
-    const dfloat d = x[ib].d;
+    const float d = x[ib].d;
 
     const int vui = x[ib].qs[iqs];
 
     v.x = vui & 0xF;
     v.y = vui >> 4;
 
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {8.0f, 8.0f});
-    v = __hmul2(v, {d, d});
-#else
     v.x = (v.x - 8.0f) * d;
     v.y = (v.y - 8.0f) * d;
-#endif // GGML_CUDA_F16
 }
 
-static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q4_1 * x = (const block_q4_1 *) vx;
 
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
+    const float2 dm = __half22float2(x[ib].dm);
 
     const int vui = x[ib].qs[iqs];
 
     v.x = vui & 0xF;
     v.y = vui >> 4;
 
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
+    v.x = (v.x * dm.x) + dm.y;
+    v.y = (v.y * dm.x) + dm.y;
 }
 
-static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q5_0 * x = (const block_q5_0 *) vx;
 
-    const dfloat d = x[ib].d;
+    const float d = x[ib].d;
 
     uint32_t qh;
     memcpy(&qh, x[ib].qh, sizeof(qh));
@@ -53,20 +42,14 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in
     v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
     v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
 
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {16.0f, 16.0f});
-    v = __hmul2(v, {d, d});
-#else
     v.x = (v.x - 16.0f) * d;
     v.y = (v.y - 16.0f) * d;
-#endif // GGML_CUDA_F16
 }
 
-static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q5_1 * x = (const block_q5_1 *) vx;
 
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
+    const float2 dm = __half22float2(x[ib].dm);
 
     uint32_t qh;
     memcpy(&qh, x[ib].qh, sizeof(qh));
@@ -77,27 +60,18 @@ static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const in
     v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
     v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
 
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
+    v.x = (v.x * dm.x) + dm.y;
+    v.y = (v.y * dm.x) + dm.y;
 }
 
-static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
+static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q8_0 * x = (const block_q8_0 *) vx;
 
-    const dfloat d = x[ib].d;
+    const float d = x[ib].d;
 
     v.x = x[ib].qs[iqs + 0];
     v.y = x[ib].qs[iqs + 1];
 
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-#else
     v.x *= d;
     v.y *= d;
-#endif // GGML_CUDA_F16
 }
diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index 68d3254fbe..3ec0e957ab 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -32,7 +32,7 @@ static __global__ void k_get_rows(
     const int y_offset = qr == 1 ? 1 : qk/2;
 
     // dequantize
-    dfloat2 v;
+    float2 v;
     dequantize_kernel(src0_row, ib, iqs, v);
 
     dst_row[iybs + iqs + 0]        = ggml_cuda_cast<dst_t>(v.x);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index d6402a8daa..8b706752bc 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3672,10 +3672,6 @@ static ggml_backend_feature * ggml_backend_cuda_get_features(ggml_backend_reg_t
         features.push_back({ "NO_PEER_COPY", "1" });
     #endif
 
-    #ifdef GGML_CUDA_F16
-        features.push_back({ "F16", "1" });
-    #endif
-
     #ifdef GGML_CUDA_USE_GRAPHS
         features.push_back({ "USE_GRAPHS", "1" });
     #endif
diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
index d8f9aa5ba6..d60292b83b 100644
--- a/ggml/src/ggml-cuda/vecdotq.cuh
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -87,7 +87,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
         sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi);
     }
 
-#ifdef GGML_CUDA_F16
+#ifdef FAST_FP16_AVAILABLE
     const float2 tmp = __half22float2(__hmul2(dm4, ds8));
     const float d4d8 = tmp.x;
     const float m4s8 = tmp.y;
@@ -96,7 +96,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
     const float2 ds8f = __half22float2(ds8);
     const float d4d8 = dm4f.x * ds8f.x;
     const float m4s8 = dm4f.y * ds8f.y;
-#endif // GGML_CUDA_F16
+#endif // FAST_FP16_AVAILABLE
 
     // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
     return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
@@ -158,7 +158,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
         sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
     }
 
-#ifdef GGML_CUDA_F16
+#ifdef FAST_FP16_AVAILABLE
     const float2 tmp = __half22float2(__hmul2(dm5, ds8));
     const float d5d8 = tmp.x;
     const float m5s8 = tmp.y;
@@ -167,7 +167,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
     const float2 ds8f = __half22float2(ds8);
     const float d5d8 = dm5f.x * ds8f.x;
     const float m5s8 = dm5f.y * ds8f.y;
-#endif // GGML_CUDA_F16
+#endif // FAST_FP16_AVAILABLE
 
     // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
     return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
@@ -201,7 +201,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
         sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
     }
 
-#ifdef GGML_CUDA_F16
+#ifdef FAST_FP16_AVAILABLE
     const float2 tmp = __half22float2(__hmul2(dm8, ds8));
     const float d8d8 = tmp.x;
     const float m8s8 = tmp.y;
@@ -210,7 +210,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
     const float2 ds8f = __half22float2(ds8);
     const float d8d8 = dm8f.x * ds8f.x;
     const float m8s8 = dm8f.y * ds8f.y;
-#endif // GGML_CUDA_F16
+#endif // FAST_FP16_AVAILABLE
 
     // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
     return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt
index 02904526ad..cdb3818c78 100644
--- a/ggml/src/ggml-musa/CMakeLists.txt
+++ b/ggml/src/ggml-musa/CMakeLists.txt
@@ -96,10 +96,6 @@ if (MUSAToolkit_FOUND)
         add_compile_definitions(GGML_CUDA_NO_FA)
     endif()
 
-    if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16)
-        add_compile_definitions(GGML_CUDA_F16)
-    endif()
-
     if (GGML_CUDA_NO_PEER_COPY)
         add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
     endif()

From 13aeb7aef284daed4ad07dc14b4b3e2a42b5ea97 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 20 Aug 2025 23:14:14 +0200
Subject: [PATCH 121/287] CUDA: refactor FA support/selection code (#15454)

---
 ggml/src/ggml-cuda/fattn-common.cuh |  22 ---
 ggml/src/ggml-cuda/fattn.cu         | 212 +++++++++++++++++++++-------
 ggml/src/ggml-cuda/fattn.cuh        |   2 +
 ggml/src/ggml-cuda/ggml-cuda.cu     |  40 +-----
 4 files changed, 165 insertions(+), 111 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index d4ed938391..b69f57d659 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -704,28 +704,6 @@ static __global__ void flash_attn_combine_results(
     dst[tid] = VKQ_numerator / VKQ_denominator;
 }
 
-[[noreturn]]
-static void on_no_fattn_vec_case(const int D) {
-    if (D == 64) {
-        fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
-        fprintf(stderr, "By default only f16 KV cache is supported.\n");
-        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
-        GGML_ABORT("fatal error");
-    } else if (D == 128) {
-        fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
-        fprintf(stderr, "Supported combinations:\n");
-        fprintf(stderr, "  - K == q4_0, V == q4_0,  4.50 BPV\n");
-        fprintf(stderr, "  - K == q8_0, V == q8_0,  8.50 BPV\n");
-        fprintf(stderr, "  - K == f16,  V == f16,  16.00 BPV\n");
-        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
-        GGML_ABORT("fatal error");
-    } else {
-        fprintf(stderr, "Unsupported KV type combination for head_size %d.\n", D);
-        fprintf(stderr, "Only f16 is supported.\n");
-        GGML_ABORT("fatal error");
-    }
-}
-
 template <int DV, int ncols1, int ncols2>
 void launch_fattn(
     ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, const int nwarps, const size_t nbytes_shared,
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index 22e90d0e7b..4883427266 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -190,7 +190,7 @@ static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, gg
     FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
 #endif // GGML_CUDA_FA_ALL_QUANTS
 
-    on_no_fattn_vec_case(Q->ne[0]);
+    GGML_ABORT("fatal error");
 }
 
 #define FATTN_VEC_F32_CASE(D, type_K, type_V)                               \
@@ -265,74 +265,184 @@ static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, gg
     FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
 #endif // GGML_CUDA_FA_ALL_QUANTS
 
-    on_no_fattn_vec_case(Q->ne[0]);
+    GGML_ABORT("fatal error");
 }
 
-void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+// Best FlashAttention kernel for a specific GPU:
+enum best_fattn_kernel {
+    BEST_FATTN_KERNEL_NONE     =   0,
+    BEST_FATTN_KERNEL_TILE_F32 = 200,
+    BEST_FATTN_KERNEL_TILE_F16 = 210,
+    BEST_FATTN_KERNEL_VEC_F32  = 100,
+    BEST_FATTN_KERNEL_VEC_F16  = 110,
+    BEST_FATTN_KERNEL_WMMA_F16 = 300,
+    BEST_FATTN_KERNEL_MMA_F16  = 400,
+};
+
+static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const ggml_tensor * dst) {
+#ifndef FLASH_ATTN_AVAILABLE
+    GGML_UNUSED(device); GGML_UNUSED(dst);
+    return BEST_FATTN_KERNEL_NONE;
+#endif// FLASH_ATTN_AVAILABLE
+
     const ggml_tensor * KQV   = dst;
     const ggml_tensor * Q     = dst->src[0];
     const ggml_tensor * K     = dst->src[1];
     const ggml_tensor * V     = dst->src[2];
     const ggml_tensor * mask  = dst->src[3];
 
-    ggml_cuda_set_device(ctx.device);
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
+    const int gqa_ratio = Q->ne[2] / K->ne[2];
+    GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
+
+    const int cc = ggml_cuda_info().devices[device].cc;
+    const int warp_size = ggml_cuda_info().devices[device].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
-#if defined(GGML_HIP_ROCWMMA_FATTN)
-    if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) {
-        ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
-        return;
-    }
-#endif // defined(GGML_HIP_ROCWMMA_FATTN)
-
-    if (!fast_fp16_available(cc)) {
-        if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
-        }
-        return;
-    }
-
-    if (!fp16_mma_available(cc)) {
-        if (prec == GGML_PREC_DEFAULT) {
-            if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
-                ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-            } else {
-                ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+    switch (K->ne[0]) {
+        case  64:
+        case 128:
+        case 256:
+            if (V->ne[0] != K->ne[0]) {
+                return BEST_FATTN_KERNEL_NONE;
             }
-        } else {
-            if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
-                ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-            } else {
-                ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
+            break;
+        case  80:
+        case  96:
+        case 112:
+            if (V->ne[0] != K->ne[0]) {
+                return BEST_FATTN_KERNEL_NONE;
             }
-        }
-        return;
+            if (!fp16_mma_available(cc) && !turing_mma_available(cc)) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        case 576:
+            if (V->ne[0] != 512) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (!turing_mma_available(cc) || gqa_ratio % 16 != 0) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        default:
+            return BEST_FATTN_KERNEL_NONE;
+    }
+
+#ifndef GGML_CUDA_FA_ALL_QUANTS
+    if (K->type != V->type) {
+        return BEST_FATTN_KERNEL_NONE;
+    }
+#endif // GGML_CUDA_FA_ALL_QUANTS
+
+    switch (K->type) {
+        case GGML_TYPE_F16:
+            break;
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+#ifndef GGML_CUDA_FA_ALL_QUANTS
+            return BEST_FATTN_KERNEL_NONE;
+#endif // GGML_CUDA_FA_ALL_QUANTS
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+#ifdef GGML_CUDA_FA_ALL_QUANTS
+            if (K->ne[0] != 128 && K->ne[0] != 64) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+#else
+            if (K->ne[0] != 128) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+#endif // GGML_CUDA_FA_ALL_QUANTS
+            break;
+        default:
+            return BEST_FATTN_KERNEL_NONE;
+    }
+
+    switch (V->type) {
+        case GGML_TYPE_F16:
+            break;
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            if (K->ne[0] != 128) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        default:
+            return BEST_FATTN_KERNEL_NONE;
+    }
+
+    if (mask && mask->ne[2] != 1) {
+        return BEST_FATTN_KERNEL_NONE;
     }
 
-    const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
-    const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
-    const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
-    const bool mma_faster_for_bs1 = turing_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
-        (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
     const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
-    if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
-        if (prec == GGML_PREC_DEFAULT) {
-            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+
+    // If Turing tensor cores available, use them except for some cases with batch size 1:
+    if (turing_mma_available(cc)) {
+        const bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask; // The mma-based kernels have GQA-specific optimizations
+        const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
+        const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (gqa_ratio > 4 && K->ne[1] >= 8192);
+        const bool mma_faster_for_bs1 = gqa_opt_applies && !mma_needs_data_conversion &&
+            (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
+        if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
+            if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+                return BEST_FATTN_KERNEL_VEC_F16;
+            }
+            return BEST_FATTN_KERNEL_VEC_F32;
         }
-        return;
+        return BEST_FATTN_KERNEL_MMA_F16;
     }
 
-    // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
-    if (fp16_mma_available(cc) && !turing_mma_available(cc)) {
-        ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
-        return;
+    // Use kernels specializes for small batch sizes if possible:
+    if (Q->ne[1] <= 8 && can_use_vector_kernel) {
+        if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+            return BEST_FATTN_KERNEL_VEC_F16;
+        }
+        return BEST_FATTN_KERNEL_VEC_F32;
     }
 
-    ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
+    // For large batch sizes, use the WMMA kernel if possible:
+    if (fp16_mma_available(cc)) {
+        return BEST_FATTN_KERNEL_WMMA_F16;
+    }
+
+    // If there is no suitable kernel for tensor cores or small batch sizes, use the generic kernel for large batch sizes:
+    if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+        return BEST_FATTN_KERNEL_TILE_F16;
+    }
+    return BEST_FATTN_KERNEL_TILE_F32;
+}
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_set_device(ctx.device);
+    switch (ggml_cuda_get_best_fattn_kernel(ggml_cuda_get_device(), dst)) {
+        case BEST_FATTN_KERNEL_NONE:
+            GGML_ABORT("fatal error");
+        case BEST_FATTN_KERNEL_TILE_F32:
+            ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_TILE_F16:
+            ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_VEC_F32:
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_VEC_F16:
+            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_WMMA_F16:
+            ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+            break;
+        case BEST_FATTN_KERNEL_MMA_F16:
+            ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
+            break;
+    }
+}
+
+bool ggml_cuda_flash_attn_ext_supported(int device, const ggml_tensor * dst) {
+    return ggml_cuda_get_best_fattn_kernel(device, dst) != BEST_FATTN_KERNEL_NONE;
 }
diff --git a/ggml/src/ggml-cuda/fattn.cuh b/ggml/src/ggml-cuda/fattn.cuh
index ad3ca7a8d8..78705d5995 100644
--- a/ggml/src/ggml-cuda/fattn.cuh
+++ b/ggml/src/ggml-cuda/fattn.cuh
@@ -1,3 +1,5 @@
 #include "common.cuh"
 
 void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+bool ggml_cuda_flash_attn_ext_supported(int device, const ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 8b706752bc..1440f2f2e9 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3499,44 +3499,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_GATED_LINEAR_ATTN:
         case GGML_OP_RWKV_WKV7:
             return true;
-        case GGML_OP_FLASH_ATTN_EXT: {
-#ifndef FLASH_ATTN_AVAILABLE
-            return false;
-#endif // FLASH_ATTN_AVAILABLE
-            if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
-                const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
-                if (!turing_mma_available(cc)) {
-                    return false;
-                }
-                const int gqa_ratio = op->src[0]->ne[2] / op->src[1]->ne[2];
-                return op->src[1]->ne[0] == 576 && op->src[2]->ne[0] == 512 && op->src[3] && gqa_ratio % 16 == 0;
-            }
-            // TODO: more general-purpose attention sink support [TAG_ATTN_SINKS]
-            if (op->src[4] && !fp16_mma_available(ggml_cuda_info().devices[dev_ctx->device].cc)
-                    && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) {
-                return false;
-            }
-            if (op->src[0]->ne[0] == 192) {
-                return false;
-            }
-            if (op->src[1]->type == GGML_TYPE_BF16 || op->src[2]->type == GGML_TYPE_BF16) {
-                return false;
-            }
-            if (op->src[0]->ne[0] ==  64 && op->src[1]->type == GGML_TYPE_F16) {
-                return true;
-            }
-            if (op->src[0]->ne[0] == 128) {
-                return true;
-            }
-            if (op->src[0]->ne[0] == 256 && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16) {
-                return true;
-            }
-            if (op->src[3] && op->src[3]->ne[2] != 1) {
-                return false;
-            }
-            return fp16_mma_available(ggml_cuda_info().devices[dev_ctx->device].cc) &&
-                op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
-        }
+        case GGML_OP_FLASH_ATTN_EXT:
+            return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op);
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:

From 1bc664a26a1d93a48baf2483a7ff95291ca8bcb8 Mon Sep 17 00:00:00 2001
From: teo <TeoZosa@users.noreply.github.com>
Date: Thu, 21 Aug 2025 07:10:08 +0900
Subject: [PATCH 122/287] server: fix OpenAI API compatibility for usage
 statistics in chat streams (#15444)

---
 tools/server/server.cpp                       | 11 +++
 .../server/tests/unit/test_chat_completion.py | 89 ++++++++++---------
 tools/server/tests/utils.py                   | 87 +++++++++---------
 3 files changed, 105 insertions(+), 82 deletions(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index ab88f3d26c..35b060674b 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -911,6 +911,17 @@ struct server_task_result_cmpl_final : server_task_result {
             {"model",              oaicompat_model},
             {"system_fingerprint", build_info},
             {"object",             "chat.completion.chunk"},
+        });
+
+        // OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
+        // https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
+        deltas.push_back({
+            {"choices", json::array()},
+            {"created",            t},
+            {"id",                 oaicompat_cmpl_id},
+            {"model",              oaicompat_model},
+            {"system_fingerprint", build_info},
+            {"object",             "chat.completion.chunk"},
             {"usage", json {
                 {"completion_tokens", n_decoded},
                 {"prompt_tokens",     n_prompt_tokens},
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index 6c6f64f5e2..509c024b75 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -72,27 +72,29 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte
     content = ""
     last_cmpl_id = None
     for i, data in enumerate(res):
-        choice = data["choices"][0]
-        if i == 0:
-            # Check first role message for stream=True
-            assert choice["delta"]["content"] is None
-            assert choice["delta"]["role"] == "assistant"
+        if data["choices"]:
+            choice = data["choices"][0]
+            if i == 0:
+                # Check first role message for stream=True
+                assert choice["delta"]["content"] is None
+                assert choice["delta"]["role"] == "assistant"
+            else:
+                assert "role" not in choice["delta"]
+            assert data["system_fingerprint"].startswith("b")
+            assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
+            if last_cmpl_id is None:
+                last_cmpl_id = data["id"]
+            assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
+            if choice["finish_reason"] in ["stop", "length"]:
+                assert "content" not in choice["delta"]
+                assert match_regex(re_content, content)
+                assert choice["finish_reason"] == finish_reason
+            else:
+                assert choice["finish_reason"] is None
+                content += choice["delta"]["content"] or ''
         else:
-            assert "role" not in choice["delta"]
-        assert data["system_fingerprint"].startswith("b")
-        assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
-        if last_cmpl_id is None:
-            last_cmpl_id = data["id"]
-        assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
-        if choice["finish_reason"] in ["stop", "length"]:
             assert data["usage"]["prompt_tokens"] == n_prompt
             assert data["usage"]["completion_tokens"] == n_predicted
-            assert "content" not in choice["delta"]
-            assert match_regex(re_content, content)
-            assert choice["finish_reason"] == finish_reason
-        else:
-            assert choice["finish_reason"] is None
-            content += choice["delta"]["content"] or ''
 
 
 def test_chat_completion_with_openai_library():
@@ -278,12 +280,14 @@ def test_chat_completion_with_timings_per_token():
             assert data["choices"][0]["delta"]["role"] == "assistant"
             assert "timings" not in data, f'First event should not have timings: {data}'
         else:
-            assert "role" not in data["choices"][0]["delta"]
-            assert "timings" in data
-            assert "prompt_per_second" in data["timings"]
-            assert "predicted_per_second" in data["timings"]
-            assert "predicted_n" in data["timings"]
-            assert data["timings"]["predicted_n"] <= 10
+            if data["choices"]:
+                assert "role" not in data["choices"][0]["delta"]
+            else:
+                assert "timings" in data
+                assert "prompt_per_second" in data["timings"]
+                assert "predicted_per_second" in data["timings"]
+                assert "predicted_n" in data["timings"]
+                assert data["timings"]["predicted_n"] <= 10
 
 
 def test_logprobs():
@@ -332,24 +336,25 @@ def test_logprobs_stream():
     output_text = ''
     aggregated_text = ''
     for i, data in enumerate(res):
-        choice = data.choices[0]
-        if i == 0:
-            # Check first role message for stream=True
-            assert choice.delta.content is None
-            assert choice.delta.role == "assistant"
-        else:
-            assert choice.delta.role is None
-            if choice.finish_reason is None:
-                if choice.delta.content:
-                    output_text += choice.delta.content
-                assert choice.logprobs is not None
-                assert choice.logprobs.content is not None
-                for token in choice.logprobs.content:
-                    aggregated_text += token.token
-                    assert token.logprob <= 0.0
-                    assert token.bytes is not None
-                    assert token.top_logprobs is not None
-                    assert len(token.top_logprobs) > 0
+        if data.choices:
+            choice = data.choices[0]
+            if i == 0:
+                # Check first role message for stream=True
+                assert choice.delta.content is None
+                assert choice.delta.role == "assistant"
+            else:
+                assert choice.delta.role is None
+                if choice.finish_reason is None:
+                    if choice.delta.content:
+                        output_text += choice.delta.content
+                    assert choice.logprobs is not None
+                    assert choice.logprobs.content is not None
+                    for token in choice.logprobs.content:
+                        aggregated_text += token.token
+                        assert token.logprob <= 0.0
+                        assert token.bytes is not None
+                        assert token.top_logprobs is not None
+                        assert len(token.top_logprobs) > 0
     assert aggregated_text == output_text
 
 
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index 49277e6000..5f42bcae61 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -318,46 +318,53 @@ class ServerProcess:
             arguments_parts = 0
 
             for chunk in self.make_stream_request(method, path, data, headers):
-                assert len(chunk['choices']) == 1, f'Expected 1 choice, got {len(chunk["choices"])}'
-                choice = chunk['choices'][0]
-                if choice['delta'].get('content') is not None:
-                    assert len(choice['delta']['content']) > 0, f'Expected non empty content delta!'
-                    content.append(choice['delta']['content'])
-                    content_parts += 1
-                if choice['delta'].get('reasoning_content') is not None:
-                    assert len(choice['delta']['reasoning_content']) > 0, f'Expected non empty reasoning_content delta!'
-                    reasoning_content.append(choice['delta']['reasoning_content'])
-                    reasoning_content_parts += 1
-                if choice['delta'].get('finish_reason') is not None:
-                    finish_reason = choice['delta']['finish_reason']
-                for tc in choice['delta'].get('tool_calls', []):
-                    if 'function' not in tc:
-                        raise ValueError(f"Expected function type, got {tc['type']}")
-                    if tc['index'] >= len(tool_calls):
-                        assert 'id' in tc
-                        assert tc.get('type') == 'function'
-                        assert 'function' in tc and 'name' in tc['function'] and len(tc['function']['name']) > 0, \
-                            f"Expected function call with name, got {tc.get('function')}"
-                        tool_calls.append(dict(
-                            id="",
-                            type="function",
-                            function=dict(
-                                name="",
-                                arguments="",
-                            )
-                        ))
-                    tool_call = tool_calls[tc['index']]
-                    if tc.get('id') is not None:
-                        tool_call['id'] = tc['id']
-                    fct = tc['function']
-                    assert 'id' not in fct, f"Function call should not have id: {fct}"
-                    if fct.get('name') is not None:
-                        tool_call['function']['name'] = tool_call['function'].get('name', '') + fct['name']
-                    if fct.get('arguments') is not None:
-                        tool_call['function']['arguments'] += fct['arguments']
-                        arguments_parts += 1
-                    tool_call_parts += 1
-
+                if chunk['choices']:
+                    assert len(chunk['choices']) == 1, f'Expected 1 choice, got {len(chunk["choices"])}'
+                    choice = chunk['choices'][0]
+                    if choice['delta'].get('content') is not None:
+                        assert len(choice['delta']['content']) > 0, f'Expected non empty content delta!'
+                        content.append(choice['delta']['content'])
+                        content_parts += 1
+                    if choice['delta'].get('reasoning_content') is not None:
+                        assert len(choice['delta']['reasoning_content']) > 0, f'Expected non empty reasoning_content delta!'
+                        reasoning_content.append(choice['delta']['reasoning_content'])
+                        reasoning_content_parts += 1
+                    if choice['delta'].get('finish_reason') is not None:
+                        finish_reason = choice['delta']['finish_reason']
+                    for tc in choice['delta'].get('tool_calls', []):
+                        if 'function' not in tc:
+                            raise ValueError(f"Expected function type, got {tc['type']}")
+                        if tc['index'] >= len(tool_calls):
+                            assert 'id' in tc
+                            assert tc.get('type') == 'function'
+                            assert 'function' in tc and 'name' in tc['function'] and len(tc['function']['name']) > 0, \
+                                f"Expected function call with name, got {tc.get('function')}"
+                            tool_calls.append(dict(
+                                id="",
+                                type="function",
+                                function=dict(
+                                    name="",
+                                    arguments="",
+                                )
+                            ))
+                        tool_call = tool_calls[tc['index']]
+                        if tc.get('id') is not None:
+                            tool_call['id'] = tc['id']
+                        fct = tc['function']
+                        assert 'id' not in fct, f"Function call should not have id: {fct}"
+                        if fct.get('name') is not None:
+                            tool_call['function']['name'] = tool_call['function'].get('name', '') + fct['name']
+                        if fct.get('arguments') is not None:
+                            tool_call['function']['arguments'] += fct['arguments']
+                            arguments_parts += 1
+                        tool_call_parts += 1
+                else:
+                    # When `include_usage` is True (the default), we expect the last chunk of the stream
+                    # immediately preceding the `data: [DONE]` message to contain a `choices` field with an empty array
+                    # and a `usage` field containing the usage statistics (n.b., llama-server also returns `timings` in
+                    # the last chunk)
+                    assert 'usage' in chunk, f"Expected finish_reason in chunk: {chunk}"
+                    assert 'timings' in chunk, f"Expected finish_reason in chunk: {chunk}"
             print(f'Streamed response had {content_parts} content parts, {reasoning_content_parts} reasoning_content parts, {tool_call_parts} tool call parts incl. {arguments_parts} arguments parts')
             result = dict(
                 choices=[

From 5682a3745f2b653dcb855d5766d8edc318fb3336 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Wed, 20 Aug 2025 16:35:28 -0700
Subject: [PATCH 123/287] sched : copy only the used experts when offloading
 prompt processing (#15346)

---
 ggml/src/ggml-backend.cpp | 96 +++++++++++++++++++++++++++++++++++----
 1 file changed, 87 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 1b9d29e911..c1e58fbb64 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -19,9 +19,8 @@
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include <string>
-#include <vector>
 #include <algorithm>
+#include <vector>
 
 #ifdef __APPLE__
 #include <sys/types.h>
@@ -1352,6 +1351,10 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
 static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
     struct ggml_backend_sched_split * splits = sched->splits;
 
+    ggml_tensor * prev_ids_tensor = nullptr;
+    std::vector<int32_t> ids;
+    std::vector<ggml_bitset_t> used_ids;
+
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &splits[i];
         int split_backend_id = split->backend_id;
@@ -1378,16 +1381,91 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 } else {
                     ggml_backend_synchronize(split_backend);
                 }
-                // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
-                // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
-                if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
+
+                // when offloading MoE weights, we can reduce the amount of data copied by copying only the experts that are used
+                ggml_tensor * node = split->graph.nodes[0];
+                if (split->graph.n_nodes > 0 &&
+                    ggml_backend_buffer_get_usage(input->buffer) == GGML_BACKEND_BUFFER_USAGE_WEIGHTS &&
+                    ggml_backend_buffer_is_host(input->buffer) && (
+                    (node->src[0] == input_cpy && node->op == GGML_OP_MUL_MAT_ID)
+                    //|| (node->src[1] == input_cpy && node->op == GGML_OP_ADD_ID) /* GGML_OP_ADD_ID weights are small and not worth splitting */
+                    )) {
+
+                    const int64_t n_expert   = node->op == GGML_OP_MUL_MAT_ID ? input->ne[2] : input->ne[1];
+                    const size_t expert_size = node->op == GGML_OP_MUL_MAT_ID ? input->nb[2] : input->nb[1];
+
                     ggml_backend_synchronize(input_backend);
-                    if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                        ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                    } else {
+
+                    // get the ids
+                    ggml_tensor * ids_tensor = node->src[2];
+                    if (ids_tensor != prev_ids_tensor) {
+                        ids.resize(ggml_nbytes(ids_tensor) / sizeof(int32_t));
+                        ggml_backend_tensor_get_async(split_backend, ids_tensor, ids.data(), 0, ggml_nbytes(ids_tensor));
                         ggml_backend_synchronize(split_backend);
+
+                        // find the used experts
+                        used_ids.clear();
+                        used_ids.resize(ggml_bitset_size(n_expert));
+                        for (int64_t i1 = 0; i1 < ids_tensor->ne[1]; i1++) {
+                            for (int64_t i0 = 0; i0 < ids_tensor->ne[0]; i0++) {
+                                int32_t id = ids[i1 * ids_tensor->nb[1]/sizeof(int32_t) + i0 * ids_tensor->nb[0]/sizeof(int32_t)];
+                                ggml_bitset_set(used_ids.data(), id);
+                            }
+                        }
+
+                        prev_ids_tensor = ids_tensor;
+                    }
+
+                    // group consecutive experts and copy them together
+                    auto copy_experts = [&](int32_t first_id, int32_t last_id) {
+                        const size_t expert_offset = first_id * expert_size;
+                        const size_t expert_size_copy =  (last_id - first_id + 1) * expert_size;
+                        const size_t padding = std::min<size_t>(expert_size, 512);
+                        const size_t padding_end = last_id < n_expert - 1 ? padding : 0;
+
+                        ggml_backend_tensor_set_async(split_backend,
+                            input_cpy,
+                            (const uint8_t *)input->data + expert_offset, expert_offset,
+                            // copy a bit extra at the to ensure there are no NaNs in the padding of the last expert
+                            // this is necessary for MMQ in the CUDA backend
+                            expert_size_copy + padding_end);
+                    };
+
+                    int id = 0;
+                    while (!ggml_bitset_get(used_ids.data(), id)) {
+                        id++;
+                    }
+                    int32_t first_id = id;
+                    int32_t last_id = first_id;
+
+                    for (++id; id < n_expert; ++id) {
+                        if (!ggml_bitset_get(used_ids.data(), id)) {
+                            continue;
+                        }
+
+                        if (id == last_id + 1) {
+                            last_id = id;
+                            continue;
+                        }
+
+                        copy_experts(first_id, last_id);
+
+                        first_id = id;
+                        last_id = id;
+                    }
+                    copy_experts(first_id, last_id);
+                } else {
+                    // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
+                    // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
+                    if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
+                        ggml_backend_synchronize(input_backend);
+                        if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                            ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
+                        } else {
+                            ggml_backend_synchronize(split_backend);
+                        }
+                        ggml_backend_tensor_copy(input, input_cpy);
                     }
-                    ggml_backend_tensor_copy(input, input_cpy);
                 }
             }
         }

From 8ad038c0fdc719ced9fbf921a02cbae9ad79287f Mon Sep 17 00:00:00 2001
From: R0CKSTAR <yeahdongcn@gmail.com>
Date: Thu, 21 Aug 2025 11:06:05 +0800
Subject: [PATCH 124/287] musa: add GGML_UNUSED_VARS (#15446)

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---
 ggml/include/ggml.h                     |  7 ++++
 ggml/src/ggml-cuda/conv-transpose-1d.cu |  5 +--
 ggml/src/ggml-cuda/convert.cu           |  4 +--
 ggml/src/ggml-cuda/cpy.cu               |  3 +-
 ggml/src/ggml-cuda/fattn-mma-f16.cuh    | 45 ++++++++++---------------
 ggml/src/ggml-cuda/fattn-tile-f16.cu    | 20 +++++------
 ggml/src/ggml-cuda/fattn-tile-f32.cu    | 29 ++++++++++------
 ggml/src/ggml-cuda/fattn-vec-f16.cuh    | 20 +++++------
 ggml/src/ggml-cuda/fattn-vec-f32.cuh    | 29 ++++++++++------
 ggml/src/ggml-cuda/fattn-wmma-f16.cu    | 19 +++++------
 ggml/src/ggml-cuda/ggml-cuda.cu         |  4 +--
 ggml/src/ggml-cuda/mma.cuh              | 44 ++++++------------------
 ggml/src/ggml-cuda/mmf.cu               |  8 ++---
 ggml/src/ggml-cuda/mmq.cu               |  5 +--
 ggml/src/ggml-cuda/mmq.cuh              |  6 ++--
 ggml/src/ggml-cuda/mmvf.cu              |  7 +---
 ggml/src/ggml-cuda/mmvq.cu              |  6 +---
 17 files changed, 113 insertions(+), 148 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index da8813fd27..b8b82e11c8 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -244,6 +244,13 @@
 #define GGML_MROPE_SECTIONS   4
 
 #define GGML_UNUSED(x) (void)(x)
+#ifdef __CUDACC__
+template<typename... Args>
+__host__ __device__ constexpr inline void ggml_unused_vars_impl(Args&&...) noexcept {}
+#define GGML_UNUSED_VARS(...) ggml_unused_vars_impl(__VA_ARGS__)
+#else
+#define GGML_UNUSED_VARS(...) do { (void)sizeof((__VA_ARGS__, 0)); } while(0)
+#endif // __CUDACC__
 
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
 
diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cu b/ggml/src/ggml-cuda/conv-transpose-1d.cu
index fe4caf674d..8418ba6673 100644
--- a/ggml/src/ggml-cuda/conv-transpose-1d.cu
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cu
@@ -34,10 +34,7 @@ static  __global__ void conv_transpose_1d_kernel(
         }
     }
     dst[global_index] = accumulator;
-    GGML_UNUSED(p0); GGML_UNUSED(d0); GGML_UNUSED(src0_ne3);
-    GGML_UNUSED(src1_ne3); GGML_UNUSED(dst_ne3);
-    GGML_UNUSED(src1_ne1); GGML_UNUSED(dst_ne1);
-    GGML_UNUSED(src1_ne2); GGML_UNUSED(dst_ne2);
+    GGML_UNUSED_VARS(p0, d0, src0_ne3, src1_ne3, dst_ne3, src1_ne1, dst_ne1, src1_ne2, dst_ne2);
 }
 
 static void conv_transpose_1d_f32_f32_cuda(
diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
index 7a8b6fdf5f..ba3d4eeb88 100644
--- a/ggml/src/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -71,9 +71,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h
         y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d));
     }
 #else
-    GGML_UNUSED(vx);
-    GGML_UNUSED(y);
-    GGML_UNUSED(k);
+    GGML_UNUSED_VARS(vx, y, k);
     NO_DEVICE_CODE;
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 }
diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index 0380784ab4..c40db08ced 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -134,8 +134,7 @@ void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_des
     CUDA_CHECK(cudaMemcpyAsync(cuda_graph->dest_ptrs_d, host_dest_ptrs, host_dest_ptrs_size*sizeof(char *), cudaMemcpyHostToDevice, stream));
     cuda_graph->graph_cpynode_index = 0; // reset index
 #else
-    GGML_UNUSED(cuda_graph); GGML_UNUSED(host_dest_ptrs);
-    GGML_UNUSED(host_dest_ptrs_size); GGML_UNUSED(stream);
+    GGML_UNUSED_VARS(cuda_graph, host_dest_ptrs, host_dest_ptrs_size, stream);
 #endif
 }
 
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 1d7e0b037e..57defb0c62 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -767,14 +767,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         }
     }
 #else
-    GGML_UNUSED(Q_f2); GGML_UNUSED(K_h2); GGML_UNUSED(V_h2);
-    GGML_UNUSED(mask_h2); GGML_UNUSED(dstk); GGML_UNUSED(dstk_fixup);
-    GGML_UNUSED(scale); GGML_UNUSED(slope); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V);
-    GGML_UNUSED(stride_mask); GGML_UNUSED(tile_K);
-    GGML_UNUSED(tile_V); GGML_UNUSED(tile_mask); GGML_UNUSED(Q_B);
-    GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
-    GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
+    GGML_UNUSED_VARS(Q_f2, K_h2, V_h2, mask_h2, dstk, dstk_fixup,
+        scale, slope, logit_softcap, ne01, ne02,
+        stride_K, stride_V, stride_mask,
+        tile_Q, tile_K, tile_V, tile_mask,
+        Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
     NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
 }
@@ -1236,14 +1233,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         }
     }
 #else
-    GGML_UNUSED(Q_f2); GGML_UNUSED(K_h2); GGML_UNUSED(V_h2);
-    GGML_UNUSED(mask_h2); GGML_UNUSED(sinks_f);
-    GGML_UNUSED(dstk); GGML_UNUSED(dstk_fixup);
-    GGML_UNUSED(scale); GGML_UNUSED(slope); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne01); GGML_UNUSED(ne02);
-    GGML_UNUSED(stride_Q1); GGML_UNUSED(stride_Q2);
-    GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
-    GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
+    GGML_UNUSED_VARS(Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dstk_fixup,
+        scale, slope, logit_softcap, ne01, ne02,
+        stride_Q1, stride_Q2, stride_K, stride_V, stride_mask,
+        jt, kb0_start, kb0_stop);
     NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
 }
@@ -1397,17 +1390,15 @@ static __global__ void flash_attn_ext_f16(
         (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
 }
diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
index 4111bcc04f..6239d184d0 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -299,17 +299,15 @@ static __global__ void flash_attn_tile_ext_f16(
         }
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && defined(FP16_AVAILABLE)
 }
diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu
index 1c1dc725d2..b96a9ef971 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -38,6 +38,15 @@ static __global__ void flash_attn_tile_ext_f32(
     return;
 #endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
+        GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+            max_bias, m0, m1, n_head_log2, logit_softcap,
+            ne00, ne01, ne02, ne03,
+                  nb01, nb02, nb03,
+            ne10, ne11, ne12, ne13,
+                  nb11, nb12, nb13,
+                  nb21, nb22, nb23,
+                  ne31, ne32, ne33,
+                  nb31, nb32, nb33);
         NO_DEVICE_CODE;
         return;
     }
@@ -301,17 +310,15 @@ static __global__ void flash_attn_tile_ext_f32(
         }
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // FLASH_ATTN_AVAILABLE
 }
diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
index 2131b5feef..27a2dd6ae4 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -349,17 +349,15 @@ static __global__ void flash_attn_vec_ext_f16(
         dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && defined(FP16_AVAILABLE)
 }
diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
index a06fba6cde..da195d0334 100644
--- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -37,6 +37,15 @@ static __global__ void flash_attn_vec_ext_f32(
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(D == 128 || D == 256)) {
+        GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+            max_bias, m0, m1, n_head_log2, logit_softcap,
+            ne00, ne01, ne02, ne03,
+                  nb01, nb02, nb03,
+            ne10, ne11, ne12, ne13,
+                  nb11, nb12, nb13,
+                  nb21, nb22, nb23,
+                  ne31, ne32, ne33,
+                  nb31, nb32, nb33);
         NO_DEVICE_CODE;
         return;
     }
@@ -334,17 +343,15 @@ static __global__ void flash_attn_vec_ext_f32(
         dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
-    GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
-    GGML_UNUSED(nb01); GGML_UNUSED(nb02); GGML_UNUSED(nb03);
-    GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // FLASH_ATTN_AVAILABLE
 }
diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index 2e2de8a093..2219191fd9 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -471,16 +471,15 @@ static __global__ void flash_attn_ext_f16(
         dst_meta[j_dst_unrolled] = dst_meta_val;
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
-    GGML_UNUSED(sinks); GGML_UNUSED(KV_max); GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
-    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
-    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
-    GGML_UNUSED(ne00); GGML_UNUSED(ne01); GGML_UNUSED(ne02); GGML_UNUSED(ne03);
-    GGML_UNUSED(ne10); GGML_UNUSED(ne11); GGML_UNUSED(ne12); GGML_UNUSED(ne13);
-    GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33); GGML_UNUSED(nb31);
-    GGML_UNUSED(nb32); GGML_UNUSED(nb33); GGML_UNUSED(nb01); GGML_UNUSED(nb02);
-    GGML_UNUSED(nb03); GGML_UNUSED(nb11); GGML_UNUSED(nb12); GGML_UNUSED(nb13);
-    GGML_UNUSED(nb21); GGML_UNUSED(nb22); GGML_UNUSED(nb23);
+    GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
+        max_bias, m0, m1, n_head_log2, logit_softcap,
+        ne00, ne01, ne02, ne03,
+              nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+              nb11, nb12, nb13,
+              nb21, nb22, nb23,
+              ne31, ne32, ne33,
+              nb31, nb32, nb33);
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || (defined(GGML_HIP_ROCWMMA_FATTN) && defined(FP16_MMA_AVAILABLE)))
 }
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 1440f2f2e9..4e17fd211e 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1328,9 +1328,7 @@ static void ggml_cuda_op_mul_mat_cublas(
                     &beta,  dst_dd_i,    ldc));
     }
 
-    GGML_UNUSED(dst);
-    GGML_UNUSED(src1_ddq_i);
-    GGML_UNUSED(src1_padded_row_size);
+    GGML_UNUSED_VARS(dst, src1_ddq_i, src1_padded_row_size);
 }
 
 static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index 83ee16b27d..667deb9c65 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -291,9 +291,7 @@ namespace ggml_cuda_mma {
             : "=r"(xi[0]), "=r"(xi[2]), "=r"(xi[1]), "=r"(xi[3])
             : "l"(xs));
 #else
-        GGML_UNUSED(t);
-        GGML_UNUSED(xs0);
-        GGML_UNUSED(stride);
+        GGML_UNUSED_VARS(t, xs0, stride);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -315,9 +313,7 @@ namespace ggml_cuda_mma {
             : "r"(A.x[1]), "r"(B.x[0]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -345,9 +341,7 @@ namespace ggml_cuda_mma {
             : "r"(A.x[3]), "r"(B.x[1]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -372,9 +366,7 @@ namespace ggml_cuda_mma {
             : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -408,9 +400,7 @@ namespace ggml_cuda_mma {
             : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -425,9 +415,7 @@ namespace ggml_cuda_mma {
             : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
             : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // AMPERE_MMA_AVAILABLE
     }
@@ -452,9 +440,7 @@ namespace ggml_cuda_mma {
             : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -469,9 +455,7 @@ namespace ggml_cuda_mma {
             : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
             : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // AMPERE_MMA_AVAILABLE
     }
@@ -505,9 +489,7 @@ namespace ggml_cuda_mma {
             : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // TURING_MMA_AVAILABLE
     }
@@ -533,9 +515,7 @@ namespace ggml_cuda_mma {
                                                       0, 0, 0);
 #endif // defined(CDNA3)
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
     }
@@ -561,9 +541,7 @@ namespace ggml_cuda_mma {
                                                      0, 0, 0);
 #endif // defined(CDNA3)
 #else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
+        GGML_UNUSED_VARS(D, A, B);
         NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
     }
diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
index 5c66fe5bb1..cfa5c5cce2 100644
--- a/ggml/src/ggml-cuda/mmf.cu
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -132,11 +132,11 @@ static __global__ void mul_mat_f(
         dst[j*stride_col_dst + row0 + threadIdx.x] = sum;
     }
 #else
+    GGML_UNUSED_VARS(x, y, ids, dst,
+        ncols, nchannels_y, stride_row, stride_col_y, stride_col_dst,
+        channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+        sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
     NO_DEVICE_CODE;
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(ids); GGML_UNUSED(dst);
-    GGML_UNUSED(ncols); GGML_UNUSED(nchannels_y); GGML_UNUSED(stride_row); GGML_UNUSED(stride_col_y); GGML_UNUSED(stride_col_dst);
-    GGML_UNUSED(channel_ratio); GGML_UNUSED(stride_channel_x); GGML_UNUSED(stride_channel_y); GGML_UNUSED(stride_channel_dst);
-    GGML_UNUSED(sample_ratio); GGML_UNUSED(stride_sample_x); GGML_UNUSED(stride_sample_y); GGML_UNUSED(stride_sample_dst);
 #endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 }
 
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 384ee7615f..576032a0ce 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -266,10 +266,7 @@ void ggml_cuda_op_mul_mat_q(
 
     ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
 
-    GGML_UNUSED(src1);
-    GGML_UNUSED(dst);
-    GGML_UNUSED(src1_ddf_i);
-    GGML_UNUSED(src1_padded_row_size);
+    GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_padded_row_size);
 }
 
 bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index c22907d404..650f708067 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -1255,7 +1255,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
         }
     }
 #else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k00);
+    GGML_UNUSED_VARS(x, y, sum, k00);
     NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
 }
@@ -1572,7 +1572,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
         }
     }
 #else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k00);
+    GGML_UNUSED_VARS(x, y, sum, k00);
     NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
 }
@@ -2301,7 +2301,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
         }
     }
 #else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k00);
+    GGML_UNUSED_VARS(x, y, sum, k00);
     NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
 }
diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu
index 16100b6804..5b21ef05b3 100644
--- a/ggml/src/ggml-cuda/mmvf.cu
+++ b/ggml/src/ggml-cuda/mmvf.cu
@@ -433,12 +433,7 @@ void ggml_cuda_op_mul_mat_vec_f(
             GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
     }
 
-    GGML_UNUSED(ctx);
-    GGML_UNUSED(src1);
-    GGML_UNUSED(dst);
-    GGML_UNUSED(src1_ddq_i);
-    GGML_UNUSED(src1_ncols);
-    GGML_UNUSED(src1_padded_row_size);
+    GGML_UNUSED_VARS(ctx, src1, dst, src1_ddq_i, src1_ncols, src1_padded_row_size);
 }
 
 bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index 5c8e5c4a7e..b7c3079308 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -596,9 +596,5 @@ void ggml_cuda_op_mul_mat_vec_q(
         src0_dd_i, src0->type, src1_ddq_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row_x, stride_col_y, nrows_dst,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, stream);
 
-    GGML_UNUSED(src1);
-    GGML_UNUSED(dst);
-    GGML_UNUSED(src1_ddf_i);
-    GGML_UNUSED(src1_ncols);
-    GGML_UNUSED(src1_padded_row_size);
+    GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_ncols, src1_padded_row_size);
 }

From 29f538ac630d6544406a0702476e36808a6bd1b3 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 21 Aug 2025 06:12:28 +0200
Subject: [PATCH 125/287] examples : remove references to `make` in examples
 [no ci] (#15457)

This commit removes references to `make` in the examples, as the build
system has been updated to use CMake directly and using `make` will now
generate an error since Commit 37f10f955f70e0158d50343d0b9a3f92d194daae
("make : remove make in favor of CMake (#15449)").
---
 examples/batched.swift/README.md | 5 +++--
 examples/passkey/README.md       | 2 +-
 examples/retrieval/README.md     | 2 +-
 examples/sycl/win-build-sycl.bat | 2 --
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/examples/batched.swift/README.md b/examples/batched.swift/README.md
index 7f2e2fcdcf..f08901506f 100644
--- a/examples/batched.swift/README.md
+++ b/examples/batched.swift/README.md
@@ -1,4 +1,5 @@
 This is a swift clone of `examples/batched`.
 
-$ `make`
-$ `./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]`
+```bash
+$ ./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]
+```
diff --git a/examples/passkey/README.md b/examples/passkey/README.md
index 2f19597c48..cbaf28fd82 100644
--- a/examples/passkey/README.md
+++ b/examples/passkey/README.md
@@ -11,5 +11,5 @@ See the following PRs for more info:
 ### Usage
 
 ```bash
-make -j && ./llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250
+llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250
 ```
diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md
index 6938a1e96e..51038cc36b 100644
--- a/examples/retrieval/README.md
+++ b/examples/retrieval/README.md
@@ -15,7 +15,7 @@ https://github.com/ggml-org/llama.cpp/pull/6193
 `retrieval` example can be tested as follows:
 
 ```bash
-make -j && ./llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
+llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
 ```
 
 This chunks and embeds all given files and starts a loop requesting query inputs:
diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat
index 6fc897b148..862998e737 100644
--- a/examples/sycl/win-build-sycl.bat
+++ b/examples/sycl/win-build-sycl.bat
@@ -18,8 +18,6 @@ if %errorlevel% neq 0 goto ERROR
 ::  for FP32
 cmake -G "Ninja" .. -DLLAMA_CURL=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
 if %errorlevel% neq 0 goto ERROR
-::  build example/main only
-::  make main
 
 ::  build all binary
 cmake --build . -j

From 1b0db8f6e08951969e2447c2d18bf638effb8f75 Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Thu, 21 Aug 2025 07:19:22 +0200
Subject: [PATCH 126/287] server : fix webui (#15462)

* Fix webui crash after streaming

* build webui
---
 tools/server/public/index.html.gz            | Bin 1914098 -> 1914087 bytes
 tools/server/webui/src/utils/app.context.tsx |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index fad36ed3390de112b91127387d323325180e2ea9..0e3e1f9834a7f22c5c3bc4445914f20cc1c6579d 100644
GIT binary patch
delta 1830415
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D(Cd$Y

diff --git a/tools/server/webui/src/utils/app.context.tsx b/tools/server/webui/src/utils/app.context.tsx
index 0b95e88f89..1860ecc721 100644
--- a/tools/server/webui/src/utils/app.context.tsx
+++ b/tools/server/webui/src/utils/app.context.tsx
@@ -255,7 +255,7 @@ export const AppContextProvider = ({
         if (chunk.error) {
           throw new Error(chunk.error?.message || 'Unknown error');
         }
-        const addedContent = chunk.choices[0].delta.content;
+        const addedContent = chunk.choices[0]?.delta.content;
         const lastContent = pendingMsg.content || '';
         if (addedContent) {
           pendingMsg = {

From 945e1f12a6b586ebf82fa4fd7f347225e58174c5 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 21 Aug 2025 07:32:26 +0200
Subject: [PATCH 127/287] ggml : fix condition of im2col on Metal backend
 (#15460)

---
 ggml/src/ggml-metal/ggml-metal.m | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index cb8eff4a77..7c70d352df 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1846,7 +1846,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_ROPE:
             return true;
         case GGML_OP_IM2COL:
-            return op->src[0]->type == GGML_TYPE_F16;
+            return op->src[1]->type == GGML_TYPE_F32 && (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
         case GGML_OP_POOL_1D:
             return false;
         case GGML_OP_UPSCALE:
@@ -4703,7 +4703,6 @@ static int ggml_metal_encode_node(
             {
                 GGML_ASSERT(ggml_is_contiguous(src0));
                 GGML_ASSERT(ggml_is_contiguous(src1));
-                GGML_ASSERT(src0->type == GGML_TYPE_F16);
                 GGML_ASSERT(src1->type == GGML_TYPE_F32);
                 GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
 

From 2f3dbffb17ef782edfd50e5a130cec6e8a7e47f8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <jiefu@tencent.com>
Date: Thu, 21 Aug 2025 16:54:34 +0800
Subject: [PATCH 128/287] common : fix incorrect print of non-ascii characters
 in the logging (#15466)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 common/common.cpp | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 67dd5404ff..decabcc2ed 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -558,13 +558,6 @@ std::string string_from(const struct llama_context * ctx, const std::vector<llam
 
         auto detokenized = common_token_to_piece(ctx, token);
 
-        detokenized.erase(
-            std::remove_if(
-                detokenized.begin(),
-                detokenized.end(),
-                [](const unsigned char c) { return !std::isprint(c); }),
-            detokenized.end());
-
         buf << "'" << detokenized << "'"
             << ":" << std::to_string(token);
     }
@@ -589,13 +582,6 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
 
         auto detokenized = common_token_to_piece(ctx, batch.token[i]);
 
-        detokenized.erase(
-                std::remove_if(
-                    detokenized.begin(),
-                    detokenized.end(),
-                    [](const unsigned char c) { return !std::isprint(c); }),
-                detokenized.end());
-
         buf << "\n"          << std::to_string(i)
             << ", token '"   << detokenized << "'"
             << ", pos "      << std::to_string(batch.pos[i])

From b2caf67db1208fd38a0570785c39f7370d906d8a Mon Sep 17 00:00:00 2001
From: Julien Denize <40604584+juliendenize@users.noreply.github.com>
Date: Thu, 21 Aug 2025 11:19:50 +0200
Subject: [PATCH 129/287] convert : make Mistral community chat templates
 optional via parameter (#15420)

* Make Mistral community chat templates optional

* Change the flag arg to disable instead of enable community chat templates

* Improve error message

* Improve help message

* Tone down the logger messages
---
 convert_hf_to_gguf.py | 41 +++++++++++++++++++++++++++++++++++------
 1 file changed, 35 insertions(+), 6 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index b45c8f1d7f..42bf10d216 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -89,13 +89,16 @@ class ModelBase:
     block_count: int
     tensor_map: gguf.TensorNameMap
 
+    # Mistral format specifics
     is_mistral_format: bool = False
+    disable_mistral_community_chat_template: bool = False
 
     def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
                  split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
-                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None):
+                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
+                 disable_mistral_community_chat_template: bool = False):
         if type(self) is ModelBase or \
                 type(self) is TextModel or \
                 type(self) is MmprojModel:
@@ -147,6 +150,9 @@ class ModelBase:
         self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
                                            split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
 
+        # Mistral specific
+        self.disable_mistral_community_chat_template = disable_mistral_community_chat_template
+
     @classmethod
     def add_prefix_to_filename(cls, path: Path, prefix: str) -> Path:
         stem, suffix = path.stem, path.suffix
@@ -2011,8 +2017,17 @@ class LlamaModel(TextModel):
 
         template_dir = Path(__file__).parent / "models/templates/"
 
-        template = MistralModel.get_community_chat_template(vocab, template_dir)
-        self.gguf_writer.add_chat_template(template)
+        if not self.is_mistral_format or not self.disable_mistral_community_chat_template:
+            # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`.
+            if self.is_mistral_format:
+                logger.info(
+                    "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. "
+                    "Mistral recommends to use `mistral-common` to perform tokenization and detokenization."
+                )
+            template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format)
+            self.gguf_writer.add_chat_template(template)
+        else:
+            logger.info("Not using a Mistral community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.")
 
     def set_vocab(self):
         if self.is_mistral_format:
@@ -8422,7 +8437,7 @@ class MistralModel(LlamaModel):
     undo_permute = False
 
     @staticmethod
-    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path):
+    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool):
         assert TokenizerVersion is not None, "mistral_common is not installed"
         assert isinstance(vocab.tokenizer, (Tekkenizer, SentencePieceTokenizer)), (
             f"Expected Tekkenizer or SentencePieceTokenizer, got {type(vocab.tokenizer)}"
@@ -8443,7 +8458,13 @@ class MistralModel(LlamaModel):
         elif vocab.tokenizer.version == TokenizerVersion.v13:
             template_file = "unsloth-mistral-Devstral-Small-2507.jinja"
         else:
-            raise ValueError(f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}")
+            err_message = f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}"
+            if is_mistral_format:
+                err_message += (
+                    " . Please pass --disable-mistral-community-chat-template argument to the CLI "
+                    "if you want to skip this error and use the Mistral official `mistral-common` pre-processing library."
+                )
+            raise ValueError(err_message)
 
         template_path = templates_dir / template_file
         if not template_path.exists():
@@ -8638,6 +8659,13 @@ def parse_args() -> argparse.Namespace:
         "--mistral-format", action="store_true",
         help="Whether the model is stored following the Mistral format.",
     )
+    parser.add_argument(
+        "--disable-mistral-community-chat-template", action="store_true",
+        help=(
+            "Whether to disable usage of Mistral community chat templates. If set, use the Mistral official `mistral-common` library for tokenization and detokenization of Mistral models. "
+            "Using `mistral-common` ensure correctness and zero-day support of tokenization for models converted from the Mistral format but requires to manually setup the tokenization server."
+        )
+    )
 
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
@@ -8744,6 +8772,7 @@ def main() -> None:
             fname_out = ModelBase.add_prefix_to_filename(fname_out, "mmproj-")
 
     is_mistral_format = args.mistral_format
+    disable_mistral_community_chat_template = args.disable_mistral_community_chat_template
 
     with torch.inference_mode():
         output_type = ftype_map[args.outtype]
@@ -8770,7 +8799,7 @@ def main() -> None:
                                      split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                      small_first_shard=args.no_tensor_first_split,
-                                     remote_hf_model_id=hf_repo_id,
+                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template
                                      )
 
         if args.vocab_only:

From 245be739df942861ddc52331b095b40f18e2a3f1 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Thu, 21 Aug 2025 11:47:52 +0200
Subject: [PATCH 130/287] ci : add copilot-instructions.md (#15286)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Initial plan

* Initialize copilot instructions exploration

* Add comprehensive .github/copilot-instructions.md file

* Update Python environment and tools directory documentation

- Add instructions for using .venv Python environment
- Include flake8 and pyright linting tools from virtual environment
- Add tools/ as core directory in project layout
- Reference existing configuration files (.flake8, pyrightconfig.json)

* add more python dependencies to .venv

* Update copilot instructions: add backend hardware note and server testing

* Apply suggestions from code review

* Apply suggestions from code review

* Replace clang-format with git clang-format to format only changed code

* Minor formatting improvements: remove extra blank line and add trailing newline

* try installing git-clang-format

* try just clang-format

* Remove --binary flag from git clang-format and add git-clang-format installation to CI

* download 18.x release

* typo--

* remove --binary flag

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 .github/copilot-instructions.md           | 262 ++++++++++++++++++++++
 .github/workflows/copilot-setup-steps.yml |   6 +-
 .gitignore                                |   1 +
 3 files changed, 268 insertions(+), 1 deletion(-)
 create mode 100644 .github/copilot-instructions.md

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 0000000000..3250e3279e
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,262 @@
+# Copilot Instructions for llama.cpp
+
+## Repository Overview
+
+llama.cpp is a large-scale C/C++ project for efficient LLM (Large Language Model) inference with minimal setup and dependencies. The project enables running language models on diverse hardware with state-of-the-art performance.
+
+**Key Facts:**
+- **Primary language**: C/C++ with Python utility scripts
+- **Size**: ~200k+ lines of code across 1000+ files
+- **Architecture**: Modular design with main library (`libllama`) and 40+ executable tools/examples
+- **Core dependency**: ggml tensor library (vendored in `ggml/` directory)
+- **Backends supported**: CPU (AVX/NEON optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
+- **License**: MIT
+
+## Build Instructions
+
+### Prerequisites
+- CMake 3.14+ (primary build system)
+- C++17 compatible compiler (GCC 13.3+, Clang, MSVC)
+- Optional: ccache for faster compilation
+
+### Basic Build (CPU-only)
+**ALWAYS run these commands in sequence:**
+```bash
+cmake -B build
+cmake --build build --config Release -j $(nproc)
+```
+
+**Build time**: ~10 minutes on 4-core system with ccache enabled, ~25 minutes without ccache.
+
+**Important Notes:**
+- The Makefile is deprecated - always use CMake
+- ccache is automatically detected and used if available
+- Built binaries are placed in `build/bin/`
+- Parallel builds (`-j`) significantly reduce build time
+
+### Backend-Specific Builds
+For CUDA support:
+```bash
+cmake -B build -DGGML_CUDA=ON
+cmake --build build --config Release -j $(nproc)
+```
+
+For Metal (macOS):
+```bash
+cmake -B build -DGGML_METAL=ON
+cmake --build build --config Release -j $(nproc)
+```
+
+**Important Note**: While all backends can be built as long as the correct requirements for that backend are installed, you will not be able to run them without the correct hardware. The only backend that can be run for testing and validation is the CPU backend.
+
+### Debug Builds
+Single-config generators:
+```bash
+cmake -B build -DCMAKE_BUILD_TYPE=Debug
+cmake --build build
+```
+
+Multi-config generators:
+```bash
+cmake -B build -G "Xcode"
+cmake --build build --config Debug
+```
+
+### Common Build Issues
+- **Issue**: Network tests fail in isolated environments
+  **Solution**: Expected behavior - core functionality tests will still pass
+
+## Testing
+
+### Running Tests
+```bash
+ctest --test-dir build --output-on-failure -j $(nproc)
+```
+
+**Test suite**: 38 tests covering tokenizers, grammar parsing, sampling, backends, and integration
+**Expected failures**: 2-3 tests may fail if network access is unavailable (they download models)
+**Test time**: ~30 seconds for passing tests
+
+### Server Unit Tests
+Run server-specific unit tests after building the server:
+```bash
+# Build the server first
+cmake --build build --target llama-server
+
+# Navigate to server tests and run
+cd tools/server/tests
+source ../../../.venv/bin/activate
+./tests.sh
+```
+**Server test dependencies**: The `.venv` environment includes the required dependencies for server unit tests (pytest, aiohttp, etc.). Tests can be run individually or with various options as documented in `tools/server/tests/README.md`.
+
+### Test Categories
+- Tokenizer tests: Various model tokenizers (BERT, GPT-2, LLaMA, etc.)
+- Grammar tests: GBNF parsing and validation
+- Backend tests: Core ggml operations across different backends
+- Integration tests: End-to-end workflows
+
+### Manual Testing Commands
+```bash
+# Test basic inference
+./build/bin/llama-cli --version
+
+# Test model loading (requires model file)
+./build/bin/llama-cli -m path/to/model.gguf -p "Hello" -n 10
+```
+
+## Code Quality and Linting
+
+### C++ Code Formatting
+**ALWAYS format C++ code before committing:**
+```bash
+git clang-format
+```
+
+Configuration is in `.clang-format` with these key rules:
+- 4-space indentation
+- 120 column limit
+- Braces on same line for functions
+- Pointer alignment: `void * ptr` (middle)
+- Reference alignment: `int & ref` (middle)
+
+### Python Code
+**ALWAYS activate the Python environment in `.venv` and use tools from that environment:**
+```bash
+# Activate virtual environment
+source .venv/bin/activate
+```
+
+Configuration files:
+- `.flake8`: flake8 settings (max-line-length=125, excludes examples/tools)
+- `pyrightconfig.json`: pyright type checking configuration
+
+### Pre-commit Hooks
+Run before committing:
+```bash
+pre-commit run --all-files
+```
+
+## Continuous Integration
+
+### GitHub Actions Workflows
+Key workflows that run on every PR:
+- `.github/workflows/build.yml`: Multi-platform builds
+- `.github/workflows/server.yml`: Server functionality tests
+- `.github/workflows/python-lint.yml`: Python code quality
+- `.github/workflows/python-type-check.yml`: Python type checking
+
+### Local CI Validation
+**Run full CI locally before submitting PRs:**
+```bash
+mkdir tmp
+
+# CPU-only build
+bash ./ci/run.sh ./tmp/results ./tmp/mnt
+```
+
+**CI Runtime**: 30-60 minutes depending on backend configuration
+
+### Triggering CI
+Add `ggml-ci` to commit message to trigger heavy CI workloads on the custom CI infrastructure.
+
+## Project Layout and Architecture
+
+### Core Directories
+- **`src/`**: Main llama library implementation (`llama.cpp`, `llama-*.cpp`)
+- **`include/`**: Public API headers, primarily `include/llama.h`
+- **`ggml/`**: Core tensor library (submodule with custom GGML framework)
+- **`examples/`**: 30+ example applications and tools
+- **`tools/`**: Additional development and utility tools (server benchmarks, tests)
+- **`tests/`**: Comprehensive test suite with CTest integration
+- **`docs/`**: Detailed documentation (build guides, API docs, etc.)
+- **`scripts/`**: Utility scripts for CI, data processing, and automation
+- **`common/`**: Shared utility code used across examples
+
+### Key Files
+- **`CMakeLists.txt`**: Primary build configuration
+- **`include/llama.h`**: Main C API header (~2000 lines)
+- **`src/llama.cpp`**: Core library implementation (~8000 lines)
+- **`CONTRIBUTING.md`**: Coding guidelines and PR requirements
+- **`.clang-format`**: C++ formatting rules
+- **`.pre-commit-config.yaml`**: Git hook configuration
+
+### Built Executables (in `build/bin/`)
+Primary tools:
+- **`llama-cli`**: Main inference tool
+- **`llama-server`**: OpenAI-compatible HTTP server
+- **`llama-quantize`**: Model quantization utility
+- **`llama-perplexity`**: Model evaluation tool
+- **`llama-bench`**: Performance benchmarking
+- **`llama-convert-llama2c-to-ggml`**: Model conversion utilities
+
+### Configuration Files
+- **CMake**: `CMakeLists.txt`, `cmake/` directory
+- **Linting**: `.clang-format`, `.clang-tidy`, `.flake8`
+- **CI**: `.github/workflows/`, `ci/run.sh`
+- **Git**: `.gitignore` (includes build artifacts, models, cache)
+
+### Dependencies
+- **System**: OpenMP, libcurl (for model downloading)
+- **Optional**: CUDA SDK, Metal framework, Vulkan SDK, Intel oneAPI
+- **Bundled**: httplib, json (header-only libraries in vendored form)
+
+## Common Validation Steps
+
+### After Making Changes
+1. **Format code**: `git clang-format`
+2. **Build**: `cmake --build build --config Release`
+3. **Test**: `ctest --test-dir build --output-on-failure`
+4. **Server tests** (if modifying server): `cd tools/server/tests && source ../../../.venv/bin/activate && ./tests.sh`
+5. **Manual validation**: Test relevant tools in `build/bin/`
+
+### Performance Validation
+```bash
+# Benchmark inference performance
+./build/bin/llama-bench -m model.gguf
+
+# Evaluate model perplexity
+./build/bin/llama-perplexity -m model.gguf -f dataset.txt
+```
+
+### Backend Validation
+```bash
+# Test backend operations
+./build/bin/test-backend-ops
+```
+
+## Environment Setup
+
+### Required Tools
+- CMake 3.14+ (install via system package manager)
+- Modern C++ compiler with C++17 support
+- Git (for submodule management)
+- Python 3.9+ with virtual environment (`.venv` is provided)
+
+### Optional but Recommended
+- ccache: `apt install ccache` or `brew install ccache`
+- clang-format 15+: Usually included with LLVM/Clang installation
+- pre-commit: `pip install pre-commit`
+
+### Backend-Specific Requirements
+- **CUDA**: NVIDIA CUDA Toolkit 11.2+
+- **Metal**: Xcode command line tools (macOS only)
+- **Vulkan**: Vulkan SDK
+- **SYCL**: Intel oneAPI toolkit
+
+## Important Guidelines
+
+### Code Changes
+- **Minimal dependencies**: Avoid adding new external dependencies
+- **Cross-platform compatibility**: Test on Linux, macOS, Windows when possible
+- **Performance focus**: This is a performance-critical inference library
+- **API stability**: Changes to `include/llama.h` require careful consideration
+
+### Git Workflow
+- Always create feature branches from `master`
+- **Never** commit build artifacts (`build/`, `.ccache/`, `*.o`, `*.gguf`)
+- Use descriptive commit messages following project conventions
+
+### Trust These Instructions
+Only search for additional information if these instructions are incomplete or found to be incorrect. This document contains validated build and test procedures that work reliably across different environments.
+
diff --git a/.github/workflows/copilot-setup-steps.yml b/.github/workflows/copilot-setup-steps.yml
index 2d61ce3035..3645e30378 100644
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -39,6 +39,10 @@ jobs:
         run: |
           sudo apt-get update
           sudo apt-get install build-essential libcurl4-openssl-dev
+          # Install git-clang-format script for formatting only changed code
+          wget -O /tmp/git-clang-format https://raw.githubusercontent.com/llvm/llvm-project/release/18.x/clang/tools/clang-format/git-clang-format
+          sudo cp /tmp/git-clang-format /usr/local/bin/git-clang-format
+          sudo chmod +x /usr/local/bin/git-clang-format
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -50,4 +54,4 @@ jobs:
           python3 -m venv .venv
           .venv/bin/activate
           pip install -r requirements/requirements-all.txt -r tools/server/tests/requirements.txt
-          pip install flake8 pyright
+          pip install flake8 pyright pre-commit
diff --git a/.gitignore b/.gitignore
index f48ce4cacd..595831accb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -147,3 +147,4 @@ poetry.toml
 # Local scripts
 /run-vim.sh
 /run-chat.sh
+.ccache/

From b108e429043ee5c9fc8fa4957a0a52c3e490d5c9 Mon Sep 17 00:00:00 2001
From: Michael Giba <michaelgiba@gmail.com>
Date: Thu, 21 Aug 2025 05:06:46 -0500
Subject: [PATCH 131/287] ci : fix -Werror=return-type in clip.cpp so ci/run.sh
 can run without issue (#15221)

* Fix -Werror=return-type so ci/run.sh can run

* Update tools/mtmd/clip.cpp

Co-authored-by: Diego Devesa <slarengh@gmail.com>

* Remove false now that we have abort

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
---
 tools/mtmd/clip.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index a32fe84fa7..1676c32836 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -3581,10 +3581,10 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         }
 
         return true;
-
+    } else {
+        GGML_ABORT("Unknown image preprocessing type");
     }
 
-    GGML_ASSERT(false && "Unknown image preprocessing type");
 }
 
 ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {

From 2758fa10dab0556e6c3f130e664750fd6773dc7c Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 21 Aug 2025 12:16:54 +0200
Subject: [PATCH 132/287] examples : add model conversion tool/example (#15455)

* examples : add model conversion tool/example

This commit adds an "example/tool" that is intended to help in the
process of converting models to GGUF. Currently it supports normal
causal models and embedding models. The readme contains instructions and
command to guide through the process.

The motivation for this to have a structured and repeatable process for
model conversions and hopefully with time improve upon it to make the
process easier and more reliable. We have started to use this for new
model conversions internally and will continue doing so and improve it
as we go along. Perhaps with time this should be placed in a different
directory than the examples directory, but for now it seems like a good
place to keep it while we are still developing it.

* squash! examples : add model conversion tool/example

Remove dependency on scikit-learn in model conversion example.

* squash! examples : add model conversion tool/example

Update transformer dep to use non-dev version. And also import
`AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility
with the latest version.

* squash! examples : add model conversion tool/example

Remove the logits requirements file from the all requirements file.
---
 examples/CMakeLists.txt                       |   1 +
 examples/model-conversion/.gitignore          |   3 +
 examples/model-conversion/CMakeLists.txt      |   5 +
 examples/model-conversion/Makefile            | 163 +++++++++
 examples/model-conversion/README.md           | 335 ++++++++++++++++++
 examples/model-conversion/logits.cpp          | 209 +++++++++++
 examples/model-conversion/requirements.txt    |   4 +
 .../causal/compare-embeddings-logits.sh       |  43 +++
 .../scripts/causal/compare-logits.py          |  88 +++++
 .../scripts/causal/convert-model.sh           |  22 ++
 .../causal/run-casual-gen-embeddings-org.sh   | 113 ++++++
 .../run-converted-model-embeddings-logits.sh  |  18 +
 .../scripts/causal/run-converted-model.sh     |  20 ++
 .../scripts/causal/run-org-model.py           | 100 ++++++
 .../embedding/compare-embeddings-logits.sh    |  42 +++
 .../scripts/embedding/convert-model.sh        |  22 ++
 .../scripts/embedding/run-converted-model.sh  |  20 ++
 .../scripts/embedding/run-original-model.py   | 116 ++++++
 .../scripts/readme.md.template                |  13 +
 .../scripts/utils/check-nmse.py               | 174 +++++++++
 .../utils/create-collection-add-model.sh      |   6 +
 .../utils/hf-add-model-to-collection.py       |  80 +++++
 .../scripts/utils/hf-create-collection.py     | 106 ++++++
 .../scripts/utils/hf-create-model.py          |  63 ++++
 .../scripts/utils/hf-upload-gguf-model.py     |  58 +++
 .../scripts/utils/inspect-converted-model.sh  |  14 +
 .../scripts/utils/inspect-org-model.py        |  67 ++++
 .../scripts/utils/perplexity-gen.sh           |  35 ++
 .../scripts/utils/perplexity-run-simple.sh    |  27 ++
 .../scripts/utils/perplexity-run.sh           |  28 ++
 .../scripts/utils/quantize.sh                 |  34 ++
 .../scripts/utils/run-embedding-server.sh     |  22 ++
 .../scripts/utils/semantic_check.py           | 179 ++++++++++
 33 files changed, 2230 insertions(+)
 create mode 100644 examples/model-conversion/.gitignore
 create mode 100644 examples/model-conversion/CMakeLists.txt
 create mode 100644 examples/model-conversion/Makefile
 create mode 100644 examples/model-conversion/README.md
 create mode 100644 examples/model-conversion/logits.cpp
 create mode 100644 examples/model-conversion/requirements.txt
 create mode 100755 examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
 create mode 100755 examples/model-conversion/scripts/causal/compare-logits.py
 create mode 100755 examples/model-conversion/scripts/causal/convert-model.sh
 create mode 100755 examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh
 create mode 100755 examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
 create mode 100755 examples/model-conversion/scripts/causal/run-converted-model.sh
 create mode 100755 examples/model-conversion/scripts/causal/run-org-model.py
 create mode 100755 examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
 create mode 100755 examples/model-conversion/scripts/embedding/convert-model.sh
 create mode 100755 examples/model-conversion/scripts/embedding/run-converted-model.sh
 create mode 100755 examples/model-conversion/scripts/embedding/run-original-model.py
 create mode 100644 examples/model-conversion/scripts/readme.md.template
 create mode 100755 examples/model-conversion/scripts/utils/check-nmse.py
 create mode 100644 examples/model-conversion/scripts/utils/create-collection-add-model.sh
 create mode 100755 examples/model-conversion/scripts/utils/hf-add-model-to-collection.py
 create mode 100755 examples/model-conversion/scripts/utils/hf-create-collection.py
 create mode 100755 examples/model-conversion/scripts/utils/hf-create-model.py
 create mode 100755 examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
 create mode 100755 examples/model-conversion/scripts/utils/inspect-converted-model.sh
 create mode 100755 examples/model-conversion/scripts/utils/inspect-org-model.py
 create mode 100755 examples/model-conversion/scripts/utils/perplexity-gen.sh
 create mode 100755 examples/model-conversion/scripts/utils/perplexity-run-simple.sh
 create mode 100755 examples/model-conversion/scripts/utils/perplexity-run.sh
 create mode 100755 examples/model-conversion/scripts/utils/quantize.sh
 create mode 100755 examples/model-conversion/scripts/utils/run-embedding-server.sh
 create mode 100644 examples/model-conversion/scripts/utils/semantic_check.py

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 11ff38762b..abc4fa1c89 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -34,6 +34,7 @@ else()
     add_subdirectory(gen-docs)
     add_subdirectory(training)
     add_subdirectory(diffusion)
+    add_subdirectory(model-conversion)
     if (NOT GGML_BACKEND_DL)
         add_subdirectory(convert-llama2c-to-ggml)
         # these examples use the backends directly and cannot be built with dynamic loading
diff --git a/examples/model-conversion/.gitignore b/examples/model-conversion/.gitignore
new file mode 100644
index 0000000000..451227547f
--- /dev/null
+++ b/examples/model-conversion/.gitignore
@@ -0,0 +1,3 @@
+.model_name
+data
+ppl
diff --git a/examples/model-conversion/CMakeLists.txt b/examples/model-conversion/CMakeLists.txt
new file mode 100644
index 0000000000..fc1746ce45
--- /dev/null
+++ b/examples/model-conversion/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET llama-logits)
+add_executable(${TARGET} logits.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
new file mode 100644
index 0000000000..27d95b4f2b
--- /dev/null
+++ b/examples/model-conversion/Makefile
@@ -0,0 +1,163 @@
+# Validation functions
+define validate_model_path
+	@if [ -z "$(MODEL_PATH)" ]; then \
+		echo "Error: MODEL_PATH must be provided either as:"; \
+		echo "  1. Environment variable: export MODEL_PATH=/path/to/model"; \
+		echo "  2. Command line argument: make $(1) MODEL_PATH=/path/to/model"; \
+		exit 1; \
+	fi
+endef
+
+define validate_embedding_model_path
+	@if [ -z "$(EMBEDDING_MODEL_PATH)" ]; then \
+		echo "Error: EMBEDDING_MODEL_PATH must be provided either as:"; \
+		echo "  1. Environment variable: export EMBEDDING_MODEL_PATH=/path/to/model"; \
+		echo "  2. Command line argument: make $(1) EMBEDDING_MODEL_PATH=/path/to/model"; \
+		exit 1; \
+	fi
+endef
+
+###
+### Casual Model targets/recipes
+###
+causal-convert-model-bf16: OUTTYPE=bf16
+causal-convert-model-bf16: causal-convert-model
+
+causal-convert-model:
+	$(call validate_model_path,causal-convert-model)
+	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
+	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
+	./scripts/causal/convert-model.sh
+
+causal-run-original-model:
+	$(call validate_model_path,causal-run-original-model)
+	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py
+
+causal-run-converted-model:
+	@CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh
+
+causal-verify-logits: causal-run-original-model causal-run-converted-model
+	@./scripts/causal/compare-logits.py
+	@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
+
+causal-run-original-embeddings:
+	@./scripts/causal/run-casual-gen-embeddings-org.sh
+
+causal-run-converted-embeddings:
+	@./scripts/causal/run-converted-model-embeddings-logits.sh
+
+causal-verify-embeddings: causal-run-original-embeddings causal-run-converted-embeddings
+	@./scripts/causal/compare-embeddings-logits.sh
+
+causal-inspect-original-model:
+	@./scripts/utils/inspect-org-model.py
+
+causal-inspect-converted-model:
+	@./scripts/utils/inspect-converted-model.sh
+
+causal-start-embedding-server:
+	@./scripts/utils/run-embedding-server.sh ${CONVERTED_MODEL}
+
+causal-curl-embedding-endpoint: causal-run-original-embeddings
+	@./scripts/utils/curl-embedding-server.sh | ./scripts/causal/compare-embeddings-logits.sh
+
+causal-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
+causal-quantize-Q8_0: causal-quantize-model
+
+causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
+causal-quantize-Q4_0: causal-quantize-model
+
+causal-quantize-model:
+	@CONVERTED_MODEL="$(CONVERTED_MODEL)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" ./scripts/utils/quantize.sh ${CONVERTED_MODEL} ${QUANTIZED_TYPE}
+	@echo "Export the quantized model path to QUANTIZED_MODEL variable in your environment"
+
+causal-run-quantized-model:
+	@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
+
+
+###
+### Embedding Model targets/recipes
+###
+
+embedding-convert-model-bf16: OUTTYPE=bf16
+embedding-convert-model-bf16: embedding-convert-model
+
+embedding-convert-model:
+	$(call validate_embedding_model_path,embedding-convert-model)
+	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
+	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
+	./scripts/embedding/convert-model.sh
+
+embedding-run-original-model:
+	$(call validate_embedding_model_path,embedding-run-original-model)
+	@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/embedding/run-original-model.py
+
+embedding-run-converted-model:
+	@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/embedding/run-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
+
+embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
+	@./scripts/embedding/compare-embeddings-logits.sh
+
+embedding-inspect-original-model:
+	$(call validate_embedding_model_path,embedding-inspect-original-model)
+	@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH}
+
+embedding-inspect-converted-model:
+	@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/utils/inspect-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
+
+embedding-start-embedding-server:
+	@./scripts/utils/run-embedding-server.sh ${CONVERTED_EMBEDDING_MODEL}
+
+embedding-curl-embedding-endpoint:
+	@./scripts/utils/curl-embedding-server.sh | ./scripts/embedding/compare-embeddings-logits.sh
+
+embedding-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
+embedding-quantize-Q8_0: embedding-quantize-model
+
+embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
+embedding-quantize-Q4_0: embedding-quantize-model
+
+embedding-quantize-model:
+	@./scripts/utils/quantize.sh ${CONVERTED_EMBEDDING_MODEL} ${QUANTIZED_TYPE}
+	@echo "Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment"
+
+embedding-run-quantized-model:
+	@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}
+
+###
+### Perplexity targets/recipes
+###
+perplexity-data-gen:
+	CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/utils/perplexity-gen.sh
+
+perplexity-run-full:
+	QUANTIZED_MODEL="$(QUANTIZED_MODEL)" LOOGITS_FILE="$(LOGITS_FILE)" \
+	./scripts/utils/perplexity-run.sh
+
+perplexity-run:
+	QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/utils/perplexity-run-simple.sh
+
+###
+### HuggingFace targets/recipes
+###
+
+hf-create-model:
+	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}"
+
+hf-create-model-private:
+	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -p
+
+hf-upload-gguf-to-model:
+	@./scripts/utils/hf-upload-gguf-model.py -m "${MODEL_PATH}" -r "${REPO_ID}" -o "${NAME_IN_REPO}"
+
+hf-create-collection:
+	@./scripts/utils/hf-create-collection.py -n "${NAME}" -d "${DESCRIPTION}" -ns "${NAMESPACE}"
+
+hf-add-model-to-collection:
+	@./scripts/utils/hf-add-model-to-collection.py -c "${COLLECTION}" -m "${MODEL}"
+
+
+.PHONY: clean
+clean:
+	@${RM} -rf data .converted_embedding_model.txt .converted_model.txt .embedding_model_name.txt .model_name.txt
+
diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md
new file mode 100644
index 0000000000..c3c5001ead
--- /dev/null
+++ b/examples/model-conversion/README.md
@@ -0,0 +1,335 @@
+# Model Conversion Example
+This directory contains scripts and code to help in the process of converting
+HuggingFace PyTorch models to GGUF format.
+
+The motivation for having this is that the conversion process can often be an
+iterative process, where the original model is inspected, converted, updates
+made to llama.cpp, converted again, etc. Once the model has been converted it
+needs to be verified against the original model, and then optionally quantified,
+and is some cases perplexity checked of the quantized model. And finally the
+model/models need to the ggml-org on Hugging Face. This tool/example tries to
+help with this process.
+
+### Overview
+The idea is that the makefile targets and scripts here can be used in the
+development/conversion process assisting with things like:
+
+* inspect/run the original model to figure out how it works
+* convert the original model to GGUF format
+* inspect/run the converted model
+* verify the logits produced by the original model and the converted model
+* quantize the model to GGUF format
+* run perplexity evaluation to verify that the quantized model is performing
+  as expected
+* upload the model to HuggingFace to make it available for others
+
+## Setup
+Create virtual python environment
+```console
+$ python3.11 -m venv venv
+$ source venv/bin/activate
+(venv) $ pip install -r requirements.txt
+```
+
+## Causal Language Model Conversion
+This section describes the steps to convert a causal language model to GGUF and
+to verify that the conversion was successful.
+
+### Download the original model
+First, clone the original model to some local directory:
+```console
+$ mkdir models && cd models
+$ git clone https://huggingface.co/user/model_name
+$ cd model_name
+$ git lfs install
+$ git lfs pull
+```
+
+### Set the MODEL_PATH
+The path to the downloaded model can be provided in two ways:
+
+**Option 1: Environment variable (recommended for iterative development)**
+```console
+export MODEL_PATH=~/work/ai/models/some_model
+```
+
+**Option 2: Command line argument (for one-off tasks)**
+```console
+make causal-convert-model MODEL_PATH=~/work/ai/models/some_model
+```
+
+Command line arguments take precedence over environment variables when both are provided.
+
+In cases where the transformer implementation for the model has not been released
+yet it is possible to set the environment variable `UNRELEASED_MODEL_NAME` which
+will the cause the transformer implementation to be loaded explicitely and not
+use AutoModelForCausalLM:
+```
+export UNRELEASED_MODEL_NAME=SomeNewModel
+```
+
+### Inspecting the original tensors
+```console
+# Using environment variable
+(venv) $ make causal-inspect-original-model
+
+# Or using command line argument
+(venv) $ make causal-inspect-original-model MODEL_PATH=~/work/ai/models/some_model
+```
+
+### Running the original model
+This is mainly to verify that the original model works, and to compare the output
+from the converted model.
+```console
+# Using environment variable
+(venv) $ make causal-run-original-model
+
+# Or using command line argument
+(venv) $ make causal-run-original-model MODEL_PATH=~/work/ai/models/some_model
+```
+This command will save two file to the `data` directory, one is a binary file
+containing logits which will be used for comparison with the converted model
+later, and the other is a text file which allows for manual visual inspection.
+
+### Model conversion
+After updates have been made to [gguf-py](../../gguf-py) to add support for the
+new model, the model can be converted to GGUF format using the following command:
+```console
+# Using environment variable
+(venv) $ make causal-convert-model
+
+# Or using command line argument
+(venv) $ make causal-convert-model MODEL_PATH=~/work/ai/models/some_model
+```
+
+### Inspecting the converted model
+The converted model can be inspected using the following command:
+```console
+(venv) $ make inspect-converted-model
+```
+
+### Running the converted model
+```console
+(venv) $ make run-converted-model
+```
+
+### Model logits verfication
+The following target will run the original model and the converted model and
+compare the logits:
+```console
+(venv) $ make causal-verify-logits
+```
+
+### Quantizing the model
+The causal model can be quantized to GGUF format using the following command:
+```console
+(venv) $ make causal-quantize-Q8_0
+Quantized model saved to: /path/to/quantized/model-Q8_0.gguf
+Export the quantized model path to QUANTIZED_MODEL variable in your environment
+```
+This will show the path to the quantized model in the terminal, which can then
+be used set the `QUANTIZED_MODEL` environment variable:
+```console
+export QUANTIZED_MODEL=/path/to/quantized/model-Q8_0.gguf
+```
+The the quantized model can be run using the following command:
+```console
+(venv) $ make causal-run-quantized-model
+```
+
+
+## Embedding Language Model Conversion
+
+### Download the original model
+```console
+$ mkdir models && cd models
+$ git clone https://huggingface.co/user/model_name
+$ cd model_name
+$ git lfs install
+$ git lfs pull
+```
+
+The path to the embedding model can be provided in two ways:
+
+**Option 1: Environment variable (recommended for iterative development)**
+```console
+export EMBEDDING_MODEL_PATH=~/path/to/embedding_model
+```
+
+**Option 2: Command line argument (for one-off tasks)**
+```console
+make embedding-convert-model EMBEDDING_MODEL_PATH=~/path/to/embedding_model
+```
+
+Command line arguments take precedence over environment variables when both are provided.
+
+### Running the original model
+This is mainly to verify that the original model works and to compare the output
+with the output from the converted model.
+```console
+# Using environment variable
+(venv) $ make embedding-run-original-model
+
+# Or using command line argument
+(venv) $ make embedding-run-original-model EMBEDDING_MODEL_PATH=~/path/to/embedding_model
+```
+This command will save two files to the `data` directory, one is a binary
+file containing logits which will be used for comparison with the converted
+model, and the other is a text file which allows for manual visual inspection.
+
+### Model conversion
+After updates have been made to [gguf-py](../../gguf-py) to add support for the
+new model the model can be converted to GGUF format using the following command:
+```console
+(venv) $ make embedding-convert-model
+```
+
+### Run the converted model
+```console
+(venv) $ make embedding-run-converted-model
+```
+
+### Model logits verfication
+The following target will run the original model and the converted model (which
+was done manually in the previous steps) and compare the logits:
+```console
+(venv) $ make embedding-verify-logits
+```
+
+### llama-server verification
+To verify that the converted model works with llama-server, the following
+command can be used:
+```console
+(venv) $ make embedding-start-embedding-server
+```
+Then open another terminal and set the `EMBEDDINGS_MODEL_PATH` environment
+variable as this will not be inherited by the new terminal:
+```console
+(venv) $ make embedding-curl-embedding-endpoint
+```
+This will call the `embedding` endpoing and the output will be piped into
+the same verification script as used by the target `embedding-verify-logits`.
+
+The causal model can also be used to produce embeddings and this can be verified
+using the following commands:
+```console
+(venv) $ make causal-start-embedding-server
+```
+Then open another terminal and set the `MODEL_PATH` environment
+variable as this will not be inherited by the new terminal:
+```console
+(venv) $ make casual-curl-embedding-endpoint
+```
+
+### Quantizing the model
+The embedding model can be quantized to GGUF format using the following command:
+```console
+(venv) $ make embedding-quantize-Q8_0
+Quantized model saved to: /path/to/quantized/model-Q8_0.gguf
+Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment
+```
+This will show the path to the quantized model in the terminal, which can then
+be used set the `QUANTIZED_EMBEDDING_MODEL` environment variable:
+```console
+export QUANTIZED_EMBEDDING_MODEL=/path/to/quantized/model-Q8_0.gguf
+```
+The the quantized model can be run using the following command:
+```console
+(venv) $ make embedding-run-quantized-model
+```
+
+## Perplexity Evaluation
+
+### Simple perplexity evaluation
+This allows to run the perplexity evaluation without having to generate a
+token/logits file:
+```console
+(venv) $ make perplexity-run QUANTIZED_MODEL=~/path/to/quantized/model.gguf
+```
+This will use the wikitext dataset to run the perplexity evaluation and and
+output the perplexity score to the terminal. This value can then be compared
+with the perplexity score of the unquantized model.
+
+### Full perplexity evaluation
+First use the converted, non-quantized, model to generate the perplexity evaluation
+dataset using the following command:
+```console
+$ make perplexity-data-gen CONVERTED_MODEL=~/path/to/converted/model.gguf
+```
+This will generate a file in the `data` directory named after the model and with
+a `.kld` suffix which contains the tokens and the logits for the wikitext dataset.
+
+After the dataset has been generated, the perplexity evaluation can be run using
+the quantized model:
+```console
+$ make perplexity-run-full QUANTIZED_MODEL=~/path/to/quantized/model-Qxx.gguf LOGITS_FILE=data/model.gguf.ppl
+```
+
+> 📝 **Note:** The `LOGITS_FILE` is the file generated by the previous command
+> can be very large, so make sure you have enough disk space available.
+
+## HuggingFace utilities
+The following targets are useful for creating collections and model repositories
+on Hugging Face in the the ggml-org. These can be used when preparing a relase
+to script the process for new model releases.
+
+For the following targets a `HF_TOKEN` environment variable is required.
+
+> 📝 **Note:** Don't forget to logout from Hugging Face after running these
+> commands, otherwise you might have issues pulling/cloning repositories as
+> the token will still be in use:
+> $ huggingface-cli logout
+> $ unset HF_TOKEN
+
+### Create a new Hugging Face Model (model repository)
+This will create a new model repsository on Hugging Face with the specified
+model name.
+```console
+(venv) $ make hf-create-model MODEL_NAME='TestModel' NAMESPACE="danbev"
+Repository ID:  danbev/TestModel-GGUF
+Repository created: https://huggingface.co/danbev/TestModel-GGUF
+```
+Note that we append a `-GGUF` suffix to the model name to ensure a consistent
+naming convention for GGUF models.
+
+### Upload a GGUF model to model repository
+The following target uploads a model to an existing Hugging Face model repository.
+```console
+(venv) $ make hf-upload-gguf-to-model MODEL_PATH=dummy-model1.gguf REPO_ID=danbev/TestModel-GGUF
+📤 Uploading dummy-model1.gguf to danbev/TestModel-GGUF/dummy-model1.gguf
+✅ Upload successful!
+🔗 File available at: https://huggingface.co/danbev/TestModel-GGUF/blob/main/dummy-model1.gguf
+```
+This command can also be used to update an existing model file in a repository.
+
+### Create a new Collection
+```console
+(venv) $ make hf-new-collection NAME=TestCollection DESCRIPTION="Collection for testing scripts" NAMESPACE=danbev
+🚀 Creating Hugging Face Collection
+Title: TestCollection
+Description: Collection for testing scripts
+Namespace: danbev
+Private: False
+✅ Authenticated as: danbev
+📚 Creating collection: 'TestCollection'...
+✅ Collection created successfully!
+📋 Collection slug: danbev/testcollection-68930fcf73eb3fc200b9956d
+🔗 Collection URL: https://huggingface.co/collections/danbev/testcollection-68930fcf73eb3fc200b9956d
+
+🎉 Collection created successfully!
+Use this slug to add models: danbev/testcollection-68930fcf73eb3fc200b9956d
+```
+
+### Add model to a Collection
+```console
+(venv) $ make hf-add-model-to-collection COLLECTION=danbev/testcollection-68930fcf73eb3fc200b9956d MODEL=danbev/TestModel-GGUF
+✅ Authenticated as: danbev
+🔍 Checking if model exists: danbev/TestModel-GGUF
+✅ Model found: danbev/TestModel-GGUF
+📚 Adding model to collection...
+✅ Model added to collection successfully!
+🔗 Collection URL: https://huggingface.co/collections/danbev/testcollection-68930fcf73eb3fc200b9956d
+
+🎉 Model added successfully!
+
+```
diff --git a/examples/model-conversion/logits.cpp b/examples/model-conversion/logits.cpp
new file mode 100644
index 0000000000..2cac6a3b3e
--- /dev/null
+++ b/examples/model-conversion/logits.cpp
@@ -0,0 +1,209 @@
+#include "llama.h"
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include <vector>
+#include <ctype.h>
+#include <filesystem>
+
+static void print_usage(int, char ** argv) {
+    printf("\nexample usage:\n");
+    printf("\n    %s -m model.gguf [-ngl n_gpu_layers] -embd-mode [prompt]\n", argv[0]);
+    printf("\n");
+}
+
+int main(int argc, char ** argv) {
+    std::string model_path;
+    std::string prompt = "Hello, my name is";
+    int ngl = 0;
+    bool embedding_mode = false;
+
+    {
+        int i = 1;
+        for (; i < argc; i++) {
+            if (strcmp(argv[i], "-m") == 0) {
+                if (i + 1 < argc) {
+                    model_path = argv[++i];
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else if (strcmp(argv[i], "-ngl") == 0) {
+                if (i + 1 < argc) {
+                    try {
+                        ngl = std::stoi(argv[++i]);
+                    } catch (...) {
+                        print_usage(argc, argv);
+                        return 1;
+                    }
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else if (strcmp(argv[i], "-embd-mode") == 0) {
+                if (i + 1 < argc) {
+                    try {
+                        embedding_mode = true;
+                    } catch (...) {
+                        print_usage(argc, argv);
+                        return 1;
+                    }
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else {
+                // prompt starts here
+                break;
+            }
+        }
+
+        if (model_path.empty()) {
+            print_usage(argc, argv);
+            return 1;
+        }
+
+        if (i < argc) {
+            prompt = argv[i++];
+            for (; i < argc; i++) {
+                prompt += " ";
+                prompt += argv[i];
+            }
+        }
+    }
+
+    ggml_backend_load_all();
+    llama_model_params model_params = llama_model_default_params();
+    model_params.n_gpu_layers = ngl;
+
+    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
+
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    // Extract basename from model_path
+    const char * basename = strrchr(model_path.c_str(), '/');
+    basename = (basename == NULL) ? model_path.c_str() : basename + 1;
+
+    char model_name[256];
+    strncpy(model_name, basename, 255);
+    model_name[255] = '\0';
+
+    char * dot = strrchr(model_name, '.');
+    if (dot != NULL && strcmp(dot, ".gguf") == 0) {
+        *dot = '\0';
+    }
+    printf("Model name: %s\n", model_name);
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    const int n_prompt = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+
+    std::vector<llama_token> prompt_tokens(n_prompt);
+    if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
+        fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
+        return 1;
+    }
+
+    llama_context_params ctx_params = llama_context_default_params();
+    ctx_params.n_ctx = n_prompt;
+    ctx_params.n_batch = n_prompt;
+    ctx_params.no_perf = false;
+    if (embedding_mode) {
+        ctx_params.embeddings = true;
+        ctx_params.n_ubatch = ctx_params.n_batch;
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    printf("Input prompt: \"%s\"\n", prompt.c_str());
+    printf("Tokenized prompt (%d tokens): ", n_prompt);
+    for (auto id : prompt_tokens) {
+        char buf[128];
+        int n = llama_token_to_piece(vocab, id, buf, sizeof(buf), 0, true);
+        if (n < 0) {
+            fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
+            return 1;
+        }
+        std::string s(buf, n);
+        printf("%s", s.c_str());
+    }
+    printf("\n");
+
+    llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to eval\n", __func__);
+        return 1;
+    }
+
+    float * logits;
+    int n_logits;
+    const char * type;
+
+    if (embedding_mode) {
+        logits = llama_get_embeddings(ctx);
+        n_logits = llama_model_n_embd(model) * batch.n_tokens;
+        type = "-embeddings";
+        printf("Embeddings size: %d\n", n_logits);
+    } else {
+        logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
+        n_logits = llama_vocab_n_tokens(vocab);
+        type = "";
+        printf("Vocab size: %d\n", n_logits);
+    }
+
+    std::filesystem::create_directory("data");
+
+    // Save logits to binary file
+    char bin_filename[512];
+    snprintf(bin_filename, sizeof(bin_filename), "data/llamacpp-%s%s.bin", model_name, type);
+    printf("Saving logits to %s\n", bin_filename);
+
+    FILE * f = fopen(bin_filename, "wb");
+    if (f == NULL) {
+        fprintf(stderr, "%s: error: failed to open binary output file\n", __func__);
+        return 1;
+    }
+    fwrite(logits, sizeof(float), n_logits, f);
+    fclose(f);
+
+    // Also save as text for debugging
+    char txt_filename[512];
+    snprintf(txt_filename, sizeof(txt_filename), "data/llamacpp-%s%s.txt", model_name, type);
+    f = fopen(txt_filename, "w");
+    if (f == NULL) {
+        fprintf(stderr, "%s: error: failed to open text output file\n", __func__);
+        return 1;
+    }
+    for (int i = 0; i < n_logits; i++) {
+        fprintf(f, "%d: %.6f\n", i, logits[i]);  // Added index and changed format
+    }
+    fclose(f);
+
+    // Print first and last 10 logits for quick verification
+    printf("First 10 logits: ");
+    for (int i = 0; i < 10 && i < n_logits; i++) {
+        printf("%.6f ", logits[i]);
+    }
+    printf("\n");
+
+    printf("Last 10 logits: ");
+    for (int i = n_logits - 10; i < n_logits; i++) {
+        if (i >= 0) printf("%.6f ", logits[i]);
+    }
+    printf("\n\n");
+
+    printf("Logits saved to %s\n", bin_filename);
+    printf("Logits saved to %s\n", txt_filename);
+
+    llama_free(ctx);
+    llama_model_free(model);
+
+    return 0;
+}
diff --git a/examples/model-conversion/requirements.txt b/examples/model-conversion/requirements.txt
new file mode 100644
index 0000000000..e1aa259e94
--- /dev/null
+++ b/examples/model-conversion/requirements.txt
@@ -0,0 +1,4 @@
+torch~=2.6.0
+torchvision~=0.21.0
+transformers~=4.55.0
+huggingface-hub~=0.34.0
diff --git a/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh b/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
new file mode 100755
index 0000000000..287158f638
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
@@ -0,0 +1,43 @@
+#/bin/bash
+
+set -e
+
+MODEL_PATH="${1:-"$MODEL_PATH"}"
+MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
+
+if [ -t 0 ]; then
+    CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
+else
+    # Process piped JSON data and convert to binary (matching logits.cpp format)
+    TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
+    python3 -c "
+import json
+import sys
+import struct
+
+data = json.load(sys.stdin)
+
+# Flatten all embeddings completely
+flattened = []
+for item in data:
+    embedding = item['embedding']
+    for token_embedding in embedding:
+        flattened.extend(token_embedding)
+
+print(f'Total embedding values: {len(flattened)}', file=sys.stderr)
+
+# Write as binary floats - matches logitc.cpp fwrite format
+with open('$TEMP_FILE', 'wb') as f:
+    for value in flattened:
+        f.write(struct.pack('f', value))
+"
+    CPP_EMBEDDINGS="$TEMP_FILE"
+    trap "rm -f $TEMP_FILE" EXIT
+fi
+
+python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
+    --python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
+    --cpp-embeddings $CPP_EMBEDDINGS \
+    --prompt "Hello world today" \
+    --causal
+
diff --git a/examples/model-conversion/scripts/causal/compare-logits.py b/examples/model-conversion/scripts/causal/compare-logits.py
new file mode 100755
index 0000000000..fb959f0d56
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/compare-logits.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import sys
+import os
+from pathlib import Path
+
+def quick_logits_check(pytorch_file, llamacpp_file):
+    """Lightweight sanity check before NMSE"""
+
+    try:
+        pytorch_logits = np.fromfile(pytorch_file, dtype=np.float32)
+        llamacpp_logits = np.fromfile(llamacpp_file, dtype=np.float32)
+    except Exception as e:
+        print(f"❌ NOK: Failed to load files - {e}")
+        return False
+
+    # Check shapes match
+    if pytorch_logits.shape != llamacpp_logits.shape:
+        print(f"❌ NOK: Shape mismatch - PyTorch: {pytorch_logits.shape}, llama.cpp: {llamacpp_logits.shape}")
+        return False
+
+    # Calculate key metrics
+    diff = pytorch_logits - llamacpp_logits
+    abs_diff = np.abs(diff)
+    max_diff = np.max(abs_diff)
+
+    # Get top 10 predictions from both models
+    pytorch_top10 = np.argsort(pytorch_logits)[-10:][::-1]
+    llamacpp_top10 = np.argsort(llamacpp_logits)[-10:][::-1]
+    print(f"Top 10 PyTorch logits: {pytorch_logits[pytorch_top10]}")
+    print(f"Top 10 llama.cpp logits: {llamacpp_logits[llamacpp_top10]}")
+    print(f"Max absolute difference: {max_diff:.4f}")
+
+    if max_diff > 1.0:
+        print(f"❌ NOK: Large differences detected - max diff: {max_diff:.4f}")
+        return False
+
+    return True
+
+def main():
+    model_path = os.getenv('MODEL_PATH')
+    if not model_path:
+        print("Error: MODEL_PATH environment variable not set")
+        sys.exit(1)
+
+    if not os.path.exists(model_path):
+        print(f"Error: Model file not found: {model_path}")
+        sys.exit(1)
+
+    model_name = os.path.splitext(os.path.basename(model_path))[0]
+    data_dir = Path("data")
+
+    pytorch_file = data_dir / f"pytorch-{model_name}.bin"
+    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    if not pytorch_file.exists():
+        print(f"Error: PyTorch logits file not found: {pytorch_file}")
+        print("Please run scripts/run-org-model.sh first to generate this file.")
+        sys.exit(1)
+
+    if not llamacpp_file.exists():
+        print(f"Error: llama.cpp logits file not found: {llamacpp_file}")
+        print("Please run scripts/run-converted-model.sh first to generate this file.")
+        sys.exit(1)
+
+    print("Checked all required files were found. Proceeding...\n")
+
+
+    print("🔍 GGML Model Validation for model ", model_name)
+    print("=" * 40)
+    print(f"PyTorch logits  : {pytorch_file}")
+    print(f"llama.cpp logits: {llamacpp_file}")
+    print()
+
+    success = quick_logits_check(pytorch_file, llamacpp_file)
+
+    # Exit with appropriate code
+    if success:
+        print("✅ OK: Lightweight model check successful!")
+        print("       Ok to proceed with NMSE check...")
+        sys.exit(0)
+    else:
+        print(f"❌ NOK: Top 10 predictions don't match - generation will differ")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/model-conversion/scripts/causal/convert-model.sh b/examples/model-conversion/scripts/causal/convert-model.sh
new file mode 100755
index 0000000000..56b21f9baa
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/convert-model.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
+OUTPUT_DIR="${OUTPUT_DIR:-../../models}"
+TYPE="${OUTTYPE:-f16}"
+METADATA_OVERRIDE="${METADATA_OVERRIDE:-}"
+CONVERTED_MODEL="${OUTPUT_DIR}/${MODEL_NAME}.gguf"
+
+echo "Model path: ${MODEL_PATH}"
+echo "Model name: ${MODEL_NAME}"
+echo "Data  type: ${TYPE}"
+echo "Converted model path:: ${CONVERTED_MODEL}"
+echo "Metadata override: ${METADATA_OVERRIDE}"
+python ../../convert_hf_to_gguf.py --verbose \
+    ${MODEL_PATH} \
+    --outfile ${CONVERTED_MODEL} \
+    --outtype ${TYPE} \
+    --metadata "${METADATA_OVERRIDE}"
+
+echo ""
+echo "The environment variable CONVERTED_MODEL can be set to this path using:"
+echo "export CONVERTED_MODEL=$(realpath ${CONVERTED_MODEL})"
diff --git a/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh b/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh
new file mode 100755
index 0000000000..2fb54ab990
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh
@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import importlib
+import sys
+import torch
+import numpy as np
+
+from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForCausalLM
+from pathlib import Path
+
+unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
+
+parser = argparse.ArgumentParser(description='Process model with specified path')
+parser.add_argument('--model-path', '-m', help='Path to the model')
+args = parser.parse_args()
+
+model_path = os.environ.get('MODEL_PATH', args.model_path)
+if model_path is None:
+    parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
+
+config = AutoConfig.from_pretrained(model_path)
+
+print("Model type:       ", config.model_type)
+print("Vocab size:       ", config.vocab_size)
+print("Hidden size:      ", config.hidden_size)
+print("Number of layers: ", config.num_hidden_layers)
+print("BOS token id:     ", config.bos_token_id)
+print("EOS token id:     ", config.eos_token_id)
+
+print("Loading model and tokenizer using AutoTokenizer:", model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+if unreleased_model_name:
+    model_name_lower = unreleased_model_name.lower()
+    unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
+    class_name = f"{unreleased_model_name}ForCausalLM"
+    print(f"Importing unreleased model module: {unreleased_module_path}")
+
+    try:
+        model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
+        model = model_class.from_pretrained(model_path)
+    except (ImportError, AttributeError) as e:
+        print(f"Failed to import or load model: {e}")
+else:
+    model = AutoModelForCausalLM.from_pretrained(model_path)
+print(f"Model class: {type(model)}")
+#print(f"Model file: {type(model).__module__}")
+
+model_name = os.path.basename(model_path)
+print(f"Model name: {model_name}")
+
+prompt = "Hello world today"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+print(f"Input tokens: {input_ids}")
+print(f"Input text: {repr(prompt)}")
+print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
+
+with torch.no_grad():
+    outputs = model(input_ids, output_hidden_states=True)
+
+    # Extract hidden states from the last layer
+    # outputs.hidden_states is a tuple of (num_layers + 1) tensors
+    # Index -1 gets the last layer, shape: [batch_size, seq_len, hidden_size]
+    last_hidden_states = outputs.hidden_states[-1]
+
+    # Get embeddings for all tokens
+    token_embeddings = last_hidden_states[0].cpu().numpy()  # Remove batch dimension
+
+    print(f"Hidden states shape: {last_hidden_states.shape}")
+    print(f"Token embeddings shape: {token_embeddings.shape}")
+    print(f"Hidden dimension: {token_embeddings.shape[-1]}")
+    print(f"Number of tokens: {token_embeddings.shape[0]}")
+
+    # Save raw token embeddings
+    data_dir = Path("data")
+    data_dir.mkdir(exist_ok=True)
+    bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
+    txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
+
+    # Save all token embeddings as binary
+    print(token_embeddings)
+    token_embeddings.astype(np.float32).tofile(bin_filename)
+
+    # Save as text for inspection
+    with open(txt_filename, "w") as f:
+        for i, embedding in enumerate(token_embeddings):
+            for j, val in enumerate(embedding):
+                f.write(f"{i} {j} {val:.6f}\n")
+
+    # Print embeddings per token in the requested format
+    print("\nToken embeddings:")
+    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
+    for i, embedding in enumerate(token_embeddings):
+        # Format: show first few values, ..., then last few values
+        if len(embedding) > 10:
+            # Show first 3 and last 3 values with ... in between
+            first_vals = " ".join(f"{val:8.6f}" for val in embedding[:3])
+            last_vals = " ".join(f"{val:8.6f}" for val in embedding[-3:])
+            print(f"embedding {i}: {first_vals}  ... {last_vals}")
+        else:
+            # If embedding is short, show all values
+            vals = " ".join(f"{val:8.6f}" for val in embedding)
+            print(f"embedding {i}: {vals}")
+
+    # Also show token info for reference
+    print(f"\nToken reference:")
+    for i, token in enumerate(tokens):
+        print(f"  Token {i}: {repr(token)}")
+
+    print(f"Saved bin logits to: {bin_filename}")
+    print(f"Saved txt logist to: {txt_filename}")
diff --git a/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh b/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
new file mode 100755
index 0000000000..64709f1798
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+set -e
+
+# First try command line argument, then environment variable, then file
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+cmake --build ../../build --target llama-logits -j8
+
+../../build/bin/llama-logits -m $CONVERTED_MODEL -embd-mode "Hello world today"
diff --git a/examples/model-conversion/scripts/causal/run-converted-model.sh b/examples/model-conversion/scripts/causal/run-converted-model.sh
new file mode 100755
index 0000000000..e2762729e7
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/run-converted-model.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+set -e
+
+# First try command line argument, then environment variable, then file
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+echo $CONVERTED_MODEL
+
+cmake --build ../../build --target llama-logits -j8
+
+../../build/bin/llama-logits -m "$CONVERTED_MODEL" "Hello, my name is"
diff --git a/examples/model-conversion/scripts/causal/run-org-model.py b/examples/model-conversion/scripts/causal/run-org-model.py
new file mode 100755
index 0000000000..f6188ea6f3
--- /dev/null
+++ b/examples/model-conversion/scripts/causal/run-org-model.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import importlib
+from pathlib import Path
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+import torch
+import numpy as np
+
+unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
+
+parser = argparse.ArgumentParser(description='Process model with specified path')
+parser.add_argument('--model-path', '-m', help='Path to the model')
+args = parser.parse_args()
+
+model_path = os.environ.get('MODEL_PATH', args.model_path)
+if model_path is None:
+    parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
+
+config = AutoConfig.from_pretrained(model_path)
+
+print("Model type:       ", config.model_type)
+print("Vocab size:       ", config.vocab_size)
+print("Hidden size:      ", config.hidden_size)
+print("Number of layers: ", config.num_hidden_layers)
+print("BOS token id:     ", config.bos_token_id)
+print("EOS token id:     ", config.eos_token_id)
+
+print("Loading model and tokenizer using AutoTokenizer:", model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+config = AutoConfig.from_pretrained(model_path)
+
+if unreleased_model_name:
+    model_name_lower = unreleased_model_name.lower()
+    unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
+    class_name = f"{unreleased_model_name}ForCausalLM"
+    print(f"Importing unreleased model module: {unreleased_module_path}")
+
+    try:
+        model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
+        model = model_class.from_pretrained(model_path)  # Note: from_pretrained, not fromPretrained
+    except (ImportError, AttributeError) as e:
+        print(f"Failed to import or load model: {e}")
+        exit(1)
+else:
+    model = AutoModelForCausalLM.from_pretrained(model_path)
+
+model_name = os.path.basename(model_path)
+# Printing the Model class to allow for easier debugging. This can be useful
+# when working with models that have not been publicly released yet and this
+# migth require that the concrete class is imported and used directly instead
+# of using AutoModelForCausalLM.
+print(f"Model class: {model.__class__.__name__}")
+
+prompt = "Hello, my name is"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+print(f"Input tokens: {input_ids}")
+print(f"Input text: {repr(prompt)}")
+print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
+
+with torch.no_grad():
+    outputs = model(input_ids)
+    logits = outputs.logits
+
+    # Extract logits for the last token (next token prediction)
+    last_logits = logits[0, -1, :].cpu().numpy()
+
+    print(f"Logits shape: {logits.shape}")
+    print(f"Last token logits shape: {last_logits.shape}")
+    print(f"Vocab size: {len(last_logits)}")
+
+    data_dir = Path("data")
+    data_dir.mkdir(exist_ok=True)
+    bin_filename = data_dir / f"pytorch-{model_name}.bin"
+    txt_filename = data_dir / f"pytorch-{model_name}.txt"
+
+    # Save to file for comparison
+    last_logits.astype(np.float32).tofile(bin_filename)
+
+    # Also save as text file for easy inspection
+    with open(txt_filename, "w") as f:
+        for i, logit in enumerate(last_logits):
+            f.write(f"{i}: {logit:.6f}\n")
+
+    # Print some sample logits for quick verification
+    print(f"First 10 logits: {last_logits[:10]}")
+    print(f"Last 10 logits: {last_logits[-10:]}")
+
+    # Show top 5 predicted tokens
+    top_indices = np.argsort(last_logits)[-5:][::-1]
+    print("Top 5 predictions:")
+    for idx in top_indices:
+        token = tokenizer.decode([idx])
+        print(f"  Token {idx} ({repr(token)}): {last_logits[idx]:.6f}")
+
+    print(f"Saved bin logits to: {bin_filename}")
+    print(f"Saved txt logist to: {txt_filename}")
diff --git a/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
new file mode 100755
index 0000000000..35b5d71984
--- /dev/null
+++ b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
@@ -0,0 +1,42 @@
+#/bin/bash
+
+set -e
+
+MODEL_PATH="${1:-"$EMBEDDING_MODEL_PATH"}"
+MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
+
+if [ -t 0 ]; then
+    CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
+else
+    # Process piped JSON data and convert to binary (matching logits.cpp format)
+    TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
+    python3 -c "
+import json
+import sys
+import struct
+
+data = json.load(sys.stdin)
+
+# Flatten all embeddings completely
+flattened = []
+for item in data:
+    embedding = item['embedding']
+    for token_embedding in embedding:
+        flattened.extend(token_embedding)
+
+print(f'Total embedding values: {len(flattened)}', file=sys.stderr)
+
+# Write as binary floats - matches logitc.cpp fwrite format
+with open('$TEMP_FILE', 'wb') as f:
+    for value in flattened:
+        f.write(struct.pack('f', value))
+"
+    CPP_EMBEDDINGS="$TEMP_FILE"
+    trap "rm -f $TEMP_FILE" EXIT
+fi
+
+python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
+    --python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
+    --cpp-embeddings $CPP_EMBEDDINGS \
+    --prompt "Hello world today"
+
diff --git a/examples/model-conversion/scripts/embedding/convert-model.sh b/examples/model-conversion/scripts/embedding/convert-model.sh
new file mode 100755
index 0000000000..0609e35357
--- /dev/null
+++ b/examples/model-conversion/scripts/embedding/convert-model.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+set -e
+
+MODEL_NAME="${MODEL_NAME:-$(basename "$EMBEDDING_MODEL_PATH")}"
+OUTPUT_DIR="${OUTPUT_DIR:-../../models}"
+TYPE="${OUTTYPE:-f16}"
+METADATA_OVERRIDE="${METADATA_OVERRIDE:-}"
+CONVERTED_MODEL="${OUTPUT_DIR}/${MODEL_NAME}.gguf"
+
+echo "Model path: ${EMBEDDING_MODEL_PATH}"
+echo "Model name: ${MODEL_NAME}"
+echo "Data  type: ${TYPE}"
+echo "Converted model path:: ${CONVERTED_MODEL}"
+python ../../convert_hf_to_gguf.py --verbose \
+    ${EMBEDDING_MODEL_PATH} \
+    --outfile ${CONVERTED_MODEL} \
+    --outtype ${TYPE}
+
+echo ""
+echo "The environment variable CONVERTED_EMBEDDING MODEL can be set to this path using:"
+echo "export CONVERTED_EMBEDDING_MODEL=$(realpath ${CONVERTED_MODEL})"
diff --git a/examples/model-conversion/scripts/embedding/run-converted-model.sh b/examples/model-conversion/scripts/embedding/run-converted-model.sh
new file mode 100755
index 0000000000..5896090411
--- /dev/null
+++ b/examples/model-conversion/scripts/embedding/run-converted-model.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+set -e
+
+# First try command line argument, then environment variable, then file
+CONVERTED_MODEL="${1:-"$CONVERTED_EMBEDDING_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_EMBEDDING_MODEL environment variable" >&2
+    exit 1
+fi
+
+echo $CONVERTED_MODEL
+
+cmake --build ../../build --target llama-logits -j8
+
+../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "Hello world today"
diff --git a/examples/model-conversion/scripts/embedding/run-original-model.py b/examples/model-conversion/scripts/embedding/run-original-model.py
new file mode 100755
index 0000000000..b9db0b893f
--- /dev/null
+++ b/examples/model-conversion/scripts/embedding/run-original-model.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import numpy as np
+import importlib
+from pathlib import Path
+
+from transformers import AutoTokenizer, AutoConfig, AutoModel
+import torch
+
+unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
+
+parser = argparse.ArgumentParser(description='Process model with specified path')
+parser.add_argument('--model-path', '-m', help='Path to the model')
+args = parser.parse_args()
+
+model_path = os.environ.get('EMBEDDING_MODEL_PATH', args.model_path)
+if model_path is None:
+    parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable")
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+if unreleased_model_name:
+    model_name_lower = unreleased_model_name.lower()
+    unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
+    class_name = f"{unreleased_model_name}Model"
+    print(f"Importing unreleased model module: {unreleased_module_path}")
+
+    try:
+        model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
+        model = model_class.from_pretrained(model_path)  # Note: from_pretrained, not fromPretrained
+    except (ImportError, AttributeError) as e:
+        print(f"Failed to import or load model: {e}")
+        exit(1)
+else:
+    model = AutoModel.from_pretrained(model_path)
+print(f"Model class: {type(model)}")
+#print(f"Model file: {type(model).__module__}")
+config = AutoConfig.from_pretrained(model_path)
+
+model_name = os.path.basename(model_path)
+
+texts = [ "Hello world today" ]
+
+encoded = tokenizer(
+    texts,
+    padding=True,
+    truncation=True,
+    return_tensors="pt"
+)
+
+tokens = encoded['input_ids'][0]
+token_strings = tokenizer.convert_ids_to_tokens(tokens)
+for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)):
+    print(f"{token_id:6d} -> '{token_str}'")
+
+with torch.no_grad():
+    outputs = model(**encoded)
+    hidden_states = outputs.last_hidden_state  # Shape: [batch_size, seq_len, hidden_size]
+
+    # Extract embeddings for each token (matching LLAMA_POOLING_TYPE_NONE behavior)
+    all_embeddings = hidden_states[0].cpu().numpy()  # Shape: [seq_len, hidden_size]
+
+    print(f"Hidden states shape: {hidden_states.shape}")
+    print(f"All embeddings shape: {all_embeddings.shape}")
+    print(f"Embedding dimension: {all_embeddings.shape[1]}")
+
+    # Print embeddings exactly like embedding.cpp does for LLAMA_POOLING_TYPE_NONE
+    n_embd = all_embeddings.shape[1]
+    n_embd_count = all_embeddings.shape[0]
+
+    print()  # Empty line to match C++ output
+
+    for j in range(n_embd_count):
+        embedding = all_embeddings[j]
+        print(f"embedding {j}: ", end="")
+
+        # Print first 3 values
+        for i in range(min(3, n_embd)):
+            print(f"{embedding[i]:9.6f} ", end="")
+
+        print(" ... ", end="")
+
+        # Print last 3 values
+        for i in range(n_embd - 3, n_embd):
+            print(f"{embedding[i]:9.6f} ", end="")
+
+        print()  # New line
+
+    print()  # Final empty line to match C++ output
+
+    data_dir = Path("data")
+    data_dir.mkdir(exist_ok=True)
+    bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
+    txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
+
+    # Save all embeddings flattened (matching what embedding.cpp would save if it did)
+    flattened_embeddings = all_embeddings.flatten()
+    flattened_embeddings.astype(np.float32).tofile(bin_filename)
+
+    with open(txt_filename, "w") as f:
+        f.write(f"# Model class: {model_name}\n")
+        f.write(f"# Tokens: {token_strings}\n")
+        f.write(f"# Shape: {all_embeddings.shape}\n")
+        f.write(f"# n_embd_count: {n_embd_count}, n_embd: {n_embd}\n\n")
+
+        for j in range(n_embd_count):
+            f.write(f"# Token {j} ({token_strings[j]}):\n")
+            for i, value in enumerate(all_embeddings[j]):
+                f.write(f"{j}_{i}: {value:.6f}\n")
+            f.write("\n")
+    print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} tokens × {n_embd} dimensions)")
+    print("")
+    print(f"Saved bin embeddings to: {bin_filename}")
+    print(f"Saved txt embeddings to: {txt_filename}")
diff --git a/examples/model-conversion/scripts/readme.md.template b/examples/model-conversion/scripts/readme.md.template
new file mode 100644
index 0000000000..87800a1b93
--- /dev/null
+++ b/examples/model-conversion/scripts/readme.md.template
@@ -0,0 +1,13 @@
+---
+base_model:
+- {base_model}
+---
+# {model_name} GGUF
+
+Recommended way to run this model:
+
+```sh
+llama-server -hf {namespace}/{model_name}-GGUF -c 0 -fa
+```
+
+Then, access http://localhost:8080
diff --git a/examples/model-conversion/scripts/utils/check-nmse.py b/examples/model-conversion/scripts/utils/check-nmse.py
new file mode 100755
index 0000000000..196a6210f0
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/check-nmse.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import sys
+import os
+import argparse
+from pathlib import Path
+
+def calculate_nmse(reference, test):
+    mse = np.mean((test - reference) ** 2)
+    ref_var = np.var(reference)
+    if ref_var == 0:
+        nmse = float('inf') if mse > 0 else 0.0
+        return mse, mse, ref_var
+
+    nmse = mse / ref_var
+
+    return nmse, mse, ref_var
+
+def load_logits(file_path):
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    if file_path.suffix == '.npy':
+        return np.load(file_path)
+    elif file_path.suffix == '.bin':
+        return np.fromfile(file_path, dtype=np.float32)
+    else:
+        # Try to load as text file
+        try:
+            # If it has index format "0: value", extract just values
+            data = []
+            with open(file_path, 'r') as f:
+                for line in f:
+                    if ':' in line:
+                        # Format: "index: value"
+                        value = float(line.split(':')[1].strip())
+                    else:
+                        # Just the value
+                        value = float(line.strip())
+                    data.append(value)
+            return np.array(data, dtype=np.float32)
+        except:
+            return np.loadtxt(file_path, dtype=np.float32)
+
+def interpret_nmse(nmse):
+    """Provide interpretation of NMSE value"""
+    if nmse == 0:
+        return "Perfect match", "🎉"
+    elif nmse < 1e-6:
+        return "Essentially identical", "✅"
+    elif nmse < 1e-4:
+        return "Excellent match", "✅"
+    elif nmse < 1e-3:
+        return "Very good match", "👍"
+    elif nmse < 1e-2:
+        return "Good match", "👍"
+    elif nmse < 0.1:
+        return "Acceptable match", "⚠️"
+    elif nmse < 1.0:
+        return "Poor match", "❌"
+    else:
+        return "Very poor match (worse than noise)", "❌"
+
+def main():
+    parser = argparse.ArgumentParser(description='Validate model logits')
+    parser.add_argument('-m', '--model-path', required=True,  help='Path to the model directory')
+    args = parser.parse_args()
+
+    model_name = os.path.splitext(os.path.basename(args.model_path))[0]
+    data_dir = Path("data")
+
+    pytorch_file = data_dir / f"pytorch-{model_name}.bin"
+    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    print(f"Model name: {model_name}")
+    print(f"PyTorch logits file: {pytorch_file}")
+    print(f"llama.cpp logits file: {llamacpp_file}")
+
+    reference_file = pytorch_file
+    test_file = llamacpp_file
+
+    print("📊 NMSE Check for Model Comparison")
+    print("=" * 50)
+    print(f"Reference (ground truth): {reference_file}")
+    print(f"Test (to evaluate):       {test_file}")
+    print()
+
+    try:
+        print("Loading reference logits...")
+        reference = load_logits(reference_file)
+        print(f"  Shape: {reference.shape}, Type: {reference.dtype}")
+
+        print("Loading test logits...")
+        test = load_logits(test_file)
+        print(f"  Shape: {test.shape}, Type: {test.dtype}")
+
+        # Check shapes match
+        if reference.shape != test.shape:
+            print(f"\n❌ Error: Shape mismatch!")
+            print(f"  Reference: {reference.shape}")
+            print(f"  Test: {test.shape}")
+            sys.exit(1)
+
+        print(f"\n✅ Shapes match: {reference.shape}")
+
+        nmse, mse, ref_var = calculate_nmse(reference, test)
+
+        # Additional metrics
+        max_abs_error = np.max(np.abs(test - reference))
+        mean_abs_error = np.mean(np.abs(test - reference))
+
+        # Results
+        print(f"\n📈 METRICS")
+        print("=" * 30)
+        print(f"MSE (Mean Squared Error):     {mse:.6e}")
+        print(f"Reference Variance:           {ref_var:.6e}")
+        print(f"NMSE:                         {nmse:.6e}")
+        print(f"Max Absolute Error:           {max_abs_error:.6f}")
+        print(f"Mean Absolute Error:          {mean_abs_error:.6f}")
+
+        # NMSE in dB (common in signal processing)
+        if nmse > 0:
+            nmse_db = 10 * np.log10(nmse)
+            print(f"NMSE (dB):                    {nmse_db:.2f} dB")
+
+        # Interpretation
+        interpretation, emoji = interpret_nmse(nmse)
+        print(f"\n🎯 INTERPRETATION")
+        print("=" * 30)
+        print(f"{emoji} {interpretation}")
+
+        # Detailed guidance
+        print(f"\n📋 GUIDANCE")
+        print("=" * 30)
+        if nmse < 1e-3:
+            print("✅ EXCELLENT: Your GGML conversion is working very well!")
+            print("   The differences are negligible for practical use.")
+        elif nmse < 1e-2:
+            print("👍 GOOD: Your GGML conversion is working well.")
+            print("   Small differences are likely due to precision/quantization.")
+        elif nmse < 0.1:
+            print("⚠️  ACCEPTABLE: Conversion is working but with some differences.")
+            print("   Check if you're using quantization (Q4, Q8, etc.)")
+            print("   Test generation quality to see if it's acceptable.")
+        else:
+            print("❌ PROBLEMATIC: Large differences detected.")
+            print("   Check your conversion process for potential issues.")
+            print("   Verify you're using the same model weights.")
+
+        # NMSE benchmarks
+        print(f"\n📚 NMSE BENCHMARKS")
+        print("=" * 30)
+        print("< 1e-6:  Essentially identical")
+        print("< 1e-4:  Excellent (typical for good conversions)")
+        print("< 1e-3:  Very good")
+        print("< 1e-2:  Good (acceptable for most use cases)")
+        print("< 0.1:   Acceptable (may need verification)")
+        print("> 1.0:   Poor (worse than random)")
+
+        # Exit code based on NMSE
+        if nmse < 1e-2:
+            print(f"\n✅ RESULT: PASS (NMSE = {nmse:.2e})")
+            sys.exit(0)
+        else:
+            print(f"\n❌ RESULT: NEEDS REVIEW (NMSE = {nmse:.2e})")
+            sys.exit(1)
+
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/model-conversion/scripts/utils/create-collection-add-model.sh b/examples/model-conversion/scripts/utils/create-collection-add-model.sh
new file mode 100644
index 0000000000..4809da6cb6
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/create-collection-add-model.sh
@@ -0,0 +1,6 @@
+
+COLLECTION_SLUG=$(python ./create_collection.py --return-slug)
+echo "Created collection: $COLLECTION_SLUG"
+
+# Use it in the next command
+python add_model_to_collection.py "$COLLECTION_SLUG" "username/my-model"
diff --git a/examples/model-conversion/scripts/utils/hf-add-model-to-collection.py b/examples/model-conversion/scripts/utils/hf-add-model-to-collection.py
new file mode 100755
index 0000000000..7e38af3c13
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/hf-add-model-to-collection.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+
+from huggingface_hub import HfApi
+import argparse
+import sys
+
+def add_model_to_collection(collection_slug, model_id, note=""):
+    """
+    Add a model to an existing collection
+
+    Args:
+        collection_slug: The slug of the collection (e.g., "username/collection-name-12345")
+        model_id: The model repository ID (e.g., "username/model-name")
+        note: Optional note about the model
+
+    Returns:
+        True if successful, False if failed
+    """
+
+    # Initialize API
+    api = HfApi()
+
+    try:
+        user_info = api.whoami()
+        print(f"✅ Authenticated as: {user_info['name']}")
+
+        # Verify the model exists
+        print(f"🔍 Checking if model exists: {model_id}")
+        try:
+            model_info = api.model_info(model_id)
+        except Exception as e:
+            print(f"❌ Model not found or not accessible: {model_id}")
+            print(f"Error: {e}")
+            return False
+
+        print(f"📚 Adding model to collection...")
+        api.add_collection_item(
+            collection_slug=collection_slug,
+            item_id=model_id,
+            item_type="model",
+            note=note
+        )
+
+        print(f"✅ Model added to collection successfully!")
+        print(f"🔗 Collection URL: https://huggingface.co/collections/{collection_slug}")
+
+        return True
+
+    except Exception as e:
+        print(f"❌ Error adding model to collection: {e}")
+        return False
+
+def main():
+    # This script requires that the environment variable HF_TOKEN is set with your
+    # Hugging Face API token.
+    api = HfApi()
+
+    parser = argparse.ArgumentParser(description='Add model to a Huggingface Collection')
+    parser.add_argument('--collection', '-c', help='The collection slug username/collection-hash', required=True)
+    parser.add_argument('--model', '-m', help='The model to add to the Collection', required=True)
+    parser.add_argument('--note', '-n', help='An optional note/description', required=False)
+    args = parser.parse_args()
+
+    collection = args.collection
+    model = args.model
+    note = args.note
+
+    success = add_model_to_collection(
+        collection_slug=collection,
+        model_id=model,
+        note=note
+    )
+
+    if success:
+        print("\n🎉 Model added successfully!")
+    else:
+        print("\n❌ Failed to add model to collection")
+        sys.exit(1)
+if __name__ == "__main__":
+    main()
diff --git a/examples/model-conversion/scripts/utils/hf-create-collection.py b/examples/model-conversion/scripts/utils/hf-create-collection.py
new file mode 100755
index 0000000000..e0fa60af1a
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/hf-create-collection.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+
+from huggingface_hub import HfApi
+import argparse
+import os
+import sys
+
+
+def create_collection(title, description, private=False, namespace=None, return_slug=False):
+    """
+    Create a new collection on Hugging Face
+
+    Args:
+        title: Collection title
+        description: Collection description
+        private: Whether the collection should be private (default: False)
+        namespace: Optional namespace (defaults to your username)
+
+    Returns:
+        Collection object if successful, None if failed
+    """
+
+    # Check if HF_TOKEN is available
+    token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
+    if not token:
+        print("❌ No HF_TOKEN or HUGGINGFACE_HUB_TOKEN found in environment variables")
+        print("Please set your Hugging Face token as an environment variable")
+        return None
+
+    # Initialize API
+    api = HfApi()
+
+    try:
+        # Test authentication first
+        user_info = api.whoami()
+        if not return_slug:
+            print(f"✅ Authenticated as: {user_info['name']}")
+
+        # Create the collection
+        if not return_slug:
+            print(f"📚 Creating collection: '{title}'...")
+        collection = api.create_collection(
+            title=title,
+            description=description,
+            private=private,
+            namespace=namespace
+        )
+
+        if not return_slug:
+            print(f"✅ Collection created successfully!")
+            print(f"📋 Collection slug: {collection.slug}")
+            print(f"🔗 Collection URL: https://huggingface.co/collections/{collection.slug}")
+
+        return collection
+
+    except Exception as e:
+        print(f"❌ Error creating collection: {e}")
+        return None
+
+def main():
+    # This script requires that the environment variable HF_TOKEN is set with your
+    # Hugging Face API token.
+    api = HfApi()
+
+    parser = argparse.ArgumentParser(description='Create a Huggingface Collection')
+    parser.add_argument('--name', '-n', help='The name/title of the Collection', required=True)
+    parser.add_argument('--description', '-d', help='The description for the Collection', required=True)
+    parser.add_argument('--namespace', '-ns', help='The namespace to add the Collection to', required=True)
+    parser.add_argument('--private', '-p', help='Create a private Collection', action='store_true')  # Fixed
+    parser.add_argument('--return-slug', '-s', help='Only output the collection slug', action='store_true')  # Fixed
+
+    args = parser.parse_args()
+
+    name = args.name
+    description = args.description
+    private = args.private
+    namespace = args.namespace
+    return_slug = args.return_slug
+
+    if not return_slug:
+        print("🚀 Creating Hugging Face Collection")
+        print(f"Title: {name}")
+        print(f"Description: {description}")
+        print(f"Namespace: {namespace}")
+        print(f"Private: {private}")
+
+    collection = create_collection(
+        title=name,
+        description=description,
+        private=private,
+        namespace=namespace,
+        return_slug=return_slug
+    )
+
+    if collection:
+        if return_slug:
+            print(collection.slug)
+        else:
+            print("\n🎉 Collection created successfully!")
+            print(f"Use this slug to add models: {collection.slug}")
+    else:
+        print("\n❌ Failed to create collection")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/model-conversion/scripts/utils/hf-create-model.py b/examples/model-conversion/scripts/utils/hf-create-model.py
new file mode 100755
index 0000000000..09bb8511ef
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/hf-create-model.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+
+from huggingface_hub import HfApi
+import argparse
+
+# This script requires that the environment variable HF_TOKEN is set with your
+# Hugging Face API token.
+api = HfApi()
+
+def load_template_and_substitute(template_path, **kwargs):
+    try:
+        with open(template_path, 'r', encoding='utf-8') as f:
+            template_content = f.read()
+
+        return template_content.format(**kwargs)
+    except FileNotFoundError:
+        print(f"Template file '{template_path}' not found!")
+        return None
+    except KeyError as e:
+        print(f"Missing template variable: {e}")
+        return None
+
+parser = argparse.ArgumentParser(description='Create a new Hugging Face model repository')
+parser.add_argument('--model-name', '-m', help='Name for the model', required=True)
+parser.add_argument('--namespace', '-ns', help='Namespace to add the model to', required=True)
+parser.add_argument('--org-base-model', '-b', help='Original Base model name', default="")
+parser.add_argument('--no-card', action='store_true', help='Skip creating model card')
+parser.add_argument('--private', '-p', action='store_true', help='Create private model')
+
+args = parser.parse_args()
+
+repo_id = f"{args.namespace}/{args.model_name}-GGUF"
+print("Repository ID: ", repo_id)
+
+repo_url = api.create_repo(
+    repo_id=repo_id,
+    repo_type="model",
+    private=args.private,
+    exist_ok=False
+)
+
+if not args.no_card:
+    template_path = "scripts/readme.md.template"
+    model_card_content = load_template_and_substitute(
+        template_path,
+        model_name=args.model_name,
+        namespace=args.namespace,
+        base_model=args.org_base_model,
+    )
+
+    if model_card_content:
+        api.upload_file(
+            path_or_fileobj=model_card_content.encode('utf-8'),
+            path_in_repo="README.md",
+            repo_id=repo_id
+        )
+        print("Model card created successfully.")
+    else:
+        print("Failed to create model card.")
+
+print(f"Repository created: {repo_url}")
+
+
diff --git a/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py b/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
new file mode 100755
index 0000000000..15ccb1150e
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+
+from huggingface_hub import HfApi
+import argparse
+import os
+
+def upload_gguf_file(local_file_path, repo_id, filename_in_repo=None):
+    """
+    Upload a GGUF file to a Hugging Face model repository
+
+    Args:
+        local_file_path: Path to your local GGUF file
+        repo_id: Your repository ID (e.g., "username/model-name")
+        filename_in_repo: Optional custom name for the file in the repo
+    """
+
+    if not os.path.exists(local_file_path):
+        print(f"❌ File not found: {local_file_path}")
+        return False
+
+    if filename_in_repo is None:
+        filename_in_repo = os.path.basename(local_file_path)
+
+    if filename_in_repo is None or filename_in_repo == "":
+        filename_in_repo = os.path.basename(local_file_path)
+
+    print(f"📤 Uploading {local_file_path} to {repo_id}/{filename_in_repo}")
+
+    api = HfApi()
+
+    try:
+        api.upload_file(
+            path_or_fileobj=local_file_path,
+            path_in_repo=filename_in_repo,
+            repo_id=repo_id,
+            repo_type="model",
+            commit_message=f"Upload {filename_in_repo}"
+        )
+
+        print("✅ Upload successful!")
+        print(f"🔗 File available at: https://huggingface.co/{repo_id}/blob/main/{filename_in_repo}")
+        return True
+
+    except Exception as e:
+        print(f"❌ Upload failed: {e}")
+        return False
+
+# This script requires that the environment variable HF_TOKEN is set with your
+# Hugging Face API token.
+api = HfApi()
+
+parser = argparse.ArgumentParser(description='Upload a GGUF model to a Huggingface model repository')
+parser.add_argument('--gguf-model-path', '-m', help='The GGUF model file to upload', required=True)
+parser.add_argument('--repo-id', '-r', help='The repository to upload to', required=True)
+parser.add_argument('--name', '-o', help='The name in the model repository', required=False)
+args = parser.parse_args()
+
+upload_gguf_file(args.gguf_model_path, args.repo_id, args.name)
diff --git a/examples/model-conversion/scripts/utils/inspect-converted-model.sh b/examples/model-conversion/scripts/utils/inspect-converted-model.sh
new file mode 100755
index 0000000000..e5b9324542
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/inspect-converted-model.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# First try command line argument, then environment variable, then file
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+../../gguf-py/gguf/scripts/gguf_dump.py $CONVERTED_MODEL
diff --git a/examples/model-conversion/scripts/utils/inspect-org-model.py b/examples/model-conversion/scripts/utils/inspect-org-model.py
new file mode 100755
index 0000000000..bc6f45a5fb
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/inspect-org-model.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import json
+from safetensors import safe_open
+from collections import defaultdict
+
+parser = argparse.ArgumentParser(description='Process model with specified path')
+parser.add_argument('--model-path', '-m', help='Path to the model')
+args = parser.parse_args()
+
+model_path = os.environ.get('MODEL_PATH', args.model_path)
+if model_path is None:
+    parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
+
+# Check if there's an index file (multi-file model)
+index_path = os.path.join(model_path, "model.safetensors.index.json")
+single_file_path = os.path.join(model_path, "model.safetensors")
+
+if os.path.exists(index_path):
+    # Multi-file model
+    print("Multi-file model detected")
+
+    with open(index_path, 'r') as f:
+        index_data = json.load(f)
+
+    # Get the weight map (tensor_name -> file_name)
+    weight_map = index_data.get("weight_map", {})
+
+    # Group tensors by file for efficient processing
+    file_tensors = defaultdict(list)
+    for tensor_name, file_name in weight_map.items():
+        file_tensors[file_name].append(tensor_name)
+
+    print("Tensors in model:")
+
+    # Process each shard file
+    for file_name, tensor_names in file_tensors.items():
+        file_path = os.path.join(model_path, file_name)
+        print(f"\n--- From {file_name} ---")
+
+        with safe_open(file_path, framework="pt") as f:
+            for tensor_name in sorted(tensor_names):
+                tensor = f.get_tensor(tensor_name)
+                print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
+
+elif os.path.exists(single_file_path):
+    # Single file model (original behavior)
+    print("Single-file model detected")
+
+    with safe_open(single_file_path, framework="pt") as f:
+        keys = f.keys()
+        print("Tensors in model:")
+        for key in sorted(keys):
+            tensor = f.get_tensor(key)
+            print(f"- {key} : shape = {tensor.shape}, dtype = {tensor.dtype}")
+
+else:
+    print(f"Error: Neither 'model.safetensors.index.json' nor 'model.safetensors' found in {model_path}")
+    print("Available files:")
+    if os.path.exists(model_path):
+        for item in sorted(os.listdir(model_path)):
+            print(f"  {item}")
+    else:
+        print(f"  Directory {model_path} does not exist")
+    exit(1)
diff --git a/examples/model-conversion/scripts/utils/perplexity-gen.sh b/examples/model-conversion/scripts/utils/perplexity-gen.sh
new file mode 100755
index 0000000000..3db0b3fd27
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/perplexity-gen.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+set -e
+
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+# Check if data/wikitext-2-raw directory exists
+if [ ! -d "ppl/wikitext-2-raw" ]; then
+    echo "ppl/wikitext-2-raw directory does not exist. Downloading..." >&2
+    mkdir -p ppl
+    pushd ppl
+    ./../../../scripts/get-wikitext-2.sh
+    popd
+fi
+
+mkdir -p ppl
+OUTPUTFILE="ppl/$(basename $CONVERTED_MODEL).kld"
+echo "Model: $CONVERTED_MODEL"
+
+cmake --build ../../build --target llama-perplexity -j8
+
+../.././build/bin/llama-perplexity -m $CONVERTED_MODEL \
+    -f ppl/wikitext-2-raw/wiki.test.raw \
+    --kl-divergence-base $OUTPUTFILE
+
+echo "Generated logits in $OUTPUTFILE"
+
diff --git a/examples/model-conversion/scripts/utils/perplexity-run-simple.sh b/examples/model-conversion/scripts/utils/perplexity-run-simple.sh
new file mode 100755
index 0000000000..69b3438f59
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/perplexity-run-simple.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+set -e
+
+QUANTIZED_MODEL="${1:-"$QUANTIZED_MODEL"}"
+
+if [ -z "$QUANTIZED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. QUANTIZED_MODEL environment variable" >&2
+    exit 1
+fi
+
+# Check if data/wikitext-2-raw directory exists
+if [ ! -d "ppl/wikitext-2-raw" ]; then
+    echo "ppl/wikitext-2-raw directory does not exist. Downloading..." >&2
+    mkdir -p ppl
+    pushd ppl
+    ./../../../scripts/get-wikitext-2.sh
+    popd
+fi
+
+cmake --build ../../build --target llama-perplexity -j8
+
+../.././build/bin/llama-perplexity -m $QUANTIZED_MODEL -f ppl/wikitext-2-raw/wiki.test.raw
+
+
diff --git a/examples/model-conversion/scripts/utils/perplexity-run.sh b/examples/model-conversion/scripts/utils/perplexity-run.sh
new file mode 100755
index 0000000000..3bce7c8472
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/perplexity-run.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+set -e
+
+QUANTIZED_MODEL="${1:-"$QUANTIZED_MODEL"}"
+LOGITS_FILE="${1:-"$LOGITS_FILE"}"
+
+if [ -z "$QUANTIZED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. QUANTIZED_MODEL environment variable" >&2
+    exit 1
+fi
+
+if [ ! -f ${LOGITS_FILE} ]; then
+    echo "Error: logits file '${LOGITS_FILE} was not found"
+    echo "Did you run the perplexity-gen.sh script?"
+    exit 1
+fi
+
+echo "Model: $QUANTIZED_MODEL"
+echo "Data file: $LOGITS_FILE"
+
+cmake --build ../../build --target llama-perplexity -j8
+
+../.././build/bin/llama-perplexity -m $QUANTIZED_MODEL \
+    --kl-divergence-base $LOGITS_FILE \
+    --kl-divergence
diff --git a/examples/model-conversion/scripts/utils/quantize.sh b/examples/model-conversion/scripts/utils/quantize.sh
new file mode 100755
index 0000000000..bcb8775754
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/quantize.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+set -e
+
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
+QUANTIZED_MODEL=$CONVERTED_MODEL
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+echo $CONVERTED_MODEL
+
+# Process the quantized model filename
+if [[ "$QUANTIZED_MODEL" == *.gguf ]]; then
+    # Remove .gguf suffix, add quantized type, then add .gguf back
+    BASE_NAME="${QUANTIZED_MODEL%.gguf}"
+    QUANTIZED_MODEL="${BASE_NAME}-${QUANTIZED_TYPE}.gguf"
+else
+    echo "Error: QUANTIZED_MODEL must end with .gguf extension" >&2
+    exit 1
+fi
+
+
+cmake --build ../../build --target llama-quantize -j8
+
+../../build/bin/llama-quantize $CONVERTED_MODEL $QUANTIZED_MODEL $QUANTIZED_TYPE
+
+echo "Quantized model saved to: $QUANTIZED_MODEL"
diff --git a/examples/model-conversion/scripts/utils/run-embedding-server.sh b/examples/model-conversion/scripts/utils/run-embedding-server.sh
new file mode 100755
index 0000000000..828fc47069
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/run-embedding-server.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+set -e
+#
+# First try command line argument, then environment variable, then file
+CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+
+# Final check if we have a model path
+if [ -z "$CONVERTED_MODEL" ]; then
+    echo "Error: Model path must be provided either as:" >&2
+    echo "  1. Command line argument" >&2
+    echo "  2. CONVERTED_MODEL environment variable" >&2
+    exit 1
+fi
+
+echo $CONVERTED_MODEL
+
+cmake --build ../../build --target llama-server
+
+../../build/bin/llama-server -m $CONVERTED_MODEL \
+    --embedding \
+    --pooling none
diff --git a/examples/model-conversion/scripts/utils/semantic_check.py b/examples/model-conversion/scripts/utils/semantic_check.py
new file mode 100644
index 0000000000..d211048097
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/semantic_check.py
@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import argparse
+import os
+import importlib
+
+from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, AutoModel
+
+unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
+
+def cosine_similarity(a, b=None):
+    a = np.asarray(a)
+    if b is None:
+        b = a
+    else:
+        b = np.asarray(b)
+
+    if a.ndim == 1:
+        a = a.reshape(1, -1)
+    if b.ndim == 1:
+        b = b.reshape(1, -1)
+
+    a_norms = np.linalg.norm(a, axis=1, keepdims=True)
+    b_norms = np.linalg.norm(b, axis=1, keepdims=True)
+
+    a_norms = np.where(a_norms == 0, 1e-8, a_norms)
+    b_norms = np.where(b_norms == 0, 1e-8, b_norms)
+
+    a_normalized = a / a_norms
+    b_normalized = b / b_norms
+
+    # Compute cosine similarity
+    return np.dot(a_normalized, b_normalized.T)
+
+def load_embeddings_from_file(filename, n_tokens, n_embd):
+    embeddings = np.fromfile(filename, dtype=np.float32)
+    return embeddings.reshape(n_tokens, n_embd)
+
+def test_single_prompt_similarity(python_emb, cpp_emb, tokens, prompt):
+    np.set_printoptions(suppress=True, precision=6)
+    print("pytorch embeddings:");
+    print(python_emb)
+    print("llama.cpp embeddings:");
+    print(cpp_emb)
+    print(f"\n=== Prompt: '{prompt}' ===")
+    print(f"Tokens: {tokens}")
+    print(f"Embeddings shape: Python {python_emb.shape}, llama.cpp {cpp_emb.shape}")
+
+    n_tokens = len(tokens)
+
+    # 1. Direct embedding comparison
+    print(f"\n1. Raw Embedding Magnitude Comparison:")
+    # Check if the distance of each token embedding from the origin and compare
+    # if the vectors are on the same "sphere". This does not tell us about
+    # direction (meaning of the token embedding), just magnitude.
+    for i in range(n_tokens):
+        py_mag = np.linalg.norm(python_emb[i]) # calculate standard euclidean norm for Python embeddings
+        cpp_mag = np.linalg.norm(cpp_emb[i])   # calculate standard euclidean norm for llama.cpp embeddings
+        ratio = py_mag / cpp_mag if cpp_mag > 0 else float('inf')
+        print(f"   Token {i} ({tokens[i]}): Python={py_mag:.3f}, llama.cpp={cpp_mag:.3f}, ratio={ratio:.3f}")
+
+    # 2. Cosine similarity between tokens within each model
+    # Here we check the direction of token embeddings to see if the have the
+    # same meaning (similarity). This is done by calculating cosine similarity
+    # of a pair of token embeddings within each model.
+    print(f"\n2. Within-Model Token Similarities:")
+    print("   Python model:")
+    for i in range(n_tokens):
+        for j in range(i+1, n_tokens):
+            sim = cosine_similarity([python_emb[i]], [python_emb[j]])[0][0]
+            print(f"     {tokens[i]} ↔ {tokens[j]}: {sim:.4f}")
+
+    print("   llama.cpp model:")
+    for i in range(n_tokens):
+        for j in range(i+1, n_tokens):
+            sim = cosine_similarity([cpp_emb[i]], [cpp_emb[j]])[0][0]
+            print(f"     {tokens[i]} ↔ {tokens[j]}: {sim:.4f}")
+
+    # 3. Cross-model similarity (same token position)
+    print(f"\n3. Cross-Model Same-Token Similarities:")
+    for i in range(n_tokens):
+        sim = cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0]
+        print(f"   Token {i} ({tokens[i]}): {sim:.4f}")
+
+    # 4. Similarity matrix comparison
+    print(f"\n4. Similarity Matrix Differences:")
+    py_sim_matrix = cosine_similarity(python_emb)
+    cpp_sim_matrix = cosine_similarity(cpp_emb)
+    diff_matrix = np.abs(py_sim_matrix - cpp_sim_matrix)
+
+    print(f"   Max difference: {np.max(diff_matrix):.4f}")
+    print(f"   Mean difference: {np.mean(diff_matrix):.4f}")
+    print(f"   RMS difference: {np.sqrt(np.mean(diff_matrix**2)):.4f}")
+
+    return {
+        'cross_model_similarities': [cosine_similarity([python_emb[i]], [cpp_emb[i]])[0][0] for i in range(n_tokens)],
+        'similarity_matrix_diff': diff_matrix,
+        'max_diff': np.max(diff_matrix),
+        'mean_diff': np.mean(diff_matrix),
+        'rms_diff': np.sqrt(np.mean(diff_matrix**2))
+    }
+
+def main():
+    parser = argparse.ArgumentParser(description='Test semantic similarity between Python and llama.cpp embeddings')
+    parser.add_argument('--model-path', '-m', required=True, help='Path to the original Python model')
+    parser.add_argument('--python-embeddings', '-pe', help='Path to pytorch embeddings "logits" binary file')
+    parser.add_argument('--cpp-embeddings', '-ce', help='Path to llama.cpp embeddings "logits" binary file')
+    parser.add_argument('--causal', '-c', default=False, help='if the model is causal (default: false)', action='store_true')
+    parser.add_argument('--prompt', '-p', default='Hello world today', help='Test prompt')
+
+    args = parser.parse_args()
+
+    print("Semantic Similarity Test Between Python and llama.cpp Embedding Models")
+    print("=" * 70)
+
+    # Single prompt detailed comparison
+    print(f"\nTesting with prompt: '{args.prompt}'")
+
+    # Load the python model to get configuration information and also to load the tokenizer.
+    print("Loading model and tokenizer using AutoTokenizer:", args.model_path)
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+    config = AutoConfig.from_pretrained(args.model_path)
+
+    if unreleased_model_name:
+        model_name_lower = unreleased_model_name.lower()
+        unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
+        if args.causal:
+            class_name = f"{unreleased_model_name}ForCausalLM"
+        else:
+            class_name = f"{unreleased_model_name}Model"
+        print(f"Model class: {class_name}")
+        print(f"Importing unreleased model module: {unreleased_module_path}")
+
+        try:
+            model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
+            model = model_class.from_pretrained(args.model_path)
+        except (ImportError, AttributeError) as e:
+            print(f"Failed to import or load model: {e}")
+            exit(1)
+    else:
+        if args.causal:
+            model = AutoModelForCausalLM.from_pretrained(args.model_path)
+        else:
+            model = AutoModel.from_pretrained(args.model_path)
+
+    encoded = tokenizer(args.prompt, return_tensors="pt")
+    tokens = tokenizer.convert_ids_to_tokens(encoded['input_ids'][0])
+    n_tokens = len(tokens)
+    print(f"n_tokens: {n_tokens}");
+    print(f"hidden_size: {model.config.hidden_size}")
+
+    # Load binary embeddings from data directory.
+    llamacpp_embeddings = load_embeddings_from_file(args.cpp_embeddings, n_tokens, model.config.hidden_size)
+    python_embeddings = load_embeddings_from_file(args.python_embeddings, n_tokens, model.config.hidden_size)
+
+    # Run comparison
+    results = test_single_prompt_similarity(python_embeddings, llamacpp_embeddings, tokens, args.prompt)
+
+    # Summary
+    print(f"\n=== SUMMARY ===")
+    avg_cross_sim = np.mean(results['cross_model_similarities'])
+    print(f"Average cross-model similarity: {avg_cross_sim:.4f}")
+    print(f"Similarity matrix RMS difference: {results['rms_diff']:.4f}")
+
+    # Quality assessment
+    if avg_cross_sim > 0.95:
+        print("✅ EXCELLENT: Models are highly similar")
+    elif avg_cross_sim > 0.90:
+        print("✅ VERY GOOD: Models are very similar")
+    elif avg_cross_sim > 0.80:
+        print("⚠️  GOOD: Models are reasonably similar")
+    elif avg_cross_sim > 0.70:
+        print("⚠️  FAIR: Models have some differences")
+    else:
+        print("❌ POOR: Models are significantly different")
+
+if __name__ == "__main__":
+    main()

From 30649cab657d87ac46692332a76e1b75d5d22e00 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 21 Aug 2025 13:42:55 +0300
Subject: [PATCH 133/287] ci : continue file download with wget (#15471)

ggml-ci
---
 ci/run.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/run.sh b/ci/run.sh
index 4d3abf9232..d51ba44385 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -106,7 +106,7 @@ function gg_wget {
     cd $out
 
     # should not re-download if file is the same
-    wget -nv -N $url
+    wget -nv -c -N $url
 
     cd $cwd
 }

From 029bb39eb1e7ae0e5df817ce97931abcd5fa52a9 Mon Sep 17 00:00:00 2001
From: Ali Tariq <alitariq4589@gmail.com>
Date: Thu, 21 Aug 2025 17:52:16 +0500
Subject: [PATCH 134/287] ci : enable RVV1.0 native build (#15386)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Changed the CI file to hw

* Changed the CI file to hw

* Added to sudoers for apt

* Removed the clone command and used checkout

* Added libcurl

* Added gcc-14

* Checking gcc --version

* added gcc-14 symlink

* added CC and C++ variables

* Added the gguf weight

* Changed the weights path

* Added system specification

* Removed white spaces

* ci: Replace Jenkins riscv native build Cloud-V pipeline with GitHub Actions workflow

Removed the legacy .devops/cloud-v-pipeline Jenkins CI configuration and introduced .github/workflows/build-riscv-native.yml for native RISC-V builds using GitHub Actions.

* removed trailing whitespaces

* Added the trigger at PR creation

* Corrected OS name

* Added ccache as setup package

* Added ccache for self-hosted runner

* Added directory for ccache size storage

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Changed the build command and added ccache debug log

* Added the base dir for the ccache

* Re-trigger CI

* Cleanup and refactored ccache steps

* Cleanup and refactored ccache steps

---------

Co-authored-by: Akif Ejaz <akifejaz40@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 .github/workflows/build-riscv-native.yml | 49 ++++++++++++++++--------
 1 file changed, 33 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/build-riscv-native.yml b/.github/workflows/build-riscv-native.yml
index a968f6d519..86dc0ff76e 100644
--- a/.github/workflows/build-riscv-native.yml
+++ b/.github/workflows/build-riscv-native.yml
@@ -1,10 +1,11 @@
 name: Build on RISCV Linux Machine by Cloud-V
 on:
+  pull_request:
   workflow_dispatch:
   workflow_call:
 
 jobs:
-  bianbu-riscv64-native: # Bianbu 2.2
+  debian-13-riscv64-native: # Bianbu 2.2
     runs-on: self-hosted
 
     steps:
@@ -20,24 +21,40 @@ jobs:
                   build-essential \
                   gcc-14-riscv64-linux-gnu \
                   g++-14-riscv64-linux-gnu \
+                  ccache \
                   cmake
 
+      - name: Setup ccache
+        run: |
+          mkdir -p $HOME/.ccache
+          ccache -M 5G -d $HOME/.ccache
+          export CCACHE_LOGFILE=/home/runneruser/ccache_debug/ccache.log
+          export CCACHE_DEBUGDIR="/home/runneruser/ccache_debug"
+          echo "$GITHUB_WORKSPACE"
+          echo "CCACHE_LOGFILE=$CCACHE_LOGFILE" >> $GITHUB_ENV
+          echo "CCACHE_DEBUGDIR=$CCACHE_DEBUGDIR" >> $GITHUB_ENV
+          echo "CCACHE_BASEDIR=$GITHUB_WORKSPACE" >> $GITHUB_ENV
+          echo "CCACHE_DIR=$HOME/.ccache" >> $GITHUB_ENV
+
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_OPENMP=OFF \
-                         -DLLAMA_BUILD_EXAMPLES=ON \
-                         -DLLAMA_BUILD_TOOLS=ON \
-                         -DLLAMA_BUILD_TESTS=OFF \
-                         -DCMAKE_SYSTEM_NAME=Linux \
-                         -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
-                         -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-                         -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
-                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
-                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+          cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_OPENMP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=ON \
+            -DLLAMA_BUILD_TOOLS=ON \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DCMAKE_SYSTEM_NAME=Linux \
+            -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
+            -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
+            -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
+            -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+            -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+            -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+            -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
+            -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+            -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+            -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
           cmake --build build --config Release -j $(nproc)

From ad294df03ff2dccd227c3fee653166f3d78b23a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <jiefu@tencent.com>
Date: Thu, 21 Aug 2025 21:42:34 +0800
Subject: [PATCH 135/287] examples : install torch-cpu for model conversion
 tool/example (#15475)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 examples/model-conversion/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/model-conversion/requirements.txt b/examples/model-conversion/requirements.txt
index e1aa259e94..b8148b269a 100644
--- a/examples/model-conversion/requirements.txt
+++ b/examples/model-conversion/requirements.txt
@@ -1,3 +1,4 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
 torch~=2.6.0
 torchvision~=0.21.0
 transformers~=4.55.0

From 715a6db02ccb16284837885f2c6fab05d8f7a6ee Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 21 Aug 2025 17:00:33 +0300
Subject: [PATCH 136/287] kv-cache : drop the "unified" prefix (#15467)

* kv-cache : drop the "unified" prefix

ggml-ci

* cont : fix comment [no ci]
---
 include/llama.h                               |   4 -
 src/CMakeLists.txt                            |   4 +-
 src/llama-context.cpp                         |   5 -
 src/llama-graph.cpp                           |  54 ++---
 src/llama-graph.h                             |  50 ++---
 ...ified-iswa.cpp => llama-kv-cache-iswa.cpp} |  94 ++++-----
 ...e-unified-iswa.h => llama-kv-cache-iswa.h} |  52 ++---
 ...v-cache-unified.cpp => llama-kv-cache.cpp} | 170 ++++++++--------
 ...ma-kv-cache-unified.h => llama-kv-cache.h} |  40 ++--
 src/llama-kv-cells.h                          |  14 +-
 src/llama-memory-hybrid.cpp                   |  10 +-
 src/llama-memory-hybrid.h                     |  12 +-
 src/llama-memory-recurrent.h                  |   2 +-
 src/llama-memory.h                            |   9 +-
 src/llama-model.cpp                           | 186 +++++++++---------
 15 files changed, 346 insertions(+), 360 deletions(-)
 rename src/{llama-kv-cache-unified-iswa.cpp => llama-kv-cache-iswa.cpp} (62%)
 rename src/{llama-kv-cache-unified-iswa.h => llama-kv-cache-iswa.h} (68%)
 rename src/{llama-kv-cache-unified.cpp => llama-kv-cache.cpp} (90%)
 rename src/{llama-kv-cache-unified.h => llama-kv-cache.h} (92%)

diff --git a/include/llama.h b/include/llama.h
index 135eaf1b65..c465ced4ff 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -64,8 +64,6 @@ extern "C" {
 
     typedef struct llama_memory_i * llama_memory_t;
 
-    struct llama_kv_cache; // DEPRECATED (use llama_memory instead)
-
     typedef int32_t llama_pos;
     typedef int32_t llama_token;
     typedef int32_t llama_seq_id;
@@ -469,8 +467,6 @@ extern "C" {
     LLAMA_API           llama_memory_t   llama_get_memory  (const struct llama_context * ctx);
     LLAMA_API  enum llama_pooling_type   llama_pooling_type(const struct llama_context * ctx); // TODO: rename to llama_get_pooling_type
 
-    DEPRECATED(LLAMA_API struct llama_kv_cache * llama_get_kv_self(struct llama_context * ctx), "use llama_get_memory instead");
-
     LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
     LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
 
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 8f9cd65244..18cfc76564 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -20,8 +20,8 @@ add_library(llama
             llama-hparams.cpp
             llama-impl.cpp
             llama-io.cpp
-            llama-kv-cache-unified.cpp
-            llama-kv-cache-unified-iswa.cpp
+            llama-kv-cache.cpp
+            llama-kv-cache-iswa.cpp
             llama-memory.cpp
             llama-memory-hybrid.cpp
             llama-memory-recurrent.cpp
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 1ebfc88ab6..fb6fbe982c 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2338,11 +2338,6 @@ const llama_model * llama_get_model(const llama_context * ctx) {
     return &ctx->get_model();
 }
 
-// deprecated
-llama_kv_cache * llama_get_kv_self(llama_context * ctx) {
-    return dynamic_cast<llama_kv_cache *>(ctx->get_memory());
-}
-
 // deprecated
 void llama_kv_self_update(llama_context * ctx) {
     ctx->kv_self_update(false);
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 053c72d6dc..04baf03ea0 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -4,8 +4,8 @@
 #include "llama-batch.h"
 #include "llama-cparams.h"
 
-#include "llama-kv-cache-unified.h"
-#include "llama-kv-cache-unified-iswa.h"
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-iswa.h"
 #include "llama-memory-hybrid.h"
 #include "llama-memory-recurrent.h"
 
@@ -277,7 +277,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                 for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) {
                     const llama_seq_id s0 = ubatch->seq_id[i0][0];
 
-                    // TODO: reimplement this like in llama_kv_cache_unified
+                    // TODO: reimplement this like in llama_kv_cache
                     if (s0 == s1 && (!cparams.causal_attn || ubatch->pos[i0] <= ubatch->pos[i1])) {
                         if (hparams.use_alibi) {
                             f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
@@ -294,15 +294,15 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     }
 }
 
-void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
+void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
     mctx->set_input_k_idxs(self_k_idxs, ubatch);
     mctx->set_input_v_idxs(self_v_idxs, ubatch);
 
     mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
 }
 
-bool llm_graph_input_attn_kv_unified::can_reuse(const llm_graph_params & params) {
-    const auto * mctx = static_cast<const llama_kv_cache_unified_context *>(params.mctx);
+bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
 
     this->mctx = mctx;
 
@@ -319,7 +319,7 @@ bool llm_graph_input_attn_kv_unified::can_reuse(const llm_graph_params & params)
     return res;
 }
 
-void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
+void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
     mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
     mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
 
@@ -331,8 +331,8 @@ void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch
     mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
 }
 
-bool llm_graph_input_attn_kv_unified_iswa::can_reuse(const llm_graph_params & params) {
-    const auto * mctx = static_cast<const llama_kv_cache_unified_iswa_context *>(params.mctx);
+bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_iswa_context *>(params.mctx);
 
     this->mctx = mctx;
 
@@ -1186,7 +1186,7 @@ ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
-    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
+    const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);
 
     auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);
 
@@ -1399,17 +1399,17 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-static std::unique_ptr<llm_graph_input_attn_kv_unified> build_attn_inp_kv_unified_impl(
+static std::unique_ptr<llm_graph_input_attn_kv> build_attn_inp_kv_impl(
            ggml_context * ctx0,
      const llama_ubatch & ubatch,
     const llama_hparams & hparams,
     const llama_cparams & cparams,
-    const llama_kv_cache_unified_context * mctx_cur) {
+    const llama_kv_cache_context * mctx_cur) {
 
-    auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
+    auto inp = std::make_unique<llm_graph_input_attn_kv>(hparams, cparams, mctx_cur);
 
     {
-        GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
+        GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA");
 
         const auto n_kv     = mctx_cur->get_n_kv();
         const auto n_tokens = ubatch.n_tokens;
@@ -1427,16 +1427,16 @@ static std::unique_ptr<llm_graph_input_attn_kv_unified> build_attn_inp_kv_unifie
     return inp;
 }
 
-llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
-    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
+llm_graph_input_attn_kv * llm_graph_context::build_attn_inp_kv() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);
 
-    auto inp = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
+    auto inp = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
 
-    return (llm_graph_input_attn_kv_unified *) res->add_input(std::move(inp));
+    return (llm_graph_input_attn_kv *) res->add_input(std::move(inp));
 }
 
 ggml_tensor * llm_graph_context::build_attn(
-        llm_graph_input_attn_kv_unified * inp,
+        llm_graph_input_attn_kv * inp,
         ggml_tensor * wo,
         ggml_tensor * wo_b,
         ggml_tensor * q_cur,
@@ -1488,7 +1488,7 @@ ggml_tensor * llm_graph_context::build_attn(
 }
 
 ggml_tensor * llm_graph_context::build_attn(
-        llm_graph_input_attn_kv_unified_iswa * inp,
+        llm_graph_input_attn_kv_iswa * inp,
         ggml_tensor * wo,
         ggml_tensor * wo_b,
         ggml_tensor * q_cur,
@@ -1513,7 +1513,7 @@ ggml_tensor * llm_graph_context::build_attn(
 }
 
 ggml_tensor * llm_graph_context::build_attn_with_sinks(
-        llm_graph_input_attn_kv_unified_iswa * inp,
+        llm_graph_input_attn_kv_iswa * inp,
         ggml_tensor * wo,
         ggml_tensor * wo_b,
         ggml_tensor * q_cur,
@@ -1636,10 +1636,10 @@ ggml_tensor * llm_graph_context::build_attn(
 // TODO: maybe separate the inner implementation into a separate function
 //       like with the non-sliding window equivalent
 //       once sliding-window hybrid caches are a thing.
-llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
-    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
+llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_iswa_context *>(mctx);
 
-    auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, mctx_cur);
+    auto inp = std::make_unique<llm_graph_input_attn_kv_iswa>(hparams, cparams, mctx_cur);
 
     const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;
 
@@ -1656,7 +1656,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
     }
 
     {
-        GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA");
+        GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache for non-SWA");
 
         const auto n_kv = mctx_cur->get_swa()->get_n_kv();
 
@@ -1669,7 +1669,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
     }
 
-    return (llm_graph_input_attn_kv_unified_iswa *) res->add_input(std::move(inp));
+    return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
 }
 
 ggml_tensor * llm_graph_context::build_rs(
@@ -1792,7 +1792,7 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
 
     auto inp_rs   = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
-    auto inp_attn = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
+    auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
 
     auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 6ff49de3a1..6636fa256f 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -19,8 +19,8 @@ struct llama_cparams;
 
 struct llama_memory_context_i;
 
-class llama_kv_cache_unified_context;
-class llama_kv_cache_unified_iswa_context;
+class llama_kv_cache_context;
+class llama_kv_cache_iswa_context;
 class llama_memory_recurrent_context;
 class llama_memory_hybrid_context;
 
@@ -152,7 +152,7 @@ class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
 public:
     llm_graph_input_pos_bucket_kv(
             const llama_hparams & hparams,
-            const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {}
+            const llama_kv_cache_context * mctx) : hparams(hparams), mctx(mctx) {}
     virtual ~llm_graph_input_pos_bucket_kv() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -161,7 +161,7 @@ public:
 
     const llama_hparams hparams;
 
-    const llama_kv_cache_unified_context * mctx;
+    const llama_kv_cache_context * mctx;
 };
 
 class llm_graph_input_out_ids : public llm_graph_input_i {
@@ -257,17 +257,17 @@ public:
     const llama_cparams cparams;
 };
 
-class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
+class llm_graph_input_attn_kv : public llm_graph_input_i {
 public:
-    llm_graph_input_attn_kv_unified(
+    llm_graph_input_attn_kv(
             const llama_hparams & hparams,
             const llama_cparams & cparams,
-            const llama_kv_cache_unified_context * mctx) :
+            const llama_kv_cache_context * mctx) :
         hparams(hparams),
         cparams(cparams),
         mctx(mctx) {
     }
-    ~llm_graph_input_attn_kv_unified() = default;
+    ~llm_graph_input_attn_kv() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
@@ -290,20 +290,20 @@ public:
     const llama_hparams hparams;
     const llama_cparams cparams;
 
-    const llama_kv_cache_unified_context * mctx;
+    const llama_kv_cache_context * mctx;
 };
 
-class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
+class llm_graph_input_attn_kv_iswa : public llm_graph_input_i {
 public:
-    llm_graph_input_attn_kv_unified_iswa(
+    llm_graph_input_attn_kv_iswa(
             const llama_hparams & hparams,
             const llama_cparams & cparams,
-            const llama_kv_cache_unified_iswa_context * mctx) :
+            const llama_kv_cache_iswa_context * mctx) :
         hparams(hparams),
         cparams(cparams),
         mctx(mctx) {
     }
-    ~llm_graph_input_attn_kv_unified_iswa() = default;
+    ~llm_graph_input_attn_kv_iswa() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
@@ -330,7 +330,7 @@ public:
     const llama_hparams hparams;
     const llama_cparams cparams;
 
-    const llama_kv_cache_unified_iswa_context * mctx;
+    const llama_kv_cache_iswa_context * mctx;
 };
 
 class llm_graph_input_attn_cross : public llm_graph_input_i {
@@ -351,7 +351,7 @@ public:
 class llm_graph_input_mem_hybrid : public llm_graph_input_i {
 public:
     llm_graph_input_mem_hybrid(
-            std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn,
+            std::unique_ptr<llm_graph_input_attn_kv> inp_attn,
             std::unique_ptr<llm_graph_input_rs>              inp_rs,
             const llama_memory_hybrid_context *              mctx) :
         inp_attn(std::move(inp_attn)),
@@ -361,11 +361,11 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
-    std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn;
-    std::unique_ptr<llm_graph_input_rs>              inp_rs;
+    std::unique_ptr<llm_graph_input_attn_kv> inp_attn;
+    std::unique_ptr<llm_graph_input_rs>      inp_rs;
 
-    llm_graph_input_attn_kv_unified * get_attn() const { return inp_attn.get(); }
-    llm_graph_input_rs              * get_recr() const { return inp_rs.get(); }
+    llm_graph_input_attn_kv * get_attn() const { return inp_attn.get(); }
+    llm_graph_input_rs      * get_recr() const { return inp_rs.get(); }
 
     const llama_memory_hybrid_context * mctx;
 };
@@ -703,10 +703,10 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
-    llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
+    llm_graph_input_attn_kv * build_attn_inp_kv() const;
 
     ggml_tensor * build_attn(
-            llm_graph_input_attn_kv_unified * inp,
+            llm_graph_input_attn_kv * inp,
             ggml_tensor * wo,
             ggml_tensor * wo_b,
             ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
@@ -717,11 +717,11 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
-    llm_graph_input_attn_kv_unified_iswa * build_attn_inp_kv_unified_iswa() const;
+    llm_graph_input_attn_kv_iswa * build_attn_inp_kv_iswa() const;
 
     // note: if k_cur or v_cur are not provided, they will not be stored in the memory
     ggml_tensor * build_attn(
-            llm_graph_input_attn_kv_unified_iswa * inp,
+            llm_graph_input_attn_kv_iswa * inp,
             ggml_tensor * wo,
             ggml_tensor * wo_b,
             ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
@@ -734,7 +734,7 @@ struct llm_graph_context {
 
     // TODO: temporary to keep the diff small. after the code is public will refactor to simplify this
     ggml_tensor * build_attn_with_sinks(
-            llm_graph_input_attn_kv_unified_iswa * inp,
+            llm_graph_input_attn_kv_iswa * inp,
             ggml_tensor * wo,
             ggml_tensor * wo_b,
             ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
@@ -765,7 +765,7 @@ struct llm_graph_context {
     //
 
     // TODO: move this implementation to llama_memory_recurrent.
-    //       this is analogous to llama_kv_cache_unified::cpy_k / cpy_v
+    //       this is analogous to llama_kv_cache::cpy_k / cpy_v
     //       when moving, avoid passing `ggml_cgraph` - only pass `ggml_context`. would likely need to split the
     //         implementation in 2 separate methods. the goal is to avoid calling `ggml_build_forward_expand` in
     //         `llama_memory_recurrent`
diff --git a/src/llama-kv-cache-unified-iswa.cpp b/src/llama-kv-cache-iswa.cpp
similarity index 62%
rename from src/llama-kv-cache-unified-iswa.cpp
rename to src/llama-kv-cache-iswa.cpp
index 1e363fff2a..a11ee5a5b1 100644
--- a/src/llama-kv-cache-unified-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -1,4 +1,4 @@
-#include "llama-kv-cache-unified-iswa.h"
+#include "llama-kv-cache-iswa.h"
 
 #include "llama-impl.h"
 #include "llama-batch.h"
@@ -8,10 +8,10 @@
 #include <cassert>
 
 //
-// llama_kv_cache_unified_iswa
+// llama_kv_cache_iswa
 //
 
-llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
+llama_kv_cache_iswa::llama_kv_cache_iswa(
         const llama_model & model,
                 ggml_type   type_k,
                 ggml_type   type_v,
@@ -23,8 +23,8 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
                  uint32_t   n_seq_max,
                  uint32_t   n_ubatch,
                  uint32_t   n_pad) : hparams(model.hparams), unified(unified) {
-    llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
-    llama_kv_cache_unified::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
+    llama_kv_cache::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
+    llama_kv_cache::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
 
     const uint32_t size_base = kv_size;
 
@@ -40,25 +40,25 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
 
     LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
 
-    kv_base = std::make_unique<llama_kv_cache_unified>(
+    kv_base = std::make_unique<llama_kv_cache>(
             model, std::move(filter_base), type_k, type_v,
             v_trans, offload, unified, size_base, n_seq_max, n_pad,
             0, LLAMA_SWA_TYPE_NONE);
 
     LLAMA_LOG_INFO("%s: creating     SWA KV cache, size = %u cells\n", __func__, size_swa);
 
-    kv_swa = std::make_unique<llama_kv_cache_unified>(
+    kv_swa = std::make_unique<llama_kv_cache>(
             model, std::move(filter_swa), type_k, type_v,
             v_trans, offload, unified, size_swa, n_seq_max, n_pad,
             hparams.n_swa, hparams.swa_type);
 }
 
-void llama_kv_cache_unified_iswa::clear(bool data) {
+void llama_kv_cache_iswa::clear(bool data) {
     kv_base->clear(data);
     kv_swa ->clear(data);
 }
 
-bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+bool llama_kv_cache_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
     bool res = true;
 
     res = res & kv_base->seq_rm(seq_id, p0, p1);
@@ -67,36 +67,36 @@ bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llam
     return res;
 }
 
-void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+void llama_kv_cache_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
     kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
     kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
 }
 
-void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
+void llama_kv_cache_iswa::seq_keep(llama_seq_id seq_id) {
     kv_base->seq_keep(seq_id);
     kv_swa ->seq_keep(seq_id);
 }
 
-void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+void llama_kv_cache_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
     kv_base->seq_add(seq_id, p0, p1, shift);
     kv_swa ->seq_add(seq_id, p0, p1, shift);
 }
 
-void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+void llama_kv_cache_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
     kv_base->seq_div(seq_id, p0, p1, d);
     kv_swa ->seq_div(seq_id, p0, p1, d);
 }
 
-llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const {
+llama_pos llama_kv_cache_iswa::seq_pos_min(llama_seq_id seq_id) const {
     // the base cache is a superset of the SWA cache, so we can just check the SWA cache
     return kv_swa->seq_pos_min(seq_id);
 }
 
-llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
+llama_pos llama_kv_cache_iswa::seq_pos_max(llama_seq_id seq_id) const {
     return kv_swa->seq_pos_max(seq_id);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+llama_memory_context_ptr llama_kv_cache_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
     GGML_UNUSED(embd_all);
 
     // first try simple split
@@ -136,7 +136,7 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
 
         assert(sinfos_base.size() == sinfos_swa.size());
 
-        return std::make_unique<llama_kv_cache_unified_iswa_context>(
+        return std::make_unique<llama_kv_cache_iswa_context>(
                 this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
     } while (false);
 
@@ -172,29 +172,29 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
 
         assert(sinfos_base.size() == sinfos_swa.size());
 
-        return std::make_unique<llama_kv_cache_unified_iswa_context>(
+        return std::make_unique<llama_kv_cache_iswa_context>(
                 this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
     } while (false);
 
     // TODO: if we fail again, we should attempt different splitting strategies
     //       but to do that properly, we first have to refactor the batches to be more flexible
 
-    return std::make_unique<llama_kv_cache_unified_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+    return std::make_unique<llama_kv_cache_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified_iswa::init_full() {
-    return std::make_unique<llama_kv_cache_unified_iswa_context>(this);
+llama_memory_context_ptr llama_kv_cache_iswa::init_full() {
+    return std::make_unique<llama_kv_cache_iswa_context>(this);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
-    return std::make_unique<llama_kv_cache_unified_iswa_context>(this, lctx, optimize);
+llama_memory_context_ptr llama_kv_cache_iswa::init_update(llama_context * lctx, bool optimize) {
+    return std::make_unique<llama_kv_cache_iswa_context>(this, lctx, optimize);
 }
 
-bool llama_kv_cache_unified_iswa::get_can_shift() const {
+bool llama_kv_cache_iswa::get_can_shift() const {
     return kv_base->get_size() == kv_swa->get_size();
 }
 
-void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
     if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
         kv_base->state_write(io, seq_id, flags);
     }
@@ -202,7 +202,7 @@ void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_i
     kv_swa->state_write(io, seq_id, flags);
 }
 
-void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+void llama_kv_cache_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
     if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
         kv_base->state_read(io, seq_id, flags);
     }
@@ -210,29 +210,29 @@ void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id
     kv_swa->state_read(io, seq_id, flags);
 }
 
-llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const {
+llama_kv_cache * llama_kv_cache_iswa::get_base() const {
     return kv_base.get();
 }
 
-llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
+llama_kv_cache * llama_kv_cache_iswa::get_swa() const {
     return kv_swa.get();
 }
 
 //
-// llama_kv_cache_unified_iswa_context
+// llama_kv_cache_iswa_context
 //
 
-llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(llama_memory_status status) : status(status) {}
+llama_kv_cache_iswa_context::llama_kv_cache_iswa_context(llama_memory_status status) : status(status) {}
 
-llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
-        llama_kv_cache_unified_iswa * kv) :
+llama_kv_cache_iswa_context::llama_kv_cache_iswa_context(
+        llama_kv_cache_iswa * kv) :
     ctx_base(kv->get_base()->init_full()),
     ctx_swa (kv->get_swa ()->init_full()),
     status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
 }
 
-llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
-        llama_kv_cache_unified_iswa * kv,
+llama_kv_cache_iswa_context::llama_kv_cache_iswa_context(
+        llama_kv_cache_iswa * kv,
         llama_context * lctx,
         bool optimize) :
     ctx_base(kv->get_base()->init_update(lctx, optimize)),
@@ -240,21 +240,21 @@ llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
     status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
 }
 
-llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
-        llama_kv_cache_unified_iswa * kv,
+llama_kv_cache_iswa_context::llama_kv_cache_iswa_context(
+        llama_kv_cache_iswa * kv,
         slot_info_vec_t sinfos_base,
         slot_info_vec_t sinfos_swa,
         std::vector<llama_ubatch> ubatches) :
     ubatches(std::move(ubatches)),
     // note: here we copy the ubatches. not sure if this is ideal
-    ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(sinfos_base), this->ubatches)),
-    ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(sinfos_swa),  this->ubatches)),
+    ctx_base(new llama_kv_cache_context(kv->get_base(), std::move(sinfos_base), this->ubatches)),
+    ctx_swa (new llama_kv_cache_context(kv->get_swa (), std::move(sinfos_swa),  this->ubatches)),
     status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
 }
 
-llama_kv_cache_unified_iswa_context:: ~llama_kv_cache_unified_iswa_context() = default;
+llama_kv_cache_iswa_context:: ~llama_kv_cache_iswa_context() = default;
 
-bool llama_kv_cache_unified_iswa_context::next() {
+bool llama_kv_cache_iswa_context::next() {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
     ctx_base->next();
@@ -267,7 +267,7 @@ bool llama_kv_cache_unified_iswa_context::next() {
     return true;
 }
 
-bool llama_kv_cache_unified_iswa_context::apply() {
+bool llama_kv_cache_iswa_context::apply() {
     assert(!llama_memory_status_is_fail(status));
 
     bool res = true;
@@ -278,24 +278,24 @@ bool llama_kv_cache_unified_iswa_context::apply() {
     return res;
 }
 
-llama_memory_status llama_kv_cache_unified_iswa_context::get_status() const {
+llama_memory_status llama_kv_cache_iswa_context::get_status() const {
     return status;
 }
 
-const llama_ubatch & llama_kv_cache_unified_iswa_context::get_ubatch() const {
+const llama_ubatch & llama_kv_cache_iswa_context::get_ubatch() const {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
     return ubatches[i_next];
 }
 
-const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_base() const {
+const llama_kv_cache_context * llama_kv_cache_iswa_context::get_base() const {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
-    return static_cast<const llama_kv_cache_unified_context *>(ctx_base.get());
+    return static_cast<const llama_kv_cache_context *>(ctx_base.get());
 }
 
-const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_swa()  const {
+const llama_kv_cache_context * llama_kv_cache_iswa_context::get_swa()  const {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
-    return static_cast<const llama_kv_cache_unified_context *>(ctx_swa.get());
+    return static_cast<const llama_kv_cache_context *>(ctx_swa.get());
 }
diff --git a/src/llama-kv-cache-unified-iswa.h b/src/llama-kv-cache-iswa.h
similarity index 68%
rename from src/llama-kv-cache-unified-iswa.h
rename to src/llama-kv-cache-iswa.h
index 7bc4df718d..dd673f18e7 100644
--- a/src/llama-kv-cache-unified-iswa.h
+++ b/src/llama-kv-cache-iswa.h
@@ -1,32 +1,32 @@
 #pragma once
 
-#include "llama-kv-cache-unified.h"
+#include "llama-kv-cache.h"
 
 #include <vector>
 
 //
-// llama_kv_cache_unified_iswa
+// llama_kv_cache_iswa
 //
 
-// utilizes two instances of llama_kv_cache_unified
+// utilizes two instances of llama_kv_cache
 //   the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
 
-class llama_kv_cache_unified_iswa : public llama_memory_i {
+class llama_kv_cache_iswa : public llama_memory_i {
 public:
-    llama_kv_cache_unified_iswa(
+    llama_kv_cache_iswa(
             const llama_model & model,
                     ggml_type   type_k,
                     ggml_type   type_v,
                          bool   v_trans,
                          bool   offload,
                          bool   swa_full,
-                         bool   unified,
+                         bool  ,
                      uint32_t   kv_size,
                      uint32_t   n_seq_max,
                      uint32_t   n_ubatch,
                      uint32_t   n_pad);
 
-    ~llama_kv_cache_unified_iswa() = default;
+    ~llama_kv_cache_iswa() = default;
 
     //
     // llama_memory_i
@@ -60,46 +60,46 @@ public:
     void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
 
     //
-    // llama_kv_cache_unified_iswa specific API
+    // llama_kv_cache_iswa specific API
     //
 
-    llama_kv_cache_unified * get_base() const;
-    llama_kv_cache_unified * get_swa () const;
+    llama_kv_cache * get_base() const;
+    llama_kv_cache * get_swa () const;
 
 private:
     const llama_hparams & hparams;
 
     const bool unified;
 
-    std::unique_ptr<llama_kv_cache_unified> kv_base;
-    std::unique_ptr<llama_kv_cache_unified> kv_swa;
+    std::unique_ptr<llama_kv_cache> kv_base;
+    std::unique_ptr<llama_kv_cache> kv_swa;
 };
 
-class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
+class llama_kv_cache_iswa_context : public llama_memory_context_i {
 public:
-    using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
 
     // used for errors
-    llama_kv_cache_unified_iswa_context(llama_memory_status status);
+    llama_kv_cache_iswa_context(llama_memory_status status);
 
     // used to create a full-cache context
-    llama_kv_cache_unified_iswa_context(
-            llama_kv_cache_unified_iswa * kv);
+    llama_kv_cache_iswa_context(
+            llama_kv_cache_iswa * kv);
 
     // used to create an update context
-    llama_kv_cache_unified_iswa_context(
-            llama_kv_cache_unified_iswa * kv,
+    llama_kv_cache_iswa_context(
+            llama_kv_cache_iswa * kv,
             llama_context * lctx,
             bool optimize);
 
     // used to create a batch processing context from a batch
-    llama_kv_cache_unified_iswa_context(
-            llama_kv_cache_unified_iswa * kv,
+    llama_kv_cache_iswa_context(
+            llama_kv_cache_iswa * kv,
             slot_info_vec_t sinfos_base,
             slot_info_vec_t sinfos_swa,
             std::vector<llama_ubatch> ubatches);
 
-    virtual ~llama_kv_cache_unified_iswa_context();
+    virtual ~llama_kv_cache_iswa_context();
 
     //
     // llama_memory_context_i
@@ -112,14 +112,14 @@ public:
     const llama_ubatch & get_ubatch() const override;
 
     //
-    // llama_kv_cache_unified_iswa_context specific API
+    // llama_kv_cache_iswa_context specific API
     //
 
-    const llama_kv_cache_unified_context * get_base() const;
-    const llama_kv_cache_unified_context * get_swa()  const;
+    const llama_kv_cache_context * get_base() const;
+    const llama_kv_cache_context * get_swa()  const;
 
 private:
-    //llama_kv_cache_unified_iswa * kv;
+    //llama_kv_cache_iswa * kv;
 
     // the index of the next ubatch to process
     size_t i_next = 0;
diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache.cpp
similarity index 90%
rename from src/llama-kv-cache-unified.cpp
rename to src/llama-kv-cache.cpp
index 478ebffac0..bb490cf9e8 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache.cpp
@@ -1,4 +1,4 @@
-#include "llama-kv-cache-unified.h"
+#include "llama-kv-cache.h"
 
 #include "llama-impl.h"
 #include "llama-io.h"
@@ -13,10 +13,10 @@
 #include <stdexcept>
 
 //
-// llama_kv_cache_unified
+// llama_kv_cache
 //
 
-llama_kv_cache_unified::llama_kv_cache_unified(
+llama_kv_cache::llama_kv_cache(
         const llama_model &  model,
           layer_filter_cb && filter,
                 ggml_type    type_k,
@@ -209,7 +209,7 @@ llama_kv_cache_unified::llama_kv_cache_unified(
     }
 }
 
-void llama_kv_cache_unified::clear(bool data) {
+void llama_kv_cache::clear(bool data) {
     for (uint32_t s = 0; s < n_stream; ++s) {
         v_cells[s].reset();
         v_heads[s] = 0;
@@ -222,7 +222,7 @@ void llama_kv_cache_unified::clear(bool data) {
     }
 }
 
-bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+bool llama_kv_cache::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
     GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));
 
     if (p0 < 0) {
@@ -285,7 +285,7 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
     return true;
 }
 
-void llama_kv_cache_unified::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+void llama_kv_cache::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
     GGML_ASSERT(seq_id_src >= 0 && (size_t) seq_id_src < seq_to_stream.size());
     GGML_ASSERT(seq_id_dst >= 0 && (size_t) seq_id_dst < seq_to_stream.size());
 
@@ -368,7 +368,7 @@ void llama_kv_cache_unified::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id
     //}
 }
 
-void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
+void llama_kv_cache::seq_keep(llama_seq_id seq_id) {
     GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
 
     auto & cells = v_cells[seq_to_stream[seq_id]];
@@ -390,7 +390,7 @@ void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
     }
 }
 
-void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+void llama_kv_cache::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
     GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
 
     auto & cells = v_cells[seq_to_stream[seq_id]];
@@ -434,7 +434,7 @@ void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_po
     head = new_head != cells.size() ? new_head : 0;
 }
 
-void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+void llama_kv_cache::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
     GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
 
     auto & cells = v_cells[seq_to_stream[seq_id]];
@@ -467,7 +467,7 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po
     }
 }
 
-llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
+llama_pos llama_kv_cache::seq_pos_min(llama_seq_id seq_id) const {
     GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
 
     const auto & cells = v_cells[seq_to_stream[seq_id]];
@@ -475,7 +475,7 @@ llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
     return cells.seq_pos_min(seq_id);
 }
 
-llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
+llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const {
     GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
 
     const auto & cells = v_cells[seq_to_stream[seq_id]];
@@ -483,7 +483,7 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
     return cells.seq_pos_max(seq_id);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified::init_batch(
+llama_memory_context_ptr llama_kv_cache::init_batch(
             llama_batch_allocr & balloc,
             uint32_t n_ubatch,
             bool embd_all) {
@@ -513,18 +513,18 @@ llama_memory_context_ptr llama_kv_cache_unified::init_batch(
             break;
         }
 
-        return std::make_unique<llama_kv_cache_unified_context>(
+        return std::make_unique<llama_kv_cache_context>(
                 this, std::move(sinfos), std::move(ubatches));
     } while (false);
 
-    return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+    return std::make_unique<llama_kv_cache_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified::init_full() {
-    return std::make_unique<llama_kv_cache_unified_context>(this);
+llama_memory_context_ptr llama_kv_cache::init_full() {
+    return std::make_unique<llama_kv_cache_context>(this);
 }
 
-llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
+llama_memory_context_ptr llama_kv_cache::init_update(llama_context * lctx, bool optimize) {
     bool do_shift = get_has_shift();
 
     defrag_info dinfo;
@@ -557,18 +557,18 @@ llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lct
         }
     }
 
-    return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo), std::move(sc_info));
+    return std::make_unique<llama_kv_cache_context>(this, lctx, do_shift, std::move(dinfo), std::move(sc_info));
 }
 
-llama_kv_cache_unified::slot_info_vec_t llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
-    llama_kv_cache_unified::slot_info_vec_t res;
+llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_ubatch> & ubatches) {
+    llama_kv_cache::slot_info_vec_t res;
 
     struct state_t {
         slot_info sinfo; // slot info for the ubatch
 
         std::vector<uint32_t> v_heads_old; // old positions of the heads, before placing the ubatch
 
-        std::vector<llama_kv_cells_unified> v_cells; // copy of the old cells, before placing the ubatch
+        std::vector<llama_kv_cells> v_cells; // copy of the old cells, before placing the ubatch
     };
 
     // remember the old state of the cells so we can restore it in the end
@@ -629,7 +629,7 @@ llama_kv_cache_unified::slot_info_vec_t llama_kv_cache_unified::prepare(const st
     return res;
 }
 
-bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const defrag_info & dinfo, const stream_copy_info & sc_info) {
+bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const defrag_info & dinfo, const stream_copy_info & sc_info) {
     bool updated = false;
 
     auto * sched = lctx->get_sched();
@@ -749,7 +749,7 @@ bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const d
     return updated;
 }
 
-llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch, bool cont) const {
+llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch, bool cont) const {
 
     if (debug > 0) {
         for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
@@ -948,7 +948,7 @@ llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_
     return res;
 }
 
-void llama_kv_cache_unified::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
+void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
     // keep track of the max sequence position that we would overwrite with this ubatch
     // for non-SWA cache, this would be always empty
     llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
@@ -1013,21 +1013,21 @@ void llama_kv_cache_unified::apply_ubatch(const slot_info & sinfo, const llama_u
     }
 }
 
-bool llama_kv_cache_unified::get_can_shift() const {
+bool llama_kv_cache::get_can_shift() const {
     return true;
 }
 
-uint32_t llama_kv_cache_unified::get_size() const {
+uint32_t llama_kv_cache::get_size() const {
     const auto & cells = v_cells[seq_to_stream[0]];
 
     return cells.size();
 }
 
-uint32_t llama_kv_cache_unified::get_n_stream() const {
+uint32_t llama_kv_cache::get_n_stream() const {
     return n_stream;
 }
 
-bool llama_kv_cache_unified::get_has_shift() const {
+bool llama_kv_cache::get_has_shift() const {
     bool result = false;
 
     for (uint32_t s = 0; s < n_stream; ++s) {
@@ -1037,7 +1037,7 @@ bool llama_kv_cache_unified::get_has_shift() const {
     return result;
 }
 
-uint32_t llama_kv_cache_unified::get_n_kv() const {
+uint32_t llama_kv_cache::get_n_kv() const {
     uint32_t result = 0;
 
     for (uint32_t s = 0; s < n_stream; ++s) {
@@ -1049,11 +1049,11 @@ uint32_t llama_kv_cache_unified::get_n_kv() const {
     return result;
 }
 
-bool llama_kv_cache_unified::get_supports_set_rows() const {
+bool llama_kv_cache::get_supports_set_rows() const {
     return supports_set_rows;
 }
 
-ggml_tensor * llama_kv_cache_unified::get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
+ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * k = layers[ikv].k;
@@ -1073,7 +1073,7 @@ ggml_tensor * llama_kv_cache_unified::get_k(ggml_context * ctx, int32_t il, uint
             ggml_row_size(k->type, n_embd_k_gqa*kv_size)*sinfo.s0);
 }
 
-ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
+ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * v = layers[ikv].v;
@@ -1105,7 +1105,7 @@ ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint
             ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0);
 }
 
-ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
+ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * k = layers[ikv].k;
@@ -1135,7 +1135,7 @@ ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_
     return ggml_cpy(ctx, k_cur, k_view);
 }
 
-ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
+ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * v = layers[ikv].v;
@@ -1189,7 +1189,7 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
     return ggml_cpy(ctx, v_cur, v_view);
 }
 
-ggml_tensor * llama_kv_cache_unified::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+ggml_tensor * llama_kv_cache::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
     const uint32_t n_tokens = ubatch.n_tokens;
 
     ggml_tensor * k_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
@@ -1199,7 +1199,7 @@ ggml_tensor * llama_kv_cache_unified::build_input_k_idxs(ggml_context * ctx, con
     return k_idxs;
 }
 
-ggml_tensor * llama_kv_cache_unified::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+ggml_tensor * llama_kv_cache::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
     const uint32_t n_tokens = ubatch.n_tokens;
 
     ggml_tensor * v_idxs;
@@ -1215,7 +1215,7 @@ ggml_tensor * llama_kv_cache_unified::build_input_v_idxs(ggml_context * ctx, con
     return v_idxs;
 }
 
-void llama_kv_cache_unified::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
+void llama_kv_cache::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
     if (!supports_set_rows) {
         return;
     }
@@ -1235,7 +1235,7 @@ void llama_kv_cache_unified::set_input_k_idxs(ggml_tensor * dst, const llama_uba
     }
 }
 
-void llama_kv_cache_unified::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
+void llama_kv_cache::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
     if (!supports_set_rows) {
         return;
     }
@@ -1272,7 +1272,7 @@ void llama_kv_cache_unified::set_input_v_idxs(ggml_tensor * dst, const llama_uba
     }
 }
 
-void llama_kv_cache_unified::set_input_k_shift(ggml_tensor * dst) const {
+void llama_kv_cache::set_input_k_shift(ggml_tensor * dst) const {
     GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
 
     int32_t * data = (int32_t *) dst->data;
@@ -1286,7 +1286,7 @@ void llama_kv_cache_unified::set_input_k_shift(ggml_tensor * dst) const {
     }
 }
 
-void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
     const uint32_t n_tokens = ubatch->n_tokens;
 
     GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
@@ -1358,7 +1358,7 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
     }
 }
 
-void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+void llama_kv_cache::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
     const int64_t n_tokens = ubatch->n_tokens;
 
     GGML_ASSERT(n_stream == 1 && "TODO: support multiple streams");
@@ -1383,7 +1383,7 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
     }
 }
 
-size_t llama_kv_cache_unified::total_size() const {
+size_t llama_kv_cache::total_size() const {
     size_t size = 0;
 
     for (const auto & buf : bufs) {
@@ -1393,7 +1393,7 @@ size_t llama_kv_cache_unified::total_size() const {
     return size;
 }
 
-size_t llama_kv_cache_unified::size_k_bytes() const {
+size_t llama_kv_cache::size_k_bytes() const {
     size_t size_k_bytes = 0;
 
     for (const auto & layer : layers) {
@@ -1403,7 +1403,7 @@ size_t llama_kv_cache_unified::size_k_bytes() const {
     return size_k_bytes;
 }
 
-size_t llama_kv_cache_unified::size_v_bytes() const {
+size_t llama_kv_cache::size_v_bytes() const {
     size_t size_v_bytes = 0;
 
     for (const auto & layer : layers) {
@@ -1413,7 +1413,7 @@ size_t llama_kv_cache_unified::size_v_bytes() const {
     return size_v_bytes;
 }
 
-ggml_tensor * llama_kv_cache_unified::build_rope_shift(
+ggml_tensor * llama_kv_cache::build_rope_shift(
         const llama_cparams & cparams,
                ggml_context * ctx,
                 ggml_tensor * cur,
@@ -1465,14 +1465,14 @@ ggml_tensor * llama_kv_cache_unified::build_rope_shift(
 
 class llm_graph_input_k_shift : public llm_graph_input_i {
 public:
-    llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
+    llm_graph_input_k_shift(const llama_kv_cache * kv_self) : kv_self(kv_self) {}
     virtual ~llm_graph_input_k_shift() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * k_shift; // I32 [kv_size*n_stream]
 
-    const llama_kv_cache_unified * kv_self;
+    const llama_kv_cache * kv_self;
 };
 
 void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) {
@@ -1483,7 +1483,7 @@ void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) {
     }
 }
 
-ggml_cgraph * llama_kv_cache_unified::build_graph_shift(llm_graph_result * res, llama_context * lctx) const {
+ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_context * lctx) const {
     auto * ctx = res->get_ctx();
     auto * gf  = res->get_gf();
 
@@ -1525,7 +1525,7 @@ ggml_cgraph * llama_kv_cache_unified::build_graph_shift(llm_graph_result * res,
     return gf;
 }
 
-ggml_cgraph * llama_kv_cache_unified::build_graph_defrag(
+ggml_cgraph * llama_kv_cache::build_graph_defrag(
          llm_graph_result * res,
             llama_context * lctx,
         const defrag_info & dinfo) const {
@@ -1679,7 +1679,7 @@ ggml_cgraph * llama_kv_cache_unified::build_graph_defrag(
     return gf;
 }
 
-llama_kv_cache_unified::defrag_info llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) const {
+llama_kv_cache::defrag_info llama_kv_cache::defrag_prepare(int32_t n_max_nodes) const {
     GGML_ASSERT(n_stream == 1 && "n_stream > 1 does not support defrag");
 
     const auto & cells = v_cells[0];
@@ -1802,7 +1802,7 @@ llama_kv_cache_unified::defrag_info llama_kv_cache_unified::defrag_prepare(int32
     return res;
 }
 
-bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
+bool llama_kv_cache::is_masked_swa(llama_pos p0, llama_pos p1) const {
     assert(p0 >= 0 && p1 >= 0);
 
     switch (swa_type) {
@@ -1828,7 +1828,7 @@ bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
     return false;
 }
 
-void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+void llama_kv_cache::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
     GGML_UNUSED(flags);
 
     io.write(&n_stream, sizeof(n_stream));
@@ -1881,7 +1881,7 @@ void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq
     }
 }
 
-void llama_kv_cache_unified::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
     GGML_UNUSED(flags);
 
     GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));
@@ -1917,7 +1917,7 @@ void llama_kv_cache_unified::state_read(llama_io_read_i & io, llama_seq_id seq_i
     }
 }
 
-void llama_kv_cache_unified::state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id) const {
+void llama_kv_cache::state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id) const {
     const auto & cells = v_cells[cr.strm];
 
     for (const auto & range : cr.data) {
@@ -1945,7 +1945,7 @@ void llama_kv_cache_unified::state_write_meta(llama_io_write_i & io, const cell_
     }
 }
 
-void llama_kv_cache_unified::state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const {
+void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const {
     const auto & cells = v_cells[cr.strm];
 
     const uint32_t v_trans = this->v_trans ? 1 : 0;
@@ -2040,7 +2040,7 @@ void llama_kv_cache_unified::state_write_data(llama_io_write_i & io, const cell_
     }
 }
 
-bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) {
+bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) {
     auto & cells = v_cells[strm];
     auto & head  = v_heads[strm];
 
@@ -2137,7 +2137,7 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t strm
     return true;
 }
 
-bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) {
+bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) {
     auto & cells = v_cells[strm];
     auto & head  = v_heads[strm];
 
@@ -2274,13 +2274,13 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t strm
 }
 
 //
-// llama_kv_cache_unified_context
+// llama_kv_cache_context
 //
 
-llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {}
+llama_kv_cache_context::llama_kv_cache_context(llama_memory_status status) : status(status) {}
 
-llama_kv_cache_unified_context::llama_kv_cache_unified_context(
-        llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
+llama_kv_cache_context::llama_kv_cache_context(
+        llama_kv_cache * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
     n_kv = kv->get_size();
 
     const uint32_t n_stream = kv->get_n_stream();
@@ -2296,8 +2296,8 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(
     }
 }
 
-llama_kv_cache_unified_context::llama_kv_cache_unified_context(
-        llama_kv_cache_unified * kv,
+llama_kv_cache_context::llama_kv_cache_context(
+        llama_kv_cache * kv,
         llama_context * lctx,
         bool do_shift,
         defrag_info dinfo,
@@ -2307,15 +2307,15 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(
     }
 }
 
-llama_kv_cache_unified_context::llama_kv_cache_unified_context(
-        llama_kv_cache_unified * kv,
-        llama_kv_cache_unified::slot_info_vec_t sinfos,
+llama_kv_cache_context::llama_kv_cache_context(
+        llama_kv_cache * kv,
+        llama_kv_cache::slot_info_vec_t sinfos,
         std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sinfos(std::move(sinfos)), ubatches(std::move(ubatches)) {
 }
 
-llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
+llama_kv_cache_context::~llama_kv_cache_context() = default;
 
-bool llama_kv_cache_unified_context::next() {
+bool llama_kv_cache_context::next() {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
     if (++i_cur >= ubatches.size()) {
@@ -2325,7 +2325,7 @@ bool llama_kv_cache_unified_context::next() {
     return true;
 }
 
-bool llama_kv_cache_unified_context::apply() {
+bool llama_kv_cache_context::apply() {
     assert(!llama_memory_status_is_fail(status));
 
     // no ubatches -> this is a KV cache update
@@ -2342,69 +2342,69 @@ bool llama_kv_cache_unified_context::apply() {
     return true;
 }
 
-llama_memory_status llama_kv_cache_unified_context::get_status() const {
+llama_memory_status llama_kv_cache_context::get_status() const {
     return status;
 }
 
-const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
+const llama_ubatch & llama_kv_cache_context::get_ubatch() const {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
     return ubatches[i_cur];
 }
 
-uint32_t llama_kv_cache_unified_context::get_n_kv() const {
+uint32_t llama_kv_cache_context::get_n_kv() const {
     return n_kv;
 }
 
-bool llama_kv_cache_unified_context::get_supports_set_rows() const {
+bool llama_kv_cache_context::get_supports_set_rows() const {
     return kv->get_supports_set_rows();
 }
 
-ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const {
+ggml_tensor * llama_kv_cache_context::get_k(ggml_context * ctx, int32_t il) const {
     return kv->get_k(ctx, il, n_kv, sinfos[i_cur]);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const {
+ggml_tensor * llama_kv_cache_context::get_v(ggml_context * ctx, int32_t il) const {
     return kv->get_v(ctx, il, n_kv, sinfos[i_cur]);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
+ggml_tensor * llama_kv_cache_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
     return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const {
+ggml_tensor * llama_kv_cache_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const {
     return kv->cpy_v(ctx, v_cur, v_idxs, il, sinfos[i_cur]);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+ggml_tensor * llama_kv_cache_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
     return kv->build_input_k_idxs(ctx, ubatch);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+ggml_tensor * llama_kv_cache_context::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
     return kv->build_input_v_idxs(ctx, ubatch);
 }
 
-void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
+void llama_kv_cache_context::set_input_k_shift(ggml_tensor * dst) const {
     kv->set_input_k_shift(dst);
 }
 
-void llama_kv_cache_unified_context::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+void llama_kv_cache_context::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
     kv->set_input_k_idxs(dst, ubatch, sinfos[i_cur]);
 }
 
-void llama_kv_cache_unified_context::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+void llama_kv_cache_context::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
     kv->set_input_v_idxs(dst, ubatch, sinfos[i_cur]);
 }
 
-void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+void llama_kv_cache_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
     kv->set_input_kq_mask(dst, ubatch, causal_attn);
 }
 
-void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+void llama_kv_cache_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
     kv->set_input_pos_bucket(dst, ubatch);
 }
 
-uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) {
+uint32_t llama_kv_cache::get_padding(const llama_cparams & cparams) {
     // the FA kernels require padding to avoid extra runtime boundary checks
     return cparams.flash_attn ? 256u : 32u;
 }
diff --git a/src/llama-kv-cache-unified.h b/src/llama-kv-cache.h
similarity index 92%
rename from src/llama-kv-cache-unified.h
rename to src/llama-kv-cache.h
index 07a7c9e4e4..5ca618e1b8 100644
--- a/src/llama-kv-cache-unified.h
+++ b/src/llama-kv-cache.h
@@ -14,10 +14,10 @@ struct llama_model;
 struct llama_context;
 
 //
-// llama_kv_cache_unified
+// llama_kv_cache
 //
 
-class llama_kv_cache_unified : public llama_memory_i {
+class llama_kv_cache : public llama_memory_i {
 public:
     static uint32_t get_padding(const llama_cparams & cparams);
 
@@ -92,7 +92,7 @@ public:
 
     using slot_info_vec_t = std::vector<slot_info>;
 
-    llama_kv_cache_unified(
+    llama_kv_cache(
             const llama_model &  model,
               layer_filter_cb && filter,
                     ggml_type    type_k,
@@ -106,7 +106,7 @@ public:
                      uint32_t    n_swa,
                llama_swa_type    swa_type);
 
-    ~llama_kv_cache_unified() = default;
+    ~llama_kv_cache() = default;
 
     //
     // llama_memory_i
@@ -140,7 +140,7 @@ public:
     void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
 
     //
-    // llama_kv_cache_unified specific API
+    // llama_kv_cache specific API
     //
 
     uint32_t get_size()     const;
@@ -241,7 +241,7 @@ private:
     // note: this is not part of the KV state and it's only used to speed-up the find_slot() method
     std::vector<uint32_t> v_heads;
 
-    std::vector<llama_kv_cells_unified> v_cells;
+    std::vector<llama_kv_cells> v_cells;
 
     // maps from a sequence id to a stream id
     std::vector<uint32_t> seq_to_stream;
@@ -295,35 +295,35 @@ private:
     bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count);
 };
 
-class llama_kv_cache_unified_context : public llama_memory_context_i {
+class llama_kv_cache_context : public llama_memory_context_i {
 public:
     // some shorthands
-    using slot_info_vec_t  = llama_kv_cache_unified::slot_info_vec_t;
-    using defrag_info      = llama_kv_cache_unified::defrag_info;
-    using stream_copy_info = llama_kv_cache_unified::stream_copy_info;
+    using slot_info_vec_t  = llama_kv_cache::slot_info_vec_t;
+    using defrag_info      = llama_kv_cache::defrag_info;
+    using stream_copy_info = llama_kv_cache::stream_copy_info;
 
     // used for errors
-    llama_kv_cache_unified_context(llama_memory_status status);
+    llama_kv_cache_context(llama_memory_status status);
 
     // used to create a full-cache context
-    llama_kv_cache_unified_context(
-            llama_kv_cache_unified * kv);
+    llama_kv_cache_context(
+            llama_kv_cache * kv);
 
     // used to create an update context
-    llama_kv_cache_unified_context(
-            llama_kv_cache_unified * kv,
+    llama_kv_cache_context(
+            llama_kv_cache * kv,
             llama_context * lctx,
             bool do_shift,
             defrag_info dinfo,
             stream_copy_info sc_info);
 
     // used to create a batch procesing context from a batch
-    llama_kv_cache_unified_context(
-            llama_kv_cache_unified * kv,
+    llama_kv_cache_context(
+            llama_kv_cache * kv,
             slot_info_vec_t sinfos,
             std::vector<llama_ubatch> ubatches);
 
-    virtual ~llama_kv_cache_unified_context();
+    virtual ~llama_kv_cache_context();
 
     //
     // llama_memory_context_i
@@ -336,7 +336,7 @@ public:
     const llama_ubatch & get_ubatch() const override;
 
     //
-    // llama_kv_cache_unified_context specific API
+    // llama_kv_cache_context specific API
     //
 
     uint32_t get_n_kv() const;
@@ -365,7 +365,7 @@ public:
 private:
     llama_memory_status status;
 
-    llama_kv_cache_unified * kv;
+    llama_kv_cache * kv;
     llama_context * lctx;
 
     //
diff --git a/src/llama-kv-cells.h b/src/llama-kv-cells.h
index 0d0dd316fd..2651e30331 100644
--- a/src/llama-kv-cells.h
+++ b/src/llama-kv-cells.h
@@ -11,7 +11,7 @@
 
 // meta information about KV cells that can be part of multiple sequences at the same time
 // TODO: add unit tests
-class llama_kv_cells_unified {
+class llama_kv_cells {
 public:
     void reset() {
         for (uint32_t i = 0; i < pos.size(); ++i) {
@@ -97,10 +97,10 @@ public:
     }
 
     // copy the state of cells [i, i + n) (used for save/restore the state of the cells)
-    llama_kv_cells_unified cp(uint32_t i, uint32_t n) const {
+    llama_kv_cells cp(uint32_t i, uint32_t n) const {
         assert(i + n <= pos.size());
 
-        llama_kv_cells_unified res;
+        llama_kv_cells res;
 
         res.resize(n);
 
@@ -117,8 +117,8 @@ public:
     }
 
     // copy the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
-    llama_kv_cells_unified cp(const std::vector<uint32_t> & idxs) const {
-        llama_kv_cells_unified res;
+    llama_kv_cells cp(const std::vector<uint32_t> & idxs) const {
+        llama_kv_cells res;
 
         res.resize(idxs.size());
 
@@ -135,7 +135,7 @@ public:
     }
 
     // set the state of cells [i, i + other.pos.size()) (used for save/restore the state of the cells)
-    void set(uint32_t i, const llama_kv_cells_unified & other) {
+    void set(uint32_t i, const llama_kv_cells & other) {
         assert(i + other.pos.size() <= pos.size());
 
         for (uint32_t j = 0; j < other.pos.size(); ++j) {
@@ -165,7 +165,7 @@ public:
     }
 
     // set the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
-    void set(const std::vector<uint32_t> & idxs, const llama_kv_cells_unified & other) {
+    void set(const std::vector<uint32_t> & idxs, const llama_kv_cells & other) {
         assert(idxs.size() == other.pos.size());
 
         for (uint32_t j = 0; j < other.pos.size(); ++j) {
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index cbeeb21344..f8303dacbf 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -30,7 +30,7 @@ llama_memory_hybrid::llama_memory_hybrid(
       layer_filter_cb && filter_attn,
       layer_filter_cb && filter_recr) :
     hparams(model.hparams),
-    mem_attn(new llama_kv_cache_unified(
+    mem_attn(new llama_kv_cache(
         model,
         filter_attn == nullptr ?
             [&](int32_t il) { return !hparams.is_recurrent(il); }
@@ -179,7 +179,7 @@ void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id,
     mem_recr->state_read(io, seq_id);
 }
 
-llama_kv_cache_unified * llama_memory_hybrid::get_mem_attn() const {
+llama_kv_cache * llama_memory_hybrid::get_mem_attn() const {
     return mem_attn.get();
 }
 
@@ -210,7 +210,7 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
         std::vector<llama_ubatch>   ubatches) :
     ubatches(std::move(ubatches)),
     // note: here we copy the ubatches. not sure if this is ideal
-    ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
+    ctx_attn(new llama_kv_cache_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
     ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(),                        this->ubatches)),
     status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
 }
@@ -248,8 +248,8 @@ const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const {
     return ubatches[i_next];
 }
 
-const llama_kv_cache_unified_context * llama_memory_hybrid_context::get_attn() const {
-    return static_cast<const llama_kv_cache_unified_context *>(ctx_attn.get());
+const llama_kv_cache_context * llama_memory_hybrid_context::get_attn() const {
+    return static_cast<const llama_kv_cache_context *>(ctx_attn.get());
 }
 
 const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const {
diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h
index acdbc26bfb..e9c64ee40a 100644
--- a/src/llama-memory-hybrid.h
+++ b/src/llama-memory-hybrid.h
@@ -2,7 +2,7 @@
 
 #include "llama-batch.h"
 #include "llama-graph.h"
-#include "llama-kv-cache-unified.h"
+#include "llama-kv-cache.h"
 #include "llama-memory.h"
 #include "llama-memory-recurrent.h"
 
@@ -13,7 +13,7 @@
 // llama_memory_hybrid
 //
 
-// utilizes instances of llama_memory_recurrent and llama_kv_cache_unified to
+// utilizes instances of llama_memory_recurrent and llama_kv_cache to
 //   support models where each layer may be either attention-based or recurrent
 
 class llama_memory_hybrid : public llama_memory_i {
@@ -81,19 +81,19 @@ public:
     // llama_memory_hybrid specific API
     //
 
-    llama_kv_cache_unified * get_mem_attn() const;
+    llama_kv_cache * get_mem_attn() const;
     llama_memory_recurrent * get_mem_recr() const;
 
 private:
     const llama_hparams & hparams;
 
-    const std::unique_ptr<llama_kv_cache_unified> mem_attn;
+    const std::unique_ptr<llama_kv_cache> mem_attn;
     const std::unique_ptr<llama_memory_recurrent> mem_recr;
 };
 
 class llama_memory_hybrid_context : public llama_memory_context_i {
 public:
-    using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
 
     // init failure
     explicit llama_memory_hybrid_context(llama_memory_status status);
@@ -125,7 +125,7 @@ public:
     // llama_memory_hybrid_context
     //
 
-    const llama_kv_cache_unified_context * get_attn() const;
+    const llama_kv_cache_context * get_attn() const;
     const llama_memory_recurrent_context * get_recr() const;
 
 private:
diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h
index 95c617b2c9..c8e8623602 100644
--- a/src/llama-memory-recurrent.h
+++ b/src/llama-memory-recurrent.h
@@ -12,7 +12,7 @@
 //
 
 // TODO: extract the cache state used for graph computation into llama_memory_recurrent_context_i
-//       see the implementation of llama_kv_cache_unified_context_i for an example how to do it
+//       see the implementation of llama_kv_cache_context_i for an example how to do it
 class llama_memory_recurrent : public llama_memory_i {
 public:
 
diff --git a/src/llama-memory.h b/src/llama-memory.h
index 171d312cc9..42a7145c2f 100644
--- a/src/llama-memory.h
+++ b/src/llama-memory.h
@@ -36,8 +36,8 @@ bool llama_memory_status_is_fail(llama_memory_status status);
 
 // the interface for managing the memory context during batch processing
 // this interface is implemented per memory type. see:
-//   - llama_kv_cache_unified_context
-//   - llama_kv_cache_unified_iswa_context
+//   - llama_kv_cache_context
+//   - llama_kv_cache_iswa_context
 //   ...
 //
 // the only method that should mutate the memory and the memory context is llama_memory_i::apply()
@@ -109,8 +109,3 @@ struct llama_memory_i {
 };
 
 using llama_memory_ptr = std::unique_ptr<llama_memory_i>;
-
-// TODO: temporary until the llama_kv_cache is removed from the public API
-struct llama_kv_cache : public llama_memory_i {
-    virtual ~llama_kv_cache() = default;
-};
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 431102edea..cbb7bc8758 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6,8 +6,8 @@
 #include "llama-cparams.h"
 #include "llama-model-loader.h"
 
-#include "llama-kv-cache-unified.h"
-#include "llama-kv-cache-unified-iswa.h"
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-iswa.h"
 #include "llama-memory-hybrid.h"
 #include "llama-memory-recurrent.h"
 
@@ -5986,7 +5986,7 @@ struct llm_build_llama : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -6146,7 +6146,7 @@ struct llm_build_llama_iswa : public llm_graph_context {
         ggml_tensor * inp_attn_scale = nullptr;
         inp_attn_scale = build_inp_attn_scale();
 
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -6325,7 +6325,7 @@ struct llm_build_deci : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -6481,7 +6481,7 @@ struct llm_build_baichuan : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -6603,7 +6603,7 @@ struct llm_build_xverse : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -6717,7 +6717,7 @@ struct llm_build_falcon : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -6841,7 +6841,7 @@ struct llm_build_grok : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -7001,7 +7001,7 @@ struct llm_build_dbrx : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -7125,7 +7125,7 @@ struct llm_build_starcoder : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -7230,7 +7230,7 @@ struct llm_build_refact : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -7632,7 +7632,7 @@ struct llm_build_bloom : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         inpL = build_norm(inpL,
                 model.tok_norm,
@@ -7739,7 +7739,7 @@ struct llm_build_mpt : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         if (model.pos_embd) {
             // inp_pos - contains the positions
@@ -7889,7 +7889,7 @@ struct llm_build_stablelm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -8041,7 +8041,7 @@ struct llm_build_qwen : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -8156,7 +8156,7 @@ struct llm_build_qwen2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -8481,7 +8481,7 @@ struct llm_build_qwen2vl : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         int sections[4];
         std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
@@ -8602,7 +8602,7 @@ struct llm_build_qwen2moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -8761,7 +8761,7 @@ struct llm_build_qwen3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -8882,7 +8882,7 @@ struct llm_build_qwen3moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9012,7 +9012,7 @@ struct llm_build_phi2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9141,13 +9141,13 @@ struct llm_build_phi3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_unified_iswa, llm_graph_input_attn_kv_unified>;
+        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
         inp_attn_type * inp_attn = nullptr;
 
         if constexpr (iswa) {
-            inp_attn = build_attn_inp_kv_unified_iswa();
+            inp_attn = build_attn_inp_kv_iswa();
         } else {
-            inp_attn = build_attn_inp_kv_unified();
+            inp_attn = build_attn_inp_kv();
         }
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
@@ -9299,7 +9299,7 @@ struct llm_build_plamo : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9415,7 +9415,7 @@ struct llm_build_gpt2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -9525,7 +9525,7 @@ struct llm_build_codeshell : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9638,7 +9638,7 @@ struct llm_build_orion : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9765,7 +9765,7 @@ struct llm_build_internlm2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -9901,7 +9901,7 @@ struct llm_build_minicpm3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -10096,7 +10096,7 @@ struct llm_build_gemma : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -10212,7 +10212,7 @@ struct llm_build_gemma2_iswa : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -10346,7 +10346,7 @@ struct llm_build_gemma3_iswa : public llm_graph_context {
         ggml_tensor * inp_pos = build_inp_pos();
 
         // TODO: is causal == true correct? might need some changes
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -10497,7 +10497,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
         ggml_tensor * inp_pos = build_inp_pos();
 
         // TODO: is causal == true correct? might need some changes
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         // inp_per_layer shape: [n_embd_altup, n_tokens, n_layer]
         ggml_tensor * inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
@@ -10904,7 +10904,7 @@ struct llm_build_starcoder2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -11473,7 +11473,7 @@ struct llm_build_command_r : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -11620,7 +11620,7 @@ struct llm_build_cohere2_iswa : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -11755,7 +11755,7 @@ struct llm_build_olmo : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -11883,7 +11883,7 @@ struct llm_build_olmo2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12012,7 +12012,7 @@ struct llm_build_olmoe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12138,7 +12138,7 @@ struct llm_build_openelm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12269,7 +12269,7 @@ struct llm_build_gptneox : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12415,7 +12415,7 @@ struct llm_build_arctic : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12553,7 +12553,7 @@ struct llm_build_deepseek : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -12730,7 +12730,7 @@ struct llm_build_deepseek2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12977,7 +12977,7 @@ struct llm_build_bitnet : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -13241,7 +13241,7 @@ struct llm_build_t5_dec : public llm_graph_context {
 
         const int64_t n_outputs_enc = embd_enc->ne[1];
 
-        auto * inp_attn_self  = build_attn_inp_kv_unified();
+        auto * inp_attn_self  = build_attn_inp_kv();
         auto * inp_attn_cross = build_attn_inp_cross();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
@@ -13406,7 +13406,7 @@ struct llm_build_jais : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -13504,7 +13504,7 @@ struct llm_build_chatglm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -13637,7 +13637,7 @@ struct llm_build_glm4 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -13787,7 +13787,7 @@ struct llm_build_glm4_moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -13947,7 +13947,7 @@ struct llm_build_nemotron : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -14076,7 +14076,7 @@ struct llm_build_exaone : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -14208,13 +14208,13 @@ struct llm_build_exaone4 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_unified_iswa, llm_graph_input_attn_kv_unified>;
+        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
         inp_attn_type * inp_attn = nullptr;
 
         if constexpr (iswa) {
-            inp_attn = build_attn_inp_kv_unified_iswa();
+            inp_attn = build_attn_inp_kv_iswa();
         } else {
-            inp_attn = build_attn_inp_kv_unified();
+            inp_attn = build_attn_inp_kv();
         }
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
@@ -15097,7 +15097,7 @@ struct llm_build_granite : public llm_graph_context {
             inp_pos = build_inp_pos();
         }
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -15148,12 +15148,12 @@ struct llm_build_granite : public llm_graph_context {
     }
 
     ggml_tensor * build_attention_layer(
-              ggml_tensor                     * cur,
-              ggml_tensor                     * inp_pos,
-              llm_graph_input_attn_kv_unified * inp_attn,
-        const llama_model                     & model,
-        const int64_t                           n_embd_head,
-        const int                               il) {
+              ggml_tensor             * cur,
+              ggml_tensor             * inp_pos,
+              llm_graph_input_attn_kv * inp_attn,
+        const llama_model             & model,
+        const int64_t                 n_embd_head,
+        const int                     il) {
 
         // compute Q and K and (optionally) RoPE them
         ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
@@ -15367,12 +15367,12 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba {
     }
 
     ggml_tensor * build_attention_layer(
-              ggml_tensor                     * cur,
-              ggml_tensor                     * inp_pos,
-              llm_graph_input_attn_kv_unified * inp_attn,
-        const llama_model                     & model,
-        const int64_t                           n_embd_head,
-        const int                               il) {
+              ggml_tensor             * cur,
+              ggml_tensor             * inp_pos,
+              llm_graph_input_attn_kv * inp_attn,
+        const llama_model             & model,
+        const int64_t                 n_embd_head,
+        const int                     il) {
 
         // compute Q and K and (optionally) RoPE them
         ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
@@ -15529,7 +15529,7 @@ struct llm_build_chameleon : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -15860,7 +15860,7 @@ struct llm_build_plm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -16025,7 +16025,7 @@ struct llm_build_bailingmoe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -16174,7 +16174,7 @@ struct llm_build_dots1 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -16324,7 +16324,7 @@ struct llm_build_ernie4_5 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -16454,7 +16454,7 @@ struct llm_build_ernie4_5_moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -16828,7 +16828,7 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {
 
 private:
     ggml_tensor * build_plamo2_attn_layer(
-            llm_graph_input_attn_kv_unified * inp,
+            llm_graph_input_attn_kv * inp,
             ggml_tensor * inp_pos,
             ggml_tensor * cur,
             const llama_model & model,
@@ -17061,7 +17061,7 @@ struct llm_build_arcee : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -17196,7 +17196,7 @@ struct llm_build_hunyuan_moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
 
@@ -17357,7 +17357,7 @@ struct llm_build_hunyuan_dense : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
 
@@ -17495,7 +17495,7 @@ struct llm_build_smollm3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified();
+        auto * inp_attn = build_attn_inp_kv();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -17627,7 +17627,7 @@ struct llm_build_openai_moe_iswa : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified_iswa();
+        auto * inp_attn = build_attn_inp_kv_iswa();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -17809,10 +17809,10 @@ struct llm_build_lfm2 : public llm_graph_context {
         return cur;
     }
 
-    ggml_tensor * build_attn_block(ggml_tensor                     * cur,
-                                   ggml_tensor                     * inp_pos,
-                                   llm_graph_input_attn_kv_unified * inp_attn,
-                                   int                               il) const {
+    ggml_tensor * build_attn_block(ggml_tensor             * cur,
+                                   ggml_tensor             * inp_pos,
+                                   llm_graph_input_attn_kv * inp_attn,
+                                   int                     il) const {
         GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
         auto const n_embd_head = hparams.n_embd_head_v;
         auto const n_head_kv = hparams.n_head_kv(il);
@@ -17940,13 +17940,13 @@ struct llm_build_smallthinker : public llm_graph_context{
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_unified_iswa, llm_graph_input_attn_kv_unified>;
+        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
         inp_attn_type * inp_attn = nullptr;
 
         if constexpr (iswa) {
-            inp_attn = build_attn_inp_kv_unified_iswa();
+            inp_attn = build_attn_inp_kv_iswa();
         } else {
-            inp_attn = build_attn_inp_kv_unified();
+            inp_attn = build_attn_inp_kv();
         }
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
@@ -18076,7 +18076,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             std::max((uint32_t) 1, cparams.n_seq_max),
                             cparams.n_seq_max);
                 } else if (llm_arch_is_hybrid(arch)) {
-                    const auto padding = llama_kv_cache_unified::get_padding(cparams);
+                    const auto padding = llama_kv_cache::get_padding(cparams);
 
                     cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding);
 
@@ -18098,7 +18098,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         /* filter_attn       */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr,
                         /* filter_recr       */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr);
                 } else {
-                    const auto padding = llama_kv_cache_unified::get_padding(cparams);
+                    const auto padding = llama_kv_cache::get_padding(cparams);
 
                     uint32_t n_ctx_per_stream = cparams.n_ctx;
 
@@ -18118,7 +18118,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                     if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
                         GGML_ASSERT(hparams.is_swa_any());
 
-                        res = new llama_kv_cache_unified_iswa(
+                        res = new llama_kv_cache_iswa(
                                 *this,
                                 params.type_k,
                                 params.type_v,
@@ -18133,7 +18133,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                     } else {
                         GGML_ASSERT(!hparams.is_swa_any());
 
-                        res = new llama_kv_cache_unified(
+                        res = new llama_kv_cache(
                                 *this,
                                 nullptr,
                                 params.type_k,

From 9ad5e60dba38a6718366b7ac43e7d8e8abdc36c9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <jiefu@tencent.com>
Date: Thu, 21 Aug 2025 22:53:13 +0800
Subject: [PATCH 137/287] examples : fix some typos in
 examples/model-conversion/README.md (#15477)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 examples/model-conversion/README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md
index c3c5001ead..c924a6be3c 100644
--- a/examples/model-conversion/README.md
+++ b/examples/model-conversion/README.md
@@ -6,7 +6,7 @@ The motivation for having this is that the conversion process can often be an
 iterative process, where the original model is inspected, converted, updates
 made to llama.cpp, converted again, etc. Once the model has been converted it
 needs to be verified against the original model, and then optionally quantified,
-and is some cases perplexity checked of the quantized model. And finally the
+and in some cases perplexity checked of the quantized model. And finally the
 model/models need to the ggml-org on Hugging Face. This tool/example tries to
 help with this process.
 
@@ -62,7 +62,7 @@ Command line arguments take precedence over environment variables when both are
 
 In cases where the transformer implementation for the model has not been released
 yet it is possible to set the environment variable `UNRELEASED_MODEL_NAME` which
-will the cause the transformer implementation to be loaded explicitely and not
+will then cause the transformer implementation to be loaded explicitely and not
 use AutoModelForCausalLM:
 ```
 export UNRELEASED_MODEL_NAME=SomeNewModel
@@ -87,7 +87,7 @@ from the converted model.
 # Or using command line argument
 (venv) $ make causal-run-original-model MODEL_PATH=~/work/ai/models/some_model
 ```
-This command will save two file to the `data` directory, one is a binary file
+This command will save two files to the `data` directory, one is a binary file
 containing logits which will be used for comparison with the converted model
 later, and the other is a text file which allows for manual visual inspection.
 
@@ -128,11 +128,11 @@ Quantized model saved to: /path/to/quantized/model-Q8_0.gguf
 Export the quantized model path to QUANTIZED_MODEL variable in your environment
 ```
 This will show the path to the quantized model in the terminal, which can then
-be used set the `QUANTIZED_MODEL` environment variable:
+be used to set the `QUANTIZED_MODEL` environment variable:
 ```console
 export QUANTIZED_MODEL=/path/to/quantized/model-Q8_0.gguf
 ```
-The the quantized model can be run using the following command:
+Then the quantized model can be run using the following command:
 ```console
 (venv) $ make causal-run-quantized-model
 ```
@@ -229,11 +229,11 @@ Quantized model saved to: /path/to/quantized/model-Q8_0.gguf
 Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment
 ```
 This will show the path to the quantized model in the terminal, which can then
-be used set the `QUANTIZED_EMBEDDING_MODEL` environment variable:
+be used to set the `QUANTIZED_EMBEDDING_MODEL` environment variable:
 ```console
 export QUANTIZED_EMBEDDING_MODEL=/path/to/quantized/model-Q8_0.gguf
 ```
-The the quantized model can be run using the following command:
+Then the quantized model can be run using the following command:
 ```console
 (venv) $ make embedding-run-quantized-model
 ```
@@ -246,7 +246,7 @@ token/logits file:
 ```console
 (venv) $ make perplexity-run QUANTIZED_MODEL=~/path/to/quantized/model.gguf
 ```
-This will use the wikitext dataset to run the perplexity evaluation and and
+This will use the wikitext dataset to run the perplexity evaluation and
 output the perplexity score to the terminal. This value can then be compared
 with the perplexity score of the unquantized model.
 

From 96452a3fa426de83494fb0268a636aa1bfe557fe Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Thu, 21 Aug 2025 09:55:00 -0500
Subject: [PATCH 138/287] vulkan: Reuse conversion results in prealloc_y
 (#15410)

* vulkan: Reuse conversion results in prealloc_y

Cache the pipeline and tensor that were most recently used to fill prealloc_y,
and skip the conversion if the current pipeline/tensor match.

* don't use shared pointer for prealloc_y_last_pipeline_used
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 75 +++++++++++++++++++++-------
 tests/test-backend-ops.cpp           | 42 +++++++++++++---
 2 files changed, 94 insertions(+), 23 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c59a588b97..a5bb1820b3 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1193,6 +1193,10 @@ struct ggml_backend_vk_context {
     vk::Fence fence, almost_ready_fence;
     bool almost_ready_fence_pending {};
 
+    // Cache most recent tensor that was converted into prealloc_y, and what pipeline it used to convert.
+    vk_pipeline_struct * prealloc_y_last_pipeline_used {};
+    const ggml_tensor * prealloc_y_last_tensor_used {};
+
     vk_buffer buffer_pool[MAX_VK_BUFFERS];
 
     vk_context_ref compute_ctx;
@@ -5651,10 +5655,20 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc, { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
     }
     if (y_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
     }
     if (quantize_y) {
-        ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13);
+        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13);
+            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
     }
 
     uint32_t stride_batch_x = ne00*ne01;
@@ -5829,7 +5843,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     }
     if (y_non_contig) {
         GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
     }
 
     // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride
@@ -6259,7 +6278,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
             { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc, { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
     }
     if (y_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
     }
 
     uint32_t stride_batch_x = ne00*ne01;
@@ -6447,7 +6471,12 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     }
     if (y_non_contig) {
         GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
     }
 
     uint32_t stride_batch_y = ne10*ne11;
@@ -6491,22 +6520,29 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
         GGML_ASSERT(nei0 <= 4096);
         const uint32_t split_size = std::min(nei1, 4096u / nei0);
 
-        ggml_tensor src1_copy = *src1;
-        ggml_tensor src2_copy = *src2;
-        ggml_tensor dst_copy = *dst;
+        if (split_size == nei1) {
+            ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
+        } else {
+            ggml_tensor src1_copy = *src1;
+            ggml_tensor src2_copy = *src2;
+            ggml_tensor dst_copy = *dst;
 
-        for (uint32_t token_start = 0; token_start < nei1; token_start += split_size) {
-            const uint32_t n_tokens = std::min(split_size, nei1 - token_start);
+            for (uint32_t token_start = 0; token_start < nei1; token_start += split_size) {
+                const uint32_t n_tokens = std::min(split_size, nei1 - token_start);
 
-            src1_copy.view_offs = src1->view_offs + token_start * src1_copy.nb[2];
-            src2_copy.view_offs = src2->view_offs + token_start * src2_copy.nb[1];
-            dst_copy.view_offs = dst->view_offs + token_start * dst_copy.nb[2];
+                src1_copy.view_offs = src1->view_offs + token_start * src1_copy.nb[2];
+                src2_copy.view_offs = src2->view_offs + token_start * src2_copy.nb[1];
+                dst_copy.view_offs = dst->view_offs + token_start * dst_copy.nb[2];
 
-            src1_copy.ne[2] = n_tokens;
-            src2_copy.ne[1] = n_tokens;
-            dst_copy.ne[2] = n_tokens;
+                src1_copy.ne[2] = n_tokens;
+                src2_copy.ne[1] = n_tokens;
+                dst_copy.ne[2] = n_tokens;
 
-            ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, &src1_copy, &src2_copy, &dst_copy, dryrun);
+                ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, &src1_copy, &src2_copy, &dst_copy, dryrun);
+                // invalidate cached prealloc_y, can't cache based on the copy of the ggml_tensor
+                ctx->prealloc_y_last_pipeline_used = {};
+                ctx->prealloc_y_last_tensor_used = nullptr;
+            }
         }
     }
 }
@@ -10311,6 +10347,7 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
         ggml_vk_pool_free(ctx, buffer);
     }
     ctx->gc.temp_buffers.clear();
+    ctx->prealloc_y_last_pipeline_used = {};
 
     ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);
     ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool);
@@ -10346,6 +10383,7 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
     ggml_vk_destroy_buffer(ctx->prealloc_x);
     ggml_vk_destroy_buffer(ctx->prealloc_y);
     ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+    ctx->prealloc_y_last_pipeline_used = nullptr;
 
     for (auto& buffer : ctx->buffer_pool) {
         ggml_vk_destroy_buffer(buffer);
@@ -10894,6 +10932,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, 0);
     }
 
+    ctx->prealloc_y_last_pipeline_used = nullptr;
+    ctx->prealloc_y_last_tensor_used = nullptr;
+
     // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
     // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
     // (and scaled down based on model size, so smaller models submit earlier).
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 4623605f66..e21e904278 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3098,9 +3098,10 @@ struct test_mul_mat : public test_case {
     const std::array<int64_t, 2> nr;  // repeat in dims 3 and 4
     const std::array<int64_t, 4> per; // permutation of dimensions
     const bool v; // whether a and b are non-contiguous views
+    const uint32_t o; // number of outputs
 
     std::string vars() override {
-        return VARS_TO_STR9(type_a, type_b, m, n, k, bs, nr, per, v);
+        return VARS_TO_STR10(type_a, type_b, m, n, k, bs, nr, per, v, o);
     }
 
     double max_nmse_err() override {
@@ -3121,8 +3122,8 @@ struct test_mul_mat : public test_case {
             std::array<int64_t, 2> bs = {10, 10},
             std::array<int64_t, 2> nr = {2, 2},
             std::array<int64_t, 4> per = {0, 1, 2, 3},
-            bool v = false)
-        : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per), v(v) {}
+            bool v = false, uint32_t o = 1)
+        : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per), v(v), o(o) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         // C^T = A * B^T: (k, m) * (k, n) => (m, n)
@@ -3186,9 +3187,21 @@ struct test_mul_mat : public test_case {
 
         ggml_tensor * out = ggml_mul_mat(ctx, a, b);
         ggml_set_name(out, "out");
+        for (uint32_t i = 1; i < o; ++i) {
+            ggml_tensor * out2 = ggml_mul_mat(ctx, a, b);
+            ggml_set_name(out2, "out2");
+            out = ggml_add(ctx, out, out2);
+        }
 
         return out;
     }
+
+    bool run_whole_graph() override { return o > 1; }
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return ggml_op_name(GGML_OP_MUL_MAT);
+    }
 };
 
 // GGML_OP_MUL_MAT_ID
@@ -3201,9 +3214,10 @@ struct test_mul_mat_id : public test_case {
     const int64_t m;
     const int64_t n;
     const int64_t k;
+    const uint32_t o; // number of outputs
 
     std::string vars() override {
-        return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k);
+        return VARS_TO_STR9(type_a, type_b, n_mats, n_used, b, m, n, k, o);
     }
 
     double max_nmse_err() override {
@@ -3217,9 +3231,9 @@ struct test_mul_mat_id : public test_case {
 
     test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
             int n_mats = 8, int n_used = 2, bool b = false,
-            int64_t m = 32, int64_t n = 32, int64_t k = 32)
+            int64_t m = 32, int64_t n = 32, int64_t k = 32, uint32_t o = 1)
         : type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
-            m(m), n(n), k(k) {
+            m(m), n(n), k(k), o(o) {
             GGML_ASSERT(n_used <= n_mats);
         }
 
@@ -3241,6 +3255,13 @@ struct test_mul_mat_id : public test_case {
         ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
         ggml_set_name(out, "out");
 
+        for (uint32_t i = 1; i < o; ++i) {
+            ggml_tensor * a2 = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
+            ggml_tensor * out2 = ggml_mul_mat_id(ctx, a2, b, ids);
+            ggml_set_name(out2, "out2");
+            out = ggml_add(ctx, out, out2);
+        }
+
         return out;
     }
 
@@ -3264,6 +3285,13 @@ struct test_mul_mat_id : public test_case {
             }
         }
     }
+
+    bool run_whole_graph() override { return o > 1; }
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return ggml_op_name(GGML_OP_MUL_MAT_ID);
+    }
 };
 
 // GGML_OP_OUT_PROD
@@ -5798,6 +5826,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45,  64, { 8,  1}, {4, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 193, {1,  1}, {4, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67,  {1,  1}, {4, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 16, 32, 32, { 1,  1}, {1, 1}, {0, 1, 2, 3}, true, 3));
 
     for (auto bs2 : {1,3}) {
         for (auto bs : {1,2,4,8}) {
@@ -5826,6 +5855,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     }
 
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
 
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {

From 20c2dac8c6e05f2ad5295ddef1aebaf2d266090e Mon Sep 17 00:00:00 2001
From: Dong Won Kim <63934649+ddwkim@users.noreply.github.com>
Date: Fri, 22 Aug 2025 00:00:16 +0900
Subject: [PATCH 139/287] vulkan: add exp operation (#15456)

Co-authored-by: aeseulgi <kim2h7903@gmail.com>
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 11 ++++++++++
 ggml/src/ggml-vulkan/vulkan-shaders/exp.comp  | 20 +++++++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  2 ++
 3 files changed, 33 insertions(+)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/exp.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a5bb1820b3..2d5254dfd2 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -490,6 +490,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_l2_norm_f32;
 
     // [src/dst 0=fp32,1=fp16]
+    vk_pipeline pipeline_exp[2];
     vk_pipeline pipeline_gelu[2];
     vk_pipeline pipeline_gelu_erf[2];
     vk_pipeline pipeline_gelu_quick[2];
@@ -3066,6 +3067,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);  \
     ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
+    CREATE_UNARY(exp)
     CREATE_UNARY(gelu)
     CREATE_UNARY(gelu_erf)
     CREATE_UNARY(gelu_quick)
@@ -7133,6 +7135,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
 
         switch (ggml_get_unary_op(dst)) {
+            case GGML_UNARY_OP_EXP:
+                return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_SILU:
                 return ctx->device->pipeline_silu[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_GELU:
@@ -9738,6 +9742,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         return false;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_EXP:
         case GGML_UNARY_OP_SILU:
         case GGML_UNARY_OP_GELU:
         case GGML_UNARY_OP_GELU_ERF:
@@ -10015,6 +10020,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         break;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_EXP:
         case GGML_UNARY_OP_SILU:
         case GGML_UNARY_OP_GELU:
         case GGML_UNARY_OP_GELU_ERF:
@@ -10251,6 +10257,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
         break;
     case GGML_OP_UNARY:
         switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_EXP:
         case GGML_UNARY_OP_SILU:
         case GGML_UNARY_OP_GELU:
         case GGML_UNARY_OP_GELU_ERF:
@@ -11166,6 +11173,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_ERF:
                 case GGML_UNARY_OP_GELU_QUICK:
@@ -11965,6 +11973,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         }
     } else if (tensor->op == GGML_OP_UNARY) {
         switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_EXP:
+            tensor_clone = ggml_exp(ggml_ctx, src_clone[0]);
+            break;
         case GGML_UNARY_OP_SILU:
             tensor_clone = ggml_silu(ggml_ctx, src_clone[0]);
             break;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/exp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/exp.comp
new file mode 100644
index 0000000000..abecd2d3dc
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/exp.comp
@@ -0,0 +1,20 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+    data_d[i] = D_TYPE(exp(float(data_a[i])));
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 75c572d6fc..4e0bffe6da 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -586,6 +586,8 @@ void process_shaders() {
 
     string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
 
+    string_to_spv("exp_f16",        "exp.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("exp_f32",        "exp.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("gelu_f16",       "gelu.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("gelu_f32",       "gelu.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("gelu_erf_f16",   "gelu_erf.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});

From 97ae5961a4d9ff9c60f51bac304b97de18e75eaf Mon Sep 17 00:00:00 2001
From: Acly <aclysia@gmail.com>
Date: Thu, 21 Aug 2025 17:01:51 +0200
Subject: [PATCH 140/287] vulkan : support conv_2d_dw with f16 weights (#15392)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp                 | 12 ++++++++++--
 .../vulkan-shaders/vulkan-shaders-gen.cpp            |  2 ++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2d5254dfd2..fb18a55cda 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -530,8 +530,8 @@ struct vk_device_struct {
     vk_pipeline pipeline_opt_step_sgd_f32;
     vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT];
     vk_pipeline pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
-    vk_pipeline pipeline_conv2d_dw_whcn_f32;
-    vk_pipeline pipeline_conv2d_dw_cwhn_f32;
+    vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
+    vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;
 
     // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
     vk_pipeline pipeline_flash_attn_f32_f16_cm2[GGML_TYPE_COUNT][FA_HEAD_SIZE_COUNT][2][2][2];
@@ -3257,6 +3257,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
 
     for (auto &c : compiles) {
         c.wait();
@@ -7346,6 +7348,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             } else if (ggml_is_contiguous_channels(src1)) {
                 return ctx->device->pipeline_conv2d_dw_cwhn_f32;
             }
+        } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+            if (ggml_is_contiguous(src1)) {
+                return ctx->device->pipeline_conv2d_dw_whcn_f16_f32;
+            } else if (ggml_is_contiguous_channels(src1)) {
+                return ctx->device->pipeline_conv2d_dw_cwhn_f16_f32;
+            }
         }
         return nullptr;
     default:
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 4e0bffe6da..123ae04491 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -680,6 +680,8 @@ void process_shaders() {
 
     string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
     string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));
+    string_to_spv("conv2d_dw_whcn_f16_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
+    string_to_spv("conv2d_dw_cwhn_f16_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));
 
     string_to_spv("roll_f32", "roll.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 

From 3f196be84b1376945737163e35a91e29e3e24d2f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 21 Aug 2025 18:44:45 +0300
Subject: [PATCH 141/287] graph : remove build_attn_with_sinks overload
 (#15469)

ggml-ci
---
 src/llama-graph.cpp |  40 +++--------
 src/llama-graph.h   |  34 ++++-----
 src/llama-model.cpp | 166 +++++++++++++++++++++++---------------------
 3 files changed, 107 insertions(+), 133 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 04baf03ea0..6419d739bd 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1223,8 +1223,8 @@ ggml_tensor * llm_graph_context::build_attn_mha(
          ggml_tensor * v,
          ggml_tensor * kq_b,
          ggml_tensor * kq_mask,
-         ggml_tensor * v_mla,
          ggml_tensor * sinks,
+         ggml_tensor * v_mla,
              float     kq_scale) const {
     const bool v_trans = v->nb[1] > v->nb[2];
 
@@ -1360,6 +1360,7 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_tensor * k_cur,
         ggml_tensor * v_cur,
         ggml_tensor * kq_b,
+        ggml_tensor * sinks,
         ggml_tensor * v_mla,
             float     kq_scale,
             int       il) const {
@@ -1381,7 +1382,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = k_cur;
     ggml_tensor * v = v_cur;
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, nullptr, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1443,6 +1444,7 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_tensor * k_cur,
         ggml_tensor * v_cur,
         ggml_tensor * kq_b,
+        ggml_tensor * sinks,
         ggml_tensor * v_mla,
             float     kq_scale,
             int       il) const {
@@ -1469,7 +1471,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, nullptr, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1495,33 +1497,8 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_tensor * k_cur,
         ggml_tensor * v_cur,
         ggml_tensor * kq_b,
-        ggml_tensor * v_mla,
-            float     kq_scale,
-            int       il) const {
-    return build_attn_with_sinks(
-            inp,
-            wo,
-            wo_b,
-            q_cur,
-            k_cur,
-            v_cur,
-            kq_b,
-            v_mla,
-            nullptr,
-            kq_scale,
-            il);
-}
-
-ggml_tensor * llm_graph_context::build_attn_with_sinks(
-        llm_graph_input_attn_kv_iswa * inp,
-        ggml_tensor * wo,
-        ggml_tensor * wo_b,
-        ggml_tensor * q_cur,
-        ggml_tensor * k_cur,
-        ggml_tensor * v_cur,
-        ggml_tensor * kq_b,
-        ggml_tensor * v_mla,
         ggml_tensor * sinks,
+        ggml_tensor * v_mla,
             float     kq_scale,
             int       il) const {
     // these nodes are added to the graph together so that they are not reordered
@@ -1561,7 +1538,7 @@ ggml_tensor * llm_graph_context::build_attn_with_sinks(
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, sinks, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1600,6 +1577,7 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_tensor * k_cur,
         ggml_tensor * v_cur,
         ggml_tensor * kq_b,
+        ggml_tensor * sinks,
         ggml_tensor * v_mla,
             float     kq_scale,
             int       il) const {
@@ -1615,7 +1593,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = k_cur;
     ggml_tensor * v = v_cur;
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, v_mla, nullptr, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
     cb(cur, "kqv_out", il);
 
     if (wo) {
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 6636fa256f..e11d91d529 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -680,14 +680,14 @@ struct llm_graph_context {
     //
 
     ggml_tensor * build_attn_mha(
-             ggml_tensor * q,       // [n_embd_head_q, n_head_q, n_tokens]
-             ggml_tensor * k,       // [n_embd_head_k, n_head_k, n_tokens]
-             ggml_tensor * v,       // [n_embd_head_v, n_head_v, n_tokens] (v_trans == false)
-             ggml_tensor * kq_b,
-             ggml_tensor * kq_mask,
-             ggml_tensor * sinks,
-             ggml_tensor * v_mla,   // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                   float   kq_scale) const;
+            ggml_tensor * q,       // [n_embd_head_q, n_head_q, n_tokens]
+            ggml_tensor * k,       // [n_embd_head_k, n_head_k, n_tokens]
+            ggml_tensor * v,       // [n_embd_head_v, n_head_v, n_tokens] (v_trans == false)
+            ggml_tensor * kq_b,
+            ggml_tensor * kq_mask,
+            ggml_tensor * sinks,   // [n_head_q]
+            ggml_tensor * v_mla,   // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
+                  float   kq_scale) const;
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
 
@@ -699,6 +699,7 @@ struct llm_graph_context {
             ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
             ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
             ggml_tensor * kq_b,
+            ggml_tensor * sinks, // [n_head_q]
             ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
                   float   kq_scale,
                     int   il) const;
@@ -713,6 +714,7 @@ struct llm_graph_context {
             ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
             ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
             ggml_tensor * kq_b,
+            ggml_tensor * sinks, // [n_head_q]
             ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
                   float   kq_scale,
                     int   il) const;
@@ -728,21 +730,8 @@ struct llm_graph_context {
             ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens] optional
             ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens] optional
             ggml_tensor * kq_b,
-            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                  float   kq_scale,
-                    int   il) const;
-
-    // TODO: temporary to keep the diff small. after the code is public will refactor to simplify this
-    ggml_tensor * build_attn_with_sinks(
-            llm_graph_input_attn_kv_iswa * inp,
-            ggml_tensor * wo,
-            ggml_tensor * wo_b,
-            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
-            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens] optional
-            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens] optional
-            ggml_tensor * kq_b,
-            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
             ggml_tensor * sinks, // [n_head_q]
+            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
                   float   kq_scale,
                     int   il) const;
 
@@ -756,6 +745,7 @@ struct llm_graph_context {
             ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
             ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
             ggml_tensor * kq_b,
+            ggml_tensor * sinks, // [n_head_q]
             ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
                   float   kq_scale,
                     int   il) const;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index cbb7bc8758..c4f0b12f24 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6050,7 +6050,7 @@ struct llm_build_llama : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -6224,7 +6224,7 @@ struct llm_build_llama_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -6401,7 +6401,7 @@ struct llm_build_deci : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -6533,7 +6533,7 @@ struct llm_build_baichuan : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -6648,7 +6648,7 @@ struct llm_build_xverse : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -6771,7 +6771,7 @@ struct llm_build_falcon : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -6901,7 +6901,7 @@ struct llm_build_grok : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7050,7 +7050,7 @@ struct llm_build_dbrx : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7164,7 +7164,7 @@ struct llm_build_starcoder : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7263,7 +7263,7 @@ struct llm_build_refact : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7426,7 +7426,7 @@ struct llm_build_bert : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
                 cb(cur, "kqv_out", il);
             }
 
@@ -7571,7 +7571,7 @@ struct llm_build_neo_bert : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
                 cb(cur, "kqv_out", il);
             }
 
@@ -7671,7 +7671,7 @@ struct llm_build_bloom : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7819,7 +7819,7 @@ struct llm_build_mpt : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -7965,7 +7965,7 @@ struct llm_build_stablelm : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8086,7 +8086,7 @@ struct llm_build_qwen : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8206,7 +8206,7 @@ struct llm_build_qwen2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8320,8 +8320,9 @@ struct llm_build_dream : public llm_graph_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr,
-                                 nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8420,8 +8421,9 @@ struct llm_build_llada : public llm_graph_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr,
-                                 1.0f / sqrtf(float(n_embd_head)), il);
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8534,7 +8536,7 @@ struct llm_build_qwen2vl : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8661,7 +8663,7 @@ struct llm_build_qwen2moe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8814,7 +8816,7 @@ struct llm_build_qwen3 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -8935,7 +8937,7 @@ struct llm_build_qwen3moe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9075,7 +9077,7 @@ struct llm_build_phi2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9212,7 +9214,7 @@ struct llm_build_phi3 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9346,7 +9348,7 @@ struct llm_build_plamo : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9454,7 +9456,7 @@ struct llm_build_gpt2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9568,7 +9570,7 @@ struct llm_build_codeshell : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9697,7 +9699,7 @@ struct llm_build_orion : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -9824,7 +9826,7 @@ struct llm_build_internlm2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -10012,7 +10014,7 @@ struct llm_build_minicpm3 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        q_states, k_states, v_states, nullptr, nullptr, kq_scale, il);
+                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -10142,7 +10144,7 @@ struct llm_build_gemma : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -10257,7 +10259,7 @@ struct llm_build_gemma2_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -10399,7 +10401,7 @@ struct llm_build_gemma3_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -10580,7 +10582,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, hparams.f_attention_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
             } else {
                 // no KV layers
                 ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
@@ -10598,7 +10600,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                     model.layers[il].wo, NULL,
-                    Qcur, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
+                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
             }
 
             cur = build_norm(cur,
@@ -10963,7 +10965,7 @@ struct llm_build_starcoder2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -11390,7 +11392,9 @@ struct llm_build_jamba : public llm_graph_context_mamba {
                 cb(Vcur, "Vcur", il);
 
                 // No RoPE :)
-                cur = build_attn(inp_hybrid->get_attn(), model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);
+                cur = build_attn(inp_hybrid->get_attn(),
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -11548,7 +11552,7 @@ struct llm_build_command_r : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -11683,7 +11687,7 @@ struct llm_build_cohere2_iswa : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -11814,7 +11818,7 @@ struct llm_build_olmo : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -11934,7 +11938,7 @@ struct llm_build_olmo2 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12067,7 +12071,7 @@ struct llm_build_olmoe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12200,7 +12204,7 @@ struct llm_build_openelm : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12312,7 +12316,7 @@ struct llm_build_gptneox : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12462,7 +12466,7 @@ struct llm_build_arctic : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12617,7 +12621,7 @@ struct llm_build_deepseek : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -12845,7 +12849,7 @@ struct llm_build_deepseek2 : public llm_graph_context {
                     // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
                     cur = build_attn(inp_attn,
                             model.layers[il].wo, NULL,
-                            Qcur, Kcur, Vcur, nullptr, model.layers[il].wv_b, kq_scale, il);
+                            Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
                 } else {
                     ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);
                     cb(kv, "kv", il);
@@ -12879,7 +12883,7 @@ struct llm_build_deepseek2 : public llm_graph_context {
                     // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
                     cur = build_attn(inp_attn,
                             model.layers[il].wo, NULL,
-                            Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 }
             }
 
@@ -13046,7 +13050,7 @@ struct llm_build_bitnet : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         NULL, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 
                 cur = build_norm(cur,
                         model.layers[il].attn_sub_norm, NULL,
@@ -13169,7 +13173,7 @@ struct llm_build_t5_enc : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo_enc, nullptr,
-                        Qcur, Kcur, Vcur, kq_b, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
                 cb(cur, "kqv_out", il);
             }
 
@@ -13275,7 +13279,7 @@ struct llm_build_t5_dec : public llm_graph_context {
 
                 cur = build_attn(inp_attn_self,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, kq_b, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
                 cb(cur, "kqv_out", il);
             }
 
@@ -13307,7 +13311,7 @@ struct llm_build_t5_dec : public llm_graph_context {
 
                 cur = build_attn(inp_attn_cross,
                         model.layers[il].wo_cross, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
                 cb(cur, "kqv_out", il);
 
                 //ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
@@ -13439,7 +13443,7 @@ struct llm_build_jais : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/float(n_embd_head), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/float(n_embd_head), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -13571,7 +13575,7 @@ struct llm_build_chatglm : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -13704,7 +13708,7 @@ struct llm_build_glm4 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -13853,7 +13857,7 @@ struct llm_build_glm4_moe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_transformer_layers - 1 && inp_out_ids) {
@@ -14007,7 +14011,7 @@ struct llm_build_nemotron : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -14138,7 +14142,7 @@ struct llm_build_exaone : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -14269,7 +14273,7 @@ struct llm_build_exaone4 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
                 cb(cur, "attn_out", il);
             }
 
@@ -15204,7 +15208,7 @@ struct llm_build_granite : public llm_graph_context {
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
         cur = build_attn(inp_attn,
                 model.layers[il].wo, model.layers[il].bo,
-                Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
         return cur;
     }
@@ -15423,7 +15427,7 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba {
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
         cur = build_attn(inp_attn,
                 model.layers[il].wo, model.layers[il].bo,
-                Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
         return cur;
     }
@@ -15608,7 +15612,7 @@ struct llm_build_chameleon : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -15964,7 +15968,7 @@ struct llm_build_plm : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        q_states, k_states, v_states, nullptr, nullptr, kq_scale, il);
+                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -16087,7 +16091,7 @@ struct llm_build_bailingmoe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -16227,7 +16231,7 @@ struct llm_build_dots1 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {
@@ -16382,7 +16386,7 @@ struct llm_build_ernie4_5 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1) {
@@ -16515,7 +16519,7 @@ struct llm_build_ernie4_5_moe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
                 cb(cur, "attn_out", il);
             }
 
@@ -16668,7 +16672,7 @@ struct llm_build_falcon_h1 : public llm_graph_context_mamba {
 
             ggml_tensor * attn_out = build_attn(inp->get_attn(),
                     model.layers[il].wo, NULL,
-                    Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             cb(attn_out, "attn_out", il);
 
             cur = build_norm(inpL,
@@ -16878,7 +16882,9 @@ private:
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
 
-            cur = build_attn(inp, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f/sqrtf(float(n_embd_head_v)), il);
+            cur = build_attn(inp,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head_v)), il);
         }
 
         cb(cur, "attn_out", il);
@@ -17125,7 +17131,7 @@ struct llm_build_arcee : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -17270,7 +17276,7 @@ struct llm_build_hunyuan_moe : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -17430,7 +17436,7 @@ struct llm_build_hunyuan_dense : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -17560,7 +17566,7 @@ struct llm_build_smollm3 : public llm_graph_context {
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
                 cb(cur, "attn_out", il);
             }
 
@@ -17682,9 +17688,9 @@ struct llm_build_openai_moe_iswa : public llm_graph_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                cur = build_attn_with_sinks(inp_attn,
+                cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].attn_sinks, 1.0f/sqrtf(float(n_rot)), il);
+                        Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f/sqrtf(float(n_rot)), il);
 
                 cb(cur, "attn_out", il);
             }
@@ -17847,7 +17853,7 @@ struct llm_build_lfm2 : public llm_graph_context {
                 );
 
         cur = build_attn(inp_attn, model.layers[il].wo, NULL,
-                q, k, v, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                q, k, v, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 
         cb(cur, "model.layers.{}.self_attn.out_proj", il);
 
@@ -17991,7 +17997,7 @@ struct llm_build_smallthinker : public llm_graph_context{
 
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
             }
 
             if (il == n_layer - 1 && inp_out_ids) {

From cd36b5e5c7fed2a3ac671dd542d579ca40b48b54 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 21 Aug 2025 19:13:45 +0300
Subject: [PATCH 142/287] llama : remove deprecated llama_kv_self API (#15472)

ggml-ci
---
 include/llama.h       | 105 -----------------------
 src/llama-context.cpp | 189 +-----------------------------------------
 src/llama-context.h   |   9 +-
 3 files changed, 6 insertions(+), 297 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index c465ced4ff..662e0971df 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -663,111 +663,6 @@ extern "C" {
     // Check if the memory supports shifting
     LLAMA_API bool llama_memory_can_shift(llama_memory_t mem);
 
-    //
-    // KV cache for self-attention (TODO: deprecate in favor of llama_memory)
-    //
-
-    // Returns the number of tokens in the KV cache (slow, use only for debug)
-    // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-    DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx),
-               "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");
-
-    // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-    DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx),
-               "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");
-
-    // Clear the KV cache - both cell info is erased and KV data is zeroed
-    DEPRECATED(LLAMA_API void llama_kv_self_clear(
-                struct llama_context * ctx),
-            "Use llama_memory_clear() instead");
-
-    // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
-    // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
-    // seq_id < 0 : match any sequence
-    // p0 < 0     : [0,  p1]
-    // p1 < 0     : [p0, inf)
-    DEPRECATED(LLAMA_API bool llama_kv_self_seq_rm(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1),
-            "Use llama_memory_seq_rm() instead");
-
-    // Copy all tokens that belong to the specified sequence to another sequence
-    // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
-    // p0 < 0 : [0,  p1]
-    // p1 < 0 : [p0, inf)
-    DEPRECATED(LLAMA_API void llama_kv_self_seq_cp(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id_src,
-                    llama_seq_id   seq_id_dst,
-                       llama_pos   p0,
-                       llama_pos   p1),
-            "Use llama_memory_seq_cp() instead");
-
-    // Removes all tokens that do not belong to the specified sequence
-    DEPRECATED(LLAMA_API void llama_kv_self_seq_keep(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id),
-            "Use llama_memory_seq_keep() instead");
-
-    // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
-    // If the KV cache is RoPEd, the KV data is updated accordingly:
-    //   - lazily on next llama_decode()
-    // p0 < 0 : [0,  p1]
-    // p1 < 0 : [p0, inf)
-    DEPRECATED(LLAMA_API void llama_kv_self_seq_add(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1,
-                       llama_pos   delta),
-            "Use llama_memory_seq_add() instead");
-
-    // Integer division of the positions by factor of `d > 1`
-    // If the KV cache is RoPEd, the KV data is updated accordingly:
-    //   - lazily on next llama_decode()
-    // p0 < 0 : [0,  p1]
-    // p1 < 0 : [p0, inf)
-    DEPRECATED(LLAMA_API void llama_kv_self_seq_div(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id,
-                       llama_pos   p0,
-                       llama_pos   p1,
-                             int   d),
-            "Use llama_memory_seq_div() instead");
-
-    // Returns the smallest position present in the KV cache for the specified sequence
-    // This is typically non-zero only for SWA caches
-    // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
-    // Return -1 if the sequence is empty
-    DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_min(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id),
-            "Use llama_memory_seq_pos_min() instead");
-
-    // Returns the largest position present in the KV cache for the specified sequence
-    // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
-    // Return -1 if the sequence is empty
-    DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_max(
-            struct llama_context * ctx,
-                    llama_seq_id   seq_id),
-            "Use llama_memory_seq_pos_max() instead");
-
-    // Defragment the KV cache
-    // This will be applied:
-    //   - lazily on next llama_decode()
-    DEPRECATED(LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx),
-            "simply remove this call, the context will automatically decide when to do a defragmentation based on 'defrag_thold'");
-
-    // Check if the context supports KV cache shifting
-    DEPRECATED(LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx),
-            "use llama_memory_can_shift() instead");
-
-    // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
-    DEPRECATED(LLAMA_API void llama_kv_self_update(struct llama_context * ctx),
-            "simply remove this call, updates are applied lazily on the next llama_decode()");
-
     //
     // State / sessions
     //
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index fb6fbe982c..e8e8b3450a 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -93,7 +93,7 @@ llama_context::llama_context(
     // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
     // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
     // ref: https://github.com/ggerganov/llama.cpp/pull/5021
-    // TODO: this padding is not needed for the cache-less context so we should probably move it to llama_context_kv_self
+    // TODO: this padding is not needed for the cache-less context so we should probably move it to llama_memory
     if (cparams.n_batch < GGML_KQ_MASK_PAD) {
         LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
         cparams.n_batch = GGML_KQ_MASK_PAD;
@@ -439,26 +439,12 @@ llama_memory_t llama_context::get_memory() const {
     return memory.get();
 }
 
-// deprecated
-void llama_context::kv_self_defrag_sched() {
-    if (!memory) {
-        return;
-    }
-
-    memory_force_optimize = true;
-}
-
-// deprecated
-bool llama_context::kv_self_update(bool optimize) {
+bool llama_context::memory_update(bool optimize) {
     if (!memory) {
         return false;
     }
 
     {
-        // TODO: remove in the future
-        optimize |= memory_force_optimize;
-        memory_force_optimize = false;
-
         const auto mctx = memory->init_update(this, optimize);
         switch (mctx->get_status()) {
             case LLAMA_MEMORY_STATUS_SUCCESS:
@@ -993,7 +979,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     bool did_optimize = false;
 
     // handle any pending defrags/shifts
-    kv_self_update(false);
+    memory_update(false);
 
     llama_memory_context_ptr mctx;
 
@@ -1018,7 +1004,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
                     if (!did_optimize) {
                         did_optimize = true;
 
-                        if (kv_self_update(true)) {
+                        if (memory_update(true)) {
                             LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, balloc->get_n_tokens());
 
                             continue;
@@ -2338,11 +2324,6 @@ const llama_model * llama_get_model(const llama_context * ctx) {
     return &ctx->get_model();
 }
 
-// deprecated
-void llama_kv_self_update(llama_context * ctx) {
-    ctx->kv_self_update(false);
-}
-
 enum llama_pooling_type llama_pooling_type(const llama_context * ctx) {
     return ctx->pooling_type();
 }
@@ -2560,168 +2541,6 @@ bool llama_memory_can_shift(llama_memory_t mem) {
     return mem->get_can_shift();
 }
 
-//
-// kv cache
-//
-
-// deprecated
-int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
-    const auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return 0;
-    }
-
-    int32_t res = 0;
-
-    for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
-        const llama_pos p0 = kv->seq_pos_min(s);
-        const llama_pos p1 = kv->seq_pos_max(s);
-
-        if (p0 >= 0) {
-            res += (p1 - p0) + 1;
-        }
-    }
-
-    return res;
-}
-
-// deprecated
-// note: this is the same as above - will be removed anyway, so it's ok
-int32_t llama_kv_self_used_cells(const llama_context * ctx) {
-    const auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return 0;
-    }
-
-    int32_t res = 0;
-
-    for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
-        const llama_pos p0 = kv->seq_pos_min(s);
-        const llama_pos p1 = kv->seq_pos_max(s);
-
-        if (p0 >= 0) {
-            res += (p1 - p0) + 1;
-        }
-    }
-
-    return res;
-}
-
-// deprecated
-void llama_kv_self_clear(llama_context * ctx) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return;
-    }
-
-    llama_memory_clear(kv, true);
-}
-
-// deprecated
-bool llama_kv_self_seq_rm(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return true;
-    }
-
-    return llama_memory_seq_rm(kv, seq_id, p0, p1);
-}
-
-// deprecated
-void llama_kv_self_seq_cp(
-        llama_context * ctx,
-         llama_seq_id   seq_id_src,
-         llama_seq_id   seq_id_dst,
-            llama_pos   p0,
-            llama_pos   p1) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return;
-    }
-
-    llama_memory_seq_cp(kv, seq_id_src, seq_id_dst, p0, p1);
-}
-
-// deprecated
-void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return;
-    }
-
-    llama_memory_seq_keep(kv, seq_id);
-}
-
-// deprecated
-void llama_kv_self_seq_add(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1,
-            llama_pos   delta) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return;
-    }
-
-    llama_memory_seq_add(kv, seq_id, p0, p1, delta);
-}
-
-// deprecated
-void llama_kv_self_seq_div(
-        llama_context * ctx,
-         llama_seq_id   seq_id,
-            llama_pos   p0,
-            llama_pos   p1,
-                  int   d) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return;
-    }
-
-    llama_memory_seq_div(kv, seq_id, p0, p1, d);
-}
-
-// deprecated
-llama_pos llama_kv_self_seq_pos_min(llama_context * ctx, llama_seq_id seq_id) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return -1;
-    }
-
-    return llama_memory_seq_pos_min(kv, seq_id);
-}
-
-// deprecated
-llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return -1;
-    }
-
-    return llama_memory_seq_pos_max(kv, seq_id);
-}
-
-// deprecated
-void llama_kv_self_defrag(llama_context * ctx) {
-    // force defrag
-    ctx->kv_self_defrag_sched();
-}
-
-// deprecated
-bool llama_kv_self_can_shift(const llama_context * ctx) {
-    auto * kv = llama_get_memory(ctx);
-    if (!kv) {
-        return false;
-    }
-
-    return llama_memory_can_shift(kv);
-}
-
 // llama state API
 
 // deprecated
diff --git a/src/llama-context.h b/src/llama-context.h
index 230ef8962b..3dd9205446 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -46,10 +46,8 @@ struct llama_context {
 
     llama_memory_t get_memory() const;
 
-    // return true of the KV cache was updated
-    // TODO: remove
-    bool kv_self_update(bool optimize);
-    void kv_self_defrag_sched();
+    // return true if the memory was updated
+    bool memory_update(bool optimize);
 
     enum llama_pooling_type pooling_type() const;
 
@@ -230,9 +228,6 @@ private:
 
     std::unique_ptr<llama_memory_i> memory;
 
-    // TODO: temporary, until the llama_kv_self_defrag() API is removed
-    bool memory_force_optimize = false;
-
     // decode output (2-dimensional array: [n_outputs][n_vocab])
     size_t  logits_size = 0; // capacity (of floats) for logits
     float * logits      = nullptr;

From 54a241f505d515d625767b993bfd573ecee306b9 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Thu, 21 Aug 2025 14:09:32 -0700
Subject: [PATCH 143/287] sched : fix possible use of wrong ids tensor when
 offloading moe prompt processing (#15488)

---
 common/arg.cpp            |  2 +-
 ggml/src/ggml-backend.cpp | 27 ++++++++++++++++++++-------
 2 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index eeb6c94af4..1227aeb2a3 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1755,7 +1755,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index c1e58fbb64..e34feccc98 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1355,15 +1355,15 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
     std::vector<int32_t> ids;
     std::vector<ggml_bitset_t> used_ids;
 
-    for (int i = 0; i < sched->n_splits; i++) {
-        struct ggml_backend_sched_split * split = &splits[i];
+    for (int split_id = 0; split_id < sched->n_splits; split_id++) {
+        struct ggml_backend_sched_split * split = &splits[split_id];
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
         // copy the input tensors to the split backend
-        for (int j = 0; j < split->n_inputs; j++) {
-            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
-            struct ggml_tensor * input = split->inputs[j];
+        for (int input_id = 0; input_id < split->n_inputs; input_id++) {
+            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
+            struct ggml_tensor * input = split->inputs[input_id];
             struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);
 
             if (input->flags & GGML_TENSOR_FLAG_INPUT) {
@@ -1398,10 +1398,22 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
 
                     // get the ids
                     ggml_tensor * ids_tensor = node->src[2];
+                    ggml_backend_t ids_backend = split_backend;
+
+                    // if the ids tensor is also an input of the split, it may not have been copied yet to the split backend
+                    // in that case, we use the original ids tensor
+                    for (int i = input_id + 1; i < split->n_inputs; i++) {
+                        if (ids_tensor == tensor_copy(split->inputs[i], split_backend_id, sched->cur_copy)) {
+                            ids_tensor = split->inputs[i];
+                            ids_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[i]);
+                            break;
+                        }
+                    }
+
                     if (ids_tensor != prev_ids_tensor) {
                         ids.resize(ggml_nbytes(ids_tensor) / sizeof(int32_t));
-                        ggml_backend_tensor_get_async(split_backend, ids_tensor, ids.data(), 0, ggml_nbytes(ids_tensor));
-                        ggml_backend_synchronize(split_backend);
+                        ggml_backend_tensor_get_async(ids_backend, ids_tensor, ids.data(), 0, ggml_nbytes(ids_tensor));
+                        ggml_backend_synchronize(ids_backend);
 
                         // find the used experts
                         used_ids.clear();
@@ -1409,6 +1421,7 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                         for (int64_t i1 = 0; i1 < ids_tensor->ne[1]; i1++) {
                             for (int64_t i0 = 0; i0 < ids_tensor->ne[0]; i0++) {
                                 int32_t id = ids[i1 * ids_tensor->nb[1]/sizeof(int32_t) + i0 * ids_tensor->nb[0]/sizeof(int32_t)];
+                                GGML_ASSERT(id >= 0 && id < n_expert);
                                 ggml_bitset_set(used_ids.data(), id);
                             }
                         }

From a0f98dd604d34826eb5ea2560d1e23fe726921df Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Fri, 22 Aug 2025 14:12:07 +0800
Subject: [PATCH 144/287] CANN: Optimize RMS_NORM using cache (#15419)

* [CANN] Optimize RMS_NORM using cache

Signed-off-by: noemotiovon <757486878@qq.com>

* fix typo

Signed-off-by: noemotiovon <757486878@qq.com>

* fix review comment

Signed-off-by: noemotiovon <757486878@qq.com>

* codestyle adjustment

Signed-off-by: noemotiovon <757486878@qq.com>

---------

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 153 +++++++++++++++++++++++--------
 ggml/src/ggml-cann/common.h      |   4 +
 2 files changed, 121 insertions(+), 36 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 2a5cb8abfa..8f65904b8f 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -867,6 +867,86 @@ static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer,
     return acl_tensor;
 }
 
+/**
+ * @brief Fills a tensor with a scalar value.
+ *
+ * This function fills the destination tensor `acl_dst` with the scalar value
+ * `scalar`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param scalar The scalar value used to fill the tensor.
+ * @param acl_dst The destination tensor to be filled with the scalar value.
+ */
+static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar,
+                              aclTensor* acl_dst) {
+    auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst, acl_scalar);
+    ggml_cann_release_resources(ctx, acl_scalar);
+}
+
+/**
+ * @brief Get or expand a cached float32 tensor filled with a scalar value.
+ *
+ * This function manages cached device memory for float32 tensors. If the current
+ * cache size is insufficient for the requested tensor shape, the old memory will
+ * be released and new memory will be allocated. The allocated buffer is then
+ * initialized either with zeros (when @p value == 0.0f) or with the given scalar
+ * value using CANN operations. Finally, an aclTensor object is created from the
+ * cached memory and returned.
+ *
+ * @param ctx           The CANN backend context that manages device memory.
+ * @param buffer        A pointer to the cached device buffer (will be allocated
+ *                      or reallocated if necessary).
+ * @param cache_element The current number of cached elements. This will be
+ *                      updated when the cache is expanded.
+ * @param ne            The tensor shape array (number of elements in each dimension).
+ * @param nb            The stride size for each dimension.
+ * @param dims          The number of tensor dimensions.
+ * @param value         The scalar value used to fill the tensor (supports zero
+ *                      initialization via memset or arbitrary values via fill_scalar).
+ * @return              An aclTensor pointer created from the cached buffer.
+ */
+static aclTensor* get_f32_cache_acl_tensor(
+    ggml_backend_cann_context& ctx,
+    void** buffer,
+    int64_t &cache_element,
+    int64_t* ne,
+    size_t* nb,
+    int64_t dims,
+    float value) {
+    // Calculate total number of elements
+    int64_t n_element = 1;
+    for (int i = 0; i < dims; i++) {
+        n_element *= ne[i];
+    }
+    size_t size = n_element * sizeof(float);
+
+    // Allocate or expand cache if needed
+    if (cache_element < n_element) {
+        if (*buffer != nullptr) {
+            aclrtFree(*buffer);
+            *buffer = nullptr;
+        }
+
+        ACL_CHECK(aclrtMalloc(buffer, size, ACL_MEM_MALLOC_HUGE_FIRST));
+        cache_element = n_element;
+
+        // Initialize cache
+        if (value == 0.0f) {
+            ACL_CHECK(aclrtMemsetAsync(*buffer, size, 0, size, ctx.stream()));
+        } else {
+            int64_t pool_ne[1] = { n_element };
+            size_t pool_nb[1] = { sizeof(float) };
+            aclTensor* acl_value = ggml_cann_create_tensor(
+                *buffer, ACL_FLOAT, sizeof(float), pool_ne, pool_nb, 1);
+            aclnn_fill_scalar(ctx, 1, acl_value);
+            ggml_cann_release_resources(ctx, acl_value);
+        }
+    }
+
+    return ggml_cann_create_tensor(*buffer, ACL_FLOAT, sizeof(float), ne, nb, dims);
+}
+
 void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     ggml_tensor* src = dst->src[0];
 
@@ -875,20 +955,39 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
-    size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src);
-    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
 
-    aclTensor* acl_gamma = aclnn_values(
-        ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1,
-        ggml_cann_type_mapping(src->type), ggml_element_size(src));
+    // build gamma, one...
+    size_t acl_gamma_nb[GGML_MAX_DIMS];
+    acl_gamma_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        acl_gamma_nb[i] = acl_gamma_nb[i - 1] * src->ne[i - 1];
+    }
+    aclTensor* acl_gamma = get_f32_cache_acl_tensor(
+        ctx,
+        &ctx.f32_one_cache,
+        ctx.f32_one_cache_element,
+        src->ne,
+        acl_gamma_nb,
+        1,        // dims
+        1.0f      // value
+    );
+
+    // build rstd, zero...
+    size_t acl_rstd_nb[GGML_MAX_DIMS];
+    acl_rstd_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * src->ne[i - 1];
+    }
+    aclTensor* acl_rstd = get_f32_cache_acl_tensor(
+        ctx,
+        &ctx.f32_zero_cache,
+        ctx.f32_zero_cache_element,
+        src->ne,
+        acl_rstd_nb,
+        GGML_MAX_DIMS,
+        0.0f      // value
+    );
 
-    size_t zero_tensor_n_bytes =
-        src->ne[1] * src->ne[2] * src->ne[3] * ggml_element_size(src);
-    ggml_cann_pool_alloc zero_tensor_allocator(ctx.pool(), zero_tensor_n_bytes);
-    aclTensor* acl_rstd =
-        aclnn_zero(ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes,
-                   src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
-                   ggml_element_size(src));
     GGML_CANN_CALL_ACLNN_OP(ctx, RmsNorm, acl_src, acl_gamma, eps, acl_dst, acl_rstd);
     ggml_cann_release_resources(ctx, acl_src, acl_dst, acl_gamma, acl_rstd);
 }
@@ -903,14 +1002,13 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     const int n_past = ((int32_t*)dst->op_params)[0];
 
-    size_t one_tensor_n_bytes = src->ne[0] * src->ne[1] * src->ne[2] *
-                                src->ne[3] * ggml_element_size(src);
-    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), ggml_nbytes(src));
+    void* buffer = one_tensor_allocator.get();
 
-    aclTensor* mask_tensor =
-        aclnn_values(ctx, one_tensor_allocator.get(), one_tensor_n_bytes,
-                     src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
-                     ggml_element_size(src), value);
+    aclTensor* mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type),
+        ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS);
+
+    aclnn_fill_scalar(ctx, value, mask_tensor);
 
     aclScalar* alpha = nullptr;
     float alphaValue = 1.0f;
@@ -1277,23 +1375,6 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx,
         tmp_permute_tensor, tmp_mul_tensor, acl_dst);
 }
 
-/**
- * @brief Fills a tensor with a scalar value.
- *
- * This function fills the destination tensor `acl_dst` with the scalar value
- * `scalar`.
- *
- * @param ctx The context for the CANN backend operations.
- * @param scalar The scalar value used to fill the tensor.
- * @param acl_dst The destination tensor to be filled with the scalar value.
- */
-static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar,
-                              aclTensor* acl_dst) {
-    auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst, acl_scalar);
-    ggml_cann_release_resources(ctx, acl_scalar);
-}
-
 /**
  * @brief Raises each element of a tensor to the power of the corresponding
  * element in another tensor.
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 2c2033bfba..5858bd3f6a 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -379,6 +379,10 @@ struct ggml_backend_cann_context {
     cann_task_queue task_queue;
     bool async_mode;
     bool support_set_rows;
+    void* f32_zero_cache = nullptr;
+    void* f32_one_cache = nullptr;
+    int64_t f32_zero_cache_element = 0;
+    int64_t f32_one_cache_element = 0;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 

From e288693669cf9d0a71e2f2b8bd57305f06340257 Mon Sep 17 00:00:00 2001
From: Tarek Dakhran <tarek@liquid.ai>
Date: Fri, 22 Aug 2025 09:29:08 +0200
Subject: [PATCH 145/287] readme : model : mtdm : lfm2 improvements (#15476)

* Support untied embeddings

* Increase number of image tokens to 1024

* Add LFM2-VL to readme

* Actually use untied embeddings
---
 README.md                 |  1 +
 gguf-py/gguf/constants.py |  1 +
 src/llama-arch.cpp        |  1 +
 src/llama-model.cpp       | 10 +++++++---
 tools/mtmd/clip.cpp       |  2 +-
 5 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 8446756384..a01ef6d503 100644
--- a/README.md
+++ b/README.md
@@ -151,6 +151,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
 - [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
 - [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
+- [x] [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)
 
 </details>
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 41804f3a2b..61ebe6e5e7 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -2590,6 +2590,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_K,
         MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.OUTPUT,
     ],
     MODEL_ARCH.SMALLTHINKER: [
         MODEL_TENSOR.TOKEN_EMBD,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 18dcc6ddfe..c759a9c6d9 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -2010,6 +2010,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
             { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
             { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT,            "output" },
         }
     },
     {
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c4f0b12f24..3c8440a8f6 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -5474,8 +5474,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_LFM2:
                 {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
                     tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT,          "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
 
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
@@ -17787,8 +17792,7 @@ struct llm_build_lfm2 : public llm_graph_context {
         cb(cur, "model.embedding_norm", -1);
         res->t_embd = cur;
 
-        // lm_head is tied with embeddings
-        cur = build_lora_mm(model.tok_embd, cur);
+        cur = build_lora_mm(model.output, cur);
         cb(cur, "lm_head", -1);
 
         res->t_logits = cur;
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 1676c32836..b3628db64f 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -3513,7 +3513,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         const int height = img->ny;
         const int total_factor = params.patch_size * params.proj_scale_factor;
         constexpr int min_image_tokens = 64;
-        constexpr int max_image_tokens = 256;
+        constexpr int max_image_tokens = 1024;
         const float min_pixels = min_image_tokens * total_factor * total_factor;
         const float max_pixels = max_image_tokens * total_factor * total_factor;
 

From 4afb0a746f22abaa545b3ebdb76a400d7da3a713 Mon Sep 17 00:00:00 2001
From: 65a <10104049+65a@users.noreply.github.com>
Date: Fri, 22 Aug 2025 08:10:14 +0000
Subject: [PATCH 146/287] server : Support multimodal completion and embeddings
 prompts in JSON format (#15108)

- Use server_tokens in more places in server and util.cpp
- Convert most functions that used llama_tokens to server_tokens
- Modify input tokenizer to handle JSON objects as subprompts
- Break out MTMD prompt parsing into utility function
- Support JSON objects with multimodal_data arrays for MTMD prompts along with other existing types
- Add capability to model endpoint to indicate if client can send multimodal data
- Add tests.
---
 tools/server/README.md                     |  17 +-
 tools/server/server.cpp                    |  77 ++-----
 tools/server/tests/unit/test_completion.py |  38 ++++
 tools/server/tests/unit/test_vision_api.py |  93 +++++++-
 tools/server/utils.hpp                     | 236 ++++++++++++++-------
 5 files changed, 323 insertions(+), 138 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index af9264ddd3..86844225ff 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -226,6 +226,10 @@ services:
 ### Multimodal support
 
 Multimodal support was added in [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) and is currently an experimental feature.
+It is currently available in the following endpoints:
+- The OAI-compatible chat endpoint.
+- The non-OAI-compatible completions endpoint.
+- The non-OAI-compatible embeddings endpoint.
 
 For more details, please refer to [multimodal documentation](../../docs/multimodal.md)
 
@@ -400,12 +404,15 @@ These input shapes and data type are allowed for `prompt`:
   - Single string: `"string"`
   - Single sequence of tokens: `[12, 34, 56]`
   - Mixed tokens and strings: `[12, 34, "string", 56, 78]`
+  - A JSON object which optionally contains multimodal data: `{ "prompt_string": "string", "multimodal_data": ["base64"] }`
 
 Multiple prompts are also supported. In this case, the completion result will be an array.
 
   - Only strings: `["string1", "string2"]`
-  - Strings and sequences of tokens: `["string1", [12, 34, 56]]`
-  - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]`
+  - Strings, JSON objects, and sequences of tokens: `["string1", [12, 34, 56], { "prompt_string": "string", "multimodal_data": ["base64"]}]`
+  - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string", { "prompt_string": "string" }]`
+
+Note for `multimodal_data` in JSON object prompts. This should be an array of strings, containing base64 encoded multimodal data such as images and audio. There must be an identical number of MTMD media markers in the string prompt element which act as placeholders for the data provided to this parameter. The multimodal data files will be substituted in order. The marker string (e.g. `<__media__>`) can be found by calling `mtmd_default_marker()` defined in [the MTMD C API](https://github.com/ggml-org/llama.cpp/blob/5fd160bbd9d70b94b5b11b0001fd7f477005e4a0/tools/mtmd/mtmd.h#L87). A client *must not* specify this field unless the server has the multimodal capability. Clients should check `/models` or `/v1/models` for the `multimodal` capability before a multimodal request.
 
 `temperature`: Adjust the randomness of the generated text. Default: `0.8`
 
@@ -477,8 +484,6 @@ These words will not be included in the completion, so make sure to add them to
 
 `t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled.
 
-`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
-
 `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
 
 `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`
@@ -638,12 +643,12 @@ Returns a JSON object with a field `prompt` containing a string of the input mes
 
 The same as [the embedding example](../embedding) does.
 
+This endpoint also supports multimodal embeddings. See the documentation for the `/completions` endpoint for details on how to send a multimodal prompt.
+
 *Options:*
 
 `content`: Set the text to process.
 
-`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
-
 `embd_normalize`: Normalization for pooled embeddings. Can be one of the following values:
 ```
   -1: No normalization
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 35b060674b..6eb5aeb582 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -4309,6 +4309,7 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_api_show = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+        bool has_mtmd = ctx_server.mctx != nullptr;
         json data = {
             {
                 "template", common_chat_templates_source(ctx_server.chat_templates.get()),
@@ -4330,7 +4331,7 @@ int main(int argc, char ** argv) {
                 {"quantization_level", ""}
             }},
             {"model_info", ""},
-            {"capabilities", {"completion"}}
+            {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})}
         };
 
         res_ok(res, data);
@@ -4356,56 +4357,15 @@ int main(int argc, char ** argv) {
             // TODO: this log can become very long, put it behind a flag or think about a more compact format
             //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
 
-            // process files
-            mtmd::bitmaps bitmaps;
-            const bool has_mtmd = ctx_server.mctx != nullptr;
-            {
-                if (!has_mtmd && !files.empty()) {
-                    throw std::runtime_error("This server does not support multimodal");
-                }
-                for (auto & file : files) {
-                    mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_buf(ctx_server.mctx, file.data(), file.size()));
-                    if (!bmp.ptr) {
-                        throw std::runtime_error("Failed to load image or audio file");
-                    }
-                    // calculate bitmap hash (for KV caching)
-                    std::string hash = fnv_hash(bmp.data(), bmp.n_bytes());
-                    bmp.set_id(hash.c_str());
-                    bitmaps.entries.push_back(std::move(bmp));
-                }
-            }
-
             // process prompt
             std::vector<server_tokens> inputs;
 
-            if (oaicompat && has_mtmd) {
-                // multimodal
-                std::string prompt_str = prompt.get<std::string>();
-                mtmd_input_text inp_txt = {
-                    prompt_str.c_str(),
-                    /* add_special */   true,
-                    /* parse_special */ true,
-                };
-                mtmd::input_chunks chunks(mtmd_input_chunks_init());
-                auto bitmaps_c_ptr = bitmaps.c_ptr();
-                int32_t tokenized = mtmd_tokenize(ctx_server.mctx,
-                                                    chunks.ptr.get(),
-                                                    &inp_txt,
-                                                    bitmaps_c_ptr.data(),
-                                                    bitmaps_c_ptr.size());
-                if (tokenized != 0) {
-                    throw std::runtime_error("Failed to tokenize prompt");
-                }
-
-                server_tokens tmp(chunks, true);
-                inputs.push_back(std::move(tmp));
+            if (oaicompat && ctx_server.mctx != nullptr) {
+                // This is the case used by OAI compatible chat path with MTMD. TODO It can be moved to the path below.
+                inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files));
             } else {
-                // non-multimodal version
-                auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
-                for (auto & p : tokenized_prompts) {
-                    auto tmp = server_tokens(p, ctx_server.mctx != nullptr);
-                    inputs.push_back(std::move(tmp));
-                }
+                // Everything else, including multimodal completions.
+                inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
             }
 
             tasks.reserve(inputs.size());
@@ -4574,7 +4534,7 @@ int main(int argc, char ** argv) {
         data["input_extra"] = input_extra; // default to empty array if it's not exist
 
         std::string prompt = json_value(data, "prompt", std::string());
-        std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, false, true);
+        std::vector<server_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, false, true);
         SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
         data["prompt"] = format_infill(
             ctx_server.vocab,
@@ -4585,7 +4545,7 @@ int main(int argc, char ** argv) {
             ctx_server.params_base.n_predict,
             ctx_server.slots[0].n_ctx, // TODO: there should be a better way
             ctx_server.params_base.spm_infill,
-            tokenized_prompts[0]
+            tokenized_prompts[0].get_text_tokens() // TODO: this could maybe be multimodal.
         );
 
         std::vector<raw_buffer> files; // dummy
@@ -4634,7 +4594,7 @@ int main(int argc, char ** argv) {
         if (current_state == SERVER_STATE_READY) {
             model_meta = ctx_server.model_meta();
         }
-
+        bool has_mtmd = ctx_server.mctx != nullptr;
         json models = {
             {"models", {
                 {
@@ -4646,7 +4606,7 @@ int main(int argc, char ** argv) {
                     {"type", "model"},
                     {"description", ""},
                     {"tags", {""}},
-                    {"capabilities", {"completion"}},
+                    {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})},
                     {"parameters", ""},
                     {"details", {
                         {"parent_model", ""},
@@ -4763,7 +4723,7 @@ int main(int argc, char ** argv) {
             }
         }
 
-        auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
+        auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
         for (const auto & tokens : tokenized_prompts) {
             // this check is necessary for models that do not add BOS token to the input
             if (tokens.empty()) {
@@ -4791,7 +4751,7 @@ int main(int argc, char ** argv) {
 
                 task.id            = ctx_server.queue_tasks.get_new_id();
                 task.index         = i;
-                task.prompt_tokens = server_tokens(tokenized_prompts[i], ctx_server.mctx != nullptr);
+                task.prompt_tokens = std::move(tokenized_prompts[i]);
 
                 // OAI-compat
                 task.params.oaicompat = oaicompat;
@@ -4878,7 +4838,10 @@ int main(int argc, char ** argv) {
             return;
         }
 
-        llama_tokens tokenized_query = tokenize_input_prompts(ctx_server.vocab, query, /* add_special */ false, true)[0];
+        std::vector<server_tokens> tokenized_queries = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, query, /* add_special */ false, true);
+        if (tokenized_queries.size() != 1) {
+            res_error(res, format_error_response("\"query\" must contain only a single prompt", ERROR_TYPE_INVALID_REQUEST));
+        }
 
         // create and queue the task
         json responses = json::array();
@@ -4886,14 +4849,14 @@ int main(int argc, char ** argv) {
         std::unordered_set<int> task_ids;
         {
             std::vector<server_task> tasks;
-            auto tokenized_docs = tokenize_input_prompts(ctx_server.vocab, documents, /* add_special */ false, true);
+            auto tokenized_docs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, documents, /* add_special */ false, true);
             tasks.reserve(tokenized_docs.size());
             for (size_t i = 0; i < tokenized_docs.size(); i++) {
-                auto tmp = format_rerank(ctx_server.vocab, tokenized_query, tokenized_docs[i]);
+                auto tmp = format_rerank(ctx_server.vocab, tokenized_queries[0], tokenized_docs[i]);
                 server_task task   = server_task(SERVER_TASK_TYPE_RERANK);
                 task.id            = ctx_server.queue_tasks.get_new_id();
                 task.index         = i;
-                task.prompt_tokens = server_tokens(tmp, ctx_server.mctx != nullptr);
+                task.prompt_tokens = std::move(tmp);
                 tasks.push_back(std::move(task));
             }
 
diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py
index adb6f27864..11483e679a 100644
--- a/tools/server/tests/unit/test_completion.py
+++ b/tools/server/tests/unit/test_completion.py
@@ -6,6 +6,8 @@ from utils import *
 
 server = ServerPreset.tinyllama2()
 
+JSON_MULTIMODAL_KEY = "multimodal_data"
+JSON_PROMPT_STRING_KEY = "prompt_string"
 
 @pytest.fixture(autouse=True)
 def create_server():
@@ -231,6 +233,28 @@ def test_nocache_long_input_prompt():
     })
     assert res.status_code == 400
 
+def test_json_prompt_no_mtmd():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": { JSON_PROMPT_STRING_KEY: "I believe the meaning of life is" },
+        "seed": 42,
+        "temperature": 1.0,
+        "cache_prompt": False,
+    })
+    assert res.status_code == 200
+
+def test_json_prompt_mtm_error_when_not_supported():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": { JSON_PROMPT_STRING_KEY: "I believe the meaning of life is <__media__>", JSON_MULTIMODAL_KEY: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=" },
+        "seed": 42,
+        "temperature": 1.0,
+        "cache_prompt": False,
+    })
+    # MTMD is disabled on this model, so this should fail.
+    assert res.status_code != 200
 
 def test_completion_with_tokens_input():
     global server
@@ -269,6 +293,20 @@ def test_completion_with_tokens_input():
     assert len(res.body) == 2
     assert res.body[0]["content"] == res.body[1]["content"]
 
+    # mixed JSON and tokens
+    res = server.make_request("POST", "/completion", data={
+        "prompt": [
+            tokens,
+            {
+                JSON_PROMPT_STRING_KEY: "I believe the meaning of life is",
+            },
+        ],
+    })
+    assert res.status_code == 200
+    assert type(res.body) == list
+    assert len(res.body) == 2
+    assert res.body[0]["content"] == res.body[1]["content"]
+
     # mixed string and tokens in one sequence
     res = server.make_request("POST", "/completion", data={
         "prompt": [1, 2, 3, 4, 5, 6, prompt_str, 7, 8, 9, 10, prompt_str],
diff --git a/tools/server/tests/unit/test_vision_api.py b/tools/server/tests/unit/test_vision_api.py
index fc63caa134..36d14b3885 100644
--- a/tools/server/tests/unit/test_vision_api.py
+++ b/tools/server/tests/unit/test_vision_api.py
@@ -10,21 +10,48 @@ IMG_URL_1 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/9
 
 response = requests.get(IMG_URL_0)
 response.raise_for_status() # Raise an exception for bad status codes
-IMG_BASE64_0 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
+IMG_BASE64_URI_0 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
+IMG_BASE64_0 = base64.b64encode(response.content).decode("utf-8")
 
+response = requests.get(IMG_URL_1)
+response.raise_for_status() # Raise an exception for bad status codes
+IMG_BASE64_URI_1 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
+IMG_BASE64_1 = base64.b64encode(response.content).decode("utf-8")
+
+JSON_MULTIMODAL_KEY = "multimodal_data"
+JSON_PROMPT_STRING_KEY = "prompt_string"
 
 @pytest.fixture(autouse=True)
 def create_server():
     global server
     server = ServerPreset.tinygemma3()
 
+def test_models_supports_multimodal_capability():
+    global server
+    server.start() # vision model may take longer to load due to download size
+    res = server.make_request("GET", "/models", data={})
+    assert res.status_code == 200
+    model_info = res.body["models"][0]
+    print(model_info)
+    assert "completion" in model_info["capabilities"]
+    assert "multimodal" in model_info["capabilities"]
+
+def test_v1_models_supports_multimodal_capability():
+    global server
+    server.start() # vision model may take longer to load due to download size
+    res = server.make_request("GET", "/v1/models", data={})
+    assert res.status_code == 200
+    model_info = res.body["models"][0]
+    print(model_info)
+    assert "completion" in model_info["capabilities"]
+    assert "multimodal" in model_info["capabilities"]
 
 @pytest.mark.parametrize(
     "prompt, image_url, success, re_content",
     [
         # test model is trained on CIFAR-10, but it's quite dumb due to small size
         ("What is this:\n", IMG_URL_0,                True, "(cat)+"),
-        ("What is this:\n", "IMG_BASE64_0",           True, "(cat)+"), # exceptional, so that we don't cog up the log
+        ("What is this:\n", "IMG_BASE64_URI_0",       True, "(cat)+"), # exceptional, so that we don't cog up the log
         ("What is this:\n", IMG_URL_1,                True, "(frog)+"),
         ("Test test\n",     IMG_URL_1,                True, "(frog)+"), # test invalidate cache
         ("What is this:\n", "malformed",              False, None),
@@ -36,8 +63,8 @@ def create_server():
 def test_vision_chat_completion(prompt, image_url, success, re_content):
     global server
     server.start(timeout_seconds=60) # vision model may take longer to load due to download size
-    if image_url == "IMG_BASE64_0":
-        image_url = IMG_BASE64_0
+    if image_url == "IMG_BASE64_URI_0":
+        image_url = IMG_BASE64_URI_0
     res = server.make_request("POST", "/chat/completions", data={
         "temperature": 0.0,
         "top_k": 1,
@@ -58,3 +85,61 @@ def test_vision_chat_completion(prompt, image_url, success, re_content):
     else:
         assert res.status_code != 200
 
+
+@pytest.mark.parametrize(
+    "prompt, image_data, success, re_content",
+    [
+        # test model is trained on CIFAR-10, but it's quite dumb due to small size
+        ("What is this: <__media__>\n", IMG_BASE64_0,           True, "(cat)+"),
+        ("What is this: <__media__>\n", IMG_BASE64_1,           True, "(frog)+"),
+        ("What is this: <__media__>\n", "malformed",            False, None), # non-image data
+        ("What is this:\n",             "",                     False, None), # empty string
+    ]
+)
+def test_vision_completion(prompt, image_data, success, re_content):
+    global server
+    server.start() # vision model may take longer to load due to download size
+    res = server.make_request("POST", "/completions", data={
+        "temperature": 0.0,
+        "top_k": 1,
+        "prompt": { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
+    })
+    if success:
+        assert res.status_code == 200
+        content = res.body["content"]
+        assert match_regex(re_content, content)
+    else:
+        assert res.status_code != 200
+
+
+@pytest.mark.parametrize(
+    "prompt, image_data, success",
+    [
+        # test model is trained on CIFAR-10, but it's quite dumb due to small size
+        ("What is this: <__media__>\n", IMG_BASE64_0,           True), # exceptional, so that we don't cog up the log
+        ("What is this: <__media__>\n", IMG_BASE64_1,           True),
+        ("What is this: <__media__>\n", "malformed",            False), # non-image data
+        ("What is this:\n",             "base64",               False), # non-image data
+    ]
+)
+def test_vision_embeddings(prompt, image_data, success):
+    global server
+    server.server_embeddings=True
+    server.n_batch=512
+    server.start() # vision model may take longer to load due to download size
+    res = server.make_request("POST", "/embeddings", data={
+        "content": [
+            { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
+            { JSON_PROMPT_STRING_KEY: prompt, JSON_MULTIMODAL_KEY: [ image_data ] },
+            { JSON_PROMPT_STRING_KEY: prompt, },
+        ],
+    })
+    if success:
+        assert res.status_code == 200
+        content = res.body
+        # Ensure embeddings are stable when multimodal.
+        assert content[0]['embedding'] == content[1]['embedding']
+        # Ensure embeddings without multimodal but same prompt do not match multimodal embeddings.
+        assert content[0]['embedding'] != content[2]['embedding']
+    else:
+        assert res.status_code != 200
diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp
index f3dfc8225d..036060bb3e 100644
--- a/tools/server/utils.hpp
+++ b/tools/server/utils.hpp
@@ -123,6 +123,19 @@ static bool json_is_array_of_mixed_numbers_strings(const json & data) {
     return false;
 }
 
+// does array have any individual integers/tokens?
+static bool json_is_array_and_contains_numbers(const json & data) {
+    if (data.is_array()) {
+        for (const auto & e : data) {
+            if (e.is_number_integer()) {
+                return true;
+            }
+        }
+        return false;
+    }
+    return false;
+}
+
 // get value by path(key1 / key2)
 static json json_get_nested_values(const std::vector<std::string> & paths, const json & js) {
     json result = json::object();
@@ -186,48 +199,6 @@ static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_
     return prompt_tokens;
 }
 
-/**
- * break the input "prompt" object into multiple prompt if needed, then tokenize them
- * this supports these cases:
- * - "prompt": "string"
- * - "prompt": [12, 34, 56]
- * - "prompt": [12, 34, "string", 56, 78]
- * and multiple prompts (multi-tasks):
- * - "prompt": ["string1", "string2"]
- * - "prompt": ["string1", [12, 34, 56]]
- * - "prompt": [[12, 34, 56], [78, 90, 12]]
- * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
- */
-static std::vector<llama_tokens> tokenize_input_prompts(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) {
-    std::vector<llama_tokens> result;
-    if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
-        // string or mixed
-        result.push_back(tokenize_mixed(vocab, json_prompt, add_special, parse_special));
-    } else if (json_is_array_of_numbers(json_prompt)) {
-        // array of tokens
-        result.push_back(json_prompt.get<llama_tokens>());
-    } else if (json_prompt.is_array()) {
-        // array of prompts
-        result.reserve(json_prompt.size());
-        for (const auto & p : json_prompt) {
-            if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) {
-                result.push_back(tokenize_mixed(vocab, p, add_special, parse_special));
-            } else if (json_is_array_of_numbers(p)) {
-                // array of tokens
-                result.push_back(p.get<llama_tokens>());
-            } else {
-                throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens");
-            }
-        }
-    } else {
-        throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
-    }
-    if (result.empty()) {
-        throw std::runtime_error("\"prompt\" must not be empty");
-    }
-    return result;
-}
-
 // return the last index of character that can form a valid string
 // if the last character is potentially cut in half, return the index before the cut
 // if validate_utf8(text) == text.size(), then the whole text is valid utf8
@@ -262,35 +233,6 @@ static size_t validate_utf8(const std::string& text) {
 // template utils
 //
 
-// format rerank task: [BOS]query[EOS][SEP]doc[EOS]
-static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_tokens & query, const llama_tokens & doc) {
-    llama_tokens result;
-
-    // Get EOS token - use SEP token as fallback if EOS is not available
-    llama_token eos_token = llama_vocab_eos(vocab);
-    if (eos_token == LLAMA_TOKEN_NULL) {
-        eos_token = llama_vocab_sep(vocab);
-    }
-
-    result.reserve(doc.size() + query.size() + 4);
-    if (llama_vocab_get_add_bos(vocab)) {
-        result.push_back(llama_vocab_bos(vocab));
-    }
-    result.insert(result.end(), query.begin(), query.end());
-    if (llama_vocab_get_add_eos(vocab)) {
-        result.push_back(eos_token);
-    }
-    if (llama_vocab_get_add_sep(vocab)) {
-        result.push_back(llama_vocab_sep(vocab));
-    }
-    result.insert(result.end(), doc.begin(), doc.end());
-    if (llama_vocab_get_add_eos(vocab)) {
-        result.push_back(eos_token);
-    }
-
-    return result;
-}
-
 // format infill task
 static llama_tokens format_infill(
         const llama_vocab * vocab,
@@ -1186,6 +1128,24 @@ public:
         }
     }
 
+    // appends server tokens, updates the media map. copies media chunks.
+    void push_back(server_tokens & tokens) {
+        size_t start_pos = size();
+        for (size_t i = 0; i < tokens.size(); i++) {
+            push_back(tokens[i]);
+        }
+        if (tokens.has_mtmd) {
+            // Assert if we are copying MTMD chunks to a server_tokens that does not have mtmd.
+            // We could also just check, but this will prevent silently dropping MTMD data.
+            GGML_ASSERT(has_mtmd);
+            for (auto it = tokens.map_pos_to_media.begin(); it != tokens.map_pos_to_media.end(); ) {
+                auto chunk = tokens.map_pos_to_media[it->first].get();
+                mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk));
+                map_pos_to_media[start_pos+it->first] = std::move(new_chunk);
+            }
+        }
+    }
+
     // for compatibility with context shift and prompt truncation
     void insert(const llama_tokens & inp_tokens) {
         GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled
@@ -1356,3 +1316,137 @@ static std::string fnv_hash(const uint8_t * data, size_t len) {
     }
     return std::to_string(hash);
 }
+
+
+// format rerank task: [BOS]query[EOS][SEP]doc[EOS].
+static server_tokens format_rerank(const struct llama_vocab * vocab, server_tokens & query, server_tokens & doc) {
+    server_tokens result = {};
+
+    // Get EOS token - use SEP token as fallback if EOS is not available
+    llama_token eos_token = llama_vocab_eos(vocab);
+    if (eos_token == LLAMA_TOKEN_NULL) {
+        eos_token = llama_vocab_sep(vocab);
+    }
+    if (llama_vocab_get_add_bos(vocab)) {
+        result.push_back(llama_vocab_bos(vocab));
+    }
+    result.push_back(query);
+    if (llama_vocab_get_add_eos(vocab)) {
+        result.push_back(eos_token);
+    }
+    if (llama_vocab_get_add_sep(vocab)) {
+        result.push_back(llama_vocab_sep(vocab));
+    }
+    result.push_back(doc);
+    if (llama_vocab_get_add_eos(vocab)) {
+        result.push_back(eos_token);
+    }
+    return result;
+}
+
+
+static server_tokens process_mtmd_prompt(mtmd_context * mctx, std::string prompt, std::vector<raw_buffer> files) {
+    mtmd::bitmaps bitmaps;
+    for (auto & file : files) {
+        mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_buf(mctx, file.data(), file.size()));
+        if (!bmp.ptr) {
+            throw std::runtime_error("Failed to load image or audio file");
+        }
+        // calculate bitmap hash (for KV caching)
+        std::string hash = fnv_hash(bmp.data(), bmp.n_bytes());
+        bmp.set_id(hash.c_str());
+        bitmaps.entries.push_back(std::move(bmp));
+    }
+    // process prompt
+    std::vector<server_tokens> inputs;
+    // multimodal
+    mtmd_input_text inp_txt = {
+        prompt.c_str(),
+        /* add_special */   true,
+        /* parse_special */ true,
+    };
+    mtmd::input_chunks chunks(mtmd_input_chunks_init());
+    auto bitmaps_c_ptr = bitmaps.c_ptr();
+    int32_t tokenized = mtmd_tokenize(mctx,
+                                      chunks.ptr.get(),
+                                      &inp_txt,
+                                      bitmaps_c_ptr.data(),
+                                      bitmaps_c_ptr.size());
+    if (tokenized != 0) {
+        throw std::runtime_error("Failed to tokenize prompt");
+    }
+    auto result = server_tokens(chunks, true);
+    return result;
+}
+
+/**
+ * break the input "prompt" object into multiple prompt if needed, then tokenize them
+ * use tokenize_input_prompts() if the input could be an array.
+ * this supports these cases:
+ * - "prompt": "string"
+ * - "prompt": [12, 34, 56]
+ * - "prompt": [12, 34, "string", 56, 78]
+ * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] }
+ */
+static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) {
+    constexpr char JSON_STRING_PROMPT_KEY[] = "prompt_string";
+    constexpr char JSON_MTMD_DATA_KEY[] = "multimodal_data";
+    const bool has_mtmd = mctx != nullptr;
+    if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
+        // string or mixed
+        llama_tokens tmp = tokenize_mixed(vocab, json_prompt, add_special, parse_special);
+        return server_tokens(tmp, false);
+    } else if (json_is_array_of_numbers(json_prompt)) {
+        // array of tokens
+        llama_tokens tmp = json_prompt.get<llama_tokens>();
+        return server_tokens(tmp, false);
+    } else if (json_prompt.contains(JSON_STRING_PROMPT_KEY)) {
+        // JSON object with prompt key.
+        if (json_prompt.contains(JSON_MTMD_DATA_KEY)) {
+            if (!has_mtmd)
+                throw std::runtime_error("Multimodal data provided, but model does not support multimodal requests.");
+
+            // JSON object with prompt and multimodal key.
+            std::vector<raw_buffer> files;
+            for (const auto & entry : json_prompt.at(JSON_MTMD_DATA_KEY)) {
+                files.push_back(base64_decode(entry));
+            }
+            return process_mtmd_prompt(mctx, json_prompt.at(JSON_STRING_PROMPT_KEY), files);
+        } else {
+            // Not multimodal, but contains a subobject.
+            llama_tokens tmp = tokenize_mixed(vocab, json_prompt.at(JSON_STRING_PROMPT_KEY), add_special, parse_special);
+            return server_tokens(tmp, false);
+        }
+   } else {
+       throw std::runtime_error("\"prompt\" elements must be a string, a list of tokens, a JSON object containing a prompt string, or a list of mixed strings & tokens.");
+   }
+}
+
+/**
+ * break the input "prompt" object into multiple prompt if needed, then tokenize them
+ * this supports these cases:
+ * - "prompt": "string"
+ * - "prompt": [12, 34, 56]
+ * - "prompt": [12, 34, "string", 56, 78]
+ * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] }
+ * and multiple prompts (multi-tasks):
+ * - "prompt": ["string1", "string2"]
+ * - "prompt": ["string1", [12, 34, 56]]
+ * - "prompt": [[12, 34, 56], [78, 90, 12]]
+ * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56], { "prompt_string": "string", "multimodal_data": [ "base64" ]}]
+ */
+static std::vector<server_tokens> tokenize_input_prompts(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) {
+    std::vector<server_tokens> result;
+    if (json_prompt.is_array() && !json_is_array_and_contains_numbers(json_prompt)) {
+        result.reserve(json_prompt.size());
+        for (const auto & p : json_prompt) {
+            result.push_back(tokenize_input_subprompt(vocab, mctx, p,add_special, parse_special));
+        }
+    } else {
+        result.push_back(tokenize_input_subprompt(vocab, mctx, json_prompt, add_special, parse_special));
+    }
+    if (result.empty()) {
+        throw std::runtime_error("\"prompt\" must not be empty");
+    }
+    return result;
+}

From ad5c975c2d0297124fad210776ef8eed6b90d578 Mon Sep 17 00:00:00 2001
From: Aaron Teo <aaron.teo1@ibm.com>
Date: Fri, 22 Aug 2025 16:11:04 +0800
Subject: [PATCH 147/287] ggml-cpu: Support Q5_0 and Q5_1 on s390x (#15486)

* ggml-cpu: initial q5_0 impl for s390x

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: updated q5_0 code for better performance

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: use optimised hsum for better performance

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: introduce q5_1 simd + refactor q5_0

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix incorrect return type vec_hsum

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: q5_0 incomplete refactor + table_b2b_0 activation

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: refactor q5_1

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: q5_1 update loop unroll to 4

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: update q5_0 unroll to 4

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: update build-s390x docs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: update unused variables q5_0

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* docs: update the last update date

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

---------

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
---
 docs/build-s390x.md                  |   7 +-
 ggml/src/ggml-cpu/arch-fallback.h    |   2 -
 ggml/src/ggml-cpu/arch/s390/quants.c | 316 +++++++++++++++++++++++++++
 ggml/src/ggml-cpu/ggml-cpu-impl.h    |   8 +
 4 files changed, 328 insertions(+), 5 deletions(-)

diff --git a/docs/build-s390x.md b/docs/build-s390x.md
index b36a199814..f3cdd63be3 100644
--- a/docs/build-s390x.md
+++ b/docs/build-s390x.md
@@ -265,8 +265,9 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 | BF16       | 🚫          | 🚫   | ❓   | ❓    |
 | Q4_0       | ✅          | ✅   | ❓   | ❓    |
 | Q4_1       | ✅          | ✅   | ❓   | ❓    |
-| Q5_0       | 🚫          | 🚫   | ❓   | ❓    |
-| Q5_1       | 🚫          | 🚫   | ❓   | ❓    |
+| MXFP4      | 🚫          | 🚫   | ❓   | ❓    |
+| Q5_0       | ✅          | ✅   | ❓   | ❓    |
+| Q5_1       | ✅          | ✅   | ❓   | ❓    |
 | Q8_0       | ✅          | ✅   | ❓   | ❓    |
 | Q2_K       | 🚫          | 🚫   | ❓   | ❓    |
 | Q3_K       | ✅          | ✅   | ❓   | ❓    |
@@ -291,4 +292,4 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself
 
-Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on July 31, 2025.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Aug 22, 2025.
diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index 0bfb92df17..373408a9c0 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -150,8 +150,6 @@
 #elif defined(__s390x__)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
-#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
diff --git a/ggml/src/ggml-cpu/arch/s390/quants.c b/ggml/src/ggml-cpu/arch/s390/quants.c
index 7e4229d0e4..1c8176fb4d 100644
--- a/ggml/src/ggml-cpu/arch/s390/quants.c
+++ b/ggml/src/ggml-cpu/arch/s390/quants.c
@@ -23,6 +23,27 @@
 
 #define UNUSED GGML_UNUSED
 
+#if defined(__VXE__) || defined(__VXE2__)
+#define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
+#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
+#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
+#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s)
+#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s)
+#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s)
+#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s)
+#define B8(c,s  ) B7(c,s,     c), B7(c,s,     s)
+
+// precomputed tables for expanding 8bits to 8 bytes:
+static const __attribute__((aligned(16))) uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b ) << 4
+static const __attribute__((aligned(16))) uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
+
+// permute mask for byteswapping
+static const uint8x16_t v_kperm = (const uint8x16_t){
+     7,  6,  5,  4,  3,  2, 1, 0,
+    15, 14, 13, 12, 11, 10, 9, 8
+};
+#endif
+
 void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK8_0 == 32);
     assert(k % QK8_0 == 0);
@@ -241,6 +262,301 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
+void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(qk == QK5_0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_q5_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    int ib = 0;
+    float sumf = 0.0f;
+
+#if defined(__VXE__) || defined(__VXE2__)
+    float32x4_t v_sum0 = vec_splats(0.0f);
+    float32x4_t v_sum1 = vec_splats(0.0f);
+
+    uint32_t qh0, qh1;
+    uint64_t tmp0[4], tmp1[4];
+
+    const uint8x16_t v_m = vec_splats((uint8_t)0x0F);
+
+    #pragma GCC unroll 4
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_0 * GGML_RESTRICT x0 = &x[ib + 0];
+        const block_q5_0 * GGML_RESTRICT x1 = &x[ib + 1];
+        const block_q8_0 * GGML_RESTRICT y0 = &y[ib + 0];
+        const block_q8_0 * GGML_RESTRICT y1 = &y[ib + 1];
+
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_1[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_1[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_1[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_1[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_1[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_1[(qh1 >> 24)       ];
+
+        int8x16_t v_qh0l = vec_xl(0, (const int8_t *)(tmp0 + 0));
+        int8x16_t v_qh0h = vec_xl(0, (const int8_t *)(tmp0 + 2));
+        int8x16_t v_qh1l = vec_xl(0, (const int8_t *)(tmp1 + 0));
+        int8x16_t v_qh1h = vec_xl(0, (const int8_t *)(tmp1 + 2));
+
+        // required for fixing the byteorder
+        v_qh0l = vec_perm(v_qh0l, v_qh0l, v_kperm);
+        v_qh0h = vec_perm(v_qh0h, v_qh0h, v_kperm);
+        v_qh1l = vec_perm(v_qh1l, v_qh1l, v_kperm);
+        v_qh1h = vec_perm(v_qh1h, v_qh1h, v_kperm);
+
+        const uint8x16_t v_x0 = vec_xl(0, (const uint8_t *)x0->qs);
+        const uint8x16_t v_x1 = vec_xl(0, (const uint8_t *)x1->qs);
+
+        int8x16_t v_x0l = (int8x16_t)vec_and(v_x0, v_m);
+        int8x16_t v_x0h = (int8x16_t)vec_sr(v_x0, 4);
+        int8x16_t v_x1l = (int8x16_t)vec_and(v_x1, v_m);
+        int8x16_t v_x1h = (int8x16_t)vec_sr(v_x1, 4);
+
+        const int8x16_t v_x0lf = vec_sub(v_x0l, v_qh0l);
+        const int8x16_t v_x0hf = vec_sub(v_x0h, v_qh0h);
+        const int8x16_t v_x1lf = vec_sub(v_x1l, v_qh1l);
+        const int8x16_t v_x1hf = vec_sub(v_x1h, v_qh1h);
+
+        const int8x16_t v_y0l = vec_xl(0,       (const int8_t *)y0->qs);
+        const int8x16_t v_y0h = vec_xl(QK8_0/2, (const int8_t *)y0->qs);
+        const int8x16_t v_y1l = vec_xl(0,       (const int8_t *)y1->qs);
+        const int8x16_t v_y1h = vec_xl(QK8_0/2, (const int8_t *)y1->qs);
+
+        const int32x4_t v_xy0 = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_x0lf, v_y0l), v_x0hf, v_y0h);
+        const int32x4_t v_xy1 = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_x1lf, v_y1l), v_x1hf, v_y1h);
+
+        const float32x4_t v_xy0f = vec_float(v_xy0);
+        const float32x4_t v_xy1f = vec_float(v_xy1);
+
+        const float32x4_t v_d0 = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
+        const float32x4_t v_d1 = vec_splats(GGML_CPU_FP16_TO_FP32(x1->d) * GGML_CPU_FP16_TO_FP32(y1->d));
+
+        v_sum0 = vec_madd(v_xy0f, v_d0, v_sum0);
+        v_sum1 = vec_madd(v_xy1f, v_d1, v_sum1);
+    }
+
+    sumf += vec_hsum(v_sum0) + vec_hsum(v_sum1);
+
+    #pragma GCC unroll 4
+    for (; ib < nb; ++ib) {
+        const block_q5_0 * GGML_RESTRICT x0 = &x[ib];
+        const block_q8_0 * GGML_RESTRICT y0 = &y[ib];
+
+        uint32_t qh;
+        memcpy(&qh, x0->qh, sizeof(qh));
+
+        uint64_t tmp[4];
+        tmp[0] = table_b2b_1[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_1[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_1[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_1[(qh >> 24)       ];
+
+        int8x16_t v_qhl = vec_xl(0, (const int8_t *)(tmp + 0));
+        int8x16_t v_qhh = vec_xl(0, (const int8_t *)(tmp + 2));
+
+        // required for fixing the byteorder
+        v_qhl = vec_perm(v_qhl, v_qhl, v_kperm);
+        v_qhh = vec_perm(v_qhh, v_qhh, v_kperm);
+
+        const uint8x16_t v_x = vec_xl(0, (const uint8_t *)x0->qs);
+        int8x16_t v_xl = (int8x16_t)vec_and(v_x, v_m);
+        int8x16_t v_xh = (int8x16_t)vec_sr(v_x, 4);
+
+        const int8x16_t v_xlf = vec_sub(v_xl, v_qhl);
+        const int8x16_t v_xhf = vec_sub(v_xh, v_qhh);
+
+        const int8x16_t v_yl = vec_xl(0,       (const int8_t *)y0->qs);
+        const int8x16_t v_yh = vec_xl(QK8_0/2, (const int8_t *)y0->qs);
+
+        const int32x4_t v_xy = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_xlf, v_yl), v_xhf, v_yh);
+        const float32x4_t v_xyf = vec_float(v_xy);
+
+        const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
+        const float32x4_t v_acc = vec_madd(v_xyf, v_d, vec_splats(0.0f));
+
+        sumf += vec_hsum(v_acc);
+    }
+
+    *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
+}
+
+void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    const int qk = QK8_1;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(qk == QK5_1);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_q5_1 * GGML_RESTRICT x = vx;
+    const block_q8_1 * GGML_RESTRICT y = vy;
+
+    int ib = 0;
+    float sumf = 0.0f;
+
+#if defined(__VXE__) || defined(__VXE2__)
+    float32x4_t v_sum0 = vec_splats(0.0f);
+    float32x4_t v_sum1 = vec_splats(0.0f);
+
+    float summs0 = 0.0f;
+    float summs1 = 0.0f;
+
+    uint32_t qh0;
+    uint32_t qh1;
+
+    uint64_t tmp0[4];
+    uint64_t tmp1[4];
+
+    const uint8x16_t v_m = vec_splats((uint8_t)0x0F);
+
+    #pragma GCC unroll 4
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_1 * GGML_RESTRICT x0 = &x[ib + 0];
+        const block_q5_1 * GGML_RESTRICT x1 = &x[ib + 1];
+        const block_q8_1 * GGML_RESTRICT y0 = &y[ib + 0];
+        const block_q8_1 * GGML_RESTRICT y1 = &y[ib + 1];
+
+        summs0 += GGML_CPU_FP16_TO_FP32(x0->m) * GGML_CPU_FP16_TO_FP32(y0->s);
+        summs1 += GGML_CPU_FP16_TO_FP32(x1->m) * GGML_CPU_FP16_TO_FP32(y1->s);
+
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_0[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_0[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_0[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_0[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_0[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_0[(qh1 >> 24)       ];
+
+        int8x16_t v_qh0l = vec_xl(0, (const int8_t *)(tmp0 + 0));
+        int8x16_t v_qh0h = vec_xl(0, (const int8_t *)(tmp0 + 2));
+        int8x16_t v_qh1l = vec_xl(0, (const int8_t *)(tmp1 + 0));
+        int8x16_t v_qh1h = vec_xl(0, (const int8_t *)(tmp1 + 2));
+
+        // required for fixing the byteorder
+        v_qh0l = vec_perm(v_qh0l, v_qh0l, v_kperm);
+        v_qh0h = vec_perm(v_qh0h, v_qh0h, v_kperm);
+        v_qh1l = vec_perm(v_qh1l, v_qh1l, v_kperm);
+        v_qh1h = vec_perm(v_qh1h, v_qh1h, v_kperm);
+
+        const uint8x16_t v_x0 = vec_xl(0, x0->qs);
+        const uint8x16_t v_x1 = vec_xl(0, x1->qs);
+
+        const int8x16_t v_x0l = (int8x16_t)vec_and(v_x0, v_m);
+        const int8x16_t v_x0h = (int8x16_t)vec_sr(v_x0, 4);
+        const int8x16_t v_x1l = (int8x16_t)vec_and(v_x1, v_m);
+        const int8x16_t v_x1h = (int8x16_t)vec_sr(v_x1, 4);
+
+        const int8x16_t v_x0lf = vec_or(v_x0l, v_qh0l);
+        const int8x16_t v_x0hf = vec_or(v_x0h, v_qh0h);
+        const int8x16_t v_x1lf = vec_or(v_x1l, v_qh1l);
+        const int8x16_t v_x1hf = vec_or(v_x1h, v_qh1h);
+
+        const int8x16_t v_y0l = vec_xl(0      , y0->qs);
+        const int8x16_t v_y0h = vec_xl(QK8_1/2, y0->qs);
+        const int8x16_t v_y1l = vec_xl(0      , y1->qs);
+        const int8x16_t v_y1h = vec_xl(QK8_1/2, y1->qs);
+
+        const int32x4_t v_xy0 = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_x0lf, v_y0l), v_x0hf, v_y0h);
+        const int32x4_t v_xy1 = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_x1lf, v_y1l), v_x1hf, v_y1h);
+
+        const float32x4_t v_xy0f = vec_float(v_xy0);
+        const float32x4_t v_xy1f = vec_float(v_xy1);
+
+        const float32x4_t v_d0 = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
+        const float32x4_t v_d1 = vec_splats(GGML_CPU_FP16_TO_FP32(x1->d) * GGML_CPU_FP16_TO_FP32(y1->d));
+
+        v_sum0 = vec_madd(v_xy0f, v_d0, v_sum0);
+        v_sum1 = vec_madd(v_xy1f, v_d1, v_sum1);
+    }
+
+    sumf += vec_hsum(v_sum0) + vec_hsum(v_sum1) + summs0 + summs1;
+
+    #pragma GCC unroll 4
+    for (; ib < nb; ++ib) {
+        const block_q5_1 * GGML_RESTRICT x0 = &x[ib];
+        const block_q8_1 * GGML_RESTRICT y0 = &y[ib];
+
+        float summs = GGML_CPU_FP16_TO_FP32(x0->m) * GGML_CPU_FP16_TO_FP32(y0->s);
+
+        uint32_t qh;
+        memcpy(&qh, x0->qh, sizeof(qh));
+
+        uint64_t tmp[4];
+        tmp[0] = table_b2b_0[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_0[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_0[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_0[(qh >> 24)       ];
+
+        int8x16_t v_qhl = vec_xl(0, (const int8_t *)(tmp + 0));
+        int8x16_t v_qhh = vec_xl(0, (const int8_t *)(tmp + 2));
+
+        // required for fixing the byteorder
+        v_qhl = vec_perm(v_qhl, v_qhl, v_kperm);
+        v_qhh = vec_perm(v_qhh, v_qhh, v_kperm);
+
+        const uint8x16_t v_x = vec_xl(0, x0->qs);
+        const int8x16_t v_xl = (int8x16_t)vec_and(v_x, v_m);
+        const int8x16_t v_xh = (int8x16_t)vec_sr(v_x, 4);
+
+        const int8x16_t v_xlf = vec_or(v_xl, v_qhl);
+        const int8x16_t v_xhf = vec_or(v_xh, v_qhh);
+
+        const int8x16_t v_yl = vec_xl(0      , y0->qs);
+        const int8x16_t v_yh = vec_xl(QK8_1/2, y0->qs);
+
+        const int32x4_t v_xy = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_xlf, v_yl), v_xhf, v_yh);
+        const float32x4_t v_xyf = vec_float(v_xy);
+
+        const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
+        const float32x4_t v_acc = vec_madd(v_xyf, v_d, v_acc);
+
+        sumf += vec_hsum(v_acc) + summs;
+    }
+
+    *s = sumf;
+#else
+    UNUSED(nb);
+    UNUSED(x);
+    UNUSED(y);
+    UNUSED(ib);
+    UNUSED(sumf);
+    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
+}
+
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h
index d839cf5c55..1f6844e16c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-impl.h
+++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h
@@ -486,6 +486,14 @@ inline static int16x8_t vec_padd_s16(int16x8_t a, int16x8_t b) {
     return v_abo + v_abe;
 }
 
+/**
+ * @see https://github.com/ggml-org/llama.cpp/pull/14037
+ */
+inline float vec_hsum(float32x4_t v) {
+    float32x4_t v_temp = v + vec_reve(v);
+    return v_temp[0] + v_temp[1];
+}
+
 inline static int32x4_t ggml_vec_dot(int32x4_t acc, int8x16_t a, int8x16_t b) {
     const int16x8_t p = vec_mule(a, b) + vec_mulo(a, b);
     return acc + (vec_unpackh(p) + vec_unpackl(p));

From 9ebebef62fd0adf8685874f154e227ea87b7c6f4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 22 Aug 2025 12:22:13 +0300
Subject: [PATCH 148/287] llama : remove KV cache defragmentation logic
 (#15473)

ggml-ci
---
 common/arg.cpp                    |   6 +-
 common/common.cpp                 |   1 -
 common/common.h                   |   1 -
 examples/llama.vim                |   2 +-
 include/llama.h                   |   2 +-
 scripts/compare-llama-bench.py    |   1 -
 src/llama-context.cpp             |   3 +-
 src/llama-cparams.h               |   1 -
 src/llama-kv-cache.cpp            | 367 +-----------------------------
 src/llama-kv-cache.h              |  25 +-
 src/llama-kv-cells.h              |  28 +--
 src/llama-memory.h                |   2 +-
 tools/llama-bench/README.md       |   1 -
 tools/llama-bench/llama-bench.cpp |  29 +--
 tools/server/README.md            |   2 +-
 tools/server/bench/bench.py       |   1 -
 16 files changed, 32 insertions(+), 440 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 1227aeb2a3..81c4005c5e 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2254,9 +2254,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"-dt", "--defrag-thold"}, "N",
-        string_format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
+        string_format("KV cache defragmentation threshold (DEPRECATED)"),
         [](common_params & params, const std::string & value) {
-            params.defrag_thold = std::stof(value);
+            GGML_UNUSED(params);
+            GGML_UNUSED(value);
+            LOG_WRN("DEPRECATED: --defrag-thold is deprecated and no longer necessary to specify\n");
         }
     ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
     add_opt(common_arg(
diff --git a/common/common.cpp b/common/common.cpp
index decabcc2ed..fdce1dcdec 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1152,7 +1152,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
     cparams.pooling_type      = params.pooling_type;
     cparams.attention_type    = params.attention_type;
-    cparams.defrag_thold      = params.defrag_thold;
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
diff --git a/common/common.h b/common/common.h
index 614e41a246..390dda5e53 100644
--- a/common/common.h
+++ b/common/common.h
@@ -288,7 +288,6 @@ struct common_params {
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
     int32_t yarn_orig_ctx         =     0; // YaRN original context length
-    float   defrag_thold          =  0.1f; // KV cache defragmentation threshold
 
     // offload params
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
diff --git a/examples/llama.vim b/examples/llama.vim
index af3fd3935d..736802d365 100644
--- a/examples/llama.vim
+++ b/examples/llama.vim
@@ -17,7 +17,7 @@
 "
 " start the llama.cpp server with a FIM-compatible model. for example:
 "
-"   $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -dt 0.1 --ubatch-size 512 --batch-size 1024 --cache-reuse 256
+"   $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa --ubatch-size 512 --batch-size 1024 --cache-reuse 256
 "
 "   --batch-size [512, model max context]
 "
diff --git a/include/llama.h b/include/llama.h
index 662e0971df..c5622cc16b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -312,7 +312,7 @@ extern "C" {
         float    yarn_beta_fast;   // YaRN low correction dim
         float    yarn_beta_slow;   // YaRN high correction dim
         uint32_t yarn_orig_ctx;    // YaRN original context size
-        float    defrag_thold;     // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
+        float    defrag_thold;     // [DEPRECATED] defragment the KV cache if holes/size > thold, <= 0 disabled (default)
 
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 8366f89a08..0141e0a350 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -28,7 +28,6 @@ LLAMA_BENCH_DB_FIELDS = [
     "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
     "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
     "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
-    "defrag_thold",
     "use_mmap",     "embeddings",   "no_op_offload",  "n_prompt",   "n_gen",        "n_depth",
     "test_time",    "avg_ns",       "stddev_ns",      "avg_ts",     "stddev_ts",
 ]
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index e8e8b3450a..18cf25079d 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -39,7 +39,6 @@ llama_context::llama_context(
     cparams.yarn_attn_factor = params.yarn_attn_factor;
     cparams.yarn_beta_fast   = params.yarn_beta_fast;
     cparams.yarn_beta_slow   = params.yarn_beta_slow;
-    cparams.defrag_thold     = params.defrag_thold;
     cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
     cparams.flash_attn       = params.flash_attn;
@@ -978,7 +977,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     bool did_optimize = false;
 
-    // handle any pending defrags/shifts
+    // handle any pending shifts/copies
     memory_update(false);
 
     llama_memory_context_ptr mctx;
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
index 38750affc5..dbbaba9f62 100644
--- a/src/llama-cparams.h
+++ b/src/llama-cparams.h
@@ -24,7 +24,6 @@ struct llama_cparams {
     float yarn_attn_factor;
     float yarn_beta_fast;
     float yarn_beta_slow;
-    float defrag_thold;
 
     bool embeddings;
     bool causal_attn;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index bb490cf9e8..70ddd5f4b9 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -525,39 +525,11 @@ llama_memory_context_ptr llama_kv_cache::init_full() {
 }
 
 llama_memory_context_ptr llama_kv_cache::init_update(llama_context * lctx, bool optimize) {
+    GGML_UNUSED(optimize);
+
     bool do_shift = get_has_shift();
 
-    defrag_info dinfo;
-
-    // see if we need to defrag
-    if (n_stream == 1) {
-        // note : for now do not consider defrag for n_stream > 1
-        const auto & cells = v_cells[seq_to_stream[0]];
-
-        bool do_defrag = optimize;
-
-        const auto thold = lctx->get_cparams().defrag_thold;
-
-        if (!do_defrag && thold > 0.0f) {
-            const auto n_kv = cells.used_max_p1();
-
-            // - do not defrag small contexts (i.e. < 2048 tokens)
-            // - count the padding towards the number of used tokens
-            const float fragmentation = n_kv >= 2048 ? std::max(0.0f, 1.0f - (float(cells.get_used() + n_pad)/n_kv)) : 0.0f;
-
-            if (fragmentation > thold) {
-                LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation);
-
-                do_defrag = true;
-            }
-        }
-
-        if (do_defrag) {
-            dinfo = defrag_prepare(lctx->graph_max_nodes());
-        }
-    }
-
-    return std::make_unique<llama_kv_cache_context>(this, lctx, do_shift, std::move(dinfo), std::move(sc_info));
+    return std::make_unique<llama_kv_cache_context>(this, lctx, do_shift, std::move(sc_info));
 }
 
 llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_ubatch> & ubatches) {
@@ -629,7 +601,7 @@ llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_
     return res;
 }
 
-bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const defrag_info & dinfo, const stream_copy_info & sc_info) {
+bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_copy_info & sc_info) {
     bool updated = false;
 
     auto * sched = lctx->get_sched();
@@ -699,53 +671,6 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const defrag_in
         }
     }
 
-    if (!dinfo.empty()) {
-        LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
-
-        // note: for now do not consider defrag for n_stream > 1
-        auto & cells = v_cells[seq_to_stream[0]];
-        auto & head  = v_heads[seq_to_stream[0]];
-
-        // apply moves:
-        {
-            const auto n_kv = dinfo.ids.size();
-
-            for (uint32_t i = 0; i < n_kv; ++i) {
-                assert(dinfo.ids[i] <= n_kv);
-
-                if (dinfo.ids[i] == n_kv || dinfo.ids[i] == i) {
-                    continue;
-                }
-
-                cells.mv(i, dinfo.ids[i]);
-            }
-
-            // reset the head so we can find the first free slot during the next ubatch
-            head = 0;
-        }
-
-        ggml_backend_sched_reset(sched);
-
-        auto * res = lctx->get_gf_res_reserve();
-
-        res->reset();
-
-        auto * gf = build_graph_defrag(res, lctx, dinfo);
-        if (!ggml_backend_sched_alloc_graph(sched, gf)) {
-            LLAMA_LOG_ERROR("%s: failed to allocate compute graph for defrag\n", __func__);
-            return updated;
-        }
-
-        res->set_inputs(nullptr);
-
-        if (lctx->graph_compute(gf, false) != GGML_STATUS_SUCCESS) {
-            LLAMA_LOG_ERROR("%s: failed to compute defrag\n", __func__);
-            return updated;
-        }
-
-        updated = true;
-    }
-
     return updated;
 }
 
@@ -1525,283 +1450,6 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
     return gf;
 }
 
-ggml_cgraph * llama_kv_cache::build_graph_defrag(
-         llm_graph_result * res,
-            llama_context * lctx,
-        const defrag_info & dinfo) const {
-    auto * ctx = res->get_ctx();
-    auto * gf  = res->get_gf();
-
-    GGML_ASSERT(n_stream == 1 && "n_stream > 1 does not support defrag");
-
-    const auto & cells = v_cells[0];
-
-    const auto & ids = dinfo.ids;
-
-    const auto & cparams = lctx->get_cparams();
-
-#if 0
-    // CPU defrag
-    //
-    // TODO: optimizations are possible:
-    //       - multiple threads
-    //       - avoid copying to the host memory when already there
-    //
-    // likely not worth the effort, as we have ggml_graph based defrag
-    //
-
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
-
-    const uint32_t kv_size = size;
-
-    std::vector<uint8_t> buf_k;
-    std::vector<uint8_t> buf_v;
-
-    for (uint32_t il = 0; il < n_layer; ++il) {
-        const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
-        const size_t k_size     = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size);
-
-        const size_t v_size_el = ggml_type_size(v_l[il]->type);
-        const size_t v_size    = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size);
-
-        buf_k.resize(k_size);
-        buf_v.resize(v_size);
-
-        ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size());
-        ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size());
-
-        // batch move [i, i+nm) to [id, id+nm)
-        // note: cells can move only to a lower index
-        for (uint32_t i = 0; i < n_kv; ++i) {
-            const uint32_t id = ids[i];
-
-            if (i == id || id == n_kv) {
-                continue;
-            }
-
-            uint32_t nm = 1;
-
-            while (i + nm < n_kv && ids[i + nm] == id + nm) {
-                nm++;
-            }
-
-            // move keys
-            {
-                const int64_t os =  i*k_size_row;
-                const int64_t od = id*k_size_row;
-
-                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
-            }
-
-            // move values (note: they are transposed)
-            {
-                const int64_t os =  i;
-                const int64_t od = id;
-
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
-                }
-            }
-
-            i += nm - 1;
-        }
-
-        ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size());
-        ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size());
-    }
-#else
-    for (uint32_t i = 0; i < ids.size(); ++i) {
-        const uint32_t id = ids[i];
-
-        if (i == id || id == ids.size()) {
-            continue;
-        }
-
-        uint32_t nm = 1;
-
-        while (i + nm < ids.size() && ids[i + nm] == id + nm) {
-            nm++;
-        }
-
-        for (const auto & layer : layers) {
-            const uint32_t il = layer.il;
-
-            const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
-            const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
-
-            ggml_tensor * view_k_src = ggml_view_2d(ctx, layer.k,
-                    n_embd_k_gqa, nm,
-                    ggml_row_size(layer.k->type, n_embd_k_gqa),
-                    ggml_row_size(layer.k->type, n_embd_k_gqa*i));
-
-            ggml_tensor * view_k_dst = ggml_view_2d(ctx, layer.k,
-                    n_embd_k_gqa, nm,
-                    ggml_row_size(layer.k->type, n_embd_k_gqa),
-                    ggml_row_size(layer.k->type, n_embd_k_gqa*id));
-
-            ggml_tensor * view_v_src;
-            ggml_tensor * view_v_dst;
-
-            if (cparams.flash_attn) {
-                // NOTE: the V cache is not transposed when using flash attention
-                view_v_src = ggml_view_2d(ctx, layer.v,
-                        n_embd_v_gqa, nm,
-                        ggml_row_size(layer.v->type, n_embd_v_gqa),
-                        ggml_row_size(layer.v->type, n_embd_v_gqa*i));
-
-                view_v_dst = ggml_view_2d(ctx, layer.v,
-                        n_embd_v_gqa, nm,
-                        ggml_row_size(layer.v->type, n_embd_v_gqa),
-                        ggml_row_size(layer.v->type, n_embd_v_gqa*id));
-            } else {
-                view_v_src = ggml_view_2d(ctx, layer.v,
-                        nm, n_embd_v_gqa,
-                        ggml_row_size(layer.v->type, cells.size()),
-                        ggml_row_size(layer.v->type, i));
-
-                view_v_dst = ggml_view_2d(ctx, layer.v,
-                        nm, n_embd_v_gqa,
-                        ggml_row_size(layer.v->type, cells.size()),
-                        ggml_row_size(layer.v->type, id));
-            }
-
-            ggml_build_forward_expand(gf, ggml_cpy(ctx, view_k_src, view_k_dst));
-            ggml_build_forward_expand(gf, ggml_cpy(ctx, view_v_src, view_v_dst));
-        }
-
-        i += nm - 1;
-    }
-
-    //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
-#endif
-
-    return gf;
-}
-
-llama_kv_cache::defrag_info llama_kv_cache::defrag_prepare(int32_t n_max_nodes) const {
-    GGML_ASSERT(n_stream == 1 && "n_stream > 1 does not support defrag");
-
-    const auto & cells = v_cells[0];
-
-    const uint32_t n_layer = layers.size();
-
-    const uint32_t n_kv   = cells.used_max_p1();
-    const uint32_t n_used = cells.get_used();
-
-    assert(n_used <= n_kv);
-
-    //const int64_t t_start = ggml_time_us();
-
-    // number of cells moved
-    uint32_t n_moves = 0;
-
-    // each move requires 6*n_layer tensors (see graph_build_kv_self_defrag)
-    //   - source view, destination view, copy operation
-    //   - x2 for keys and values
-    //const uint32_t max_moves = max_nodes()/(6*n_layer);
-    // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
-    const uint32_t max_moves = (n_max_nodes - 2*n_layer)/(6*n_layer);
-
-    // determine which KV cells to move where
-    defrag_info res;
-    auto & ids = res.ids;
-
-    ids.resize(n_kv, n_kv);
-
-    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
-        if (!cells.is_empty(i0)) {
-            ids[i0] = i0;
-
-            continue;
-        }
-
-        // found a hole - fill it with data from the end of the cache
-
-        uint32_t nh = 1;
-
-        // determine the size of the hole
-        while (i0 + nh < n_used && cells.is_empty(i0 + nh)) {
-            nh++;
-        }
-
-        uint32_t nf = 0;
-        uint32_t is = n_kv - 1;
-
-        // starting from the end, find nh non-empty cells
-        for (; is > i0; --is) {
-            if (cells.is_empty(is) || ids[is] != n_kv) {
-                continue;
-            }
-
-            // non-empty cell which is not yet moved
-            nf++;
-
-            if (nf == nh) {
-                break;
-            }
-        }
-
-        // this can only happen if `n_used` is not accurate, which would be a bug
-        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
-
-        nf = 0;
-
-        uint32_t i1 = is;
-
-        // are we moving a continuous block of memory?
-        bool cont = false;
-
-        // should we stop searching for the next move?
-        bool stop = false;
-
-        // go back and move the nf cells to the hole
-        for (; i1 < n_kv; ++i1) {
-            if (cells.is_empty(i1) || ids[i1] != n_kv) {
-                if (n_moves == max_moves) {
-                    stop = true;
-                    break;
-                }
-
-                cont = false;
-                continue;
-            }
-
-            // this cell goes to (i0 + nf)
-            ids[i1] = i0 + nf;
-
-            if (!cont) {
-                n_moves++;
-                cont = true;
-            }
-
-            nf++;
-
-            if (nf == nh) {
-                break;
-            }
-        }
-
-        if (stop || n_moves == max_moves) {
-            break;
-        }
-
-        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
-
-        i0 += nh - 1;
-    }
-
-    if (n_moves == 0) {
-        return {};
-    }
-
-    LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves);
-
-    LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer);
-
-    return res;
-}
-
 bool llama_kv_cache::is_masked_swa(llama_pos p0, llama_pos p1) const {
     assert(p0 >= 0 && p1 >= 0);
 
@@ -2300,9 +1948,8 @@ llama_kv_cache_context::llama_kv_cache_context(
         llama_kv_cache * kv,
         llama_context * lctx,
         bool do_shift,
-        defrag_info dinfo,
-        stream_copy_info sc_info) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), dinfo(std::move(dinfo)), sc_info(std::move(sc_info)) {
-    if (!do_shift && this->dinfo.empty() && this->sc_info.empty()) {
+        stream_copy_info sc_info) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), sc_info(std::move(sc_info)) {
+    if (!do_shift && this->sc_info.empty()) {
         status = LLAMA_MEMORY_STATUS_NO_UPDATE;
     }
 }
@@ -2330,7 +1977,7 @@ bool llama_kv_cache_context::apply() {
 
     // no ubatches -> this is a KV cache update
     if (ubatches.empty()) {
-        kv->update(lctx, do_shift, dinfo, sc_info);
+        kv->update(lctx, do_shift, sc_info);
 
         return true;
     }
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 5ca618e1b8..297a0973dd 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -24,17 +24,6 @@ public:
     // this callback is used to filter out layers that should not be included in the cache
     using layer_filter_cb = std::function<bool(int32_t il)>;
 
-    struct defrag_info {
-        bool empty() const {
-            return ids.empty();
-        }
-
-        // contains information about which cell moves where:
-        //  - cell i moves to ids[i]
-        //  - if ids[i] == i || ids[i] == ids.size(), then cell i is not moved
-        std::vector<uint32_t> ids;
-    };
-
     struct stream_copy_info {
         bool empty() const {
             assert(ssrc.size() == sdst.size());
@@ -173,7 +162,7 @@ public:
     // return empty vector on failure
     slot_info_vec_t prepare(const std::vector<llama_ubatch> & ubatches);
 
-    bool update(llama_context * lctx, bool do_shift, const defrag_info & dinfo, const stream_copy_info & sc_info);
+    bool update(llama_context * lctx, bool do_shift, const stream_copy_info & sc_info);
 
     // find a slot of kv cells that can hold the ubatch
     // if cont == true, then the slot must be continuous
@@ -254,9 +243,6 @@ private:
     // model layer id -> KV cache layer id
     std::unordered_map<int32_t, int32_t> map_layer_ids;
 
-    // return non-empty vector if cells have been moved
-    defrag_info defrag_prepare(int32_t n_max_nodes) const;
-
     size_t total_size() const;
 
     size_t size_k_bytes() const;
@@ -277,11 +263,6 @@ private:
                llm_graph_result * res,
                   llama_context * lctx) const;
 
-    ggml_cgraph * build_graph_defrag(
-               llm_graph_result * res,
-                  llama_context * lctx,
-              const defrag_info & dinfo) const;
-
     struct cell_ranges_t {
         uint32_t strm;
 
@@ -299,7 +280,6 @@ class llama_kv_cache_context : public llama_memory_context_i {
 public:
     // some shorthands
     using slot_info_vec_t  = llama_kv_cache::slot_info_vec_t;
-    using defrag_info      = llama_kv_cache::defrag_info;
     using stream_copy_info = llama_kv_cache::stream_copy_info;
 
     // used for errors
@@ -314,7 +294,6 @@ public:
             llama_kv_cache * kv,
             llama_context * lctx,
             bool do_shift,
-            defrag_info dinfo,
             stream_copy_info sc_info);
 
     // used to create a batch procesing context from a batch
@@ -374,8 +353,6 @@ private:
 
     bool do_shift = false;
 
-    defrag_info dinfo;
-
     stream_copy_info sc_info;
 
     //
diff --git a/src/llama-kv-cells.h b/src/llama-kv-cells.h
index 2651e30331..8f6bf01456 100644
--- a/src/llama-kv-cells.h
+++ b/src/llama-kv-cells.h
@@ -77,24 +77,24 @@ public:
     }
 
     // move cell isrc to idst (used during defrag)
-    void mv(uint32_t isrc, uint32_t idst) {
-        assert(isrc < pos.size());
-        assert(idst < pos.size());
+    //void mv(uint32_t isrc, uint32_t idst) {
+    //    assert(isrc < pos.size());
+    //    assert(idst < pos.size());
 
-        assert(pos[idst] == -1);
-        assert(pos[isrc] != -1);
+    //    assert(pos[idst] == -1);
+    //    assert(pos[isrc] != -1);
 
-        pos  [idst] = pos  [isrc];
-        shift[idst] = shift[isrc];
-        seq  [idst] = seq  [isrc];
+    //    pos  [idst] = pos  [isrc];
+    //    shift[idst] = shift[isrc];
+    //    seq  [idst] = seq  [isrc];
 
-        pos  [isrc] = -1;
-        shift[isrc] =  0;
-        seq  [isrc].reset();
+    //    pos  [isrc] = -1;
+    //    shift[isrc] =  0;
+    //    seq  [isrc].reset();
 
-        used.erase (isrc);
-        used.insert(idst);
-    }
+    //    used.erase (isrc);
+    //    used.insert(idst);
+    //}
 
     // copy the state of cells [i, i + n) (used for save/restore the state of the cells)
     llama_kv_cells cp(uint32_t i, uint32_t n) const {
diff --git a/src/llama-memory.h b/src/llama-memory.h
index 42a7145c2f..94d858bccc 100644
--- a/src/llama-memory.h
+++ b/src/llama-memory.h
@@ -77,7 +77,7 @@ struct llama_memory_i {
     // simulate full cache, used for allocating worst-case compute buffers
     virtual llama_memory_context_ptr init_full() = 0;
 
-    // prepare for any pending memory updates, such as shifts, defrags, etc.
+    // prepare for any pending memory updates, such as shifts, copies, etc.
     // status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
     virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
 
diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md
index 31a2730874..bf7fd29c8c 100644
--- a/tools/llama-bench/README.md
+++ b/tools/llama-bench/README.md
@@ -43,7 +43,6 @@ test parameters:
   -ub, --ubatch-size <n>                    (default: 512)
   -ctk, --cache-type-k <t>                  (default: f16)
   -ctv, --cache-type-v <t>                  (default: f16)
-  -dt, --defrag-thold <f>                   (default: -1)
   -t, --threads <n>                         (default: system dependent)
   -C, --cpu-mask <hex,hex>                  (default: 0x0)
   --cpu-strict <0|1>                        (default: 0)
diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index 10b48c5568..9378706a12 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -245,7 +245,6 @@ struct cmd_params {
     std::vector<int>                 n_ubatch;
     std::vector<ggml_type>           type_k;
     std::vector<ggml_type>           type_v;
-    std::vector<float>               defrag_thold;
     std::vector<int>                 n_threads;
     std::vector<std::string>         cpu_mask;
     std::vector<bool>                cpu_strict;
@@ -282,7 +281,6 @@ static const cmd_params cmd_params_defaults = {
     /* n_ubatch             */ { 512 },
     /* type_k               */ { GGML_TYPE_F16 },
     /* type_v               */ { GGML_TYPE_F16 },
-    /* defrag_thold         */ { -1.0f },
     /* n_threads            */ { cpu_get_num_math() },
     /* cpu_mask             */ { "0x0" },
     /* cpu_strict           */ { false },
@@ -346,8 +344,6 @@ static void print_usage(int /* argc */, char ** argv) {
            join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
     printf("  -ctv, --cache-type-v <t>                  (default: %s)\n",
            join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
-    printf("  -dt, --defrag-thold <f>                   (default: %s)\n",
-           join(cmd_params_defaults.defrag_thold, ",").c_str());
     printf("  -t, --threads <n>                         (default: %s)\n",
            join(cmd_params_defaults.n_threads, ",").c_str());
     printf("  -C, --cpu-mask <hex,hex>                  (default: %s)\n",
@@ -533,13 +529,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                     break;
                 }
                 params.type_v.insert(params.type_v.end(), types.begin(), types.end());
-            } else if (arg == "-dt" || arg == "--defrag-thold") {
-                if (++i >= argc) {
-                    invalid_param = true;
-                    break;
-                }
-                auto p = string_split<float>(argv[i], split_delim);
-                params.defrag_thold.insert(params.defrag_thold.end(), p.begin(), p.end());
             } else if (arg == "-t" || arg == "--threads") {
                 if (++i >= argc) {
                     invalid_param = true;
@@ -849,9 +838,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.type_v.empty()) {
         params.type_v = cmd_params_defaults.type_v;
     }
-    if (params.defrag_thold.empty()) {
-        params.defrag_thold = cmd_params_defaults.defrag_thold;
-    }
     if (params.n_gpu_layers.empty()) {
         params.n_gpu_layers = cmd_params_defaults.n_gpu_layers;
     }
@@ -910,7 +896,6 @@ struct cmd_params_instance {
     int                n_ubatch;
     ggml_type          type_k;
     ggml_type          type_v;
-    float              defrag_thold;
     int                n_threads;
     std::string        cpu_mask;
     bool               cpu_strict;
@@ -1007,7 +992,6 @@ struct cmd_params_instance {
         cparams.n_ubatch     = n_ubatch;
         cparams.type_k       = type_k;
         cparams.type_v       = type_v;
-        cparams.defrag_thold = defrag_thold;
         cparams.offload_kqv  = !no_kv_offload;
         cparams.flash_attn   = flash_attn;
         cparams.embeddings   = embeddings;
@@ -1037,7 +1021,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
-    for (const auto & defrag_thold : params.defrag_thold)
     for (const auto & nkvo : params.no_kv_offload)
     for (const auto & fa : params.flash_attn)
     for (const auto & nt : params.n_threads)
@@ -1058,7 +1041,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
-                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1091,7 +1073,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
-                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1124,7 +1105,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
-                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1166,7 +1146,6 @@ struct test {
     int                      poll;
     ggml_type                type_k;
     ggml_type                type_v;
-    float                    defrag_thold;
     int                      n_gpu_layers;
     llama_split_mode         split_mode;
     int                      main_gpu;
@@ -1201,7 +1180,6 @@ struct test {
         poll           = inst.poll;
         type_k         = inst.type_k;
         type_v         = inst.type_v;
-        defrag_thold   = inst.defrag_thold;
         n_gpu_layers   = inst.n_gpu_layers;
         split_mode     = inst.split_mode;
         main_gpu       = inst.main_gpu;
@@ -1257,7 +1235,6 @@ struct test {
             "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
             "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
             "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
-            "defrag_thold",
             "use_mmap",     "embeddings",   "no_op_offload",   "n_prompt",       "n_gen",      "n_depth",      "test_time",
             "avg_ns",       "stddev_ns",    "avg_ts",         "stddev_ts",
         };
@@ -1277,7 +1254,7 @@ struct test {
             field == "use_mmap" || field == "embeddings") {
             return BOOL;
         }
-        if (field == "avg_ts" || field == "stddev_ts" || field == "defrag_thold") {
+        if (field == "avg_ts" || field == "stddev_ts") {
             return FLOAT;
         }
         return STRING;
@@ -1344,7 +1321,6 @@ struct test {
                                             std::to_string(flash_attn),
                                             tensor_split_str,
                                             tensor_buft_overrides_str,
-                                            std::to_string(defrag_thold),
                                             std::to_string(use_mmap),
                                             std::to_string(embeddings),
                                             std::to_string(no_op_offload),
@@ -1611,9 +1587,6 @@ struct markdown_printer : public printer {
         if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
             fields.emplace_back("type_v");
         }
-        if (params.defrag_thold.size() > 1 || params.defrag_thold != cmd_params_defaults.defrag_thold) {
-            fields.emplace_back("defrag_thold");
-        }
         if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
             fields.emplace_back("main_gpu");
         }
diff --git a/tools/server/README.md b/tools/server/README.md
index 86844225ff..baf3730add 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -66,7 +66,7 @@ The project is under active development, and we are [looking for feedback and co
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
 | `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
-| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
+| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
 | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
diff --git a/tools/server/bench/bench.py b/tools/server/bench/bench.py
index 5cc6f92ab6..0c57a2df04 100644
--- a/tools/server/bench/bench.py
+++ b/tools/server/bench/bench.py
@@ -274,7 +274,6 @@ def start_server_background(args):
     server_args.extend(['--batch-size', args.batch_size])
     server_args.extend(['--ubatch-size', args.ubatch_size])
     server_args.extend(['--n-predict', args.max_tokens * 2])
-    server_args.extend(['--defrag-thold', "0.1"])
     server_args.append('--cont-batching')
     server_args.append('--metrics')
     server_args.append('--flash-attn')

From b1ab91821f980f8993423c3f2a82a0a0f60c09d2 Mon Sep 17 00:00:00 2001
From: Yavor Ivanov <yavorgenadiev@gmail.com>
Date: Fri, 22 Aug 2025 14:06:29 +0300
Subject: [PATCH 149/287] cuda : add Pad Reflect 1D support (#14659)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add Pad Reflect 1D CUDA support

* Update ggml/src/ggml-cuda/pad_reflect_1d.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/ggml-cuda.cu       |  5 ++
 ggml/src/ggml-cuda/pad_reflect_1d.cu  | 82 +++++++++++++++++++++++++++
 ggml/src/ggml-cuda/pad_reflect_1d.cuh |  5 ++
 3 files changed, 92 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/pad_reflect_1d.cu
 create mode 100644 ggml/src/ggml-cuda/pad_reflect_1d.cuh

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 4e17fd211e..d29a0b573f 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -49,6 +49,7 @@
 #include "ggml-cuda/wkv.cuh"
 #include "ggml-cuda/gla.cuh"
 #include "ggml-cuda/set-rows.cuh"
+#include "ggml-cuda/pad_reflect_1d.cuh"
 #include "ggml.h"
 
 #include <algorithm>
@@ -2352,6 +2353,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_PAD:
             ggml_cuda_op_pad(ctx, dst);
             break;
+        case GGML_OP_PAD_REFLECT_1D:
+            ggml_cuda_op_pad_reflect_1d(ctx, dst);
+            break;
         case GGML_OP_ARANGE:
             ggml_cuda_op_arange(ctx, dst);
             break;
@@ -3490,6 +3494,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
+        case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_ARANGE:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
diff --git a/ggml/src/ggml-cuda/pad_reflect_1d.cu b/ggml/src/ggml-cuda/pad_reflect_1d.cu
new file mode 100644
index 0000000000..4ed34aec3d
--- /dev/null
+++ b/ggml/src/ggml-cuda/pad_reflect_1d.cu
@@ -0,0 +1,82 @@
+#include "pad_reflect_1d.cuh"
+
+static __global__ void pad_reflect_1d_kernel_f32(
+    const void * __restrict__ src0,
+    void * __restrict__ dst,
+    const int64_t ne0,
+    const int64_t ne00,
+    const int64_t ne01,
+    const int64_t ne02,
+    const int64_t ne03,
+    const int64_t nb00,
+    const int64_t nb01,
+    const int64_t nb02,
+    const int64_t nb03,
+    const int64_t nb0,
+    const int64_t nb1,
+    const int64_t nb2,
+    const int64_t nb3,
+    const int p0,
+    const int p1) {
+
+    const int64_t i3 = blockIdx.z;
+    const int64_t i2 = blockIdx.y;
+    const int64_t i1 = blockIdx.x;
+
+    if (i1 >= ne01 || i2 >= ne02 || i3 >= ne03) {
+        return;
+    }
+
+    const char * src0_ptr = (const char *)src0 + i3*nb03 + i2*nb02 + i1*nb01;
+    char * dst_ptr = (char *)dst + i3*nb3 + i2*nb2 + i1*nb1;
+
+    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
+        float value;
+
+        if (i0 < p0) {
+            // Left padding - reflect
+            value = *(const float *)(src0_ptr + (p0 - i0) * nb00);
+        } else if (i0 < ne0 - p1) {
+            // Middle - copy
+            value = *(const float *)(src0_ptr + (i0 - p0) * nb00);
+        } else {
+            // Right padding - reflect
+            int64_t src_idx = (ne0 - p1 - p0) - (p1 + 1 - (ne0 - i0)) - 1;
+            value = *(const float *)(src0_ptr + src_idx * nb00);
+        }
+
+        *(float *)(dst_ptr + i0 * nb0) = value;
+    }
+}
+
+void ggml_cuda_op_pad_reflect_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *) dst->op_params;
+    const int p0 = opts[0];
+    const int p1 = opts[1];
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t ne0 = dst->ne[0];
+
+    GGML_ASSERT(ne0 == ne00 + p0 + p1);
+
+    const dim3 block_dims(CUDA_PAD_REFLECT_1D_BLOCK_SIZE, 1, 1);
+    const dim3 grid_dims(ne01, ne02, ne03);
+
+    pad_reflect_1d_kernel_f32<<<grid_dims, block_dims, 0, stream>>>(
+        src0->data, dst->data,
+        ne0, ne00, ne01, ne02, ne03,
+        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+        dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3],
+        p0, p1
+    );
+}
diff --git a/ggml/src/ggml-cuda/pad_reflect_1d.cuh b/ggml/src/ggml-cuda/pad_reflect_1d.cuh
new file mode 100644
index 0000000000..15f2ed1737
--- /dev/null
+++ b/ggml/src/ggml-cuda/pad_reflect_1d.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_PAD_REFLECT_1D_BLOCK_SIZE 256
+
+void ggml_cuda_op_pad_reflect_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

From 92f7f0a53cf6484e16a8084ed90807c35a164809 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Fri, 22 Aug 2025 15:33:15 +0200
Subject: [PATCH 150/287] ggml: add `conv3d` op (#15182)

* add conv3d

* bump GGML_OP_COUNT
---
 ggml/include/ggml.h          |  18 +++++
 ggml/src/ggml-cpu/ggml-cpu.c |   6 ++
 ggml/src/ggml-cpu/ops.cpp    | 142 +++++++++++++++++++++++++++++++++++
 ggml/src/ggml-cpu/ops.h      |   1 +
 ggml/src/ggml.c              |  56 +++++++++++++-
 tests/test-backend-ops.cpp   | 124 ++++++++++++++++++++++++++++++
 6 files changed, 345 insertions(+), 2 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index b8b82e11c8..7e9c3c8c7a 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -512,6 +512,7 @@ extern "C" {
         GGML_OP_IM2COL,
         GGML_OP_IM2COL_BACK,
         GGML_OP_CONV_2D,
+        GGML_OP_CONV_3D,
         GGML_OP_CONV_2D_DW,
         GGML_OP_CONV_TRANSPOSE_2D,
         GGML_OP_POOL_1D,
@@ -1940,6 +1941,23 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
+    GGML_API struct ggml_tensor * ggml_conv_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
+            struct ggml_tensor  * b,   // input  [W, H, D, C * N]
+            int                   s0,  // stride
+            int                   s1,
+            int                   s2,
+            int                   p0,  // padding
+            int                   p1,
+            int                   p2,
+            int                   d0,  // dilation
+            int                   d1,
+            int                   d2,
+            int                   n_channels,
+            int                   n_batch,
+            int                   n_channels_out);
+
     enum ggml_op_pool {
         GGML_OP_POOL_MAX,
         GGML_OP_POOL_AVG,
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index f6bea3df34..0d5d3a3440 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1880,6 +1880,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_conv_2d(params, tensor);
             } break;
+        case GGML_OP_CONV_3D:
+            {
+                ggml_compute_forward_conv_3d(params, tensor);
+            } break;
         case GGML_OP_CONV_2D_DW:
             {
                 ggml_compute_forward_conv_2d_dw(params, tensor);
@@ -2252,6 +2256,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_IM2COL:
         case GGML_OP_IM2COL_BACK:
         case GGML_OP_CONV_2D:
+        case GGML_OP_CONV_3D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_CONV_TRANSPOSE_1D:
         case GGML_OP_CONV_TRANSPOSE_2D:
@@ -2773,6 +2778,7 @@ struct ggml_cplan ggml_graph_plan(
                         }
                     } break;
                 case GGML_OP_CONV_2D:
+                case GGML_OP_CONV_3D:
                     {
                         cur = GGML_IM2COL_WORK_SIZE;
                     } break;
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index b72a2556a5..460367cca0 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -7207,6 +7207,148 @@ void ggml_compute_forward_conv_2d(
     ggml_compute_forward_conv_2d_impl(params, src0, src1, dst, src0->type);
 }
 
+// ggml_compute_forward_conv_3d
+
+static void ggml_compute_forward_conv_3d_impl(const ggml_compute_params * params,
+                                              const ggml_tensor *         kernel,
+                                              const ggml_tensor *         src,
+                                              ggml_tensor *               dst,
+                                              ggml_type                   kernel_type) {
+
+    GGML_ASSERT(ggml_is_contiguous(kernel));
+    GGML_ASSERT(kernel_type == GGML_TYPE_F16 || kernel_type == GGML_TYPE_F32);
+    GGML_ASSERT(kernel->type == kernel_type);
+
+    const ggml_type_traits * traits = ggml_get_type_traits(kernel_type);
+
+    const int32_t s0 = dst->op_params[0];
+    const int32_t s1 = dst->op_params[1];
+    const int32_t s2 = dst->op_params[2];
+    const int32_t p0 = dst->op_params[3];
+    const int32_t p1 = dst->op_params[4];
+    const int32_t p2 = dst->op_params[5];
+    const int32_t d0 = dst->op_params[6];
+    const int32_t d1 = dst->op_params[7];
+    const int32_t d2 = dst->op_params[8];
+    const int32_t c  = dst->op_params[9];
+    const int32_t n  = dst->op_params[10];
+    const int32_t oc = dst->op_params[11];
+
+    const int64_t src_w = src->ne[0];
+    const int64_t src_h = src->ne[1];
+    const int64_t src_d = src->ne[2];
+    const int64_t knl_w = kernel->ne[0];
+    const int64_t knl_h = kernel->ne[1];
+    const int64_t knl_d = kernel->ne[2];
+    const int64_t dst_w = dst->ne[0];
+    const int64_t dst_h = dst->ne[1];
+    const int64_t dst_d = dst->ne[2];
+
+    const float * src_data = (float *) src->data;
+    void  * knl_data       = kernel->data;
+    float * dst_data       = (float *) dst->data;
+
+    const int64_t knl_n_per_channel = knl_w * knl_h * knl_d;
+    const int64_t knl_n_total       = knl_n_per_channel * c;
+    const int64_t patch_total       = n * dst_w * dst_h * dst_d;
+
+    const int64_t space_per_patch   = knl_n_total * traits->type_size + oc * sizeof(float);
+    const int64_t batch_size        = params->wsize / space_per_patch;
+    const int64_t patches_per_batch = batch_size > 8 ? (batch_size / 8) * 8 : batch_size;
+    const int64_t batch_n           = (patch_total + patches_per_batch - 1) / patches_per_batch;
+
+    GGML_ASSERT(patches_per_batch > 0 && batch_size >= 1);
+
+    void * tmp = params->wdata;
+
+    for (int64_t batch_i = 0; batch_i < batch_n; ++batch_i) {
+        const int64_t patch_start_batch = batch_i * patches_per_batch;
+        const int64_t patch_end_batch   = std::min(patch_start_batch + patches_per_batch, patch_total);
+        const int64_t patch_n_in_batch  = patch_end_batch - patch_start_batch;
+
+        const int64_t patch_per_thread  = (patch_n_in_batch + params->nth - 1) / params->nth;
+        const int64_t patch_start       = patch_start_batch + params->ith * patch_per_thread;
+        const int64_t patch_end         = std::min(patch_start + patch_per_thread, patch_end_batch);
+
+        for (int64_t p = patch_start; p < patch_end; ++p) {
+            const int64_t p_in_batch = p % (dst_w * dst_h * dst_d);
+            const int64_t p_in_depth = p_in_batch % (dst_w * dst_h);
+            const int64_t batch_idx  = p / (dst_w * dst_h * dst_d);
+            const int64_t dst_z      = p_in_batch / (dst_w * dst_h);
+            const int64_t dst_y      = p_in_depth / dst_w;
+            const int64_t dst_x      = p_in_depth % dst_w;
+
+            char * dst_row = (char *) tmp + (p % patches_per_batch) * knl_n_total * traits->type_size;
+
+            for (int64_t ic = 0; ic < c; ++ic) {
+                for (int64_t kz = 0; kz < knl_d; ++kz) {
+                    for (int64_t ky = 0; ky < knl_h; ++ky) {
+                        for (int64_t kx = 0; kx < knl_w; ++kx) {
+                            const int64_t sz = dst_z * s2 + kz * d2 - p2;
+                            const int64_t sy = dst_y * s1 + ky * d1 - p1;
+                            const int64_t sx = dst_x * s0 + kx * d0 - p0;
+
+                            int64_t dst_idx = ic * knl_n_per_channel + kz * (knl_h * knl_w) + ky * knl_w + kx;
+
+                            float src_val;
+                            if (sz < 0 || sz >= src_d || sy < 0 || sy >= src_h || sx < 0 || sx >= src_w) {
+                                src_val = 0.0f;
+                            } else {
+                                const int64_t cn_idx = batch_idx * c + ic;
+                                const float * src_ptr = (const float *)((const char *)src_data + sx*src->nb[0] + sy*src->nb[1] + sz*src->nb[2] + cn_idx*src->nb[3]);
+                                src_val = *src_ptr;
+                            }
+
+                            char * element_ptr = dst_row + dst_idx * traits->type_size;
+                            if (kernel_type == GGML_TYPE_F32) {
+                                *(float *)element_ptr = src_val;
+                            } else if (kernel_type == GGML_TYPE_F16) {
+                                *(ggml_fp16_t *)element_ptr = GGML_CPU_FP32_TO_FP16(src_val);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        ggml_barrier(params->threadpool);
+
+        float * gemm_output = (float *) ((char *) tmp + patches_per_batch * knl_n_total * traits->type_size);
+        ggml_call_mul_mat(kernel_type, params, patch_n_in_batch, oc, knl_n_total, tmp, knl_data, gemm_output);
+
+        ggml_barrier(params->threadpool);
+
+        const int64_t permute_per_thread = (patch_n_in_batch + params->nth - 1) / params->nth;
+        const int64_t permute_start = params->ith * permute_per_thread;
+        const int64_t permute_end = std::min(permute_start + permute_per_thread, patch_n_in_batch);
+
+        for (int64_t i = permute_start; i < permute_end; ++i) {
+            const int64_t p = patch_start_batch + i;
+            const int64_t p_in_batch = p % (dst_w * dst_h * dst_d);
+            const int64_t p_in_depth = p_in_batch % (dst_w * dst_h);
+            const int64_t batch_idx  = p / (dst_w * dst_h * dst_d);
+            const int64_t dst_z      = p_in_batch / (dst_w * dst_h);
+            const int64_t dst_y      = p_in_depth / dst_w;
+            const int64_t dst_x      = p_in_depth % dst_w;
+
+            for (int64_t ioc = 0; ioc < oc; ++ioc) {
+                const float value = gemm_output[i * oc + ioc];
+                const int64_t ocn_idx = batch_idx * oc + ioc;
+                float * dst_ptr = (float *)((char *)dst_data + dst_x*dst->nb[0] + dst_y*dst->nb[1] + dst_z*dst->nb[2] + ocn_idx*dst->nb[3]);
+                *dst_ptr = value;
+            }
+        }
+    }
+}
+
+void ggml_compute_forward_conv_3d(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    ggml_compute_forward_conv_3d_impl(params, src0, src1, dst, src0->type);
+}
+
 // ggml_compute_forward_conv_transpose_2d
 
 void ggml_compute_forward_conv_transpose_2d(
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index 82ea79eaa5..d0ea83843b 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -70,6 +70,7 @@ void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * p
 void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_conv_3d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_2d_dw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_pool_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index a4417f1a17..d76ea58f78 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -975,6 +975,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "IM2COL",
     "IM2COL_BACK",
     "CONV_2D",
+    "CONV_3D",
     "CONV_2D_DW",
     "CONV_TRANSPOSE_2D",
     "POOL_1D",
@@ -1017,7 +1018,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
+static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1077,6 +1078,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "im2col(x)",
     "im2col_back(x)",
     "conv_2d(x)",
+    "conv_3d(x)",
     "conv_2d_dw(x)",
     "conv_transpose_2d(x)",
     "pool_1d(x)",
@@ -1119,7 +1121,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
+static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4480,6 +4482,56 @@ struct ggml_tensor * ggml_conv_2d_direct(
     return result;
 }
 
+// ggml_conv_3d
+
+struct ggml_tensor * ggml_conv_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   s1,
+        int                   s2,
+        int                   p0,
+        int                   p1,
+        int                   p2,
+        int                   d0,
+        int                   d1,
+        int                   d2,
+        int                   c,
+        int                   n,
+        int                   oc) {
+
+    GGML_ASSERT(a->ne[3] == (int64_t) c * oc);
+    GGML_ASSERT(b->ne[3] == (int64_t) c * n);
+
+    int64_t ne[4];
+    ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
+    ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1);
+    ne[2] = ggml_calc_conv_output_size(b->ne[2], a->ne[2], s2, p2, d2);
+    ne[3] = (int64_t) oc * n;
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    ggml_set_op_params_i32(result, 0,  s0);
+    ggml_set_op_params_i32(result, 1,  s1);
+    ggml_set_op_params_i32(result, 2,  s2);
+    ggml_set_op_params_i32(result, 3,  p0);
+    ggml_set_op_params_i32(result, 4,  p1);
+    ggml_set_op_params_i32(result, 5,  p2);
+    ggml_set_op_params_i32(result, 6,  d0);
+    ggml_set_op_params_i32(result, 7,  d1);
+    ggml_set_op_params_i32(result, 8,  d2);
+    ggml_set_op_params_i32(result, 9,  c);
+    ggml_set_op_params_i32(result, 10, n);
+    ggml_set_op_params_i32(result, 11, oc);
+
+    result->op = GGML_OP_CONV_3D;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
 // ggml_conv_transpose_2d_p0
 
 static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index e21e904278..a51527ca55 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -4091,6 +4091,75 @@ struct test_conv_2d_dw : public test_case {
     }
 };
 
+// GGML_OP_CONV_3D
+struct test_conv_3d : public test_case {
+    // Logical 5D dimensions
+    const int64_t N, IC, ID, IH, IW;
+    const int64_t OC, KD, KH, KW;
+    // Conv params
+    const int s0, s1, s2;
+    const int p0, p1, p2;
+    const int d0, d1, d2;
+    // Types
+    const ggml_type type_kernel;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "CONV_3D";
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR11(N, IC, ID, IH, IW, OC, KD, KH, KW, s0, s1) + "," +
+               VARS_TO_STR8(s2, p0, p1, p2, d0, d1, d2, type_kernel);
+    }
+
+    double max_nmse_err() override {
+        return 5e-4;
+    }
+
+    uint64_t op_flops(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
+            return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+        };
+        const int64_t OD = calc_conv_output_size(ID, KD, s2, p2, d2);
+        const int64_t OH = calc_conv_output_size(IH, KH, s1, p1, d1);
+        const int64_t OW = calc_conv_output_size(IW, KW, s0, p0, d0);
+
+        return (uint64_t)N * OC * OD * OH * OW * (2 * IC * KD * KH * KW - 1);
+    }
+
+    test_conv_3d(
+        int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW,
+        int64_t OC, int64_t KD, int64_t KH, int64_t KW,
+        int s0, int s1, int s2,
+        int p0, int p1, int p2,
+        int d0, int d1, int d2,
+        ggml_type type_kernel
+    ) : N(N), IC(IC), ID(ID), IH(IH), IW(IW),
+        OC(OC), KD(KD), KH(KH), KW(KW),
+        s0(s0), s1(s1), s2(s2),
+        p0(p0), p1(p1), p2(p2),
+        d0(d0), d1(d1), d2(d2),
+        type_kernel(type_kernel) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        // GGML input tensor is packed as [W, H, D, C*N]
+        const int64_t ne_input[] = {IW, IH, ID, IC * N};
+        ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input);
+        ggml_set_name(input, "input");
+
+        // GGML kernel tensor is packed as [KW, KH, KD, IC*OC]
+        const int64_t ne_kernel[] = {KW, KH, KD, IC * OC};
+        ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel);
+        ggml_set_name(kernel, "kernel");
+
+        ggml_tensor * out = ggml_conv_3d(ctx, kernel, input, s0, s1, s2, p0, p1, p2, d0, d1, d2, (int)IC, (int)N, (int)OC);
+        ggml_set_name(out, "out");
+        return out;
+    }
+};
+
 // GGML_OP_CONCAT
 struct test_concat : public test_case {
     const ggml_type type;
@@ -5528,6 +5597,61 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, false));
     test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, true));
 
+    // CONV_3D
+    auto calc_conv_output_size_3d = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
+        return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+    };
+
+    for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+        for (int N : {1, 2}) {
+            for (int IC : {1, 3}) {
+                for (int OC : {1, 4}) {
+                    for (int s0 : {1, 2}) {
+                        for (int p1 : {0, 1}) {
+                            for (int d2 : {1, 2}) {
+                                int64_t IW = 20, IH = 22, ID = 18;
+                                int64_t KW = 3,  KH = 3,  KD = 3;
+                                int s1 = s0, s2 = s0;
+                                int p0 = p1, p2 = p1;
+                                int d0 = d2, d1 = d2;
+
+                                if (calc_conv_output_size_3d(IW, KW, s0, p0, d0) <= 0 ||
+                                    calc_conv_output_size_3d(IH, KH, s1, p1, d1) <= 0 ||
+                                    calc_conv_output_size_3d(ID, KD, s2, p2, d2) <= 0) {
+                                    continue;
+                                }
+                                test_cases.emplace_back(new test_conv_3d(
+                                    N, IC, ID, IH, IW,
+                                    OC, KD, KH, KW,
+                                    s0, s1, s2, p0, p1, p2, d0, d1, d2,
+                                    kernel_type));
+
+                                // Asymmetric kernel and params
+                                int64_t asym_KW = 5, asym_KH = 1, asym_KD = 3;
+                                int asym_s0 = 2, asym_s1 = 1, asym_s2 = 1;
+                                int asym_p0 = 2, asym_p1 = 0, asym_p2 = 1;
+                                int asym_d0 = 1, asym_d1 = 1, asym_d2 = 2;
+
+                                if (calc_conv_output_size_3d(IW, asym_KW, asym_s0, asym_p0, asym_d0) <= 0 ||
+                                    calc_conv_output_size_3d(IH, asym_KH, asym_s1, asym_p1, asym_d1) <= 0 ||
+                                    calc_conv_output_size_3d(ID, asym_KD, asym_s2, asym_p2, asym_d2) <= 0) {
+                                    continue;
+                                }
+                                test_cases.emplace_back(new test_conv_3d(
+                                    N, IC, ID, IH, IW,
+                                    OC, asym_KD, asym_KH, asym_KW,
+                                    asym_s0, asym_s1, asym_s2, asym_p0, asym_p1, asym_p2, asym_d0, asym_d1, asym_d2,
+                                    kernel_type));
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        // Case with kernel size 1
+        test_cases.emplace_back(new test_conv_3d(1, 4, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
+    }
+
     for(uint32_t Cout : {1, 9}){
         for(uint32_t Cin : {1, 7}){
             for(uint32_t K : {1, 3, 1337}){

From 32732f2459a598606055f0403f0e4ec148d06d68 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Fri, 22 Aug 2025 11:04:08 -0500
Subject: [PATCH 151/287] model : gpt-oss add response_format support (#15494)

---
 common/chat.cpp | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index 7f6809a4ed..111b4a21b3 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -1361,6 +1361,26 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         "<|end|>",
     };
 
+    if (!inputs.json_schema.is_null()) {
+        data.grammar_lazy = false;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            auto schema = inputs.json_schema;
+            builder.resolve_refs(schema);
+
+            auto not_end = builder.add_rule("not-end",
+                "[^<] | \"<\" [^|] | \"<|\" [^e] | \"<|e\" [^n] | \"<|en\" [^d] | \"<|end\" [^|] | \"<|end|\" [^>]");
+            auto analysis = builder.add_rule("analysis",
+                "\"<|channel|>analysis<|message|>\" ( " + not_end + " )* \"<|end|>\"");
+            auto constraint = builder.add_rule("constraint", "\"<|constrain|>\"? [a-zA-Z0-9_-]+");
+            auto final = builder.add_rule("final",
+                "\"<|channel|>final\" ( \" \" " + constraint + " )? \"<|message|>\" " +
+                builder.add_schema("response", schema)
+            );
+
+            builder.add_rule("root", "( " + analysis + " \"<|start|>assistant\" )? " + final);
+        });
+    }
+
     if (inputs.tools.is_array() && !inputs.tools.empty()) {
         data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
         data.grammar = build_grammar([&](const common_grammar_builder & builder) {
@@ -2121,7 +2141,7 @@ static common_chat_params common_chat_templates_apply_jinja(
     }
 
     // GPT-OSS
-    if (src.find("<|channel|>") != std::string::npos && params.json_schema.is_null()) {
+    if (src.find("<|channel|>") != std::string::npos) {
         return common_chat_params_init_gpt_oss(tmpl, params);
     }
 

From 45363632cbd593537d541e81b600242e0b3d47fc Mon Sep 17 00:00:00 2001
From: Reese Levine <reeselevine1@gmail.com>
Date: Fri, 22 Aug 2025 11:28:03 -0700
Subject: [PATCH 152/287] ggml WebGPU: add support for quantization types
 (#15440)

* Begin work on set_rows

* Work on set rows

* Add error buffers for reporting unsupported SET_ROWS indices

* Remove extra comments

* Work on templating for different types in shaders

* Work on shader type generation

* Working q4_0 mul_mat and some templating for different types

* Add q4_0_f16 matmul and fix device init

* Add matmul support for basic quantization types

* Add q2_k and q3_k quantization

* Add rest of k-quants

* Get firt i-quant working

* Closer to supporting all i-quants

* Support rest of i-quants

* Cleanup code

* Fix python formatting

* debug

* Bugfix for memset

* Add padding to end of buffers on creation

* Simplify bit-shifting

* Update usage of StringView
---
 ggml/src/ggml-webgpu/CMakeLists.txt           |    4 +-
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          |  426 ++--
 .../ggml-webgpu/wgsl-shaders/embed_wgsl.py    |   90 +-
 ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl |   16 +-
 .../wgsl-shaders/mul_mat.tmpl.wgsl            | 1794 +++++++++++++++++
 .../src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl |   56 -
 6 files changed, 2143 insertions(+), 243 deletions(-)
 create mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl
 delete mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl

diff --git a/ggml/src/ggml-webgpu/CMakeLists.txt b/ggml/src/ggml-webgpu/CMakeLists.txt
index 79ef68b85a..78a985a4d1 100644
--- a/ggml/src/ggml-webgpu/CMakeLists.txt
+++ b/ggml/src/ggml-webgpu/CMakeLists.txt
@@ -20,8 +20,8 @@ add_custom_command(
     COMMAND ${CMAKE_COMMAND} -E make_directory ${SHADER_OUTPUT_DIR}
     COMMAND ${CMAKE_COMMAND} -E env PYTHONIOENCODING=utf-8
         ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py
-            --input "${SHADER_DIR}"
-            --output "${SHADER_HEADER}"
+            --input_dir "${SHADER_DIR}"
+            --output_file "${SHADER_HEADER}"
     DEPENDS ${WGSL_SHADER_FILES} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py
     VERBATIM
 )
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index ba1addc8d9..32f1e304e1 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -118,13 +118,11 @@ struct webgpu_context_struct {
 
     std::recursive_mutex mutex;
 
-    bool device_init = false;
-
     webgpu_buf_pool param_buf_pool;
     webgpu_buf_pool set_rows_error_buf_pool;
 
     wgpu::ComputePipeline memset_pipeline;
-    wgpu::ComputePipeline mul_mat_pipeline;
+    wgpu::ComputePipeline mul_mat_pipeline[30][2];
     wgpu::ComputePipeline set_rows_pipeline;
     wgpu::ComputePipeline cpy_pipeline;
 
@@ -238,7 +236,7 @@ static void ggml_backend_webgpu_wait_on_submission(webgpu_context & ctx) {
                                   wgpu::CallbackMode::AllowSpontaneous,
                                   [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
                                       if (status != wgpu::QueueWorkDoneStatus::Success) {
-                                          GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
+                                          GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", std::string(message).c_str());
                                       }
                                   }),
                               UINT64_MAX);
@@ -278,7 +276,7 @@ static void ggml_backend_webgpu_submit_queue(webgpu_context & ctx) {
         wgpu::CallbackMode::AllowSpontaneous,
         [ctx, staged_param_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
             if (status != wgpu::QueueWorkDoneStatus::Success) {
-                GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
+                GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", std::string(message).c_str());
             }
             // Free the staged buffers
             ctx->param_buf_pool.free_bufs(staged_param_bufs);
@@ -294,7 +292,7 @@ static void ggml_backend_webgpu_submit_queue(webgpu_context & ctx) {
             wgpu::CallbackMode::AllowSpontaneous,
             [ctx, error_bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) {
                 if (status != wgpu::MapAsyncStatus::Success) {
-                    GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", message.data);
+                    GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", std::string(message).c_str());
                 } else {
                     const uint32_t * error_data = (const uint32_t *) error_bufs.host_buf.GetConstMappedRange();
                     if (*error_data) {
@@ -331,6 +329,7 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
 // To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and
 // debug statements in the shader, and then call this function after encoding the commands and submitting them.
 static void ggml_backend_webgpu_debug(webgpu_context & ctx) {
+    ggml_backend_webgpu_submit_queue(ctx);
     wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
     encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize());
     wgpu::CommandBuffer commands = encoder.Finish();
@@ -421,15 +420,6 @@ static void ggml_backend_webgpu_buffer_memset(webgpu_context & ctx,
     ggml_backend_webgpu_build_and_enqueue(ctx, ctx->memset_pipeline, params, entries, wg_x, true);
 }
 
-static size_t ggml_backend_webgpu_tensor_offset(const ggml_tensor * tensor) {
-    return webgpu_tensor_offset(tensor) + tensor->view_offs;
-}
-
-static wgpu::Buffer ggml_backend_webgpu_tensor_buf(const ggml_tensor * tensor) {
-    ggml_backend_webgpu_buffer_context * ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
-    return ctx->buffer;
-}
-
 /** End WebGPU Actions */
 
 /** GGML Backend Interface */
@@ -447,19 +437,36 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) {
     GGML_UNUSED(ctx);
 }
 
+static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
+    return webgpu_tensor_offset(tensor) + tensor->view_offs;
+}
+
+static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) {
+    ggml_backend_webgpu_buffer_context * ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
+    return ctx->buffer;
+}
+
+static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, ggml_tensor * t) {
+    size_t offset = ggml_webgpu_tensor_offset(t);
+    return offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+}
+
+static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, ggml_tensor * t) {
+    size_t offset = ggml_webgpu_tensor_offset(t);
+    return offset & ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+}
+
+static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) {
+    return (ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t) + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) &
+           ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1);
+}
+
 static void ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
-    size_t src_offset       = ggml_backend_webgpu_tensor_offset(src);
-    // assumes power of 2 offset alignment
-    size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    // align to minimum offset alignment
-    src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    size_t dst_offset       = ggml_backend_webgpu_tensor_offset(dst);
-    size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    uint32_t              ne     = (uint32_t) ggml_nelements(dst);
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
     std::vector<uint32_t> params = { ne,
-                                     (uint32_t) (src_misalignment / ggml_type_size(src->type)),
-                                     (uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
                                      // Convert byte-strides to element-strides
                                      (uint32_t) (src->nb[0] / ggml_type_size(src->type)),
                                      (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
@@ -477,15 +484,13 @@ static void ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor
 
     std::vector<wgpu::BindGroupEntry> entries = {
         { .binding = 0,
-         .buffer  = ggml_backend_webgpu_tensor_buf(src),
-         .offset  = src_offset,
-         .size    = (ggml_nbytes(src) + src_misalignment + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) &
-                  ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1) },
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
         { .binding = 1,
-         .buffer  = ggml_backend_webgpu_tensor_buf(dst),
-         .offset  = dst_offset,
-         .size    = (ggml_nbytes(dst) + dst_misalignment + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) &
-                  ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1) }
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
     };
 
     size_t   max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
@@ -504,21 +509,9 @@ static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_t
         error_bufs.host_buf.Unmap();
     }
 
-    size_t src_offset       = ggml_backend_webgpu_tensor_offset(src);
-    // assumes power of 2 offset alignment
-    size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    // align to minimum offset alignment
-    src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    size_t idx_offset       = ggml_backend_webgpu_tensor_offset(idx);
-    size_t idx_misalignment = idx_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    idx_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    size_t dst_offset       = ggml_backend_webgpu_tensor_offset(dst);
-    size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-
-    std::vector<uint32_t> params = { (uint32_t) (src_misalignment / ggml_type_size(src->type)),
-                                     (uint32_t) (idx_misalignment / ggml_type_size(idx->type)),
-                                     (uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
+    std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
                                      // Convert byte-strides to element-strides
                                      (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
                                      (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
@@ -540,18 +533,18 @@ static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_t
 
     std::vector<wgpu::BindGroupEntry> entries = {
         { .binding = 0,
-         .buffer  = ggml_backend_webgpu_tensor_buf(src),
-         .offset  = ggml_backend_webgpu_tensor_offset(src),
-         .size    = ggml_nbytes(src)                                                                       },
+         .buffer  = ggml_webgpu_tensor_buf(src),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src) },
         { .binding = 1,
-         .buffer  = ggml_backend_webgpu_tensor_buf(idx),
-         .offset  = ggml_backend_webgpu_tensor_offset(idx),
-         .size    = ggml_nbytes(idx)                                                                       },
+         .buffer  = ggml_webgpu_tensor_buf(idx),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, idx),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, idx) },
         { .binding = 2,
-         .buffer  = ggml_backend_webgpu_tensor_buf(dst),
-         .offset  = ggml_backend_webgpu_tensor_offset(dst),
-         .size    = ggml_nbytes(dst)                                                                       },
-        { .binding = 3, .buffer = error_bufs.dev_buf,    .offset = 0, .size = error_bufs.dev_buf.GetSize() }
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst) },
+        { .binding = 3, .buffer = error_bufs.dev_buf, .offset = 0, .size = error_bufs.dev_buf.GetSize() }
     };
 
     size_t   max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
@@ -565,15 +558,18 @@ static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_t
 
 static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
     std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
         (uint32_t) dst->ne[1],                                  // number of rows in result (M)
         (uint32_t) dst->ne[0],                                  // number of columns in result (N)
         (uint32_t) src0->ne[0],                                 // number of columns in src0/src1 (K)
-        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),  // stride (elements) of src0 in dimension 1
-        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),  // stride (elements) of src1 in dimension 1
-        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),  // stride (elements) of src0 in dimension 2
-        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),  // stride (elements) of src1 in dimension 2
-        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),  // stride (elements) of src0 in dimension 3
-        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),  // stride (elements) of src1 in dimension 3
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 1
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 1
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 2
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 2
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 3
+        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 3
         (uint32_t) src0->ne[2],                                 // batch size in dimension 2
         (uint32_t) src0->ne[3],                                 // batch size in dimension 3
         (uint32_t) (src1->ne[2] / src0->ne[2]),                 // broadcast in dimension 2
@@ -582,22 +578,22 @@ static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_t
 
     std::vector<wgpu::BindGroupEntry> entries = {
         { .binding = 0,
-         .buffer  = ggml_backend_webgpu_tensor_buf(src0),
-         .offset  = ggml_backend_webgpu_tensor_offset(src0),
-         .size    = ggml_nbytes(src0) },
+         .buffer  = ggml_webgpu_tensor_buf(src0),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
         { .binding = 1,
-         .buffer  = ggml_backend_webgpu_tensor_buf(src1),
-         .offset  = ggml_backend_webgpu_tensor_offset(src1),
-         .size    = ggml_nbytes(src1) },
+         .buffer  = ggml_webgpu_tensor_buf(src1),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) },
         { .binding = 2,
-         .buffer  = ggml_backend_webgpu_tensor_buf(dst),
-         .offset  = ggml_backend_webgpu_tensor_offset(dst),
-         .size    = ggml_nbytes(dst)  }
+         .buffer  = ggml_webgpu_tensor_buf(dst),
+         .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
+         .size    = ggml_webgpu_tensor_binding_size(ctx, dst)  },
     };
 
     uint32_t wg_x =
         (dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3] + WEBGPU_MUL_MAT_WG_SIZE - 1) / WEBGPU_MUL_MAT_WG_SIZE;
-    ggml_backend_webgpu_build_and_enqueue(ctx, ctx->mul_mat_pipeline, params, entries, wg_x);
+    ggml_backend_webgpu_build_and_enqueue(ctx, ctx->mul_mat_pipeline[src0->type][src1->type], params, entries, wg_x);
 }
 
 // Returns true if node has enqueued work into the queue, false otherwise
@@ -827,7 +823,7 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b
     wgpu::Buffer buf;
     ggml_webgpu_create_buffer(ctx->webgpu_ctx->device,
                               buf,
-                              size,
+                              (size + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1),
                               wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst,
                               "allocated_buffer");
 
@@ -907,7 +903,94 @@ static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) {
 }
 
 static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
-    ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline, wgsl_mul_mat, "mul_mat");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F32][GGML_TYPE_F32],
+                                wgsl_mul_mat_f32_f32,
+                                "mul_mat_f32_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F16][GGML_TYPE_F16],
+                                wgsl_mul_mat_f16_f16,
+                                "mul_mat_f16_f16");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F16][GGML_TYPE_F32],
+                                wgsl_mul_mat_f16_f32,
+                                "mul_mat_f16_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_0][GGML_TYPE_F32],
+                                wgsl_mul_mat_q4_0_f32,
+                                "mul_mat_q4_0_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_1][GGML_TYPE_F32],
+                                wgsl_mul_mat_q4_1_f32,
+                                "mul_mat_q4_1_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_0][GGML_TYPE_F32],
+                                wgsl_mul_mat_q5_0_f32,
+                                "mul_mat_q5_0_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_1][GGML_TYPE_F32],
+                                wgsl_mul_mat_q5_1_f32,
+                                "mul_mat_q5_1_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q8_0][GGML_TYPE_F32],
+                                wgsl_mul_mat_q8_0_f32,
+                                "mul_mat_q8_0_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q2_K][GGML_TYPE_F32],
+                                wgsl_mul_mat_q2_k_f32,
+                                "mul_mat_q2_k_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q3_K][GGML_TYPE_F32],
+                                wgsl_mul_mat_q3_k_f32,
+                                "mul_mat_q3_k_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_K][GGML_TYPE_F32],
+                                wgsl_mul_mat_q4_k_f32,
+                                "mul_mat_q4_k_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_K][GGML_TYPE_F32],
+                                wgsl_mul_mat_q5_k_f32,
+                                "mul_mat_q5_k_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q6_K][GGML_TYPE_F32],
+                                wgsl_mul_mat_q6_k_f32,
+                                "mul_mat_q6_k_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq2_xxs_f32,
+                                "mul_mat_iq2_xxs_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XS][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq2_xs_f32,
+                                "mul_mat_iq2_xs_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_S][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq2_s_f32,
+                                "mul_mat_iq2_s_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq3_xxs_f32,
+                                "mul_mat_iq3_xxs_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_S][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq3_s_f32,
+                                "mul_mat_iq3_s_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_S][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq1_s_f32,
+                                "mul_mat_iq1_s_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_M][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq1_m_f32,
+                                "mul_mat_iq1_m_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_NL][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq4_nl_f32,
+                                "mul_mat_iq4_nl_f32");
+    ggml_webgpu_create_pipeline(webgpu_ctx->device,
+                                webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_XS][GGML_TYPE_F32],
+                                wgsl_mul_mat_iq4_xs_f32,
+                                "mul_mat_iq4_xs_f32");
 }
 
 static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
@@ -933,79 +1016,6 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
     ggml_backend_webgpu_device_context * dev_ctx    = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
     webgpu_context                       webgpu_ctx = dev_ctx->webgpu_ctx;
 
-    // Multiple threads may try to initialize the device
-    std::lock_guard<std::recursive_mutex> lock(webgpu_ctx->mutex);
-    if (!webgpu_ctx->device_init) {
-        // Initialize device
-        std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16,
-                                                             wgpu::FeatureName::ImplicitDeviceSynchronization };
-        wgpu::DeviceDescriptor         dev_desc;
-        dev_desc.requiredLimits       = &webgpu_ctx->limits;
-        dev_desc.requiredFeatures     = required_features.data();
-        dev_desc.requiredFeatureCount = required_features.size();
-        dev_desc.SetDeviceLostCallback(
-            wgpu::CallbackMode::AllowSpontaneous,
-            [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
-                GGML_UNUSED(device);
-                GGML_LOG_ERROR(
-                    "ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason), message.data);
-            });
-        dev_desc.SetUncapturedErrorCallback(
-            [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) {
-                GGML_UNUSED(device);
-                GGML_LOG_ERROR(
-                    "ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast<int>(reason), message.data);
-            });
-        webgpu_ctx->instance.WaitAny(
-            webgpu_ctx->adapter.RequestDevice(
-                &dev_desc,
-                wgpu::CallbackMode::AllowSpontaneous,
-                [webgpu_ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) {
-                    if (status != wgpu::RequestDeviceStatus::Success) {
-                        GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", message.data);
-                        return;
-                    }
-                    webgpu_ctx->device = std::move(device);
-                }),
-            UINT64_MAX);
-        GGML_ASSERT(webgpu_ctx->device != nullptr);
-
-        // Initialize (compute) queue
-        webgpu_ctx->queue = webgpu_ctx->device.GetQueue();
-
-        // Create buffer pool for shader parameters
-        webgpu_ctx->param_buf_pool.init(webgpu_ctx->device,
-                                        WEBGPU_NUM_PARAM_BUFS,
-                                        WEBGPU_PARAMS_BUF_SIZE_BYTES,
-                                        wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
-                                        wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
-        webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->device,
-                                                 WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
-                                                 WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
-                                                 wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
-                                                 wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
-
-        ggml_webgpu_init_memset_pipeline(webgpu_ctx);
-        ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
-        ggml_webgpu_init_set_rows_pipeline(webgpu_ctx);
-        ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
-
-#ifdef GGML_WEBGPU_DEBUG
-        // Initialize debug buffers
-        ggml_webgpu_create_buffer(webgpu_ctx->device,
-                                  webgpu_ctx->debug_host_buf,
-                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
-                                  wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead,
-                                  "debug_host_buf");
-        ggml_webgpu_create_buffer(webgpu_ctx->device,
-                                  webgpu_ctx->debug_dev_buf,
-                                  WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
-                                  wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc,
-                                  "debug_dev_buf");
-#endif
-        webgpu_ctx->device_init = true;
-    }
-
     static ggml_backend_webgpu_context backend_ctx;
     backend_ctx.name       = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name;
     backend_ctx.webgpu_ctx = webgpu_ctx;
@@ -1053,10 +1063,45 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
             return true;
-        case GGML_OP_CPY | GGML_OP_SET_ROWS:
+        case GGML_OP_CPY:
+        case GGML_OP_SET_ROWS:
             return op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_MUL_MAT:
-            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
+            {
+                switch (op->src[1]->type) {
+                    case GGML_TYPE_F16:
+                        return op->src[0]->type == GGML_TYPE_F16;
+                    case GGML_TYPE_F32:
+                        switch (op->src[0]->type) {
+                            case GGML_TYPE_F32:
+                            case GGML_TYPE_F16:
+                            case GGML_TYPE_Q4_0:
+                            case GGML_TYPE_Q4_1:
+                            case GGML_TYPE_Q5_0:
+                            case GGML_TYPE_Q5_1:
+                            case GGML_TYPE_Q8_0:
+                            case GGML_TYPE_Q2_K:
+                            case GGML_TYPE_Q3_K:
+                            case GGML_TYPE_Q4_K:
+                            case GGML_TYPE_Q5_K:
+                            case GGML_TYPE_Q6_K:
+                            case GGML_TYPE_IQ2_XXS:
+                            case GGML_TYPE_IQ2_XS:
+                            case GGML_TYPE_IQ2_S:
+                            case GGML_TYPE_IQ3_XXS:
+                            case GGML_TYPE_IQ3_S:
+                            case GGML_TYPE_IQ1_S:
+                            case GGML_TYPE_IQ1_M:
+                            case GGML_TYPE_IQ4_NL:
+                            case GGML_TYPE_IQ4_XS:
+                                return true;
+                            default:
+                                return false;
+                        }
+                    default:
+                        return false;
+                }
+            }
         default:
             return false;
     }
@@ -1123,20 +1168,87 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t
     wgpu::AdapterInfo info{};
     ctx->adapter.GetInfo(&info);
 
+    // Initialize device
+    std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16,
+                                                         wgpu::FeatureName::ImplicitDeviceSynchronization };
+    wgpu::DeviceDescriptor         dev_desc;
+    dev_desc.requiredLimits       = &ctx->limits;
+    dev_desc.requiredFeatures     = required_features.data();
+    dev_desc.requiredFeatureCount = required_features.size();
+    dev_desc.SetDeviceLostCallback(
+        wgpu::CallbackMode::AllowSpontaneous,
+        [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
+            GGML_UNUSED(device);
+            GGML_LOG_ERROR(
+                "ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason), std::string(message).c_str());
+        });
+    dev_desc.SetUncapturedErrorCallback(
+        [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) {
+            GGML_UNUSED(device);
+            GGML_LOG_ERROR(
+                "ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast<int>(reason), std::string(message).c_str());
+        });
+    ctx->instance.WaitAny(ctx->adapter.RequestDevice(
+                              &dev_desc,
+                              wgpu::CallbackMode::AllowSpontaneous,
+                              [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) {
+                                  if (status != wgpu::RequestDeviceStatus::Success) {
+                                      GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", std::string(message).c_str());
+                                      return;
+                                  }
+                                  ctx->device = std::move(device);
+                              }),
+                          UINT64_MAX);
+    GGML_ASSERT(ctx->device != nullptr);
+
+    // Initialize (compute) queue
+    ctx->queue = ctx->device.GetQueue();
+
+    // Create buffer pool for shader parameters
+    ctx->param_buf_pool.init(ctx->device,
+                             WEBGPU_NUM_PARAM_BUFS,
+                             WEBGPU_PARAMS_BUF_SIZE_BYTES,
+                             wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
+                             wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
+    ctx->set_rows_error_buf_pool.init(ctx->device,
+                                      WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
+                                      WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
+                                      wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
+                                      wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
+
+    ggml_webgpu_init_memset_pipeline(ctx);
+    ggml_webgpu_init_mul_mat_pipeline(ctx);
+    ggml_webgpu_init_set_rows_pipeline(ctx);
+    ggml_webgpu_init_cpy_pipeline(ctx);
+
+#ifdef GGML_WEBGPU_DEBUG
+    // Initialize debug buffers
+    ggml_webgpu_create_buffer(ctx->device,
+                              ctx->debug_host_buf,
+                              WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                              wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead,
+                              "debug_host_buf");
+    ggml_webgpu_create_buffer(ctx->device,
+                              ctx->debug_dev_buf,
+                              WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
+                              wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc,
+                              "debug_dev_buf");
+#endif
+
     static ggml_backend_webgpu_device_context device_ctx;
     device_ctx.webgpu_ctx  = ctx;
     device_ctx.device_name = GGML_WEBGPU_NAME;
-    device_ctx.device_desc = std::string(info.description.data);
+    device_ctx.device_desc = info.description;
 
     GGML_LOG_INFO(
         "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | "
         "device_desc: %s\n",
         info.vendorID,
-        info.vendor.data,
-        info.architecture.data,
+        std::string(info.vendor).c_str(),
+        std::string(info.architecture).c_str(),
         info.deviceID,
-        info.device.data,
-        info.description.data);
+        std::string(info.device).c_str(),
+        std::string(info.description).c_str());
 
     // See GGML Backend Device Interface section
     static ggml_backend_device device = {
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py
index 962dcd6b17..cc8def7f13 100755
--- a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py
@@ -1,35 +1,85 @@
 import os
+import re
+import ast
 import argparse
 
 
-def escape_triple_quotes(wgsl):
-    # Simple defense in case of embedded """
-    return wgsl.replace('"""', '\\"""')
+def extract_block(text, name):
+    pattern = rf'#define\({name}\)\s*(.*?)#end\({name}\)'
+    match = re.search(pattern, text, re.DOTALL)
+    if not match:
+        raise ValueError(f"Missing block: {name}")
+    return match.group(1).strip()
 
 
-def to_cpp_string_literal(varname, content):
-    return f'const char* wgsl_{varname} = R"({content})";\n'
+def parse_decls(decls_text):
+    decls = {}
+    for name, code in re.findall(r'#decl\((.*?)\)\s*(.*?)#enddecl\(\1\)', decls_text, re.DOTALL):
+        decls[name.strip()] = code.strip()
+    return decls
+
+
+def replace_placeholders(shader_text, replacements):
+    for key, val in replacements.items():
+        # Match {{KEY}} literally, where KEY is escaped
+        pattern = r'{{\s*' + re.escape(key) + r'\s*}}'
+        shader_text = re.sub(pattern, str(val), shader_text)
+    return shader_text
+
+
+def write_shader(shader_name, shader_code, output_dir, outfile):
+    if output_dir:
+        wgsl_filename = os.path.join(output_dir, f"{shader_name}.wgsl")
+        with open(wgsl_filename, "w", encoding="utf-8") as f_out:
+            f_out.write(shader_code)
+    outfile.write(f'const char* wgsl_{shader_name} = R"({shader_code})";\n\n')
+
+
+def generate_variants(shader_path, output_dir, outfile):
+    shader_base_name = shader_path.split("/")[-1].split(".")[0]
+
+    with open(shader_path, "r", encoding="utf-8") as f:
+        text = f.read()
+
+    try:
+        variants = ast.literal_eval(extract_block(text, "VARIANTS"))
+    except ValueError:
+        write_shader(shader_base_name, text, output_dir, outfile)
+    else:
+        decls_map = parse_decls(extract_block(text, "DECLS"))
+        shader_template = extract_block(text, "SHADER")
+
+        for variant in variants:
+            decls = variant["DECLS"]
+            decls_code = ""
+            for key in decls:
+                if key not in decls_map:
+                    raise ValueError(f"DECLS key '{key}' not found.")
+                decls_code += decls_map[key] + "\n\n"
+
+            shader_variant = replace_placeholders(shader_template, variant["REPLS"])
+            final_shader = re.sub(r'\bDECLS\b', decls_code, shader_variant)
+
+            output_name = f"{shader_base_name}_" + "_".join([variant["REPLS"]["SRC0_TYPE"], variant["REPLS"]["SRC1_TYPE"]])
+            write_shader(output_name, final_shader, output_dir, outfile)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--input', required=True)
-    parser.add_argument('--output', required=True)
+    parser.add_argument("--input_dir", required=True)
+    parser.add_argument("--output_file", required=True)
+    parser.add_argument("--output_dir")
     args = parser.parse_args()
 
-    with open(args.output, 'w', encoding='utf-8') as out:
-        out.write("// Auto-generated shader embedding \n\n")
-        for fname in sorted(os.listdir(args.input)):
-            if not fname.endswith('.wgsl'):
-                continue
-            shader_path = os.path.join(args.input, fname)
-            varname = os.path.splitext(fname)[0]
-            with open(shader_path, 'r', encoding='utf-8') as f:
-                content = f.read()
-            content = escape_triple_quotes(content)
-            out.write(to_cpp_string_literal(varname, content))
-            out.write('\n')
+    if args.output_dir:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    with open(args.output_file, "w", encoding="utf-8") as out:
+        out.write("// Auto-generated shader embedding\n\n")
+        for fname in sorted(os.listdir(args.input_dir)):
+            if fname.endswith(".wgsl"):
+                generate_variants(os.path.join(args.input_dir, fname), args.output_dir, out)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl
index cb7c8c3e09..194d2d6f58 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl
@@ -19,20 +19,20 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     let start = params.offset;
     let end = params.offset + params.size;
 
-    for (var j: u32 = 0u; j < bytes_per_thread; j = j + 1u) {
+    for (var j: u32 = 0u; j < bytes_per_thread; j += 4) {
         let byte_index = start + i + j;
-        if (byte_index + 4u <= end) {
-            output_buffer[(byte_index >> 2u)] = params.value;
+        if (byte_index + 4 <= end) {
+            output_buffer[byte_index >> 2] = params.value;
         } else {
             // Handle tail (unaligned)
-            for (var k: u32 = 0u; k < 4u; k = k + 1u) {
+            for (var k: u32 = 0; k < 4; k++) {
                 let idx = byte_index + k;
                 if (idx < end) {
-                    let word_idx = idx >> 2u;
-                    let byte_offset = (idx & 3u) * 8u;
-                    let mask = ~(0xffu << byte_offset);
+                    let word_idx = idx >> 2;
+                    let bit_offset = (idx & 3) * 8u;
+                    let mask = ~(0xffu << bit_offset);
                     let existing = output_buffer[word_idx];
-                    output_buffer[word_idx] = (existing & mask) | ((params.value & 0xffu) << byte_offset);
+                    output_buffer[word_idx] = (existing & mask) | (params.value & (0xffu << bit_offset));
                 }
             }
         }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl
new file mode 100644
index 0000000000..79465c298d
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl
@@ -0,0 +1,1794 @@
+#define(VARIANTS)
+
+[
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "BLOCK_SIZE" : 1
+    },
+    "DECLS" : ["FLOAT"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_1",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q4_1"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_1",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q5_1"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q8_0",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32
+    },
+    "DECLS": ["BYTE_HELPERS", "Q8_0"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q2_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q2_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q3_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q3_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q4_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q4_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q5_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["Q45_K_SCALE_MIN", "BYTE_HELPERS", "Q5_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "q6_k",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "Q6_K"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_xxs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XXS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_xs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_XS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq2_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ2_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq3_xxs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_XSS"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq3_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ23_TABLES", "IQ3_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq1_s",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_TABLE","IQ1_S"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq1_m",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ1_TABLE","IQ1_M"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq4_nl",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 32,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_TABLE", "IQ4_NL"]
+  },
+  {
+    "REPLS": {
+      "SRC0_TYPE": "iq4_xs",
+      "SRC1_TYPE": "f32",
+      "BLOCK_SIZE": 256,
+    },
+    "DECLS": ["BYTE_HELPERS", "IQ4_TABLE", "IQ4_XS"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(BYTE_HELPERS)
+
+fn get_byte(value: u32, index: u32) -> u32 {
+    return (value >> (index * 8)) & 0xFF;
+}
+
+fn get_byte_i32(value: u32, index: u32) -> i32 {
+    return bitcast<i32>(((value >> (index * 8)) & 0xFF) << 24) >> 24;
+}
+
+#enddecl(BYTE_HELPERS)
+
+#decl(FLOAT)
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    return f32(src0[src0_idx_base + offset]) * f32(src1[src1_idx_base + offset]);
+}
+#enddecl(FLOAT)
+
+#decl(Q4_0)
+struct q4_0 {
+    d: f16,
+    qs: array<f16, 8>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q4_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q4_0.d);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q4_0.qs[2 * j], block_q4_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0f) * d;
+            let q_lo = (f32(q_byte & 0xF) - 8.0f) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q4_0)
+
+#decl(Q4_1)
+struct q4_1 {
+    d: f16,
+    m: f16,
+    qs: array<u32, 4>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q4_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q4_1.d);
+    let m = f32(block_q4_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q4_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = f32((q_byte >> 4) & 0xF) * d + m;
+            let q_lo = f32(q_byte & 0xF) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q4_1)
+
+#decl(Q5_0)
+struct q5_0 {
+    d: f16,
+    qh: array<f16, 2>,
+    qs: array<f16, 8>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q5_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q5_0.d);
+    var sum: f32 = 0.0;
+    let qh_packed = bitcast<u32>(vec2(block_q5_0.qh[0], block_q5_0.qh[1]));
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q5_0.qs[2 * j], block_q5_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (qh_packed >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = (f32(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d;
+            let qh_lo = ((qh_packed >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = (f32((q_byte & 0xF) | qh_lo) - 16.0) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q5_0)
+
+#decl(Q5_1)
+struct q5_1 {
+    d: f16,
+    m: f16,
+    qh: u32,
+    qs: array<u32, 4>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q5_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q5_1.d);
+    let m = f32(block_q5_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 4; j++) {
+        let q_packed = block_q5_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let qh_hi = (block_q5_1.qh >> (j * 4 + k + 12)) & 0x10;
+            let q_hi = f32(((q_byte >> 4) & 0xF) | qh_hi) * d + m;
+            let qh_lo = ((block_q5_1.qh >> (j * 4 + k)) << 4) & 0x10;
+            let q_lo = f32((q_byte & 0xF) | qh_lo) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_lo * f32(src1[src1_offset]);
+            sum += q_hi * f32(src1[src1_offset + 16]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q5_1)
+
+#decl(Q8_0)
+struct q8_0 {
+    d: f16,
+    qs: array<f16, 16>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q8_0 = src0[src0_idx_base + offset];
+    let d = f32(block_q8_0.d);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 8; j++) {
+        let q_packed = bitcast<u32>(vec2(block_q8_0.qs[2 * j], block_q8_0.qs[2 * j + 1]));
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte_i32(q_packed, k);
+            let q_val = f32(q_byte) * d;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_val * f32(src1[src1_offset]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q8_0)
+
+#decl(Q8_1)
+struct q8_1 {
+    d: f16,
+    m: f16,
+    qs: array<u32, 8>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block_q8_1 = src0[src0_idx_base + offset];
+    let d = f32(block_q8_1.d);
+    let m = f32(block_q8_1.m);
+    var sum: f32 = 0.0;
+    for (var j: u32 = 0; j < 8; j++) {
+        let q_packed = block_q8_1.qs[j];
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte_i32(q_packed, k);
+            let q_val = f32(q_byte) * d + m;
+            let src1_offset = src1_idx_base + offset * 32 + j * 4 + k;
+            sum += q_val * f32(src1[src1_offset]);
+        }
+    }
+    return sum;
+}
+#enddecl(Q8_1)
+
+#decl(Q2_K)
+// 16 blocks of 16 elements each
+struct q2_k {
+    scales: array<u32, 4>,
+    qs: array<u32, 16>,
+    d: f16,
+    dmin: f16
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(block.scales[is / 4], is % 4);
+                is++;
+                let dl = d * f32(sc & 0xF);
+                let ml = m * f32(sc >> 4);
+                for (var l: u32 = 0u; l < 16; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                    let qs_val = (q_byte >> shift) & 3;
+                    sum += (f32(qs_val) * dl - ml) * src1[src1_i];
+                    src1_i++;
+                }
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q2_K)
+
+#decl(Q3_K)
+// 16 blocks of 16 elements each
+struct q3_k {
+    hmask: array<f16, 16>,
+    qs: array<f16, 32>,
+    scales: array<f16, 6>, // 6-bit quantized values
+    d: f16
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+
+    // extract 6-bit scales, which consist of 4-bits from first 8 bytes of scale,
+    // and 2-bits from the last 4 bytes
+    let kmask1: u32 = 0x03030303;
+    let kmask2: u32 = 0x0f0f0f0f;
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+    var tmp: u32 = scale_vals[2];
+    scale_vals[2] = ((scale_vals[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+    scale_vals[3] = ((scale_vals[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+    scale_vals[0] = (scale_vals[0] & kmask2) | ((tmp & kmask1) << 4);
+    scale_vals[1] = (scale_vals[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+    // convert arrays of f16 -> u32
+    var hmask_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        hmask_vals[i] = bitcast<u32>(vec2(block.hmask[2 * i], block.hmask[2 * i + 1]));
+    }
+    var qs_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[2 * i], block.qs[2 * i + 1]));
+    }
+
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    var m: u32 = 1;
+    // 2 halves of the block (128 elements each)
+    for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) {
+        // 4 groups (each group has 2 blocks of 16 elements)
+        for (var shift: u32 = 0; shift < 8; shift += 2) {
+            // 2 blocks
+            for (var k: u32 = 0; k < 32; k += 16) {
+                let sc = get_byte(scale_vals[is / 4], is % 4);
+                is++;
+                let dl = d * (f32(sc) - 32.0);
+                for (var l: u32 = 0u; l < 16u; l++) {
+                    let q_idx = q_b_idx + k + l;
+                    let hm_idx = k + l;
+                    let q_byte = get_byte(qs_vals[q_idx / 4], q_idx % 4);
+                    let hmask_byte = get_byte(hmask_vals[hm_idx / 4], hm_idx % 4);
+                    let hm = select(4.0, 0.0, (hmask_byte & m) != 0);
+                    let qs_val = (q_byte >> shift) & 3;
+                    sum += ((f32(qs_val) - hm) * dl) * src1[src1_i];
+                    src1_i++;
+                }
+            }
+            m <<= 1;
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q3_K)
+
+#decl(Q45_K_SCALE_MIN)
+
+fn get_scale_min(is: u32, scales: array<u32, 3>) -> vec2<f32> {
+    if (is < 4) {
+        let sc_byte = get_byte(scales[is / 4], is % 4);
+        let min_byte = get_byte(scales[(is + 4) / 4], is % 4);
+        return vec2(f32(sc_byte & 63), f32(min_byte & 63));
+    } else {
+        let sc_min_lo = get_byte(scales[(is + 4) / 4], (is + 4) % 4);
+        let sc_hi = get_byte(scales[(is - 4) / 4], (is - 4) % 4);
+        let min_hi = get_byte(scales[is / 4], is % 4);
+        let sc = (sc_min_lo & 0xF) | ((sc_hi >> 6) << 4);
+        let m = (sc_min_lo >> 4) | ((min_hi >> 6) << 4);
+        return vec2(f32(sc), f32(m));
+    }
+}
+
+#enddecl(Q45_K_SCALE_MIN)
+
+#decl(Q4_K)
+// 8 blocks of 32 elements each
+struct q4_k {
+    d: f16,
+    dmin: f16,
+    scales: array<u32, 3>,
+    qs: array<u32, 32>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                sum += (f32(qs_val) * dl - ml) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q4_K)
+
+#decl(Q5_K)
+// 8 blocks of 32 elements each
+struct q5_k {
+    d: f16,
+    dmin: f16,
+    scales: array<u32, 3>,
+    qh: array<u32, 8>,
+    qs: array<u32, 32>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let m = f32(block.dmin);
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var is: u32 = 0;
+    var u: u32 = 1;
+    // 2 blocks each iteration
+    for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) {
+        for (var shift: u32 = 0; shift < 8; shift += 4) {
+            let scale_min = get_scale_min(is, block.scales);
+            is++;
+            let dl = d * scale_min.x;
+            let ml = m * scale_min.y;
+            for (var l: u32 = 0; l < 32; l++) {
+                let q_idx = q_b_idx + l;
+                let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4);
+                let qh_byte = get_byte(block.qh[l / 4], l % 4);
+                let qs_val = (q_byte >> shift) & 0xF;
+                let qh_val = select(0.0, 16.0, (qh_byte & u) != 0);
+                sum += ((f32(qs_val) + qh_val) * dl - ml) * src1[src1_i];
+               src1_i++;
+            }
+            u <<= 1;
+        }
+    }
+    return sum;
+}
+
+#enddecl(Q5_K)
+
+#decl(Q6_K)
+// 16 blocks of 16 elements each
+struct q6_k {
+    ql: array<f16, 64>,
+    qh: array<f16, 32>,
+    scales: array<f16, 8>,
+    d: f16
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+
+    // convert arrays of f16 -> u32
+    var ql_vals: array<u32, 32>;
+    for (var i: u32 = 0; i < 32; i++) {
+        ql_vals[i] = bitcast<u32>(vec2(block.ql[2 * i], block.ql[2 * i + 1]));
+    }
+    var qh_vals: array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qh_vals[i] = bitcast<u32>(vec2(block.qh[2 * i], block.qh[2 * i + 1]));
+    }
+    var scale_vals: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        scale_vals[i] = bitcast<u32>(vec2(block.scales[2 * i], block.scales[2 * i + 1]));
+    }
+
+    var sum = 0.0;
+    var src1_i = src1_idx_base + offset * 256;
+    var qh_b_idx: u32 = 0;
+    var sc_b_idx: u32 = 0;
+    for (var ql_b_idx: u32 = 0; ql_b_idx < 128; ql_b_idx += 64) {
+        for (var l: u32 = 0; l < 32; l++) {
+            let ql13_b = get_byte(ql_vals[(ql_b_idx + l) / 4], (ql_b_idx + l) % 4);
+            let ql24_b = get_byte(ql_vals[(ql_b_idx + l + 32) / 4], (ql_b_idx + l + 32) % 4);
+            let qh_b = get_byte(qh_vals[(qh_b_idx + l) / 4], (qh_b_idx + l) % 4);
+
+            let q1 = f32((ql13_b & 0xF) | ((qh_b & 3) << 4)) - 32.0;
+            let q2 = f32((ql24_b & 0xF) | (((qh_b >> 2) & 3) << 4)) - 32.0;
+            let q3 = f32((ql13_b >> 4) | (((qh_b >> 4) & 3) << 4)) - 32.0;
+            let q4 = f32((ql24_b >> 4) | (((qh_b >> 6) & 3) << 4)) - 32.0;
+
+            let is = l/16;
+            let is1 = sc_b_idx + is;
+            let sc1 = get_byte_i32(scale_vals[is1 / 4], is1 % 4);
+            let is2 = sc_b_idx + is + 2;
+            let sc2 = get_byte_i32(scale_vals[is2 / 4], is2 % 4);
+            let is3 = sc_b_idx + is + 4;
+            let sc3 = get_byte_i32(scale_vals[is3 / 4], is3 % 4);
+            let is4 = sc_b_idx + is + 6;
+            let sc4 = get_byte_i32(scale_vals[is4 / 4], is4 % 4);
+
+            sum += d * f32(sc1) * q1 * src1[src1_i + l];
+            sum += d * f32(sc2) * q2 * src1[src1_i + l + 32];
+            sum += d * f32(sc3) * q3 * src1[src1_i + l + 64];
+            sum += d * f32(sc4) * q4 * src1[src1_i + l + 96];
+        }
+        src1_i += 128;
+        qh_b_idx += 32;
+        sc_b_idx += 8;
+    }
+    return sum;
+}
+
+#enddecl(Q6_K)
+
+#decl(IQ23_TABLES)
+const kmask_iq2xs : array<u32, 2> = array<u32, 2>(
+    0x08040201u, // 1, 2, 4, 8
+    0x80402010u  // 16, 32, 64, 128
+);
+
+const ksigns_iq2xs: array<u32, 32> = array<u32, 32>(
+    0x03828100,0x87060584,0x8b0a0988,0x0f8e8d0c,
+    0x93121190,0x17969514,0x1b9a9918,0x9f1e1d9c,
+    0xa32221a0,0x27a6a524,0x2baaa928,0xaf2e2dac,
+    0x33b2b130,0xb73635b4,0xbb3a39b8,0x3fbebd3c,
+    0xc34241c0,0x47c6c544,0x4bcac948,0xcf4e4dcc,
+    0x53d2d150,0xd75655d4,0xdb5a59d8,0x5fdedd5c,
+    0x63e2e160,0xe76665e4,0xeb6a69e8,0x6feeed6c,
+    0xf37271f0,0x77f6f574,0x7bfaf978,0xff7e7dfc
+);
+#enddecl(IQ23_TABLES)
+
+#decl(IQ2_XXS)
+
+const iq2xxs_grid = array<u32, 512>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x082b0808, 0x08080808,
+    0x082b082b, 0x08080808, 0x082b2b08, 0x08080808, 0x082b2b2b, 0x08080808, 0x19080819, 0x08080808,
+    0x19081908, 0x08080808, 0x19190808, 0x08080808, 0x19192b08, 0x08080808, 0x192b0819, 0x08080808,
+    0x192b1908, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b082b2b, 0x08080808,
+    0x2b2b082b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819, 0x08190808, 0x08080819,
+    0x08191919, 0x08080819, 0x19080808, 0x08080819, 0x2b081908, 0x08080819, 0x2b192b08, 0x08080819,
+    0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x082b082b, 0x0808082b, 0x2b08082b, 0x0808082b,
+    0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x08190808, 0x08081908, 0x082b0819, 0x08081908,
+    0x082b1908, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19082b08, 0x08081908,
+    0x192b0808, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908,
+    0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919, 0x08082b08, 0x08081919,
+    0x082b0808, 0x08081919, 0x1908192b, 0x08081919, 0x192b2b19, 0x08081919, 0x2b080808, 0x08081919,
+    0x2b190819, 0x08081919, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b, 0x19080808, 0x0808192b,
+    0x2b081908, 0x0808192b, 0x2b2b1908, 0x0808192b, 0x08080808, 0x08082b08, 0x08081919, 0x08082b08,
+    0x08082b08, 0x08082b08, 0x08191908, 0x08082b08, 0x082b2b08, 0x08082b08, 0x19080819, 0x08082b08,
+    0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x2b082b08, 0x08082b08,
+    0x08081908, 0x08082b19, 0x19080808, 0x08082b19, 0x0808082b, 0x08082b2b, 0x08191908, 0x08082b2b,
+    0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x08190808, 0x08190808, 0x082b0819, 0x08190808,
+    0x19080808, 0x08190808, 0x192b0808, 0x08190808, 0x2b081908, 0x08190808, 0x2b190808, 0x08190808,
+    0x2b191919, 0x08190808, 0x08080808, 0x08190819, 0x08082b08, 0x08190819, 0x082b0808, 0x08190819,
+    0x19190808, 0x08190819, 0x19192b2b, 0x08190819, 0x2b080808, 0x08190819, 0x082b1908, 0x0819082b,
+    0x19081919, 0x0819082b, 0x08080808, 0x08191908, 0x08082b08, 0x08191908, 0x082b0808, 0x08191908,
+    0x082b1919, 0x08191908, 0x19082b19, 0x08191908, 0x2b080808, 0x08191908, 0x08192b08, 0x08191919,
+    0x192b082b, 0x08191919, 0x08080808, 0x0819192b, 0x0819192b, 0x0819192b, 0x08080819, 0x08192b08,
+    0x08081908, 0x08192b08, 0x08190808, 0x08192b08, 0x19080808, 0x08192b08, 0x2b080819, 0x08192b08,
+    0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x2b2b0808, 0x08192b19, 0x19190819, 0x08192b2b,
+    0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08082b2b, 0x082b0808, 0x19081908, 0x082b0808,
+    0x192b0819, 0x082b0808, 0x2b080808, 0x082b0808, 0x2b08082b, 0x082b0808, 0x082b2b19, 0x082b0819,
+    0x19082b08, 0x082b0819, 0x08080808, 0x082b082b, 0x0808082b, 0x082b082b, 0x08080819, 0x082b1908,
+    0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x19080808, 0x082b1908, 0x1919192b, 0x082b1908,
+    0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x192b1908, 0x082b1919, 0x2b190808, 0x082b192b,
+    0x08082b08, 0x082b2b08, 0x082b0808, 0x082b2b08, 0x2b191908, 0x082b2b08, 0x19081908, 0x082b2b2b,
+    0x08080819, 0x19080808, 0x08081908, 0x19080808, 0x08190808, 0x19080808, 0x08192b08, 0x19080808,
+    0x082b0819, 0x19080808, 0x082b1908, 0x19080808, 0x19080808, 0x19080808, 0x19082b08, 0x19080808,
+    0x1919192b, 0x19080808, 0x192b0808, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808,
+    0x2b190808, 0x19080808, 0x08080808, 0x19080819, 0x082b0808, 0x19080819, 0x192b0819, 0x19080819,
+    0x2b080808, 0x19080819, 0x2b081919, 0x19080819, 0x08080819, 0x1908082b, 0x08190808, 0x1908082b,
+    0x19082b08, 0x1908082b, 0x1919192b, 0x1908082b, 0x192b2b08, 0x1908082b, 0x08080808, 0x19081908,
+    0x08082b08, 0x19081908, 0x082b0808, 0x19081908, 0x2b080808, 0x19081908, 0x2b192b19, 0x19081908,
+    0x0819082b, 0x19081919, 0x082b1908, 0x19081919, 0x08080808, 0x1908192b, 0x08080819, 0x19082b08,
+    0x08081908, 0x19082b08, 0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08,
+    0x08080808, 0x19082b19, 0x19192b08, 0x19082b19, 0x192b0819, 0x19082b19, 0x2b08082b, 0x19082b19,
+    0x19081919, 0x19082b2b, 0x2b190808, 0x19082b2b, 0x08080808, 0x19190808, 0x08082b08, 0x19190808,
+    0x08190819, 0x19190808, 0x08192b19, 0x19190808, 0x082b0808, 0x19190808, 0x2b080808, 0x19190808,
+    0x2b082b08, 0x19190808, 0x08081908, 0x19190819, 0x1908082b, 0x19190819, 0x2b2b1908, 0x19190819,
+    0x2b190819, 0x1919082b, 0x2b190808, 0x19191908, 0x2b19082b, 0x19191908, 0x08082b2b, 0x19191919,
+    0x08080819, 0x1919192b, 0x19191908, 0x1919192b, 0x08080808, 0x19192b08, 0x08190819, 0x19192b08,
+    0x08192b19, 0x19192b08, 0x192b1908, 0x19192b08, 0x19080808, 0x19192b19, 0x08082b08, 0x19192b2b,
+    0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808, 0x192b2b08, 0x192b0808,
+    0x08080808, 0x192b0819, 0x19191919, 0x192b0819, 0x08192b08, 0x192b082b, 0x192b0808, 0x192b082b,
+    0x08080808, 0x192b1908, 0x08081919, 0x192b1908, 0x08190808, 0x192b1919, 0x0819082b, 0x192b1919,
+    0x2b081908, 0x192b1919, 0x1908082b, 0x192b2b08, 0x08080808, 0x2b080808, 0x0808082b, 0x2b080808,
+    0x08082b2b, 0x2b080808, 0x19080819, 0x2b080808, 0x2b08082b, 0x2b080808, 0x08081908, 0x2b080819,
+    0x08192b08, 0x2b080819, 0x19080808, 0x2b080819, 0x08190819, 0x2b08082b, 0x08080819, 0x2b081908,
+    0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x08191919, 0x2b081908, 0x19080808, 0x2b081908,
+    0x192b0808, 0x2b081908, 0x08080808, 0x2b081919, 0x1908192b, 0x2b081919, 0x2b191908, 0x2b081919,
+    0x08082b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x192b0808, 0x2b08192b, 0x0808082b, 0x2b082b08,
+    0x08081908, 0x2b082b19, 0x08190819, 0x2b082b2b, 0x08081908, 0x2b190808, 0x08190808, 0x2b190808,
+    0x082b1908, 0x2b190808, 0x19080808, 0x2b190808, 0x2b2b0819, 0x2b190808, 0x0819192b, 0x2b190819,
+    0x2b080808, 0x2b190819, 0x19081919, 0x2b19082b, 0x08080808, 0x2b191908, 0x082b082b, 0x2b191908,
+    0x19081908, 0x2b191908, 0x19190819, 0x2b191919, 0x2b080819, 0x2b192b08, 0x082b0808, 0x2b192b19,
+    0x0808082b, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b081919, 0x2b2b0808, 0x08082b19, 0x2b2b0819,
+    0x08080808, 0x2b2b082b, 0x08192b08, 0x2b2b1908, 0x19190808, 0x2b2b2b08, 0x08081908, 0x2b2b2b19
+);
+
+struct iq2_xxs {
+    d: f16,
+    qs: array<f16, 32>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let aux0 = bitcast<u32>(vec2(block.qs[ib], block.qs[ib + 1]));
+        let aux1 = bitcast<u32>(vec2(block.qs[ib + 2], block.qs[ib + 3]));
+        let db = d * (0.5 + f32(aux1 >> 28)) * 0.25;
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = get_byte(aux0, l) * 8;
+            let is = (aux1 >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xxs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += db * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ2_XXS)
+
+#decl(IQ2_XS)
+const iq2xs_grid = array<u32, 1024>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808,
+    0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808,
+    0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808,
+    0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808,
+    0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x2b080808, 0x08080808,
+    0x2b08082b, 0x08080808, 0x2b081919, 0x08080808, 0x2b082b08, 0x08080808, 0x2b190819, 0x08080808,
+    0x2b191908, 0x08080808, 0x2b192b19, 0x08080808, 0x2b2b0808, 0x08080808, 0x08080819, 0x08080819,
+    0x08081908, 0x08080819, 0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819,
+    0x0819082b, 0x08080819, 0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x08192b2b, 0x08080819,
+    0x082b0819, 0x08080819, 0x082b1908, 0x08080819, 0x19080808, 0x08080819, 0x1908082b, 0x08080819,
+    0x19081919, 0x08080819, 0x19082b08, 0x08080819, 0x19190819, 0x08080819, 0x19191908, 0x08080819,
+    0x192b0808, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819, 0x2b081908, 0x08080819,
+    0x2b190808, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x08081919, 0x0808082b,
+    0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b, 0x082b0808, 0x0808082b,
+    0x19080819, 0x0808082b, 0x19081908, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b,
+    0x2b080808, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908,
+    0x0808192b, 0x08081908, 0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908,
+    0x08191919, 0x08081908, 0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908,
+    0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908, 0x19082b08, 0x08081908,
+    0x19190819, 0x08081908, 0x19191908, 0x08081908, 0x1919192b, 0x08081908, 0x192b0808, 0x08081908,
+    0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908, 0x08080808, 0x08081919,
+    0x0808082b, 0x08081919, 0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08190819, 0x08081919,
+    0x08191908, 0x08081919, 0x082b0808, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919,
+    0x19190808, 0x08081919, 0x192b0819, 0x08081919, 0x2b080808, 0x08081919, 0x08080819, 0x0808192b,
+    0x08081908, 0x0808192b, 0x08190808, 0x0808192b, 0x082b192b, 0x0808192b, 0x19080808, 0x0808192b,
+    0x1908082b, 0x0808192b, 0x2b081908, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08,
+    0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08082b2b, 0x08082b08, 0x08190819, 0x08082b08,
+    0x08191908, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08, 0x19080819, 0x08082b08,
+    0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x19192b08, 0x08082b08, 0x2b080808, 0x08082b08,
+    0x2b2b0808, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19, 0x08081908, 0x08082b19,
+    0x08190808, 0x08082b19, 0x19080808, 0x08082b19, 0x2b080819, 0x08082b19, 0x2b082b19, 0x08082b19,
+    0x08080808, 0x08082b2b, 0x082b0808, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x2b19192b, 0x08082b2b,
+    0x2b2b0808, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x0808192b, 0x08190808,
+    0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808, 0x08191919, 0x08190808,
+    0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808, 0x19080808, 0x08190808,
+    0x1908082b, 0x08190808, 0x19081919, 0x08190808, 0x19082b08, 0x08190808, 0x19190819, 0x08190808,
+    0x19191908, 0x08190808, 0x192b0808, 0x08190808, 0x192b2b2b, 0x08190808, 0x2b080819, 0x08190808,
+    0x2b081908, 0x08190808, 0x2b190808, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819,
+    0x08081919, 0x08190819, 0x08082b08, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819,
+    0x082b0808, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819, 0x19190808, 0x08190819,
+    0x2b080808, 0x08190819, 0x2b191908, 0x08190819, 0x2b19192b, 0x08190819, 0x08080819, 0x0819082b,
+    0x08081908, 0x0819082b, 0x0808192b, 0x0819082b, 0x08190808, 0x0819082b, 0x19080808, 0x0819082b,
+    0x192b0808, 0x0819082b, 0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908,
+    0x08082b08, 0x08191908, 0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x082b0808, 0x08191908,
+    0x19080819, 0x08191908, 0x19081908, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908,
+    0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x08080819, 0x08191919, 0x08081908, 0x08191919,
+    0x08190808, 0x08191919, 0x19080808, 0x08191919, 0x08080808, 0x0819192b, 0x08191908, 0x0819192b,
+    0x19082b19, 0x0819192b, 0x08080819, 0x08192b08, 0x08081908, 0x08192b08, 0x08190808, 0x08192b08,
+    0x0819082b, 0x08192b08, 0x19080808, 0x08192b08, 0x19191908, 0x08192b08, 0x2b08192b, 0x08192b08,
+    0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x192b192b, 0x08192b19, 0x19190819, 0x08192b2b,
+    0x2b2b2b19, 0x08192b2b, 0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808,
+    0x08082b08, 0x082b0808, 0x08082b2b, 0x082b0808, 0x08190819, 0x082b0808, 0x08191908, 0x082b0808,
+    0x082b0808, 0x082b0808, 0x19080819, 0x082b0808, 0x19081908, 0x082b0808, 0x19190808, 0x082b0808,
+    0x2b080808, 0x082b0808, 0x2b2b0808, 0x082b0808, 0x08080819, 0x082b0819, 0x08081908, 0x082b0819,
+    0x08190808, 0x082b0819, 0x19080808, 0x082b0819, 0x19082b08, 0x082b0819, 0x192b1919, 0x082b0819,
+    0x08080808, 0x082b082b, 0x082b082b, 0x082b082b, 0x2b080808, 0x082b082b, 0x2b2b2b08, 0x082b082b,
+    0x08080819, 0x082b1908, 0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x082b2b19, 0x082b1908,
+    0x19080808, 0x082b1908, 0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x1919082b, 0x082b1919,
+    0x2b192b19, 0x082b1919, 0x08080819, 0x082b192b, 0x08192b2b, 0x082b192b, 0x2b2b192b, 0x082b192b,
+    0x08080808, 0x082b2b08, 0x08082b08, 0x082b2b08, 0x08082b2b, 0x082b2b08, 0x082b0808, 0x082b2b08,
+    0x19191919, 0x082b2b08, 0x2b082b08, 0x082b2b08, 0x2b2b082b, 0x082b2b08, 0x192b2b08, 0x082b2b19,
+    0x2b190808, 0x082b2b19, 0x08082b08, 0x082b2b2b, 0x082b0808, 0x082b2b2b, 0x2b08082b, 0x082b2b2b,
+    0x2b082b08, 0x082b2b2b, 0x2b082b2b, 0x082b2b2b, 0x08080819, 0x19080808, 0x08081908, 0x19080808,
+    0x0808192b, 0x19080808, 0x08082b19, 0x19080808, 0x08190808, 0x19080808, 0x0819082b, 0x19080808,
+    0x08191919, 0x19080808, 0x08192b08, 0x19080808, 0x082b0819, 0x19080808, 0x082b1908, 0x19080808,
+    0x19080808, 0x19080808, 0x1908082b, 0x19080808, 0x19081919, 0x19080808, 0x19082b08, 0x19080808,
+    0x19082b2b, 0x19080808, 0x19190819, 0x19080808, 0x19191908, 0x19080808, 0x192b0808, 0x19080808,
+    0x192b1919, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808, 0x2b190808, 0x19080808,
+    0x08080808, 0x19080819, 0x0808082b, 0x19080819, 0x08081919, 0x19080819, 0x08082b08, 0x19080819,
+    0x08190819, 0x19080819, 0x08191908, 0x19080819, 0x082b0808, 0x19080819, 0x19080819, 0x19080819,
+    0x19081908, 0x19080819, 0x19190808, 0x19080819, 0x2b080808, 0x19080819, 0x2b081919, 0x19080819,
+    0x2b2b082b, 0x19080819, 0x08080819, 0x1908082b, 0x08081908, 0x1908082b, 0x08190808, 0x1908082b,
+    0x0819082b, 0x1908082b, 0x082b2b19, 0x1908082b, 0x19080808, 0x1908082b, 0x08080808, 0x19081908,
+    0x0808082b, 0x19081908, 0x08081919, 0x19081908, 0x08082b08, 0x19081908, 0x08190819, 0x19081908,
+    0x08191908, 0x19081908, 0x08192b19, 0x19081908, 0x082b0808, 0x19081908, 0x19080819, 0x19081908,
+    0x19081908, 0x19081908, 0x19190808, 0x19081908, 0x2b080808, 0x19081908, 0x2b191908, 0x19081908,
+    0x08080819, 0x19081919, 0x08081908, 0x19081919, 0x08190808, 0x19081919, 0x082b1908, 0x19081919,
+    0x19080808, 0x19081919, 0x2b192b2b, 0x19081919, 0x08080808, 0x1908192b, 0x08082b2b, 0x1908192b,
+    0x19081908, 0x1908192b, 0x19190808, 0x1908192b, 0x08080819, 0x19082b08, 0x08081908, 0x19082b08,
+    0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08, 0x19191908, 0x19082b08,
+    0x192b082b, 0x19082b08, 0x08080808, 0x19082b19, 0x08190819, 0x19082b19, 0x19081908, 0x19082b19,
+    0x19190808, 0x19082b19, 0x192b2b19, 0x19082b19, 0x08081908, 0x19082b2b, 0x08080808, 0x19190808,
+    0x0808082b, 0x19190808, 0x08081919, 0x19190808, 0x08082b08, 0x19190808, 0x08190819, 0x19190808,
+    0x08191908, 0x19190808, 0x082b0808, 0x19190808, 0x082b2b08, 0x19190808, 0x19080819, 0x19190808,
+    0x19081908, 0x19190808, 0x19190808, 0x19190808, 0x2b080808, 0x19190808, 0x08080819, 0x19190819,
+    0x08081908, 0x19190819, 0x08190808, 0x19190819, 0x08191919, 0x19190819, 0x19080808, 0x19190819,
+    0x1908082b, 0x19190819, 0x08080808, 0x1919082b, 0x19081908, 0x1919082b, 0x2b2b2b2b, 0x1919082b,
+    0x08080819, 0x19191908, 0x08081908, 0x19191908, 0x08190808, 0x19191908, 0x082b0819, 0x19191908,
+    0x19080808, 0x19191908, 0x192b0808, 0x19191908, 0x2b080819, 0x19191908, 0x2b2b0819, 0x19191908,
+    0x08080808, 0x19191919, 0x08082b08, 0x19191919, 0x2b080808, 0x19191919, 0x2b082b08, 0x19191919,
+    0x082b0819, 0x1919192b, 0x192b2b08, 0x1919192b, 0x2b2b0819, 0x1919192b, 0x08080808, 0x19192b08,
+    0x08191908, 0x19192b08, 0x19080819, 0x19192b08, 0x19190808, 0x19192b08, 0x2b192b19, 0x19192b08,
+    0x08192b2b, 0x19192b19, 0x19080808, 0x19192b19, 0x1908082b, 0x19192b19, 0x2b081919, 0x19192b2b,
+    0x08080819, 0x192b0808, 0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808,
+    0x19191908, 0x192b0808, 0x192b082b, 0x192b0808, 0x2b08192b, 0x192b0808, 0x2b2b2b19, 0x192b0808,
+    0x08080808, 0x192b0819, 0x082b1908, 0x192b082b, 0x19082b2b, 0x192b082b, 0x2b19082b, 0x192b082b,
+    0x08080808, 0x192b1908, 0x0819192b, 0x192b1908, 0x08190808, 0x192b1919, 0x19080808, 0x192b1919,
+    0x19081919, 0x192b1919, 0x2b2b1908, 0x192b1919, 0x08080819, 0x192b2b08, 0x192b2b2b, 0x192b2b08,
+    0x082b1919, 0x192b2b19, 0x0808192b, 0x192b2b2b, 0x19191908, 0x192b2b2b, 0x192b082b, 0x192b2b2b,
+    0x08080808, 0x2b080808, 0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808,
+    0x08190819, 0x2b080808, 0x08191908, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b2b2b, 0x2b080808,
+    0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x2b080808, 0x2b080808,
+    0x2b08082b, 0x2b080808, 0x2b2b2b08, 0x2b080808, 0x2b2b2b2b, 0x2b080808, 0x08080819, 0x2b080819,
+    0x08081908, 0x2b080819, 0x0808192b, 0x2b080819, 0x08190808, 0x2b080819, 0x19080808, 0x2b080819,
+    0x19190819, 0x2b080819, 0x19192b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x082b0808, 0x2b08082b,
+    0x2b080808, 0x2b08082b, 0x2b08082b, 0x2b08082b, 0x2b2b0808, 0x2b08082b, 0x2b2b2b08, 0x2b08082b,
+    0x08080819, 0x2b081908, 0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908,
+    0x08191919, 0x2b081908, 0x19080808, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b082b19, 0x2b081908,
+    0x08080808, 0x2b081919, 0x19081908, 0x2b081919, 0x2b2b1919, 0x2b081919, 0x08192b08, 0x2b08192b,
+    0x192b2b2b, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08082b08, 0x2b082b08, 0x082b1919, 0x2b082b08,
+    0x19192b2b, 0x2b082b08, 0x2b080808, 0x2b082b08, 0x2b08082b, 0x2b082b08, 0x2b2b2b08, 0x2b082b08,
+    0x0808192b, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x2b080808, 0x2b082b2b, 0x2b082b08, 0x2b082b2b,
+    0x2b19192b, 0x2b082b2b, 0x2b2b2b08, 0x2b082b2b, 0x08080819, 0x2b190808, 0x08081908, 0x2b190808,
+    0x08190808, 0x2b190808, 0x19080808, 0x2b190808, 0x1919192b, 0x2b190808, 0x2b081908, 0x2b190808,
+    0x08080808, 0x2b190819, 0x082b082b, 0x2b190819, 0x192b1908, 0x2b190819, 0x1919192b, 0x2b19082b,
+    0x2b082b19, 0x2b19082b, 0x08080808, 0x2b191908, 0x08081919, 0x2b191908, 0x19081908, 0x2b191908,
+    0x19190808, 0x2b191908, 0x19192b08, 0x2b191908, 0x082b2b19, 0x2b191919, 0x2b190808, 0x2b191919,
+    0x2b19082b, 0x2b191919, 0x19080819, 0x2b19192b, 0x19190819, 0x2b192b08, 0x2b2b192b, 0x2b192b08,
+    0x19082b19, 0x2b192b19, 0x08191919, 0x2b192b2b, 0x192b0808, 0x2b192b2b, 0x08080808, 0x2b2b0808,
+    0x0808082b, 0x2b2b0808, 0x08082b08, 0x2b2b0808, 0x08082b2b, 0x2b2b0808, 0x082b0808, 0x2b2b0808,
+    0x082b2b2b, 0x2b2b0808, 0x2b2b0808, 0x2b2b0808, 0x19190819, 0x2b2b0819, 0x19192b19, 0x2b2b0819,
+    0x2b2b192b, 0x2b2b0819, 0x08080808, 0x2b2b082b, 0x0808082b, 0x2b2b082b, 0x08082b08, 0x2b2b082b,
+    0x082b2b2b, 0x2b2b082b, 0x2b080808, 0x2b2b082b, 0x2b2b0808, 0x2b2b082b, 0x19080808, 0x2b2b1908,
+    0x2b191919, 0x2b2b1908, 0x192b1919, 0x2b2b192b, 0x2b192b08, 0x2b2b192b, 0x08082b2b, 0x2b2b2b08,
+    0x082b0808, 0x2b2b2b08, 0x082b082b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b0808, 0x2b2b2b08,
+    0x2b2b2b08, 0x2b2b2b08, 0x08081908, 0x2b2b2b19, 0x2b081908, 0x2b2b2b19, 0x2b08192b, 0x2b2b2b19,
+    0x082b2b08, 0x2b2b2b2b, 0x082b2b2b, 0x2b2b2b2b, 0x2b190819, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b
+);
+
+struct iq2_xs {
+    d: f16,
+    qs: array<f16, 32>,
+    scales: array<f16, 4>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 32; ib += 4) {
+        let s = get_byte(scale_vals[ib / 16], (ib % 16) / 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let qs_val = bitcast<u32>(vec2(block.qs[ib + l], 0.0));
+            let ig = (qs_val & 511) * 8;
+            let is = qs_val >> 9;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2xs_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += dl * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ2_XS)
+
+#decl(IQ2_S)
+
+const iq2s_grid = array<u32, 2048>(
+    0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808,
+    0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808,
+    0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808,
+    0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808,
+    0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808,
+    0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x192b192b, 0x08080808,
+    0x192b2b19, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b081919, 0x08080808,
+    0x2b082b08, 0x08080808, 0x2b190819, 0x08080808, 0x2b191908, 0x08080808, 0x2b2b0808, 0x08080808,
+    0x2b2b1919, 0x08080808, 0x2b2b2b2b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819,
+    0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819, 0x0819082b, 0x08080819,
+    0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x082b0819, 0x08080819, 0x082b1908, 0x08080819,
+    0x19080808, 0x08080819, 0x1908082b, 0x08080819, 0x19081919, 0x08080819, 0x19082b08, 0x08080819,
+    0x19190819, 0x08080819, 0x19191908, 0x08080819, 0x1919192b, 0x08080819, 0x19192b19, 0x08080819,
+    0x192b0808, 0x08080819, 0x192b1919, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819,
+    0x2b081908, 0x08080819, 0x2b190808, 0x08080819, 0x2b19082b, 0x08080819, 0x2b191919, 0x08080819,
+    0x2b2b0819, 0x08080819, 0x2b2b1908, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b,
+    0x08081919, 0x0808082b, 0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b,
+    0x082b0808, 0x0808082b, 0x082b2b2b, 0x0808082b, 0x19080819, 0x0808082b, 0x19081908, 0x0808082b,
+    0x1908192b, 0x0808082b, 0x19082b19, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b,
+    0x2b080808, 0x0808082b, 0x2b081919, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x2b191908, 0x0808082b,
+    0x2b2b082b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x0808192b, 0x08081908,
+    0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908, 0x08191919, 0x08081908,
+    0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908, 0x082b192b, 0x08081908,
+    0x082b2b19, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908,
+    0x19082b08, 0x08081908, 0x19082b2b, 0x08081908, 0x19190819, 0x08081908, 0x19191908, 0x08081908,
+    0x1919192b, 0x08081908, 0x19192b19, 0x08081908, 0x192b0808, 0x08081908, 0x192b082b, 0x08081908,
+    0x192b1919, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b08192b, 0x08081908,
+    0x2b082b19, 0x08081908, 0x2b190808, 0x08081908, 0x2b191919, 0x08081908, 0x2b192b08, 0x08081908,
+    0x2b2b0819, 0x08081908, 0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919,
+    0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08082b2b, 0x08081919, 0x08190819, 0x08081919,
+    0x08191908, 0x08081919, 0x0819192b, 0x08081919, 0x08192b19, 0x08081919, 0x082b0808, 0x08081919,
+    0x082b1919, 0x08081919, 0x082b2b08, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919,
+    0x1908192b, 0x08081919, 0x19082b19, 0x08081919, 0x19190808, 0x08081919, 0x1919082b, 0x08081919,
+    0x19191919, 0x08081919, 0x19192b08, 0x08081919, 0x192b0819, 0x08081919, 0x192b1908, 0x08081919,
+    0x2b080808, 0x08081919, 0x2b08082b, 0x08081919, 0x2b081919, 0x08081919, 0x2b082b08, 0x08081919,
+    0x2b190819, 0x08081919, 0x2b191908, 0x08081919, 0x2b2b0808, 0x08081919, 0x08080819, 0x0808192b,
+    0x08081908, 0x0808192b, 0x0808192b, 0x0808192b, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b,
+    0x08191919, 0x0808192b, 0x19080808, 0x0808192b, 0x19081919, 0x0808192b, 0x19082b08, 0x0808192b,
+    0x19190819, 0x0808192b, 0x19191908, 0x0808192b, 0x192b0808, 0x0808192b, 0x2b080819, 0x0808192b,
+    0x2b081908, 0x0808192b, 0x2b190808, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08,
+    0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08190819, 0x08082b08, 0x08191908, 0x08082b08,
+    0x0819192b, 0x08082b08, 0x08192b19, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08,
+    0x082b2b2b, 0x08082b08, 0x19080819, 0x08082b08, 0x19081908, 0x08082b08, 0x1908192b, 0x08082b08,
+    0x19082b19, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x19191919, 0x08082b08,
+    0x19192b08, 0x08082b08, 0x192b0819, 0x08082b08, 0x192b1908, 0x08082b08, 0x2b080808, 0x08082b08,
+    0x2b081919, 0x08082b08, 0x2b191908, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19,
+    0x08081908, 0x08082b19, 0x08190808, 0x08082b19, 0x0819082b, 0x08082b19, 0x08191919, 0x08082b19,
+    0x08192b08, 0x08082b19, 0x082b0819, 0x08082b19, 0x19080808, 0x08082b19, 0x19081919, 0x08082b19,
+    0x19082b08, 0x08082b19, 0x19190819, 0x08082b19, 0x19191908, 0x08082b19, 0x192b0808, 0x08082b19,
+    0x2b080819, 0x08082b19, 0x2b190808, 0x08082b19, 0x08080808, 0x08082b2b, 0x08190819, 0x08082b2b,
+    0x08191908, 0x08082b2b, 0x082b082b, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x082b2b2b, 0x08082b2b,
+    0x19190808, 0x08082b2b, 0x2b192b19, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808,
+    0x0808192b, 0x08190808, 0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808,
+    0x08191919, 0x08190808, 0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808,
+    0x082b192b, 0x08190808, 0x19080808, 0x08190808, 0x1908082b, 0x08190808, 0x19081919, 0x08190808,
+    0x19082b08, 0x08190808, 0x19190819, 0x08190808, 0x19191908, 0x08190808, 0x1919192b, 0x08190808,
+    0x19192b19, 0x08190808, 0x192b0808, 0x08190808, 0x192b082b, 0x08190808, 0x192b1919, 0x08190808,
+    0x192b2b08, 0x08190808, 0x2b080819, 0x08190808, 0x2b081908, 0x08190808, 0x2b08192b, 0x08190808,
+    0x2b190808, 0x08190808, 0x2b191919, 0x08190808, 0x2b192b08, 0x08190808, 0x2b2b0819, 0x08190808,
+    0x2b2b1908, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819, 0x08081919, 0x08190819,
+    0x08082b08, 0x08190819, 0x08082b2b, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819,
+    0x0819192b, 0x08190819, 0x08192b19, 0x08190819, 0x082b0808, 0x08190819, 0x082b082b, 0x08190819,
+    0x082b1919, 0x08190819, 0x082b2b08, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819,
+    0x1908192b, 0x08190819, 0x19082b19, 0x08190819, 0x19190808, 0x08190819, 0x1919082b, 0x08190819,
+    0x19191919, 0x08190819, 0x19192b08, 0x08190819, 0x192b0819, 0x08190819, 0x192b1908, 0x08190819,
+    0x2b080808, 0x08190819, 0x2b08082b, 0x08190819, 0x2b081919, 0x08190819, 0x2b082b08, 0x08190819,
+    0x2b190819, 0x08190819, 0x2b191908, 0x08190819, 0x08080819, 0x0819082b, 0x08081908, 0x0819082b,
+    0x08082b19, 0x0819082b, 0x08190808, 0x0819082b, 0x08191919, 0x0819082b, 0x082b0819, 0x0819082b,
+    0x082b1908, 0x0819082b, 0x19080808, 0x0819082b, 0x19081919, 0x0819082b, 0x19190819, 0x0819082b,
+    0x19191908, 0x0819082b, 0x2b080819, 0x0819082b, 0x2b081908, 0x0819082b, 0x2b190808, 0x0819082b,
+    0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908, 0x08082b08, 0x08191908,
+    0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x0819192b, 0x08191908, 0x08192b19, 0x08191908,
+    0x082b0808, 0x08191908, 0x082b1919, 0x08191908, 0x082b2b08, 0x08191908, 0x19080819, 0x08191908,
+    0x19081908, 0x08191908, 0x1908192b, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908,
+    0x1919082b, 0x08191908, 0x19191919, 0x08191908, 0x19192b08, 0x08191908, 0x192b0819, 0x08191908,
+    0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x2b08082b, 0x08191908, 0x2b081919, 0x08191908,
+    0x2b082b08, 0x08191908, 0x2b190819, 0x08191908, 0x2b191908, 0x08191908, 0x2b2b0808, 0x08191908,
+    0x08080819, 0x08191919, 0x08081908, 0x08191919, 0x0808192b, 0x08191919, 0x08082b19, 0x08191919,
+    0x08190808, 0x08191919, 0x0819082b, 0x08191919, 0x08191919, 0x08191919, 0x08192b08, 0x08191919,
+    0x082b0819, 0x08191919, 0x082b1908, 0x08191919, 0x19080808, 0x08191919, 0x1908082b, 0x08191919,
+    0x19081919, 0x08191919, 0x19082b08, 0x08191919, 0x19190819, 0x08191919, 0x19191908, 0x08191919,
+    0x192b0808, 0x08191919, 0x2b080819, 0x08191919, 0x2b081908, 0x08191919, 0x2b190808, 0x08191919,
+    0x08080808, 0x0819192b, 0x08081919, 0x0819192b, 0x08082b08, 0x0819192b, 0x08190819, 0x0819192b,
+    0x08191908, 0x0819192b, 0x082b0808, 0x0819192b, 0x19080819, 0x0819192b, 0x19081908, 0x0819192b,
+    0x19190808, 0x0819192b, 0x2b080808, 0x0819192b, 0x2b2b2b2b, 0x0819192b, 0x08080819, 0x08192b08,
+    0x08081908, 0x08192b08, 0x0808192b, 0x08192b08, 0x08082b19, 0x08192b08, 0x08190808, 0x08192b08,
+    0x08191919, 0x08192b08, 0x08192b08, 0x08192b08, 0x082b0819, 0x08192b08, 0x19080808, 0x08192b08,
+    0x1908082b, 0x08192b08, 0x19081919, 0x08192b08, 0x19082b08, 0x08192b08, 0x19190819, 0x08192b08,
+    0x19191908, 0x08192b08, 0x192b0808, 0x08192b08, 0x2b080819, 0x08192b08, 0x2b081908, 0x08192b08,
+    0x08080808, 0x08192b19, 0x0808082b, 0x08192b19, 0x08081919, 0x08192b19, 0x08082b08, 0x08192b19,
+    0x08190819, 0x08192b19, 0x08191908, 0x08192b19, 0x082b0808, 0x08192b19, 0x19080819, 0x08192b19,
+    0x19081908, 0x08192b19, 0x19190808, 0x08192b19, 0x192b2b19, 0x08192b19, 0x2b2b082b, 0x08192b19,
+    0x08081908, 0x08192b2b, 0x08190808, 0x08192b2b, 0x19080808, 0x08192b2b, 0x1919192b, 0x08192b2b,
+    0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808, 0x08082b08, 0x082b0808,
+    0x08190819, 0x082b0808, 0x08191908, 0x082b0808, 0x0819192b, 0x082b0808, 0x08192b19, 0x082b0808,
+    0x082b0808, 0x082b0808, 0x082b1919, 0x082b0808, 0x082b2b2b, 0x082b0808, 0x19080819, 0x082b0808,
+    0x19081908, 0x082b0808, 0x19190808, 0x082b0808, 0x1919082b, 0x082b0808, 0x19191919, 0x082b0808,
+    0x192b1908, 0x082b0808, 0x2b080808, 0x082b0808, 0x2b082b2b, 0x082b0808, 0x2b191908, 0x082b0808,
+    0x2b2b2b2b, 0x082b0808, 0x08080819, 0x082b0819, 0x08081908, 0x082b0819, 0x08190808, 0x082b0819,
+    0x0819082b, 0x082b0819, 0x08191919, 0x082b0819, 0x082b0819, 0x082b0819, 0x19080808, 0x082b0819,
+    0x1908082b, 0x082b0819, 0x19081919, 0x082b0819, 0x19190819, 0x082b0819, 0x19191908, 0x082b0819,
+    0x192b0808, 0x082b0819, 0x2b080819, 0x082b0819, 0x2b081908, 0x082b0819, 0x2b190808, 0x082b0819,
+    0x08080808, 0x082b082b, 0x08082b2b, 0x082b082b, 0x082b082b, 0x082b082b, 0x082b2b08, 0x082b082b,
+    0x082b2b2b, 0x082b082b, 0x19081908, 0x082b082b, 0x19190808, 0x082b082b, 0x2b082b08, 0x082b082b,
+    0x2b082b2b, 0x082b082b, 0x2b2b2b08, 0x082b082b, 0x08080819, 0x082b1908, 0x08081908, 0x082b1908,
+    0x0808192b, 0x082b1908, 0x08082b19, 0x082b1908, 0x08190808, 0x082b1908, 0x08191919, 0x082b1908,
+    0x08192b08, 0x082b1908, 0x082b0819, 0x082b1908, 0x082b1908, 0x082b1908, 0x19080808, 0x082b1908,
+    0x1908082b, 0x082b1908, 0x19081919, 0x082b1908, 0x19082b08, 0x082b1908, 0x19190819, 0x082b1908,
+    0x19191908, 0x082b1908, 0x192b0808, 0x082b1908, 0x2b080819, 0x082b1908, 0x2b081908, 0x082b1908,
+    0x2b190808, 0x082b1908, 0x08080808, 0x082b1919, 0x08081919, 0x082b1919, 0x08082b08, 0x082b1919,
+    0x08190819, 0x082b1919, 0x08191908, 0x082b1919, 0x082b0808, 0x082b1919, 0x19080819, 0x082b1919,
+    0x19081908, 0x082b1919, 0x19190808, 0x082b1919, 0x192b192b, 0x082b1919, 0x2b080808, 0x082b1919,
+    0x08080819, 0x082b192b, 0x08081908, 0x082b192b, 0x08190808, 0x082b192b, 0x19080808, 0x082b192b,
+    0x19192b19, 0x082b192b, 0x08080808, 0x082b2b08, 0x08081919, 0x082b2b08, 0x08190819, 0x082b2b08,
+    0x08191908, 0x082b2b08, 0x19080819, 0x082b2b08, 0x19081908, 0x082b2b08, 0x19190808, 0x082b2b08,
+    0x2b082b2b, 0x082b2b08, 0x2b2b2b2b, 0x082b2b08, 0x08080819, 0x082b2b19, 0x08081908, 0x082b2b19,
+    0x08190808, 0x082b2b19, 0x2b191919, 0x082b2b19, 0x08082b2b, 0x082b2b2b, 0x082b082b, 0x082b2b2b,
+    0x192b1908, 0x082b2b2b, 0x2b082b08, 0x082b2b2b, 0x2b082b2b, 0x082b2b2b, 0x08080819, 0x19080808,
+    0x08081908, 0x19080808, 0x0808192b, 0x19080808, 0x08082b19, 0x19080808, 0x08190808, 0x19080808,
+    0x0819082b, 0x19080808, 0x08191919, 0x19080808, 0x08192b08, 0x19080808, 0x08192b2b, 0x19080808,
+    0x082b0819, 0x19080808, 0x082b1908, 0x19080808, 0x082b192b, 0x19080808, 0x19080808, 0x19080808,
+    0x1908082b, 0x19080808, 0x19081919, 0x19080808, 0x19082b08, 0x19080808, 0x19082b2b, 0x19080808,
+    0x19190819, 0x19080808, 0x19191908, 0x19080808, 0x1919192b, 0x19080808, 0x19192b19, 0x19080808,
+    0x192b0808, 0x19080808, 0x192b082b, 0x19080808, 0x192b1919, 0x19080808, 0x2b080819, 0x19080808,
+    0x2b081908, 0x19080808, 0x2b190808, 0x19080808, 0x2b191919, 0x19080808, 0x2b192b08, 0x19080808,
+    0x2b2b0819, 0x19080808, 0x2b2b1908, 0x19080808, 0x08080808, 0x19080819, 0x0808082b, 0x19080819,
+    0x08081919, 0x19080819, 0x08082b08, 0x19080819, 0x08190819, 0x19080819, 0x08191908, 0x19080819,
+    0x0819192b, 0x19080819, 0x08192b19, 0x19080819, 0x082b0808, 0x19080819, 0x082b082b, 0x19080819,
+    0x082b1919, 0x19080819, 0x19080819, 0x19080819, 0x19081908, 0x19080819, 0x1908192b, 0x19080819,
+    0x19082b19, 0x19080819, 0x19190808, 0x19080819, 0x1919082b, 0x19080819, 0x19191919, 0x19080819,
+    0x19192b08, 0x19080819, 0x192b0819, 0x19080819, 0x192b1908, 0x19080819, 0x2b080808, 0x19080819,
+    0x2b08082b, 0x19080819, 0x2b081919, 0x19080819, 0x2b082b08, 0x19080819, 0x2b190819, 0x19080819,
+    0x2b191908, 0x19080819, 0x2b2b0808, 0x19080819, 0x08080819, 0x1908082b, 0x08081908, 0x1908082b,
+    0x08190808, 0x1908082b, 0x0819082b, 0x1908082b, 0x08191919, 0x1908082b, 0x08192b08, 0x1908082b,
+    0x082b1908, 0x1908082b, 0x19080808, 0x1908082b, 0x19081919, 0x1908082b, 0x19082b08, 0x1908082b,
+    0x19190819, 0x1908082b, 0x19191908, 0x1908082b, 0x192b0808, 0x1908082b, 0x2b080819, 0x1908082b,
+    0x2b081908, 0x1908082b, 0x08080808, 0x19081908, 0x0808082b, 0x19081908, 0x08081919, 0x19081908,
+    0x08082b08, 0x19081908, 0x08082b2b, 0x19081908, 0x08190819, 0x19081908, 0x08191908, 0x19081908,
+    0x0819192b, 0x19081908, 0x08192b19, 0x19081908, 0x082b0808, 0x19081908, 0x082b082b, 0x19081908,
+    0x082b1919, 0x19081908, 0x082b2b08, 0x19081908, 0x19080819, 0x19081908, 0x19081908, 0x19081908,
+    0x1908192b, 0x19081908, 0x19082b19, 0x19081908, 0x19190808, 0x19081908, 0x1919082b, 0x19081908,
+    0x19191919, 0x19081908, 0x19192b08, 0x19081908, 0x192b0819, 0x19081908, 0x192b1908, 0x19081908,
+    0x2b080808, 0x19081908, 0x2b08082b, 0x19081908, 0x2b081919, 0x19081908, 0x2b082b08, 0x19081908,
+    0x2b190819, 0x19081908, 0x2b191908, 0x19081908, 0x2b2b0808, 0x19081908, 0x08080819, 0x19081919,
+    0x08081908, 0x19081919, 0x0808192b, 0x19081919, 0x08082b19, 0x19081919, 0x08190808, 0x19081919,
+    0x0819082b, 0x19081919, 0x08191919, 0x19081919, 0x08192b08, 0x19081919, 0x082b0819, 0x19081919,
+    0x082b1908, 0x19081919, 0x19080808, 0x19081919, 0x1908082b, 0x19081919, 0x19081919, 0x19081919,
+    0x19082b08, 0x19081919, 0x19190819, 0x19081919, 0x19191908, 0x19081919, 0x192b0808, 0x19081919,
+    0x192b2b2b, 0x19081919, 0x2b080819, 0x19081919, 0x2b081908, 0x19081919, 0x2b190808, 0x19081919,
+    0x08080808, 0x1908192b, 0x0808082b, 0x1908192b, 0x08081919, 0x1908192b, 0x08082b08, 0x1908192b,
+    0x08190819, 0x1908192b, 0x08191908, 0x1908192b, 0x082b0808, 0x1908192b, 0x19080819, 0x1908192b,
+    0x19081908, 0x1908192b, 0x19190808, 0x1908192b, 0x2b080808, 0x1908192b, 0x2b2b1919, 0x1908192b,
+    0x08080819, 0x19082b08, 0x08081908, 0x19082b08, 0x08082b19, 0x19082b08, 0x08190808, 0x19082b08,
+    0x0819082b, 0x19082b08, 0x08191919, 0x19082b08, 0x08192b08, 0x19082b08, 0x082b0819, 0x19082b08,
+    0x082b1908, 0x19082b08, 0x19080808, 0x19082b08, 0x1908082b, 0x19082b08, 0x19081919, 0x19082b08,
+    0x19082b08, 0x19082b08, 0x19190819, 0x19082b08, 0x19191908, 0x19082b08, 0x192b0808, 0x19082b08,
+    0x2b081908, 0x19082b08, 0x2b190808, 0x19082b08, 0x08080808, 0x19082b19, 0x0808082b, 0x19082b19,
+    0x08081919, 0x19082b19, 0x08082b08, 0x19082b19, 0x08190819, 0x19082b19, 0x08191908, 0x19082b19,
+    0x082b0808, 0x19082b19, 0x19080819, 0x19082b19, 0x19081908, 0x19082b19, 0x19190808, 0x19082b19,
+    0x2b080808, 0x19082b19, 0x2b19192b, 0x19082b19, 0x08080819, 0x19082b2b, 0x08081908, 0x19082b2b,
+    0x08190808, 0x19082b2b, 0x19080808, 0x19082b2b, 0x08080808, 0x19190808, 0x0808082b, 0x19190808,
+    0x08081919, 0x19190808, 0x08082b08, 0x19190808, 0x08190819, 0x19190808, 0x08191908, 0x19190808,
+    0x0819192b, 0x19190808, 0x08192b19, 0x19190808, 0x082b0808, 0x19190808, 0x082b082b, 0x19190808,
+    0x082b1919, 0x19190808, 0x082b2b08, 0x19190808, 0x19080819, 0x19190808, 0x19081908, 0x19190808,
+    0x1908192b, 0x19190808, 0x19082b19, 0x19190808, 0x19190808, 0x19190808, 0x1919082b, 0x19190808,
+    0x19191919, 0x19190808, 0x19192b08, 0x19190808, 0x192b0819, 0x19190808, 0x192b1908, 0x19190808,
+    0x2b080808, 0x19190808, 0x2b08082b, 0x19190808, 0x2b081919, 0x19190808, 0x2b082b08, 0x19190808,
+    0x2b190819, 0x19190808, 0x2b191908, 0x19190808, 0x08080819, 0x19190819, 0x08081908, 0x19190819,
+    0x0808192b, 0x19190819, 0x08082b19, 0x19190819, 0x08190808, 0x19190819, 0x0819082b, 0x19190819,
+    0x08191919, 0x19190819, 0x08192b08, 0x19190819, 0x082b0819, 0x19190819, 0x082b1908, 0x19190819,
+    0x19080808, 0x19190819, 0x1908082b, 0x19190819, 0x19081919, 0x19190819, 0x19082b08, 0x19190819,
+    0x19190819, 0x19190819, 0x19191908, 0x19190819, 0x192b0808, 0x19190819, 0x2b080819, 0x19190819,
+    0x2b081908, 0x19190819, 0x2b190808, 0x19190819, 0x08080808, 0x1919082b, 0x08081919, 0x1919082b,
+    0x08082b08, 0x1919082b, 0x08190819, 0x1919082b, 0x08191908, 0x1919082b, 0x082b0808, 0x1919082b,
+    0x19080819, 0x1919082b, 0x19081908, 0x1919082b, 0x19190808, 0x1919082b, 0x192b2b19, 0x1919082b,
+    0x2b080808, 0x1919082b, 0x08080819, 0x19191908, 0x08081908, 0x19191908, 0x0808192b, 0x19191908,
+    0x08082b19, 0x19191908, 0x08190808, 0x19191908, 0x0819082b, 0x19191908, 0x08191919, 0x19191908,
+    0x08192b08, 0x19191908, 0x082b0819, 0x19191908, 0x082b1908, 0x19191908, 0x19080808, 0x19191908,
+    0x1908082b, 0x19191908, 0x19081919, 0x19191908, 0x19082b08, 0x19191908, 0x19190819, 0x19191908,
+    0x19191908, 0x19191908, 0x192b0808, 0x19191908, 0x2b080819, 0x19191908, 0x2b081908, 0x19191908,
+    0x2b190808, 0x19191908, 0x08080808, 0x19191919, 0x0808082b, 0x19191919, 0x08081919, 0x19191919,
+    0x08082b08, 0x19191919, 0x08190819, 0x19191919, 0x08191908, 0x19191919, 0x082b0808, 0x19191919,
+    0x19080819, 0x19191919, 0x19081908, 0x19191919, 0x19190808, 0x19191919, 0x2b080808, 0x19191919,
+    0x08080819, 0x1919192b, 0x08081908, 0x1919192b, 0x08190808, 0x1919192b, 0x082b192b, 0x1919192b,
+    0x19080808, 0x1919192b, 0x08080808, 0x19192b08, 0x0808082b, 0x19192b08, 0x08081919, 0x19192b08,
+    0x08082b08, 0x19192b08, 0x08190819, 0x19192b08, 0x08191908, 0x19192b08, 0x082b0808, 0x19192b08,
+    0x19080819, 0x19192b08, 0x19081908, 0x19192b08, 0x19190808, 0x19192b08, 0x19192b2b, 0x19192b08,
+    0x2b080808, 0x19192b08, 0x08080819, 0x19192b19, 0x08081908, 0x19192b19, 0x08190808, 0x19192b19,
+    0x19080808, 0x19192b19, 0x08080808, 0x19192b2b, 0x08192b19, 0x19192b2b, 0x2b081919, 0x19192b2b,
+    0x2b2b2b08, 0x19192b2b, 0x08080819, 0x192b0808, 0x08081908, 0x192b0808, 0x0808192b, 0x192b0808,
+    0x08190808, 0x192b0808, 0x0819082b, 0x192b0808, 0x08191919, 0x192b0808, 0x08192b08, 0x192b0808,
+    0x082b0819, 0x192b0808, 0x082b1908, 0x192b0808, 0x19080808, 0x192b0808, 0x19081919, 0x192b0808,
+    0x19082b08, 0x192b0808, 0x19190819, 0x192b0808, 0x19191908, 0x192b0808, 0x192b0808, 0x192b0808,
+    0x2b081908, 0x192b0808, 0x2b190808, 0x192b0808, 0x08080808, 0x192b0819, 0x0808082b, 0x192b0819,
+    0x08081919, 0x192b0819, 0x08082b08, 0x192b0819, 0x08190819, 0x192b0819, 0x08191908, 0x192b0819,
+    0x082b0808, 0x192b0819, 0x19080819, 0x192b0819, 0x19081908, 0x192b0819, 0x19190808, 0x192b0819,
+    0x2b080808, 0x192b0819, 0x2b192b19, 0x192b0819, 0x08081908, 0x192b082b, 0x08190808, 0x192b082b,
+    0x19080808, 0x192b082b, 0x1919192b, 0x192b082b, 0x2b2b0819, 0x192b082b, 0x08080808, 0x192b1908,
+    0x08081919, 0x192b1908, 0x08082b08, 0x192b1908, 0x08190819, 0x192b1908, 0x08191908, 0x192b1908,
+    0x082b0808, 0x192b1908, 0x19080819, 0x192b1908, 0x19081908, 0x192b1908, 0x19190808, 0x192b1908,
+    0x2b080808, 0x192b1908, 0x08080819, 0x192b1919, 0x08081908, 0x192b1919, 0x08190808, 0x192b1919,
+    0x19080808, 0x192b1919, 0x19082b2b, 0x192b1919, 0x192b2b08, 0x192b1919, 0x2b19082b, 0x192b1919,
+    0x08080808, 0x192b192b, 0x2b191908, 0x192b192b, 0x08080819, 0x192b2b08, 0x08081908, 0x192b2b08,
+    0x08190808, 0x192b2b08, 0x192b1919, 0x192b2b08, 0x2b192b08, 0x192b2b08, 0x08080808, 0x192b2b19,
+    0x082b2b2b, 0x192b2b19, 0x1908082b, 0x192b2b2b, 0x2b2b0819, 0x192b2b2b, 0x08080808, 0x2b080808,
+    0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808, 0x08190819, 0x2b080808,
+    0x08191908, 0x2b080808, 0x08192b19, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b1919, 0x2b080808,
+    0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x1919082b, 0x2b080808,
+    0x19191919, 0x2b080808, 0x19192b08, 0x2b080808, 0x192b0819, 0x2b080808, 0x2b080808, 0x2b080808,
+    0x2b081919, 0x2b080808, 0x2b190819, 0x2b080808, 0x2b191908, 0x2b080808, 0x08080819, 0x2b080819,
+    0x08081908, 0x2b080819, 0x08082b19, 0x2b080819, 0x08190808, 0x2b080819, 0x0819082b, 0x2b080819,
+    0x08191919, 0x2b080819, 0x08192b08, 0x2b080819, 0x082b0819, 0x2b080819, 0x082b1908, 0x2b080819,
+    0x19080808, 0x2b080819, 0x1908082b, 0x2b080819, 0x19081919, 0x2b080819, 0x19082b08, 0x2b080819,
+    0x19190819, 0x2b080819, 0x19191908, 0x2b080819, 0x2b080819, 0x2b080819, 0x2b081908, 0x2b080819,
+    0x2b190808, 0x2b080819, 0x2b2b2b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x08081919, 0x2b08082b,
+    0x08082b2b, 0x2b08082b, 0x08190819, 0x2b08082b, 0x08191908, 0x2b08082b, 0x19080819, 0x2b08082b,
+    0x19081908, 0x2b08082b, 0x19190808, 0x2b08082b, 0x08080819, 0x2b081908, 0x08081908, 0x2b081908,
+    0x0808192b, 0x2b081908, 0x08082b19, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908,
+    0x08191919, 0x2b081908, 0x08192b08, 0x2b081908, 0x082b0819, 0x2b081908, 0x19080808, 0x2b081908,
+    0x1908082b, 0x2b081908, 0x19081919, 0x2b081908, 0x19082b08, 0x2b081908, 0x19190819, 0x2b081908,
+    0x19191908, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b080819, 0x2b081908, 0x2b081908, 0x2b081908,
+    0x2b190808, 0x2b081908, 0x08080808, 0x2b081919, 0x0808082b, 0x2b081919, 0x08081919, 0x2b081919,
+    0x08082b08, 0x2b081919, 0x08190819, 0x2b081919, 0x08191908, 0x2b081919, 0x082b0808, 0x2b081919,
+    0x19080819, 0x2b081919, 0x19081908, 0x2b081919, 0x19190808, 0x2b081919, 0x2b080808, 0x2b081919,
+    0x2b082b2b, 0x2b081919, 0x08080819, 0x2b08192b, 0x08081908, 0x2b08192b, 0x08190808, 0x2b08192b,
+    0x082b2b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08081919, 0x2b082b08,
+    0x08190819, 0x2b082b08, 0x08191908, 0x2b082b08, 0x19080819, 0x2b082b08, 0x19081908, 0x2b082b08,
+    0x19190808, 0x2b082b08, 0x2b2b082b, 0x2b082b08, 0x08080819, 0x2b082b19, 0x08081908, 0x2b082b19,
+    0x19080808, 0x2b082b19, 0x192b1919, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x19192b08, 0x2b082b2b,
+    0x19192b2b, 0x2b082b2b, 0x2b08082b, 0x2b082b2b, 0x2b2b082b, 0x2b082b2b, 0x08080819, 0x2b190808,
+    0x08081908, 0x2b190808, 0x08082b19, 0x2b190808, 0x08190808, 0x2b190808, 0x0819082b, 0x2b190808,
+    0x08191919, 0x2b190808, 0x08192b08, 0x2b190808, 0x082b1908, 0x2b190808, 0x19080808, 0x2b190808,
+    0x1908082b, 0x2b190808, 0x19081919, 0x2b190808, 0x19082b08, 0x2b190808, 0x19190819, 0x2b190808,
+    0x19191908, 0x2b190808, 0x192b0808, 0x2b190808, 0x2b080819, 0x2b190808, 0x2b081908, 0x2b190808,
+    0x2b190808, 0x2b190808, 0x08080808, 0x2b190819, 0x08081919, 0x2b190819, 0x08190819, 0x2b190819,
+    0x08191908, 0x2b190819, 0x19080819, 0x2b190819, 0x19081908, 0x2b190819, 0x19190808, 0x2b190819,
+    0x19192b2b, 0x2b190819, 0x08080819, 0x2b19082b, 0x08081908, 0x2b19082b, 0x08190808, 0x2b19082b,
+    0x19080808, 0x2b19082b, 0x2b2b192b, 0x2b19082b, 0x08080808, 0x2b191908, 0x0808082b, 0x2b191908,
+    0x08081919, 0x2b191908, 0x08082b08, 0x2b191908, 0x08190819, 0x2b191908, 0x08191908, 0x2b191908,
+    0x082b0808, 0x2b191908, 0x19080819, 0x2b191908, 0x19081908, 0x2b191908, 0x19190808, 0x2b191908,
+    0x2b080808, 0x2b191908, 0x2b19192b, 0x2b191908, 0x08080819, 0x2b191919, 0x08081908, 0x2b191919,
+    0x08190808, 0x2b191919, 0x19080808, 0x2b191919, 0x2b192b08, 0x2b191919, 0x2b2b0819, 0x2b191919,
+    0x08080808, 0x2b19192b, 0x1908192b, 0x2b19192b, 0x192b1908, 0x2b19192b, 0x08080819, 0x2b192b08,
+    0x08081908, 0x2b192b08, 0x08190808, 0x2b192b08, 0x082b192b, 0x2b192b08, 0x19080808, 0x2b192b08,
+    0x2b2b2b19, 0x2b192b08, 0x08080808, 0x2b192b19, 0x19082b19, 0x2b192b19, 0x1919082b, 0x2b192b19,
+    0x2b190808, 0x2b192b2b, 0x08080808, 0x2b2b0808, 0x08081919, 0x2b2b0808, 0x08082b2b, 0x2b2b0808,
+    0x08191908, 0x2b2b0808, 0x082b082b, 0x2b2b0808, 0x082b2b2b, 0x2b2b0808, 0x19080819, 0x2b2b0808,
+    0x19081908, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b2b082b, 0x2b2b0808, 0x2b2b2b2b, 0x2b2b0808,
+    0x19080808, 0x2b2b0819, 0x192b1919, 0x2b2b0819, 0x0808082b, 0x2b2b082b, 0x08082b2b, 0x2b2b082b,
+    0x082b082b, 0x2b2b082b, 0x082b2b08, 0x2b2b082b, 0x082b2b2b, 0x2b2b082b, 0x2b08082b, 0x2b2b082b,
+    0x2b082b08, 0x2b2b082b, 0x2b082b2b, 0x2b2b082b, 0x2b2b2b08, 0x2b2b082b, 0x08080819, 0x2b2b1908,
+    0x08081908, 0x2b2b1908, 0x08190808, 0x2b2b1908, 0x19080808, 0x2b2b1908, 0x2b082b19, 0x2b2b1908,
+    0x2b2b1908, 0x2b2b1908, 0x08080808, 0x2b2b1919, 0x08192b19, 0x2b2b1919, 0x19190819, 0x2b2b192b,
+    0x08082b2b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b082b, 0x2b2b2b08, 0x19191908, 0x2b2b2b19,
+    0x2b08192b, 0x2b2b2b19, 0x08082b08, 0x2b2b2b2b, 0x08082b2b, 0x2b2b2b2b, 0x082b0808, 0x2b2b2b2b,
+    0x082b082b, 0x2b2b2b2b, 0x082b2b08, 0x2b2b2b2b, 0x2b082b08, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b
+);
+
+struct iq2_s {
+    d: f16,
+    qs: array<f16, 32>,
+    qh: array<f16, 4>,
+    scales: array<f16, 4>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var qs_vals : array<u32, 16>;
+    for (var i: u32 = 0; i < 16; i++) {
+        qs_vals[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var scale_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.scales[0], block.scales[1])),
+        bitcast<u32>(vec2(block.scales[2], block.scales[3]))
+    );
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib ++) {
+        let s = get_byte(scale_vals[ib / 4], ib % 4);
+        let db = array<f32, 2>(
+            d * (0.5 + f32(s & 0xF)) * 0.25,
+            d * (0.5 + f32(s >> 4)) * 0.25
+        );
+        let qs_w = qs_vals[ib];
+        for (var l: u32 = 0; l < 4; l++) {
+            let qh_b = (get_byte(qh_vals[ib / 4], ib % 4) << (8 - 2 * l)) & 0x300;
+            let ig = (get_byte(qs_w, l) | qh_b) * 8;
+            let signs = get_byte(qs_vals[ib + 8], l);
+            let dl = db[l/2];
+            for (var j: u32 = 0; j < 8; j++) {
+                let g = get_byte(iq2s_grid[(ig + j) / 4], (ig + j) % 4);
+                let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0);
+                sum += dl * f32(g) * m * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+
+#enddecl(IQ2_S)
+
+#decl(IQ3_XSS)
+
+const iq3xxs_grid = array<u32, 256>(
+    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
+    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
+    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
+    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
+    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
+    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
+    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
+    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
+    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
+    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
+    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
+    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
+    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
+    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
+    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
+    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
+    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
+    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
+    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
+    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
+    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
+    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
+    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
+    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
+    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
+    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
+    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
+    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
+    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
+    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
+    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
+    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04
+);
+
+struct iq3_xxs {
+    d: f16,
+    qs: array<f16, 48>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 16; ib += 2) {
+        let sc_sign = bitcast<u32>(vec2(block.qs[ib + 32], block.qs[ib + 33]));
+        let db = d * (0.5 + f32(sc_sign >> 28)) * 0.5;
+        for (var l: u32 = 0; l < 4; l++) {
+            let is = (sc_sign >> (7 * l)) & 127;
+            let signs = get_byte(ksigns_iq2xs[is / 4], is % 4);
+            let ig_val = bitcast<u32>(vec2(block.qs[ib * 2 + l], 0.0));
+            let ig1 = get_byte(ig_val, 0);
+            let ig2 = get_byte(ig_val, 1);
+            for (var j: u32 = 0; j < 4; j++) {
+                let g1 = get_byte(iq3xxs_grid[ig1], j);
+                let g2 = get_byte(iq3xxs_grid[ig2], j);
+                let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                sum += db * f32(g1) * m1 * src1[src1_i];
+                sum += db * f32(g2) * m2 * src1[src1_i + 4];
+                src1_i++;
+            }
+            src1_i += 4;
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ3_XSS)
+
+#decl(IQ3_S)
+
+const iq3s_grid = array<u32, 512>(
+    0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
+    0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
+    0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
+    0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
+    0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
+    0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
+    0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
+    0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
+    0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
+    0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
+    0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
+    0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
+    0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
+    0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
+    0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
+    0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
+    0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
+    0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
+    0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
+    0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
+    0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
+    0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
+    0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
+    0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
+    0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
+    0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
+    0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
+    0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
+    0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
+    0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
+    0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
+    0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
+    0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
+    0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
+    0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
+    0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
+    0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
+    0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
+    0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
+    0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
+    0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
+    0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
+    0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
+    0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
+    0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
+    0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
+    0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
+    0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
+    0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
+    0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
+    0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
+    0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
+    0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
+    0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
+    0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
+    0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
+    0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
+    0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
+    0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
+    0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
+    0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
+    0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
+    0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
+    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101
+);
+
+struct iq3_s {
+    d: f16,
+    qs: array<f16, 32>,
+    qh: array<f16, 4>,
+    signs: array<f16, 16>,
+    scales: array<f16, 2>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var qh_vals = array<u32, 2>(
+        bitcast<u32>(vec2(block.qh[0], block.qh[1])),
+        bitcast<u32>(vec2(block.qh[2], block.qh[3]))
+    );
+    var sign_vals: array<u32, 8>;
+    for (var i: u32 = 0; i < 8; i++) {
+        sign_vals[i] = bitcast<u32>(vec2(block.signs[i * 2], block.signs[i * 2 + 1]));
+    }
+    var scale_vals = bitcast<u32>(vec2(block.scales[0], block.scales[1]));
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 4; ib++) {
+        let s = get_byte(scale_vals, ib);
+        let db = array<f32, 2>(
+            d * (1.0 + 2.0 * f32(s & 0xF)),
+            d * (1.0 + 2.0 * f32(s >> 4))
+        );
+        for (var k: u32 = 0; k < 2; k++) {
+            let dl = db[k];
+            let qh_byte = get_byte(qh_vals[ib / 2], (ib % 2) * 2 + k);
+            let sign_w = sign_vals[ib * 2 + k];
+            for (var l: u32 = 0; l < 4; l++) {
+                let signs = get_byte(sign_w, l);
+                let ig_val = bitcast<u32>(vec2(block.qs[ib * 8 + k * 4 + l], 0.0));
+                let ig1 = get_byte(ig_val, 0) | ((qh_byte << ((8 - (2 * l)))) & 256);
+                let ig2 = get_byte(ig_val, 1) | ((qh_byte << ((7 - (2 * l)))) & 256);
+                for (var j: u32 = 0; j < 4; j++) {
+                    let g1 = get_byte(iq3s_grid[ig1], j);
+                    let g2 = get_byte(iq3s_grid[ig2], j);
+                    let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0);
+                    let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0);
+                    sum += dl * f32(g1) * m1 * src1[src1_i];
+                    sum += dl * f32(g2) * m2 * src1[src1_i + 4];
+                    src1_i++;
+                }
+                src1_i += 4;
+            }
+        }
+    }
+    return sum;
+}
+#enddecl(IQ3_S)
+
+#decl(IQ1_TABLE)
+
+const IQ1_DELTA: f32 = 0.125;
+
+const iq1_grid = array<u32, 1024>(
+    0xfffdffff, 0xfff7fff0, 0xffccfff5, 0xffdfffc0, 0xffd7ffdd, 0xff30ffd5, 0xff03ff0c, 0xff10ff01,
+    0xff7dff7f, 0xff75ff77, 0xff5fff40, 0xff57ff5d, 0xfcf3ff55, 0xfcccfcf0, 0xfcc1fcc3, 0xfcc5fcc4,
+    0xfc3cfcd0, 0xfc34fc31, 0xfc00fc0d, 0xfc1cfc05, 0xfc11fc13, 0xfc70fc17, 0xfc43fc4c, 0xfc50fc41,
+    0xfdfdfdff, 0xfdf5fdf7, 0xfddffdc0, 0xfdd7fddd, 0xfd30fdd5, 0xfd04fd0c, 0xfd14fd13, 0xfd7dfd7f,
+    0xfd75fd77, 0xfd40fd4c, 0xfd5ffd44, 0xfd57fd5d, 0xf3ccfd55, 0xf3c1f3c3, 0xf33cf3d0, 0xf300f334,
+    0xf313f305, 0xf34cf310, 0xf350f344, 0xf0f3f0fc, 0xf0f1f0f0, 0xf0c7f0c0, 0xf0d4f0c5, 0xf030f03f,
+    0xf00ff035, 0xf003f00c, 0xf001f000, 0xf01ff004, 0xf010f01d, 0xf015f017, 0xf04cf07c, 0xf047f040,
+    0xf05cf045, 0xf050f053, 0xf054f051, 0xf1c4f1c3, 0xf133f13c, 0xf10df10f, 0xf107f100, 0xf11cf11f,
+    0xf114f111, 0xf14cf170, 0xf144f143, 0xf7fdf7ff, 0xf7f5f7f7, 0xf7dff7c0, 0xf7d7f7dd, 0xf730f7d5,
+    0xf701f70c, 0xf77ff710, 0xf777f77d, 0xf740f775, 0xf75df75f, 0xf755f757, 0xf4ccf4f0, 0xf4c4f4c3,
+    0xf4d0f4d3, 0xf40ff43c, 0xf400f40c, 0xf413f41c, 0xf44cf414, 0xf441f443, 0xf450f444, 0xf5fdf5ff,
+    0xf5f5f5f7, 0xf5dff5c0, 0xf5d7f5dd, 0xf530f5d5, 0xf504f50c, 0xf510f51c, 0xf57df57f, 0xf577f570,
+    0xf540f575, 0xf55df55f, 0xf555f557, 0xcfcccfcf, 0xcfc4cfc3, 0xcfd0cfd3, 0xcf33cf3c, 0xcf00cf0f,
+    0xcf1ccf07, 0xcf10cf13, 0xcf4ccf14, 0xcf41cf43, 0xcf50cf5c, 0xccf3ccfc, 0xccf4ccf1, 0xcccdcccf,
+    0xccc7ccc0, 0xccd3ccdc, 0xcc30ccd4, 0xcc0fcc35, 0xcc0dcc0c, 0xcc00cc03, 0xcc04cc01, 0xcc10cc1f,
+    0xcc4dcc73, 0xcc5ccc40, 0xcdcccc53, 0xcdc1cdc3, 0xcd3fcdd0, 0xcd34cd31, 0xcd00cd0d, 0xcd05cd07,
+    0xcd11cd13, 0xcd4ccd70, 0xcd41cd43, 0xc3fccd50, 0xc3f4c3f1, 0xc3c0c3c3, 0xc3c4c3c7, 0xc3d1c3dc,
+    0xc330c33c, 0xc337c331, 0xc30cc335, 0xc300c303, 0xc304c301, 0xc310c31d, 0xc373c317, 0xc34fc374,
+    0xc340c343, 0xc344c347, 0xc35cc345, 0xc350c353, 0xc0fdc354, 0xc0f5c0f0, 0xc0c3c0cc, 0xc0c1c0c0,
+    0xc0dfc0c4, 0xc0d0c0dd, 0xc0d5c0d7, 0xc033c03c, 0xc031c030, 0xc00dc00c, 0xc000c003, 0xc004c001,
+    0xc01cc005, 0xc010c013, 0xc014c011, 0xc07dc07f, 0xc070c073, 0xc075c077, 0xc04cc04f, 0xc040c043,
+    0xc044c041, 0xc05fc045, 0xc050c05d, 0xc1f3c1fc, 0xc1f1c1f0, 0xc1c1c1c0, 0xc1c5c1c7, 0xc1d1c1dc,
+    0xc13dc13f, 0xc130c133, 0xc135c137, 0xc100c10c, 0xc107c101, 0xc11cc104, 0xc110c113, 0xc114c117,
+    0xc171c115, 0xc14dc175, 0xc153c140, 0xc7ccc154, 0xc7d0c7c1, 0xc733c73c, 0xc734c731, 0xc700c70f,
+    0xc705c707, 0xc71cc71f, 0xc711c713, 0xc770c714, 0xc743c74c, 0xc4cfc750, 0xc4c0c4cd, 0xc4dcc4c5,
+    0xc43dc4d0, 0xc430c433, 0xc40cc437, 0xc400c403, 0xc404c401, 0xc41fc405, 0xc415c410, 0xc44cc474,
+    0xc440c44d, 0xc45cc447, 0xc454c451, 0xc5c1c5f4, 0xc5d1c5d3, 0xc531c533, 0xc50fc534, 0xc500c50d,
+    0xc51cc507, 0xc514c511, 0xc54cc570, 0xc545c541, 0xdffddfff, 0xdff5dff7, 0xdfdfdfc0, 0xdfd0dfdd,
+    0xdfd5dfd7, 0xdf0cdf30, 0xdf1cdf04, 0xdf7fdf10, 0xdf77df7d, 0xdf40df75, 0xdf5ddf5f, 0xdf57df50,
+    0xdcf0df55, 0xdcc3dccc, 0xdcd0dcc4, 0xdc33dc3d, 0xdc00dc34, 0xdc05dc07, 0xdc13dc1c, 0xdc11dc10,
+    0xdc4fdc70, 0xdc44dc41, 0xddfcdc50, 0xddf5ddf7, 0xddc0ddcc, 0xdddddddf, 0xddd5ddd7, 0xdd0cdd30,
+    0xdd04dd01, 0xdd7cdd10, 0xdd75dd77, 0xdd40dd4c, 0xdd5ddd5f, 0xdd55dd57, 0xd3c3d3f0, 0xd3c4d3c1,
+    0xd333d3d0, 0xd331d330, 0xd30dd334, 0xd307d300, 0xd311d305, 0xd34cd370, 0xd344d343, 0xd350d35c,
+    0xd0c0d0f4, 0xd0d4d0dc, 0xd030d03f, 0xd00cd037, 0xd000d003, 0xd01dd004, 0xd017d010, 0xd04fd074,
+    0xd040d043, 0xd045d047, 0xd053d05c, 0xd054d051, 0xd1cfd1f0, 0xd1c4d1cd, 0xd13cd1d0, 0xd100d134,
+    0xd11cd11f, 0xd173d114, 0xd14fd171, 0xd7ffd145, 0xd7f7d7fd, 0xd7c0d7f5, 0xd7ddd7df, 0xd7d5d7d7,
+    0xd70cd730, 0xd710d703, 0xd77dd77f, 0xd775d777, 0xd75dd75f, 0xd755d757, 0xd4ccd4f4, 0xd4c4d4c3,
+    0xd431d4d0, 0xd40dd434, 0xd41cd400, 0xd411d413, 0xd470d414, 0xd441d44f, 0xd453d444, 0xd5ffd450,
+    0xd5f7d5fd, 0xd5dfd5f5, 0xd5d7d5dd, 0xd530d5d5, 0xd501d50c, 0xd510d504, 0xd57dd57f, 0xd575d577,
+    0xd55fd540, 0xd557d55d, 0x3ff0d555, 0x3fc13fcc, 0x3f343fd0, 0x3f003f0d, 0x3f053f07, 0x3f133f1c,
+    0x3f433f11, 0x3f5c3f44, 0x3cff3f51, 0x3cf33cfc, 0x3cf43cf1, 0x3cc03ccd, 0x3cc73cc1, 0x3cdc3cc5,
+    0x3cd43cd1, 0x3c373c30, 0x3c0c3c35, 0x3c003c03, 0x3c043c01, 0x3c103c05, 0x3c153c17, 0x3c733c7c,
+    0x3c4f3c71, 0x3c403c4d, 0x3c5c3c5f, 0x3df03c5d, 0x3dc33dcc, 0x3dd03dc1, 0x3d0d3d3c, 0x3d053d00,
+    0x3d143d13, 0x3d433d74, 0x33fc3d50, 0x33c433c0, 0x333033d4, 0x33353337, 0x3303330c, 0x33013300,
+    0x331d331c, 0x33173310, 0x337c3315, 0x33743371, 0x334d334f, 0x335f3340, 0x3354335c, 0x30fd30fc,
+    0x30f530f0, 0x30c330cc, 0x30c130c0, 0x30df30c4, 0x30d530d0, 0x3033303c, 0x30313030, 0x300f3034,
+    0x3003300c, 0x30013000, 0x30043007, 0x3013301c, 0x30113010, 0x307d3014, 0x30703073, 0x304c3077,
+    0x30403043, 0x30443041, 0x30503045, 0x30553057, 0x31f031fc, 0x31c331f4, 0x31c731c0, 0x31dc31c5,
+    0x31d431d3, 0x313d313f, 0x31373130, 0x310c310f, 0x3100310d, 0x31043101, 0x3110311d, 0x317c3117,
+    0x31753170, 0x31403143, 0x3153315c, 0x37f03151, 0x37c037cc, 0x37d037c5, 0x3734373d, 0x3700370f,
+    0x371c3707, 0x37113713, 0x37703714, 0x3743374c, 0x37443741, 0x34fc3750, 0x34f134f0, 0x34cf34f5,
+    0x34c034c3, 0x34dc34c7, 0x34d134d3, 0x3430343f, 0x340c3435, 0x3403340d, 0x34013400, 0x341f3404,
+    0x3410341d, 0x34153411, 0x34743471, 0x3440344d, 0x34473441, 0x3453345c, 0x34543451, 0x353335c1,
+    0x35343531, 0x35073500, 0x35133505, 0x35433514, 0x0ffc3550, 0x0ff00ff3, 0x0ff40ff1, 0x0fc00fcd,
+    0x0fdc0fc5, 0x0fd40fd3, 0x0f300f3f, 0x0f0c0f37, 0x0f000f03, 0x0f040f01, 0x0f170f10, 0x0f740f71,
+    0x0f470f40, 0x0f5c0f5f, 0x0f540f51, 0x0cf70cf0, 0x0cf50cf4, 0x0cc30ccc, 0x0cc10cc0, 0x0cc40cc7,
+    0x0cd00cdf, 0x0cd70cd1, 0x0c3c0cd5, 0x0c300c33, 0x0c340c31, 0x0c0c0c0f, 0x0c030c0d, 0x0c010c00,
+    0x0c040c07, 0x0c1c0c05, 0x0c100c13, 0x0c140c11, 0x0c700c7d, 0x0c430c4c, 0x0c410c40, 0x0c5f0c44,
+    0x0c550c50, 0x0df10dfc, 0x0dc00dcd, 0x0ddc0dc5, 0x0d3d0dd3, 0x0d350d30, 0x0d030d0c, 0x0d010d00,
+    0x0d1d0d04, 0x0d700d10, 0x0d4d0d4f, 0x0d440d40, 0x0d530d45, 0x03f003f3, 0x03c303cc, 0x03c103c0,
+    0x03c403c7, 0x03d003dc, 0x03d503d7, 0x0333033c, 0x03310330, 0x03350334, 0x030c030f, 0x03000303,
+    0x03070301, 0x03050304, 0x031d031c, 0x03100313, 0x03140311, 0x0377037f, 0x034c0375, 0x03400343,
+    0x03440341, 0x0353035c, 0x03550350, 0x00fd00fc, 0x00f000f3, 0x00f400f1, 0x00cc00cf, 0x00c300cd,
+    0x00c100c0, 0x00c500c4, 0x00d300dc, 0x00d100d0, 0x003f00d4, 0x003d003c, 0x00300033, 0x00370031,
+    0x000f0034, 0x000d000c, 0x00000003, 0x00070001, 0x00050004, 0x001c001f, 0x00100013, 0x00170011,
+    0x00150014, 0x0073007c, 0x00740070, 0x004f0075, 0x0043004c, 0x00410040, 0x00440047, 0x0053005c,
+    0x00510050, 0x01ff0054, 0x01fd01fc, 0x01f101f3, 0x01f401f7, 0x01c301cc, 0x01c701c0, 0x01df01c4,
+    0x01dd01dc, 0x01d001d3, 0x01d701d1, 0x013c01d4, 0x01310130, 0x01340137, 0x010f0135, 0x010d010c,
+    0x01000103, 0x01070101, 0x01050104, 0x0113011c, 0x01140110, 0x0170017d, 0x01770171, 0x01750174,
+    0x0140014c, 0x015d0145, 0x01510150, 0x01540157, 0x07f007f3, 0x07f407f1, 0x07c007cf, 0x07dc07c7,
+    0x073007d5, 0x07350737, 0x0703070c, 0x07010700, 0x07040707, 0x071d071f, 0x07100713, 0x0774077d,
+    0x074d074f, 0x07470740, 0x0754075c, 0x04fd04fc, 0x04f504f0, 0x04c304cc, 0x04c104c0, 0x04d004c4,
+    0x0433043c, 0x04310430, 0x040f0434, 0x040d040c, 0x04000403, 0x04070401, 0x04050404, 0x0413041c,
+    0x04110410, 0x047c0414, 0x04740470, 0x0443044c, 0x04410440, 0x04440447, 0x05f30450, 0x05c005f7,
+    0x05df05c5, 0x05d105d0, 0x053005d4, 0x05340537, 0x0500050c, 0x05070501, 0x051d0504, 0x05170510,
+    0x057c0515, 0x054d0575, 0x05410540, 0x05450547, 0x1ff0055c, 0x1fc11fc3, 0x1fd01fc4, 0x1f0f1f33,
+    0x1f011f00, 0x1f051f07, 0x1f131f1c, 0x1f141f11, 0x1f411f7c, 0x1cfc1f50, 0x1cf11cf3, 0x1ccd1cf4,
+    0x1cdc1cc0, 0x1cd11cdd, 0x1c301cd4, 0x1c0c1c34, 0x1c011c00, 0x1c101c04, 0x1c151c11, 0x1c751c73,
+    0x1c401c4d, 0x1c511c5c, 0x1dcc1c54, 0x1dc41dc1, 0x1d3c1d3f, 0x1d001d31, 0x1d071d01, 0x1d701d1f,
+    0x1d411d4c, 0x13cc1d50, 0x13c013cd, 0x13c513c1, 0x13d113dc, 0x133f13d4, 0x1330133d, 0x13351337,
+    0x1303130c, 0x13011300, 0x13051304, 0x131d131f, 0x13731310, 0x13741370, 0x134d134f, 0x13401343,
+    0x13471341, 0x135c1345, 0x13541353, 0x10f710f0, 0x10cc10f5, 0x10c110c0, 0x103310c4, 0x10311030,
+    0x100f1034, 0x1003100c, 0x10011000, 0x101c1004, 0x10101013, 0x10141011, 0x10741071, 0x104c1075,
+    0x10411040, 0x10451044, 0x1050105d, 0x10571051, 0x11f411fd, 0x11df11c0, 0x11d711d1, 0x113f11d4,
+    0x11371130, 0x110c1135, 0x11001103, 0x11071101, 0x111f1105, 0x11171110, 0x117d117f, 0x11751170,
+    0x11411143, 0x11441147, 0x1153115f, 0x11551151, 0x17c417c1, 0x173c17d0, 0x1700170d, 0x171c1705,
+    0x17701714, 0x1747174c, 0x14fc1751, 0x14cf14f3, 0x14dc14c0, 0x14d114d3, 0x143f14d4, 0x1430143c,
+    0x14371431, 0x1403140c, 0x14011400, 0x141f1404, 0x14151410, 0x1473147d, 0x14401475, 0x1453145c,
+    0x14541450, 0x15c115cc, 0x153c15c7, 0x15341533, 0x1500150f, 0x15051507, 0x15101513, 0x15711514,
+    0x15471543, 0x15511545, 0x7ffd7fff, 0x7ff57ff7, 0x7fdd7fdf, 0x7fd57fd7, 0x7f0f7f30, 0x7f037f0c,
+    0x7f047f01, 0x7f7f7f10, 0x7f777f7d, 0x7f407f75, 0x7f5d7f5f, 0x7f557f57, 0x7ccc7cf0, 0x7cc17cc3,
+    0x7cd07cc4, 0x7c337c3c, 0x7c0f7c34, 0x7c007c0d, 0x7c077c01, 0x7c137c04, 0x7c147c11, 0x7c747c70,
+    0x7c417c43, 0x7c507c44, 0x7dfd7dff, 0x7df57df7, 0x7ddf7dc0, 0x7dd77ddd, 0x7d0c7dd5, 0x7d047d03,
+    0x7d7f7d10, 0x7d777d7d, 0x7d407d75, 0x7d5d7d5f, 0x7d557d57, 0x73c473c3, 0x7333733c, 0x7300730c,
+    0x731c7305, 0x73147313, 0x73447343, 0x70f470fc, 0x70c070cd, 0x70d170c5, 0x703f70d4, 0x7030703c,
+    0x700c7037, 0x70007003, 0x70047001, 0x70107005, 0x70177011, 0x707c7015, 0x70717073, 0x704f7074,
+    0x7040704d, 0x70517047, 0x71c171cc, 0x71d071c4, 0x7133713c, 0x71357134, 0x7100710f, 0x71057104,
+    0x7111711c, 0x71707115, 0x7145714c, 0x77ff7153, 0x77f777fd, 0x77c077f5, 0x77dd77df, 0x77d577d7,
+    0x7730773c, 0x7703770c, 0x77107704, 0x777f7714, 0x7777777d, 0x77407775, 0x775d775f, 0x77557757,
+    0x74f174f0, 0x74c374cc, 0x74d074c1, 0x7433743c, 0x74347431, 0x740d740f, 0x74057400, 0x7413741c,
+    0x74417470, 0x74507444, 0x75fd75ff, 0x75f575f7, 0x75df75c0, 0x75d775dd, 0x753075d5, 0x7503750c,
+    0x757f7501, 0x7577757d, 0x75407575, 0x755d755f, 0x75557557, 0x4fcc4ff0, 0x4fc74fc1, 0x4fd04fc4,
+    0x4f314f3c, 0x4f004f34, 0x4f054f07, 0x4f154f14, 0x4f4c4f70, 0x4f414f43, 0x4f504f44, 0x4cf34cfc,
+    0x4cf44cf1, 0x4cc04ccf, 0x4cc54cc7, 0x4cd34cdc, 0x4cd44cd1, 0x4c304c3f, 0x4c0c4c0f, 0x4c004c03,
+    0x4c044c01, 0x4c104c1d, 0x4c714c73, 0x4c404c4d, 0x4c5c4c47, 0x4c514c53, 0x4df04c54, 0x4dc34dcc,
+    0x4dd04dc4, 0x4d314d33, 0x4d0f4d34, 0x4d004d0d, 0x4d114d07, 0x4d704d14, 0x4d414d43, 0x43fc4d54,
+    0x43f143f3, 0x43c043cf, 0x43d143c7, 0x4335433f, 0x4303430c, 0x43014300, 0x43044307, 0x431c431f,
+    0x4310431d, 0x43714373, 0x4343434d, 0x43474340, 0x4354435c, 0x40f040ff, 0x40f540f7, 0x40cc40cf,
+    0x40c040c3, 0x40c440c1, 0x40d040dc, 0x40d540d4, 0x4033403c, 0x40314030, 0x400f4034, 0x400d400c,
+    0x40004003, 0x40074001, 0x40054004, 0x4013401c, 0x40114010, 0x407c4014, 0x40774070, 0x404d404c,
+    0x40404043, 0x40444041, 0x405f4045, 0x4050405d, 0x40554057, 0x41f341fc, 0x41c041cf, 0x41df41c4,
+    0x41d441d1, 0x41374130, 0x410c4134, 0x4100410d, 0x41044101, 0x41174110, 0x4173417d, 0x41754174,
+    0x4143414d, 0x41534140, 0x41544151, 0x47c147f0, 0x47d047c4, 0x4731473c, 0x470d470f, 0x47014700,
+    0x47134705, 0x47704710, 0x4741474c, 0x47504744, 0x44f144f3, 0x44cf44f4, 0x44c044cd, 0x44c544c7,
+    0x44dc44df, 0x44d144d3, 0x443d443f, 0x44374430, 0x440c4435, 0x44004403, 0x44044401, 0x4410441d,
+    0x44154411, 0x4473447c, 0x444d444f, 0x44454440, 0x4451445c, 0x45c045f0, 0x453345d0, 0x45344531,
+    0x4500450f, 0x451c4507, 0x454c4570, 0x45404543, 0x5fff4541, 0x5ff75ffd, 0x5fc05ff5, 0x5fdd5fdf,
+    0x5fd55fd7, 0x5f0c5f30, 0x5f015f03, 0x5f7f5f04, 0x5f775f7d, 0x5f405f75, 0x5f5d5f5f, 0x5f555f57,
+    0x5cf45cf0, 0x5cc35ccc, 0x5cc45cc1, 0x5c315cc5, 0x5c0c5c34, 0x5c075c00, 0x5c1c5c05, 0x5c705c13,
+    0x5c4d5c4f, 0x5c445c41, 0x5df75dfd, 0x5dcf5df5, 0x5ddd5dc4, 0x5dd55dd7, 0x5d0c5d30, 0x5d045d01,
+    0x5d7f5d10, 0x5d775d7d, 0x5d405d75, 0x5d5d5d5f, 0x5d555d57, 0x53d053c4, 0x5333533c, 0x5303530f,
+    0x53075300, 0x531c5305, 0x53115310, 0x53145317, 0x50f15370, 0x50cf50f4, 0x50c050cd, 0x50d150c7,
+    0x503d50d4, 0x500c5030, 0x50005003, 0x50045001, 0x50155010, 0x5073507c, 0x50715070, 0x504d5074,
+    0x50475040, 0x51cc51f0, 0x51c551c1, 0x51d051dc, 0x51315133, 0x510d5135, 0x51015100, 0x511f5107,
+    0x5171511d, 0x5140514f, 0x51445141, 0x5153515c, 0x57ff5151, 0x57f757fd, 0x57df57f5, 0x57d757dd,
+    0x570c57d5, 0x57015703, 0x577f5704, 0x5777577d, 0x57405775, 0x575d575f, 0x57555757, 0x54c354f0,
+    0x54dc54c4, 0x543c54d0, 0x5400540f, 0x541c5405, 0x54145411, 0x5441544f, 0x55fd55ff, 0x55f555f7,
+    0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557
+);
+
+#enddecl(IQ1_TABLE)
+
+#decl(IQ1_S)
+
+struct iq1_s {
+    d: f16,
+    qs: array<f16, 16>,
+    qh: array<f16, 8>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let qh = bitcast<u32>(vec2(block.qh[ib], 0.0));
+        let dl = d * (2 * f32((qh >> 12) & 7) + 1);
+        let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000) != 0);
+        let qs_w = bitcast<u32>(vec2(block.qs[ib * 2], block.qs[ib * 2 + 1]));
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = (get_byte(qs_w, l) | (((qh >> (3 * l)) & 7) << 8)) * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                sum += dl * (f32(gs) + delta) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ1_S)
+
+#decl(IQ1_M)
+
+struct iq1_m {
+    qs: array<u32, 8>,
+    qh: array<u32, 4>,
+    scales: array<u32, 2>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+
+    let scale = ((block.scales[0] >> 12) & 0xF) | ((block.scales[0] >> 24) & 0x00F0) | ((block.scales[1] >> 4) & 0x0F00) | ((block.scales[1] >> 16) & 0xF000);
+    let d = f32(bitcast<vec2<f16>>(scale).x);
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let sw = (block.scales[ib / 4] >> (16 * ((ib / 2) % 2))) & 0xFFFF;
+        let s1 : u32 = (sw >> (6 * (ib % 2))) & 0x7;
+        let s2 : u32 = (sw >> (6 * (ib % 2) + 3)) & 0x7;
+        var dl = array<f32, 2>(
+            d * f32(2 * s1 + 1),
+            d * f32(2 * s2 + 1)
+        );
+
+        let qh = block.qh[ib / 2] >> (16 * (ib % 2));
+        var idx = array<u32, 4>(
+            get_byte(block.qs[ib], 0) | ((qh << 8) & 0x700),
+            get_byte(block.qs[ib], 1) | ((qh << 4) & 0x700),
+            get_byte(block.qs[ib], 2) | ((qh) & 0x700),
+            get_byte(block.qs[ib], 3) | ((qh >> 4) & 0x700)
+        );
+        var delta = array<f32, 4>(
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x80) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x08) != 0),
+            select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x80) != 0)
+        );
+        for (var l: u32 = 0; l < 4; l++) {
+            let ig = idx[l] * 8;
+            for (var j: u32 = 0; j < 8; j++) {
+                let gw = iq1_grid[(ig + j) / 16];
+                let g = (gw >> (((ig + j) % 16) * 2)) & 3;
+                let gs = bitcast<i32>(g << 30) >> 30;
+                sum += dl[l/2] * (f32(gs) + delta[l]) * src1[src1_i];
+                src1_i++;
+            }
+        }
+    }
+    return sum;
+}
+
+#enddecl(IQ1_M)
+
+#decl(IQ4_TABLE)
+
+const kvalues_iq4nl = array<i32, 16>(
+    -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113
+);
+
+#enddecl(IQ4_TABLE)
+
+#decl(IQ4_NL)
+
+struct iq4_nl {
+    d: f16,
+    qs: array<f16, 8>,
+}
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    var src1_i = src1_idx_base + offset * 32;
+    var sum = 0.0;
+    var qs: array<u32, 4>;
+    for (var i: u32 = 0; i < 4; i++) {
+        qs[i] = bitcast<u32>(vec2(block.qs[i * 2], block.qs[i * 2 + 1]));
+    }
+    for (var j: u32 = 0; j < 16; j++) {
+        let qsb = get_byte(qs[j / 4], j % 4);
+        sum += d * f32(kvalues_iq4nl[qsb & 0xF]) * src1[src1_i];
+        sum += d * f32(kvalues_iq4nl[qsb >> 4]) * src1[src1_i + 16];
+        src1_i++;
+    }
+    return sum;
+}
+
+#enddecl(IQ4_NL)
+
+#decl(IQ4_XS)
+
+struct iq4_xs {
+    d: f16,
+    scales_h: f16,
+    scales_l: u32,
+    qs: array<u32, 32>
+};
+
+fn multiply_add(src0_idx_base: u32, src1_idx_base: u32, offset: u32) -> f32 {
+    let block = src0[src0_idx_base + offset];
+    let d = f32(block.d);
+    let scales_h = bitcast<u32>(vec2(block.scales_h, 0.0));
+    var src1_i = src1_idx_base + offset * 256;
+    var sum = 0.0;
+    for (var ib: u32 = 0; ib < 8; ib++) {
+        let ls = ((get_byte(block.scales_l, ib / 2) >> (4 * (ib % 2))) & 0xF) | (((scales_h >> (2 * ib)) & 3) << 4);
+        let dl = d * (f32(ls) - 32.0);
+        for (var j: u32 = 0; j < 16; j++) {
+            let iqs = ib * 16 + j;
+            let qsb = get_byte(block.qs[iqs / 4], iqs % 4);
+            sum += dl * f32(kvalues_iq4nl[qsb & 0xF]) * src1[src1_i];
+            sum += dl * f32(kvalues_iq4nl[qsb >> 4]) * src1[src1_i + 16];
+            src1_i++;
+        }
+        src1_i += 16;
+    }
+    return sum;
+}
+
+#enddecl(IQ4_XS)
+
+#end(DECLS)
+
+#define(SHADER)
+
+enable f16;
+
+DECLS
+
+struct MulMatParams {
+    offset_src0: u32, // in elements/blocks
+    offset_src1: u32, // in elements/blocks
+    offset_dst: u32, // in elements/blocks
+    m: u32,
+    n: u32,
+    k: u32,
+    // all strides are in elements/blocks
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // N rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // M rows, K columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<f32>; // M rows, N columns
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
+    let total = params.m * params.n * params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3;
+    if (global_id.x >= total) {
+        return;
+    }
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = global_id.x / dst3_stride;
+    let src03_idx = dst3_idx / params.broadcast3; // src0 may be broadcast along the third dimension
+    let src13_idx = dst3_idx; // src1 is not broadcast
+    let dst3_rem = global_id.x % dst3_stride;
+
+    let dst2_idx = dst3_rem / dst2_stride;
+    let src02_idx = dst2_idx / params.broadcast2; // src0 may also be broadcast along the second dimension
+    let src12_idx = dst2_idx; // src1 is not broadcast
+
+    let dst2_rem = dst3_rem % dst2_stride;
+
+    let row = dst2_rem / params.n; // output row
+    let col = dst2_rem % params.n; // output column
+
+    let src0_idx_base = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02 + col * params.stride_01;
+    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12 + row * params.stride_11;
+
+    var sum = 0.0;
+    for (var i: u32 = 0u; i < params.k/{{BLOCK_SIZE}}; i = i + 1u) {
+        sum += multiply_add(src0_idx_base, src1_idx_base, i);
+    }
+    dst[params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row * params.n + col] = sum;
+}
+
+#end(SHADER)
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl
deleted file mode 100644
index 054aab566f..0000000000
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl
+++ /dev/null
@@ -1,56 +0,0 @@
-struct MulMatParams {
-    m: u32,
-    n: u32,
-    k: u32,
-    // all strides are in elements
-    stride_01: u32,
-    stride_11: u32,
-    stride_02: u32,
-    stride_12: u32,
-    stride_03: u32,
-    stride_13: u32,
-
-    bs02: u32,
-    bs03: u32,
-    broadcast2: u32,
-    broadcast3: u32
-};
-
-@group(0) @binding(0) var<storage, read_write> src0: array<f32>; // N rows, K columns
-@group(0) @binding(1) var<storage, read_write> src1: array<f32>; // M rows, K columns (transposed)
-@group(0) @binding(2) var<storage, read_write> dst: array<f32>; // M rows, N columns
-
-@group(0) @binding(3) var<uniform> params: MulMatParams;
-
-@compute @workgroup_size(64)
-fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
-    let total = params.m * params.n * params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3;
-    if (global_id.x >= total) {
-        return;
-    }
-
-    let dst2_stride = params.m * params.n;
-    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
-
-    let dst3_idx = global_id.x / dst3_stride;
-    let src03_idx = dst3_idx / params.broadcast3; // src0 may be broadcast along the third dimension
-    let src13_idx = dst3_idx; // src1 is not broadcast
-    let dst3_rem = global_id.x % dst3_stride;
-
-    let dst2_idx = dst3_rem / dst2_stride;
-    let src02_idx = dst2_idx / params.broadcast2; // src0 may also be broadcast along the second dimension
-    let src12_idx = dst2_idx; // src1 is not broadcast
-
-    let dst2_rem = dst3_rem % dst2_stride;
-
-    let row = dst2_rem / params.n; // output row
-    let col = dst2_rem % params.n; // output column
-
-    var sum = 0.0;
-    for (var i: u32 = 0u; i < params.k; i = i + 1u) {
-        let src0_idx = src03_idx * params.stride_03 + src02_idx * params.stride_02 + col * params.stride_01 + i;
-        let src1_idx = src13_idx * params.stride_13 + src12_idx * params.stride_12 + row * params.stride_11 + i;
-        sum = sum + src0[src0_idx] * src1[src1_idx];
-    }
-    dst[dst3_idx * dst3_stride + dst2_idx * dst2_stride + row * params.n + col] = sum;
-}

From e92734d51bcb82cc35f0a6b5a14928f0036b2c90 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 22 Aug 2025 23:47:01 +0200
Subject: [PATCH 153/287] test-opt: allow slight inprecision (#15503)

---
 tests/test-opt.cpp | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index f02b4cad8c..18d3fcf2cb 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -358,7 +358,7 @@ static std::pair<int, int> test_forward_backward(
         double accuracy;
         double accuracy_unc;
         ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
-        const bool subtest_ok = ndata == 0 && loss == 0.0 && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
+        const bool subtest_ok = ndata == 0 && almost_equal(loss, 0.0, 1e-6) && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
         helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass);
     }
 
@@ -381,10 +381,12 @@ static std::pair<int, int> test_forward_backward(
     {
         float weights;
         ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
-        const bool subtest_ok = weights == ndata/2;
+        const bool subtest_ok = almost_equal(weights, ndata/2, 1e-10);
         helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass);
     }
     {
+        constexpr double atol = 1e-10;
+
         int64_t ndata;
         ggml_opt_result_ndata(cd.result, &ndata);
         bool subtest_ok = ndata == 6;
@@ -392,7 +394,7 @@ static std::pair<int, int> test_forward_backward(
         double loss;
         double loss_unc;
         ggml_opt_result_loss(cd.result, &loss, &loss_unc);
-        subtest_ok = subtest_ok && loss == 33.0 && almost_equal(loss_unc, sqrt(3.5), 1e-10);
+        subtest_ok = subtest_ok && almost_equal(loss, 33.0, atol) && almost_equal(loss_unc, sqrt(3.5), atol);
 
         double accuracy;
         double accuracy_unc;
@@ -437,7 +439,7 @@ static std::pair<int, int> test_forward_backward(
     {
         float weights;
         ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
-        const bool subtest_ok = weights == -ndata * .5;
+        const bool subtest_ok = almost_equal(weights, -ndata * 0.5, 1e-10);
         helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass);
     }
     {
@@ -448,7 +450,7 @@ static std::pair<int, int> test_forward_backward(
         double loss;
         double loss_unc;
         ggml_opt_result_loss(cd.result, &loss, &loss_unc);
-        subtest_ok = subtest_ok && loss == 18.0 && (shuffle || loss_unc == 0.0);
+        subtest_ok = subtest_ok && almost_equal(loss, 18.0, 1e-10) && (shuffle || loss_unc == 0.0);
 
         double accuracy;
         double accuracy_unc;
@@ -550,10 +552,12 @@ static std::pair<int, int> test_idata_split(
         if (adamw) {
             float weights;
             ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
-            const bool subtest_ok = weights == ndata/2 - epoch*idata_split;
+            const bool subtest_ok = almost_equal(weights, ndata/2 - epoch*idata_split, 1e-10);
             helper_after_test_idata_split(optim, __func__, high_level, epoch, "weights", subtest_ok, ntest, npass);
         }
         if (adamw) {
+            constexpr double atol = 1e-10;
+
             int64_t ndata_result;
             ggml_opt_result_ndata(cd.result, &ndata_result);
             bool subtest_ok = ndata_result == idata_split;
@@ -561,7 +565,7 @@ static std::pair<int, int> test_idata_split(
             double loss;
             double loss_unc;
             ggml_opt_result_loss(cd.result, &loss, &loss_unc);
-            subtest_ok = subtest_ok && loss == 28.0 - epoch*16.0 && loss_unc == 0.0;
+            subtest_ok = subtest_ok && almost_equal(loss, 28.0 - epoch*16.0, atol) && almost_equal(loss_unc, 0.0, atol);
 
             double accuracy;
             double accuracy_unc;
@@ -571,6 +575,8 @@ static std::pair<int, int> test_idata_split(
             helper_after_test_idata_split(optim, __func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass);
         }
         if (adamw) {
+            constexpr double atol = 1e-10;
+
             int64_t ndata_result;
             ggml_opt_result_ndata(cd.result2, &ndata_result);
             bool subtest_ok = ndata_result == ndata - idata_split;
@@ -578,7 +584,7 @@ static std::pair<int, int> test_idata_split(
             double loss;
             double loss_unc;
             ggml_opt_result_loss(cd.result2, &loss, &loss_unc);
-            subtest_ok = subtest_ok && loss == 15.0 - epoch*8 && almost_equal(loss_unc, sqrt(0.5), 1e-10);
+            subtest_ok = subtest_ok && almost_equal(loss, 15.0 - epoch*8, atol) && almost_equal(loss_unc, sqrt(0.5), atol);
 
             double accuracy;
             double accuracy_unc;
@@ -687,22 +693,24 @@ static std::pair<int, int> test_gradient_accumulation(
         }
         bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
         if (adamw) {
+            constexpr double atol = 1e-6;
             float weights;
             ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
-            const bool subtest_ok = weights == (ndata/2) - epoch;
+            const bool subtest_ok = almost_equal(weights, (ndata/2) - epoch, atol);
             helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass);
         }
         {
+            constexpr double atol = 1e-6;
             int64_t ndata_result;
             ggml_opt_result_ndata(cd.result, &ndata_result);
-            bool subtest_ok = ndata_result == ndata/nbatch_physical;
+            bool subtest_ok = almost_equal(ndata_result, ndata/nbatch_physical, atol);
 
             double loss;
             ggml_opt_result_loss(cd.result, &loss, /*loss_unc =*/ nullptr);
             if (loss_type == GGML_OPT_LOSS_TYPE_SUM) {
-                subtest_ok = subtest_ok && loss == (39.0 - epoch*6.0);
+                subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0), atol);
             } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
-                subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, 1e-6);
+                subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, atol);
             } else {
                 GGML_ASSERT(false);
             }

From 330c3d2d21b55bca5517db7d2eea2ea8f131df4a Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 23 Aug 2025 01:31:54 -0500
Subject: [PATCH 154/287] vulkan: optimize mul_mat_id loading row ids into
 shared memory (#15427)

- Spread the work across the whole workgroup. Using more threads seems to
far outweigh the synchronization overhead.
- Specialize the code for when the division is by a power of two.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |   6 +-
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    | 105 +++++++++++-------
 .../vulkan-shaders/mul_mm_cm2.comp            | 103 ++++++++++-------
 3 files changed, 133 insertions(+), 81 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index fb18a55cda..2c5678f488 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2168,9 +2168,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
         s_mmq_wg_denoms_k = { 32,  64,  1 };
 
         // spec constants and tile sizes for quant matmul_id
-        l_warptile_mmqid = { 256, 128, 128, 16, 0 };
-        m_warptile_mmqid = { 256, 128, 64, 16, 0 };
-        s_warptile_mmqid = { 256, 128, 64, 16, 0 };
+        l_warptile_mmqid = { 256, 128, 128, 16, 0, device->subgroup_size };
+        m_warptile_mmqid = { 256, 128, 64, 16, 0, device->subgroup_size };
+        s_warptile_mmqid = { 256, 128, 64, 16, 0, device->subgroup_size };
         l_mmqid_wg_denoms = { 128, 128, 1 };
         m_mmqid_wg_denoms = { 128, 64, 1 };
         s_mmqid_wg_denoms = { 128, 64, 1 };
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index a61a464c7b..d57cc6bdec 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -103,16 +103,74 @@ layout (constant_id = 10) const uint WARP = 32;
 shared FLOAT_TYPE buf_a[BM * SHMEM_STRIDE];
 shared FLOAT_TYPE buf_b[BN * SHMEM_STRIDE];
 
+#define NUM_WARPS (BLOCK_SIZE / WARP)
+
 #ifdef MUL_MAT_ID
 shared u16vec2 row_ids[4096];
 uint _ne1;
 #ifdef COOPMAT
-shared uint _ne1_sh;
+shared uvec4 ballots_sh[NUM_WARPS];
+void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
+    _ne1 = 0;
+    uint num_elements = p.nei1 * p.nei0;
+    uint nei0shift = findLSB(p.nei0);
+
+    uint ids[16];
+    uint iter = 0;
+
+    for (uint j = 0; j < num_elements; j += BLOCK_SIZE) {
+        // prefetch up to 16 elements
+        if (iter == 0) {
+            [[unroll]] for (uint k = 0; k < 16; ++k) {
+                uint i = j + gl_LocalInvocationIndex + k*BLOCK_SIZE;
+                bool in_range = i < num_elements;
+                uint ii1;
+                if (nei0_is_pow2) {
+                    ii1 = i >> nei0shift;
+                } else {
+                    ii1 = i / p.nei0;
+                }
+                uint ii0 = i - ii1 * p.nei0;
+                ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
+            }
+        }
+        uint i = j + gl_LocalInvocationIndex;
+        bool in_range = i < num_elements;
+        uint ii1;
+        if (nei0_is_pow2) {
+            ii1 = i >> nei0shift;
+        } else {
+            ii1 = i / p.nei0;
+        }
+        uint ii0 = i - ii1 * p.nei0;
+        uint id = ids[iter++];
+        uvec4 ballot = subgroupBallot(in_range && id == expert_idx);
+
+        ballots_sh[gl_SubgroupID] = ballot;
+        barrier();
+
+        uint subgroup_base = 0;
+        uint total = 0;
+        for (uint k = 0; k < gl_NumSubgroups; ++k) {
+            if (k == gl_SubgroupID) {
+                subgroup_base = total;
+            }
+            total += subgroupBallotBitCount(ballots_sh[k]);
+        }
+        barrier();
+
+        uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot);
+        if (in_range && id == expert_idx) {
+            row_ids[_ne1 + idx] = u16vec2(ii0, ii1);
+        }
+        _ne1 += total;
+        iter &= 15;
+    }
+    barrier();
+}
 #endif
 #endif // MUL_MAT_ID
 
-#define NUM_WARPS (BLOCK_SIZE / WARP)
-
 #ifdef COOPMAT
 shared ACC_TYPE coopmat_stage[TM * TN * NUM_WARPS];
 #endif
@@ -178,44 +236,11 @@ void main() {
 
 #ifdef MUL_MAT_ID
 #ifdef COOPMAT
-    // Spread the search across all elements in the first subgroup
-    if (gl_SubgroupID == 0) {
-        _ne1 = 0;
-        uint num_elements = p.nei1 * p.nei0;
-
-        uint ids[16];
-        uint iter = 0;
-
-        for (uint j = 0; j < num_elements; j += gl_SubgroupSize) {
-            // prefetch up to 16 elements
-            if (iter == 0) {
-                [[unroll]] for (uint k = 0; k < 16; ++k) {
-                    uint i = j + gl_SubgroupInvocationID + k*gl_SubgroupSize;
-                    bool in_range = i < num_elements;
-                    uint ii1 = i / p.nei0;
-                    uint ii0 = i % p.nei0;
-                    ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
-                }
-            }
-            uint i = j + gl_SubgroupInvocationID;
-            bool in_range = i < num_elements;
-            uint ii1 = i / p.nei0;
-            uint ii0 = i % p.nei0;
-            uint id = ids[iter++];
-            uvec4 ballot = subgroupBallot(in_range && id == expert_idx);
-            uint idx = subgroupBallotExclusiveBitCount(ballot);
-            if (in_range && id == expert_idx) {
-                row_ids[_ne1 + idx] = u16vec2(ii0, ii1);
-            }
-            _ne1 += subgroupBallotBitCount(ballot);
-            iter &= 15;
-        }
-        _ne1_sh = _ne1;
+    if (bitCount(p.nei0) == 1) {
+        load_row_ids(expert_idx, true);
+    } else {
+        load_row_ids(expert_idx, false);
     }
-
-    barrier();
-
-    _ne1 = _ne1_sh;
 #else
     _ne1 = 0;
     for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 29e4b5c9ce..4d16eb0791 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -19,6 +19,7 @@
 #endif
 
 #include "types.comp"
+#include "utils.comp"
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
@@ -99,7 +100,8 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB {
 };
 
 uint _ne1;
-shared uint _ne1_sh;
+layout (constant_id = 5) const uint subgroup_size = 32;
+shared uvec4 ballots_sh[BLOCK_SIZE / subgroup_size];
 
 B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2])
 {
@@ -128,6 +130,64 @@ D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem
     return elem;
 }
 
+void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
+    _ne1 = 0;
+    uint num_elements = p.nei1 * p.nei0;
+    uint nei0shift = findLSB(p.nei0);
+
+    uint ids[16];
+    uint iter = 0;
+
+    for (uint j = 0; j < num_elements; j += BLOCK_SIZE) {
+        // prefetch up to 16 elements
+        if (iter == 0) {
+            [[unroll]] for (uint k = 0; k < 16; ++k) {
+                uint i = j + gl_LocalInvocationIndex + k*BLOCK_SIZE;
+                bool in_range = i < num_elements;
+                uint ii1;
+                if (nei0_is_pow2) {
+                    ii1 = i >> nei0shift;
+                } else {
+                    ii1 = i / p.nei0;
+                }
+                uint ii0 = i - ii1 * p.nei0;
+                ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
+            }
+        }
+        uint i = j + gl_LocalInvocationIndex;
+        bool in_range = i < num_elements;
+        uint ii1;
+        if (nei0_is_pow2) {
+            ii1 = i >> nei0shift;
+        } else {
+            ii1 = i / p.nei0;
+        }
+        uint ii0 = i - ii1 * p.nei0;
+        uint id = ids[iter++];
+        uvec4 ballot = subgroupBallot(in_range && id == expert_idx);
+
+        ballots_sh[gl_SubgroupID] = ballot;
+        barrier();
+
+        uint subgroup_base = 0;
+        uint total = 0;
+        for (uint k = 0; k < gl_NumSubgroups; ++k) {
+            if (k == gl_SubgroupID) {
+                subgroup_base = total;
+            }
+            total += subgroupBallotBitCount(ballots_sh[k]);
+        }
+        barrier();
+
+        uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot);
+        if (in_range && id == expert_idx) {
+            row_ids[_ne1 + idx] = u16vec4(fastmod(ii0, p.ne11), ii1, ii0, 0);
+        }
+        _ne1 += total;
+        iter &= 15;
+    }
+    barrier();
+}
 #endif
 
 void main() {
@@ -157,45 +217,12 @@ void main() {
     const uint ic = gl_WorkGroupID.y;
 
 #ifdef MUL_MAT_ID
-    // Spread the search across all elements in the first subgroup
-    if (gl_SubgroupID == 0) {
-        _ne1 = 0;
-        uint num_elements = p.nei1 * p.nei0;
-
-        uint ids[16];
-        uint iter = 0;
-
-        for (uint j = 0; j < num_elements; j += gl_SubgroupSize) {
-            // prefetch up to 16 elements
-            if (iter == 0) {
-                [[unroll]] for (uint k = 0; k < 16; ++k) {
-                    uint i = j + gl_SubgroupInvocationID + k*gl_SubgroupSize;
-                    bool in_range = i < num_elements;
-                    uint ii1 = i / p.nei0;
-                    uint ii0 = i % p.nei0;
-                    ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
-                }
-            }
-            uint i = j + gl_SubgroupInvocationID;
-            bool in_range = i < num_elements;
-            uint ii1 = i / p.nei0;
-            uint ii0 = i % p.nei0;
-            uint id = ids[iter++];
-            uvec4 ballot = subgroupBallot(in_range && id == expert_idx);
-            uint idx = subgroupBallotExclusiveBitCount(ballot);
-            if (in_range && id == expert_idx) {
-                row_ids[_ne1 + idx] = u16vec4(ii0 % p.ne11, ii1, ii0, 0);
-            }
-            _ne1 += subgroupBallotBitCount(ballot);
-            iter &= 15;
-        }
-        _ne1_sh = _ne1;
+    if (bitCount(p.nei0) == 1) {
+        load_row_ids(expert_idx, true);
+    } else {
+        load_row_ids(expert_idx, false);
     }
 
-    barrier();
-
-    _ne1 = _ne1_sh;
-
     // Workgroup has no work
     if (ic * BN >= _ne1) return;
 #endif

From 0a9b43e507a359ca392c037cf341f55137ad0b69 Mon Sep 17 00:00:00 2001
From: Acly <aclysia@gmail.com>
Date: Sat, 23 Aug 2025 08:35:21 +0200
Subject: [PATCH 155/287] vulkan : support ggml_mean (#15393)

* vulkan : support ggml_mean

* vulkan : support sum, sum_rows and mean with non-contiguous tensors

* vulkan : fix subbuffer size not accounting for misalign offset

* tests : add backend-op tests for non-contiguous sum_rows

* cuda : require contiguous src for SUM_ROWS, MEAN support
* sycl : require contiguous src for SUM, SUM_ROWS, ARGSORT support

* require ggml_contiguous_rows in supports_op and expect nb00=1 in the shader
---
 ggml/src/ggml-cuda/ggml-cuda.cu               |  4 +-
 ggml/src/ggml-sycl/ggml-sycl.cpp              |  3 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 82 +++++++++++++++++--
 .../ggml-vulkan/vulkan-shaders/sum_rows.comp  | 43 ++++++++--
 tests/test-backend-ops.cpp                    | 21 ++++-
 5 files changed, 135 insertions(+), 18 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index d29a0b573f..aa45ab39ed 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3485,11 +3485,11 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CONV_TRANSPOSE_2D:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM:
-        case GGML_OP_SUM_ROWS:
-        case GGML_OP_MEAN:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
             return true;
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_MEAN:
         case GGML_OP_GROUP_NORM:
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_UPSCALE:
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index a0a650e92e..12dd5dd2e6 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4391,10 +4391,11 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             return true;
         case GGML_OP_UPSCALE:
             return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
-        case GGML_OP_POOL_2D:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
+            return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_POOL_2D:
         case GGML_OP_ACC:
         case GGML_OP_PAD:
         case GGML_OP_LEAKY_RELU:
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2c5678f488..007556cf4f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1015,6 +1015,39 @@ struct vk_op_upscale_push_constants {
     float sf0; float sf1; float sf2; float sf3;
 };
 
+struct vk_op_sum_rows_push_constants
+{
+    uint32_t n_cols;
+    uint32_t ne01, ne02;
+    uint32_t nb01, nb02, nb03;
+    uint32_t nb11, nb12, nb13;
+    float weight;
+    uint32_t misalign_offsets;
+    uint32_t ne0_12mp, ne0_12L;
+    uint32_t ne0_1mp, ne0_1L;
+};
+
+vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) {
+    uint32_t type_size = (uint32_t)ggml_type_size(src->type);
+    vk_op_sum_rows_push_constants p = {};
+    p.n_cols = (uint32_t)n_cols;
+    p.ne01 = (uint32_t)src->ne[1];
+    p.ne02 = (uint32_t)src->ne[2];
+    p.nb01 = (uint32_t)src->nb[1] / type_size;
+    p.nb02 = (uint32_t)src->nb[2] / type_size;
+    p.nb03 = (uint32_t)src->nb[3] / type_size;
+    p.nb11 = (uint32_t)dst->nb[1] / type_size;
+    p.nb12 = (uint32_t)dst->nb[2] / type_size;
+    p.nb13 = (uint32_t)dst->nb[3] / type_size;
+    p.weight = 1.0f;
+    return p;
+}
+
+template <> void init_pushconst_fastdiv(vk_op_sum_rows_push_constants &p) {
+    init_fastdiv_values(p.ne01*p.ne02, p.ne0_12mp, p.ne0_12L);
+    init_fastdiv_values(p.ne01,        p.ne0_1mp,  p.ne0_1L);
+}
+
 // Allow pre-recording command buffers
 struct vk_staging_memcpy {
     vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@@ -3128,7 +3161,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_count_equal_i32, "count_equal_i32", count_equal_i32_len, count_equal_i32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, { device->subgroup_size }, 1);
 
@@ -7249,6 +7282,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         return nullptr;
     case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
+    case GGML_OP_MEAN:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_sum_rows_f32;
         }
@@ -7387,6 +7421,9 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     case GGML_OP_CONV_2D_DW:
     case GGML_OP_IM2COL:
     case GGML_OP_SET_ROWS:
+    case GGML_OP_SUM:
+    case GGML_OP_SUM_ROWS:
+    case GGML_OP_MEAN:
         return true;
     default:
         return false;
@@ -7421,6 +7458,16 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk
     GGML_UNUSED(src2);
 }
 
+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
+
+    p.misalign_offsets = (a_offset << 16) | d_offset;
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(src2);
+}
+
 template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
     const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
     const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
@@ -7571,10 +7618,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     d_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
 
     if (op_supports_incontiguous) {
-        x_sz = ggml_nbytes(src0);
-        y_sz = use_src1 ? ggml_nbytes(src1) : 0;
-        z_sz = use_src2 ? ggml_nbytes(src2) : 0;
-        d_sz = ggml_nbytes(dst);
+        x_sz = ggml_nbytes(src0) + get_misalign_bytes(ctx, src0);
+        y_sz = use_src1 ? ggml_nbytes(src1) + get_misalign_bytes(ctx, src1) : 0;
+        z_sz = use_src2 ? ggml_nbytes(src2) + get_misalign_bytes(ctx, src2) : 0;
+        d_sz = ggml_nbytes(dst) + get_misalign_bytes(ctx, dst);
 
         if (x_buf_offset + x_sz >= d_X->size) {
             x_sz = VK_WHOLE_SIZE;
@@ -7602,6 +7649,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_SOFT_MAX:
     case GGML_OP_SOFT_MAX_BACK:
     case GGML_OP_SUM_ROWS:
+    case GGML_OP_MEAN:
     case GGML_OP_ARGMAX:
         {
             const uint32_t nr = ggml_nrows(src0);
@@ -8588,11 +8636,19 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, c
 }
 
 static void ggml_vk_sum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }, dryrun);
+    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, ggml_nelements(src0));
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM, p, dryrun);
 }
 
 static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
+    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, p, dryrun);
+}
+
+static void ggml_vk_mean(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
+    p.weight = 1.0f / (float)src0->ne[0];
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_MEAN, p, dryrun);
 }
 
 static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
@@ -9815,6 +9871,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_ARGSORT:
     case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
+    case GGML_OP_MEAN:
     case GGML_OP_ARGMAX:
     case GGML_OP_COUNT_EQUAL:
     case GGML_OP_IM2COL:
@@ -9884,6 +9941,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_OP_ARGSORT:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
+        case GGML_OP_MEAN:
         case GGML_OP_ARGMAX:
         case GGML_OP_COUNT_EQUAL:
         case GGML_OP_IM2COL:
@@ -10087,6 +10145,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_SUM_ROWS:
         ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun);
 
+        break;
+    case GGML_OP_MEAN:
+        ggml_vk_mean(ctx, compute_ctx, src0, node, dryrun);
+
         break;
     case GGML_OP_ARGMAX:
         ggml_vk_argmax(ctx, compute_ctx, src0, node, dryrun);
@@ -10246,6 +10308,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     case GGML_OP_ARGSORT:
     case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
+    case GGML_OP_MEAN:
     case GGML_OP_ARGMAX:
     case GGML_OP_COUNT_EQUAL:
     case GGML_OP_IM2COL:
@@ -11483,8 +11546,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_SOFT_MAX_BACK:
+            return true;
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
+        case GGML_OP_MEAN:
+            return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_ARGMAX:
         case GGML_OP_COUNT_EQUAL:
         case GGML_OP_IM2COL:
@@ -12043,6 +12109,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         tensor_clone = ggml_sum(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_SUM_ROWS) {
         tensor_clone = ggml_sum_rows(ggml_ctx, src_clone[0]);
+    } else if (tensor->op == GGML_OP_MEAN) {
+        tensor_clone = ggml_mean(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_ARGMAX) {
         tensor_clone = ggml_argmax(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_COUNT_EQUAL) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp
index 961e5ffa1f..759204afaf 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp
@@ -1,9 +1,9 @@
 #version 450
 
-#include "generic_head.comp"
 #include "types.comp"
 
 #extension GL_EXT_control_flow_attributes : enable
+
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
@@ -11,16 +11,49 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 
 layout (constant_id = 0) const uint BLOCK_SIZE = 32;
 
+layout (push_constant) uniform parameter
+{
+    uint n_cols;
+    uint ne01, ne02;
+    uint nb01, nb02, nb03;
+    uint nb11, nb12, nb13;
+    float weight;
+    uint misalign_offsets;
+    uint ne0_12mp, ne0_12L;
+    uint ne0_1mp, ne0_1L;
+} p;
+
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_doffset() { return p.misalign_offsets & 0xFFFF; }
+
+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    // msbs = mulhi(n, mp)
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}
+
+
 shared FLOAT_TYPE tmp[BLOCK_SIZE];
 
 void main() {
     const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
     const uint col = gl_LocalInvocationID.x;
+    const float weight = p.weight;
 
-    tmp[col] = FLOAT_TYPE(0.0f);
+    const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L);
+    const uint i03_offset = i03 * p.ne01*p.ne02;
+    const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L);
+    const uint i01 = row - i03_offset - i02*p.ne01;
 
-    for (uint i = col; i < p.KX; i += BLOCK_SIZE) {
-        tmp[col] += FLOAT_TYPE(data_a[row*p.KX + i]);
+    const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03;
+    const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13;
+
+    tmp[col] = FLOAT_TYPE(0.0);
+
+    for (uint i = col; i < p.n_cols; i += BLOCK_SIZE) {
+        tmp[col] += FLOAT_TYPE(data_a[src_idx + i]);
     }
 
     barrier();
@@ -32,6 +65,6 @@ void main() {
     }
 
     if (col == 0) {
-        data_d[row] = D_TYPE(tmp[0]);
+        data_d[dst_idx] = D_TYPE(tmp[0] * weight);
     }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index a51527ca55..2e53f8e21a 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -4300,20 +4300,32 @@ struct test_sum : public test_case {
 struct test_sum_rows : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
+    const bool permute;
+    const bool slice;
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        return VARS_TO_STR4(type, ne, permute, slice);
     }
 
     test_sum_rows(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 5, 4, 3})
-        : type(type), ne(ne) {}
+            std::array<int64_t, 4> ne = {10, 5, 4, 3},
+            bool permute = false, bool slice = false)
+        : type(type), ne(ne), permute(permute), slice(slice) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
         ggml_set_param(a);
         ggml_set_name(a, "a");
 
+        if (slice) {
+            a = ggml_view_4d(ctx, a,
+                             ne[0], ne[1], ne[2] / 2, ne[3] - 1,
+                             a->nb[1], a->nb[2] * 2, a->nb[3], /*offset=*/a->nb[3]);
+        }
+        if (permute) {
+            a = ggml_permute(ctx, a, 0, 2, 3, 1);
+        }
+
         ggml_tensor * out = ggml_sum_rows(ctx, a);
         ggml_set_name(out, "out");
 
@@ -6195,6 +6207,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     test_cases.emplace_back(new test_sum());
     test_cases.emplace_back(new test_sum_rows());
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, false));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, false, true));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, true));
     test_cases.emplace_back(new test_mean());
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1, 1, 1 }));
     test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1, 1, 1 }));

From b55f06e1aa67fb10e89f53e31bbccf37eb2678ea Mon Sep 17 00:00:00 2001
From: R0CKSTAR <yeahdongcn@gmail.com>
Date: Sat, 23 Aug 2025 14:58:57 +0800
Subject: [PATCH 156/287] vulkan.Dockerfile: install vulkan SDK using tarball
 (#15282)

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---
 .devops/vulkan.Dockerfile | 30 +++++++++++++++++++++++-------
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index fcd81ffa1e..6cf87c67e8 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -2,14 +2,30 @@ ARG UBUNTU_VERSION=24.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
-# Install build tools
-RUN apt update && apt install -y git build-essential cmake wget
+# Ref: https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html
 
-# Install Vulkan SDK and cURL
-RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
-    apt update -y && \
-    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget xz-utils
+
+# Install Vulkan SDK
+ARG VULKAN_VERSION=1.4.321.1
+RUN ARCH=$(uname -m) && \
+    wget -qO /tmp/vulkan-sdk.tar.xz https://sdk.lunarg.com/sdk/download/${VULKAN_VERSION}/linux/vulkan-sdk-linux-${ARCH}-${VULKAN_VERSION}.tar.xz && \
+    mkdir -p /opt/vulkan && \
+    tar -xf /tmp/vulkan-sdk.tar.xz -C /tmp --strip-components=1 && \
+    mv /tmp/${ARCH}/* /opt/vulkan/ && \
+    rm -rf /tmp/*
+
+# Install cURL and Vulkan SDK dependencies
+RUN apt install -y libcurl4-openssl-dev curl \
+    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev
+
+# Set environment variables
+ENV VULKAN_SDK=/opt/vulkan
+ENV PATH=$VULKAN_SDK/bin:$PATH
+ENV LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
+ENV CMAKE_PREFIX_PATH=$VULKAN_SDK:$CMAKE_PREFIX_PATH
+ENV PKG_CONFIG_PATH=$VULKAN_SDK/lib/pkgconfig:$PKG_CONFIG_PATH
 
 # Build it
 WORKDIR /app

From 289bf4113ef5c02d8f5eb0cf2d86683d8b8bc4d9 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 23 Aug 2025 02:33:36 -0500
Subject: [PATCH 157/287] vulkan: Rewrite synchronization to allow some overlap
 between nodes (#15489)

Track a list of nodes that need synchronization, and only sync if the new node
depends on them (or overwrites them). This allows some overlap which can
improve performance, and centralizes a big chunk of the synchronization logic.

The remaining synchronization logic involves writes to memory other than the
nodes, e.g. for dequantization or split_k. Each of these allocations has a bool
indicating whether they were in use and need to be synced. This should be
checked before they are written to, and set to true after they are done being
consumed.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 233 ++++++++++++++++++++++-----
 1 file changed, 193 insertions(+), 40 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 007556cf4f..c7cfb6473e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1231,6 +1231,14 @@ struct ggml_backend_vk_context {
     vk_pipeline_struct * prealloc_y_last_pipeline_used {};
     const ggml_tensor * prealloc_y_last_tensor_used {};
 
+    // Track which nodes have been used since the last sync, and whether they were written to
+    std::vector<const ggml_tensor *> unsynced_nodes_written;
+    std::vector<const ggml_tensor *> unsynced_nodes_read;
+    // Track which prealloc buffers have pending reads that need to be synchronized.
+    // These are checked before writing to the buffer (and call ggml_vk_sync_buffers if set),
+    // and set to true after the buffer contents are consumed.
+    bool prealloc_x_need_sync, prealloc_y_need_sync, prealloc_split_k_need_sync;
+
     vk_buffer buffer_pool[MAX_VK_BUFFERS];
 
     vk_context_ref compute_ctx;
@@ -1906,14 +1914,18 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
     return { buf, 0, VK_WHOLE_SIZE };
 }
 
-static void ggml_vk_sync_buffers(vk_context& ctx) {
+static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subctx) {
     VK_LOG_DEBUG("ggml_vk_sync_buffers()");
 
-    const bool transfer_queue = ctx->p->q->transfer_only;
+    const bool transfer_queue = subctx->p->q->transfer_only;
 
-    ctx->s->buffer.pipelineBarrier(
-        ctx->p->q->stage_flags,
-        ctx->p->q->stage_flags,
+    if (ctx) {
+        ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;
+    }
+
+    subctx->s->buffer.pipelineBarrier(
+        subctx->p->q->stage_flags,
+        subctx->p->q->stage_flags,
         {},
         { {
           { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
@@ -4898,7 +4910,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont
             }
         }
 
-        ggml_vk_sync_buffers(subctx);
+        ggml_vk_sync_buffers(ctx, subctx);
         subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
         return;
     }
@@ -4913,7 +4925,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont
     ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size);
     VkBufferCopy buf_copy{ 0, offset, copy_size };
 
-    ggml_vk_sync_buffers(subctx);
+    ggml_vk_sync_buffers(ctx, subctx);
     vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
 
     for (uint64_t i3 = 0; i3 < ne3; i3++) {
@@ -4967,7 +4979,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
             }
         }
 
-        ggml_vk_sync_buffers(subctx);
+        ggml_vk_sync_buffers(nullptr, subctx);
         subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
         return;
     }
@@ -4988,7 +5000,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
         offset,
         copy_size};
 
-    ggml_vk_sync_buffers(subctx);
+    ggml_vk_sync_buffers(nullptr, subctx);
     vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
 
     if (width == spitch) {
@@ -5068,7 +5080,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
 
     if (buf != nullptr) {
         // Memory is pinned, use as staging buffer
-        ggml_vk_sync_buffers(subctx);
+        ggml_vk_sync_buffers(nullptr, subctx);
         subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
 
         return;
@@ -5085,7 +5097,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
 
     vk_buffer& staging_buffer = src->device->sync_staging;
 
-    ggml_vk_sync_buffers(subctx);
+    ggml_vk_sync_buffers(nullptr, subctx);
     subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices);
 
     deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys);
@@ -5275,13 +5287,16 @@ static void ggml_vk_matmul(
         uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
         uint32_t padded_n) {
         VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", padded_n: " << padded_n << ")");
-    ggml_vk_sync_buffers(subctx);
     if (split_k == 1) {
         const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k, ne02, ne12, broadcast2, broadcast3, padded_n };
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc, { m, n, batch });
         return;
     }
 
+    if (ctx->prealloc_split_k_need_sync) {
+        ggml_vk_sync_buffers(ctx, subctx);
+    }
+
     GGML_ASSERT(batch_stride_d == m * n);
 
     // Round the split size up to a multiple of 256 (k-quant alignment)
@@ -5291,9 +5306,10 @@ static void ggml_vk_matmul(
     const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k_split, ne02, ne12, broadcast2, broadcast3, padded_n };
     // Make sure enough workgroups get assigned for split k to work
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
-    ggml_vk_sync_buffers(subctx);
+    ggml_vk_sync_buffers(ctx, subctx);
     const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2, { m * n * batch, 1, 1 });
+    ctx->prealloc_split_k_need_sync = true;
 }
 
 static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type) {
@@ -5338,7 +5354,6 @@ static void ggml_vk_matmul_id(
         "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " <<
         "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " <<
         "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")");
-    ggml_vk_sync_buffers(subctx);
     const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d,
                                               nei0, nei1, nbi1, ne11, padded_n };
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids }, pc, { m, nei1, n_as });
@@ -5469,8 +5484,8 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context&
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     };
     init_pushconst_fastdiv(pc);
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
+    ggml_vk_sync_buffers(ctx, subctx);
 }
 
 static vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type) {
@@ -5488,8 +5503,8 @@ static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& sub
 
     vk_pipeline pipeline = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
 
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, std::array<uint32_t, 1>{ne}, { ne, 1, 1 });
+    ggml_vk_sync_buffers(ctx, subctx);
 }
 
 static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -5684,12 +5699,23 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         GGML_ASSERT(qy_sz == y_sz);
     }
 
+    if (x_non_contig || qx_needs_dequant) {
+        if (ctx->prealloc_x_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+    if (y_non_contig || quantize_y) {
+        if (ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+
     if (x_non_contig) {
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
     } else if (qx_needs_dequant) {
         const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc, { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+        ggml_vk_sync_buffers(ctx, subctx);
     }
     if (y_non_contig) {
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
@@ -5728,6 +5754,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21,
         split_k, ne12*ne13, ne02, ne12, r2, r3, padded_n
     );  // NOLINT
+
+    if (x_non_contig || qx_needs_dequant) {
+        ctx->prealloc_x_need_sync = true;
+    }
+    if (y_non_contig || quantize_y) {
+        ctx->prealloc_y_need_sync = true;
+    }
 }
 
 static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -5874,6 +5907,17 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         GGML_ASSERT(qy_sz == y_sz);
     }
 
+    if (x_non_contig) {
+        if (ctx->prealloc_x_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+    if (y_non_contig) {
+        if (ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+
     if (x_non_contig) {
         GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
@@ -5917,10 +5961,16 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         stride_batch_x, stride_batch_y, stride_batch_d,
         (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
     };
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
                               { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} },
                               pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z });
+
+    if (x_non_contig) {
+        ctx->prealloc_x_need_sync = true;
+    }
+    if (y_non_contig) {
+        ctx->prealloc_y_need_sync = true;
+    }
 }
 
 static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -6007,7 +6057,6 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         workgroups_z /= gqa_ratio;
     }
 
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1], { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { 1, (uint32_t)ne01, workgroups_z });
 }
 
@@ -6094,7 +6143,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
 
     // compute
     const std::array<uint32_t, 12> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), nb03, nb13, nb23 };
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
         { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
 }
@@ -6306,13 +6354,24 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
         GGML_ASSERT(qy_sz == y_sz);
     }
 
+    if (x_non_contig || qx_needs_dequant) {
+        if (ctx->prealloc_x_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+    if (y_non_contig) {
+        if (ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+
     if (x_non_contig) {
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
     } else if (qx_needs_dequant) {
         const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0,
             { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc, { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+        ggml_vk_sync_buffers(ctx, subctx);
     }
     if (y_non_contig) {
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
@@ -6343,6 +6402,13 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
         stride_batch_x, stride_batch_y, ne20*ne21,
         n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11, padded_n
     );  // NOLINT
+
+    if (x_non_contig || qx_needs_dequant) {
+        ctx->prealloc_x_need_sync = true;
+    }
+    if (y_non_contig) {
+        ctx->prealloc_y_need_sync = true;
+    }
 }
 
 static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) {
@@ -6502,6 +6568,17 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         GGML_ASSERT(qy_sz == y_sz);
     }
 
+    if (x_non_contig) {
+        if (ctx->prealloc_x_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+    if (y_non_contig) {
+        if (ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+    }
+
     if (x_non_contig) {
         GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
@@ -6538,11 +6615,17 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21),
         (uint32_t)nei0, (uint32_t)ne11,
     };
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
         { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 },
         vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23}, vk_subbuffer{ d_ids, ids_buf_offset, ids_sz } },
         pc, { groups_x, (uint32_t)nei0, groups_z });
+
+    if (x_non_contig) {
+        ctx->prealloc_x_need_sync = true;
+    }
+    if (y_non_contig) {
+        ctx->prealloc_y_need_sync = true;
+    }
 }
 
 static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
@@ -6925,9 +7008,11 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                               mask_n_head_log2, m0, m1,
                                               gqa_ratio, split_kv, split_k };
 
-    ggml_vk_sync_buffers(subctx);
-
     if (split_k > 1) {
+        if (ctx->prealloc_split_k_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
                                     {
                                         vk_subbuffer{d_Q, q_buf_offset, VK_WHOLE_SIZE},
@@ -6943,7 +7028,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                     // cancel out the divide by wg_denoms[0].
                                     pc, { workgroups_x * pipeline->wg_denoms[0], workgroups_y, workgroups_z });
 
-        ggml_vk_sync_buffers(subctx);
+        ggml_vk_sync_buffers(ctx, subctx);
         const std::array<uint32_t, 5> pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k, (sinks != nullptr) };
         ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
                                     {
@@ -6952,6 +7037,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                         vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
                                     },
                                     pc2, { (uint32_t)ne1, HSV, (uint32_t)ne3 });
+        ctx->prealloc_split_k_need_sync = true;
     } else {
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
                                     {
@@ -7820,7 +7906,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
             subbuf_y = { d_X, 0, x_sz };
         }
 
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_SOFT_MAX) {
         // Empty src1 and src2 is possible in soft_max, but the shader needs a buffer
@@ -7838,7 +7923,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
             subbuf_z = { d_X, 0, x_sz };
         }
 
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_ROPE || op == GGML_OP_ROPE_BACK) {
         // Empty src2 is possible in rope, but the shader needs a buffer
@@ -7849,30 +7933,23 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
             subbuf_z = { d_X, 0, x_sz };
         }
 
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_IM2COL) {
         // im2col uses only src1 and dst buffers
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_COUNT_EQUAL) {
-        ggml_vk_sync_buffers(subctx);
         // count_equal assumes that destination buffer is initialized with zeroes
         ggml_vk_buffer_memset_async(subctx, d_D, d_buf_offset, 0, d_sz);
-        ggml_vk_sync_buffers(subctx);
+        ggml_vk_sync_buffers(ctx, subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (op == GGML_OP_OPT_STEP_SGD) {
         // OPT_STEP_SGD works on src0, it does not need dst
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz } }, pc, elements);
     } else if (use_src2) {
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else if (use_src1) {
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     } else {
-        ggml_vk_sync_buffers(subctx);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
     }
 }
@@ -7999,7 +8076,6 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
         elements = { ne, 1, 1 };
     }
 
-    ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
         {
             vk_subbuffer{ buf[0], offset[0], VK_WHOLE_SIZE },
@@ -8112,8 +8188,6 @@ static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx
         src_buf_ctxs[i] = (ggml_backend_vk_buffer_context *)dst->src[i]->buffer->context;
     }
 
-    ggml_vk_sync_buffers(subctx);
-
     vk_buffer d_D = nullptr, d_srcs[7] = { nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr };
     size_t dst_offset = 0, src_offsets[7] = { 0, 0, 0, 0, 0, 0, 0 };
     bool dst_uma = false, srcs_uma[7] = { false, false, false, false, false, false, false };
@@ -8251,8 +8325,6 @@ static void ggml_vk_op_f32_opt_step_adamw(ggml_backend_vk_context * ctx, vk_cont
     ggml_backend_vk_buffer_context * gv_buf_ctx = (ggml_backend_vk_buffer_context *)gv->buffer->context;
     ggml_backend_vk_buffer_context * p_buf_ctx = (ggml_backend_vk_buffer_context *)p->buffer->context;
 
-    ggml_vk_sync_buffers(subctx);
-
     vk_buffer d_X = nullptr, d_G = nullptr, d_GM = nullptr, d_GV = nullptr, d_P = nullptr;
     size_t x_offset = 0, g_offset = 0, gm_offset = 0, gv_offset = 0, p_offset = 0;
     bool X_uma = false, G_uma = false, GM_uma = false, GV_uma = false, P_uma = false;
@@ -9964,6 +10036,83 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         }
     }
 
+    if (!dryrun) {
+        // This logic detects dependencies between modes in the graph and calls ggml_vk_sync_buffers
+        // to synchronize them. This handles most "normal" synchronization when computing the graph, and when
+        // there is no auxiliary memory use, it shouldn't be necessary to call ggml_vk_sync_buffers
+        // outside of this logic. When a node uses one of the prealloc buffers for something like
+        // dequantization or split_k, additional synchronization is needed between those passes.
+        bool need_sync = false;
+
+        // Check whether "node" requires synchronization. The node requires synchronization if it
+        // overlaps in memory with another unsynchronized node and at least one of them is a write.
+        // Destination nodes are checked against both the written/read lists. Source nodes are only
+        // checked against the written list. Two nodes overlap in memory if they come from the same
+        // buffer and the tensor or view ranges overlap.
+        auto const &overlaps_unsynced = [&](const ggml_tensor *node, const std::vector<const ggml_tensor *> &unsynced_nodes) -> bool {
+            if (unsynced_nodes.size() == 0) {
+                return false;
+            }
+            auto n_base = vk_tensor_offset(node) + node->view_offs;
+            auto n_size = ggml_nbytes(node);
+            ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)node->buffer->context;
+            vk_buffer a_buf = a_buf_ctx->dev_buffer;
+            for (auto &other : unsynced_nodes) {
+                ggml_backend_vk_buffer_context * o_buf_ctx = (ggml_backend_vk_buffer_context *)other->buffer->context;
+                vk_buffer o_buf = o_buf_ctx->dev_buffer;
+                if (a_buf == o_buf) {
+                    auto o_base = vk_tensor_offset(other) + other->view_offs;
+                    auto o_size = ggml_nbytes(other);
+
+                    if ((o_base <= n_base && n_base < o_base + o_size) ||
+                        (n_base <= o_base && o_base < n_base + n_size)) {
+                        return true;
+                    }
+                }
+            }
+            return false;
+        };
+
+        // For all fused ops, check if the destination node or any of the source
+        // nodes require synchronization.
+        for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1 && !need_sync; ++i) {
+            const ggml_tensor *cur_node = cgraph->nodes[node_idx + i];
+            if (overlaps_unsynced(cur_node, ctx->unsynced_nodes_read) || overlaps_unsynced(cur_node, ctx->unsynced_nodes_written)) {
+                need_sync = true;
+                break;
+            }
+            for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) {
+                if (!cur_node->src[j]) {
+                    continue;
+                }
+                if (overlaps_unsynced(cur_node->src[j], ctx->unsynced_nodes_written)) {
+                    need_sync = true;
+                    break;
+                }
+            }
+        }
+        if (need_sync) {
+            VK_LOG_DEBUG("node_idx=" << i << " sync");
+            ctx->unsynced_nodes_written.clear();
+            ctx->unsynced_nodes_read.clear();
+            ggml_vk_sync_buffers(ctx, compute_ctx);
+        } else {
+            VK_LOG_DEBUG("node_idx=" << i << " unsynced");
+        }
+        // Add the last fused node and all fused source nodes to the unsynchronized list.
+        const ggml_tensor * last_node = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
+        ctx->unsynced_nodes_written.push_back(last_node);
+        for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1; ++i) {
+            const ggml_tensor *cur_node = cgraph->nodes[node_idx + i];
+            for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) {
+                if (!cur_node->src[j]) {
+                    continue;
+                }
+                ctx->unsynced_nodes_read.push_back(cur_node->src[j]);
+            }
+        }
+    }
+
     switch (node->op) {
     case GGML_OP_REPEAT:
         ggml_vk_repeat(ctx, compute_ctx, src0, node, dryrun);
@@ -10427,6 +10576,10 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
     ctx->gc.temp_buffers.clear();
     ctx->prealloc_y_last_pipeline_used = {};
 
+    ctx->unsynced_nodes_written.clear();
+    ctx->unsynced_nodes_read.clear();
+    ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;
+
     ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);
     ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool);
 

From 21dc4ddaf21b8ed551d717e7606abd2cffbacdbf Mon Sep 17 00:00:00 2001
From: LaffeyNyaa <112215776+LaffeyNyaa@users.noreply.github.com>
Date: Sat, 23 Aug 2025 16:38:30 +0800
Subject: [PATCH 158/287] chat : fix debug build assertion in trim function
 (#15520)

---
 src/llama-chat.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 0a96a9a579..4d6fdf8226 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -16,10 +16,10 @@
 static std::string trim(const std::string & str) {
     size_t start = 0;
     size_t end = str.size();
-    while (start < end && isspace(str[start])) {
+    while (start < end && isspace(static_cast<unsigned char>(str[start]))) {
         start += 1;
     }
-    while (end > start && isspace(str[end - 1])) {
+    while (end > start && isspace(static_cast<unsigned char>(str[end - 1]))) {
         end -= 1;
     }
     return str.substr(start, end - start);

From 9ef536907de1b50c30e0369284898d30472a755a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 23 Aug 2025 12:58:58 +0200
Subject: [PATCH 159/287] scripts: fix compare-llama-bench.py (#15521)

---
 scripts/compare-llama-bench.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 0141e0a350..2cfca66e09 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -37,7 +37,6 @@ LLAMA_BENCH_DB_TYPES = [
     "TEXT",    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
     "TEXT",    "INTEGER", "INTEGER", "TEXT",    "TEXT",    "INTEGER",
     "TEXT",    "INTEGER", "INTEGER", "INTEGER", "TEXT",    "TEXT",
-    "REAL",
     "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
     "TEXT",    "INTEGER", "INTEGER", "REAL",    "REAL",
 ]

From b1afcab804e3281867a5471fbd701e32eb32e512 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Sat, 23 Aug 2025 15:21:52 +0200
Subject: [PATCH 160/287] model : add support for Seed-OSS (#15490)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* First draft

* Fix linter errors

* Added missing sinks nullptr

* Don't forget the llama-arch!

* We're through to the generation stage.

* Fix post-attention norm

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Fix RoPE type

* Fix tensor name and reorder llm_types

* Update gguf-py/gguf/constants.py

Remove nonexistent FFN_POST_NORM tensor

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.h

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Add basic chat template

* Add chat template tests

* Remake chat template test

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-chat.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Reorder llm type descriptions

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 convert_hf_to_gguf.py        |   5 +
 gguf-py/gguf/constants.py    |  16 +++
 src/llama-arch.cpp           |  18 ++++
 src/llama-arch.h             |   1 +
 src/llama-chat.cpp           |  11 +++
 src/llama-chat.h             |   1 +
 src/llama-model.cpp          | 183 +++++++++++++++++++++++++++++++++++
 src/llama-model.h            |   1 +
 tests/test-chat-template.cpp |   8 ++
 9 files changed, 244 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 42bf10d216..35fadbc83e 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -5854,6 +5854,11 @@ class OlmoModel(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("SeedOssForCausalLM")
+class SeedOssModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.SEED_OSS
+
+
 @ModelBase.register("Olmo2ForCausalLM")
 class Olmo2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.OLMO2
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 61ebe6e5e7..d03a02c7bf 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -385,6 +385,7 @@ class MODEL_ARCH(IntEnum):
     DREAM            = auto()
     SMALLTHINKER     = auto()
     LLADA            = auto()
+    SEED_OSS         = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -717,6 +718,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.DREAM:            "dream",
     MODEL_ARCH.SMALLTHINKER:     "smallthinker",
     MODEL_ARCH.LLADA:            "llada",
+    MODEL_ARCH.SEED_OSS:         "seed_oss",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -1973,6 +1975,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.SEED_OSS: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+    ],
     MODEL_ARCH.OLMOE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index c759a9c6d9..0ca0a4c22f 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -93,6 +93,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DREAM,            "dream"            },
     { LLM_ARCH_SMALLTHINKER,     "smallthinker"     },
     { LLM_ARCH_LLADA,            "llada"            },
+    { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -2068,6 +2069,23 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_SEED_OSS,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 7af587e795..7008c2514c 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -97,6 +97,7 @@ enum llm_arch {
     LLM_ARCH_DREAM,
     LLM_ARCH_SMALLTHINKER,
     LLM_ARCH_LLADA,
+    LLM_ARCH_SEED_OSS,
     LLM_ARCH_UNKNOWN,
 };
 
diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp
index 4d6fdf8226..9d8e57eac1 100644
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -69,6 +69,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "gpt-oss",           LLM_CHAT_TEMPLATE_OPENAI_MOE        },
     { "hunyuan-dense",     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE     },
     { "kimi-k2",           LLM_CHAT_TEMPLATE_KIMI_K2           },
+    { "seed_oss",          LLM_CHAT_TEMPLATE_SEED_OSS          },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -201,6 +202,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
     } else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
         return LLM_CHAT_TEMPLATE_KIMI_K2;
+    } else if (tmpl_contains("<seed:bos>")) {
+        return LLM_CHAT_TEMPLATE_SEED_OSS;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -752,6 +755,14 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|im_assistant|>assistant<|im_middle|>";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_SEED_OSS) {
+        for (auto message: chat) {
+            std::string role(message->role);
+            ss << "<seed:bos>" << role << "\n" << (role == "assistant" ? trim(message->content) : message->content) << "<seed:eos>";
+        }
+        if (add_ass) {
+            ss << "<seed:bos>assistant\n";
+        }
     } else {
         // template not supported
         return -1;
diff --git a/src/llama-chat.h b/src/llama-chat.h
index 35a943856f..21d53ed08b 100644
--- a/src/llama-chat.h
+++ b/src/llama-chat.h
@@ -49,6 +49,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_OPENAI_MOE,
     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
     LLM_CHAT_TEMPLATE_KIMI_K2,
+    LLM_CHAT_TEMPLATE_SEED_OSS,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 3c8440a8f6..d5148f7df3 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -83,6 +83,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_32B:           return "32B";
         case LLM_TYPE_34B:           return "34B";
         case LLM_TYPE_35B:           return "35B";
+        case LLM_TYPE_36B:           return "36B";
         case LLM_TYPE_40B:           return "40B";
         case LLM_TYPE_65B:           return "65B";
         case LLM_TYPE_70B:           return "70B";
@@ -1288,6 +1289,14 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_SEED_OSS:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 64: type = LLM_TYPE_36B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_OLMOE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -3967,6 +3976,43 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_SEED_OSS:
+                {
+                    const uint32_t head_dim             = hparams.n_embd_head_k;
+                    const int64_t n_qo_dim              = n_head * head_dim;
+                    const int64_t n_kv_dim              = n_head_kv * head_dim;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_qo_dim}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_kv_dim}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_kv_dim}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, 0);
+
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_qo_dim},   TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_kv_dim},   TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_kv_dim},   TENSOR_NOT_REQUIRED);
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                    }
+                } break;
+
             case LLM_ARCH_OLMOE:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -17934,6 +17980,137 @@ struct llm_build_lfm2 : public llm_graph_context {
     }
 };
 
+struct llm_build_seed_oss : public llm_graph_context {
+    llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 template <bool iswa>
 struct llm_build_smallthinker : public llm_graph_context{
     llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
@@ -18472,6 +18649,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_bailingmoe>(*this, params);
             } break;
+        case LLM_ARCH_SEED_OSS:
+            {
+                llm = std::make_unique<llm_build_seed_oss>(*this, params);
+            } break;
         case LLM_ARCH_DOTS1:
             {
                 llm = std::make_unique<llm_build_dots1>(*this, params);
@@ -18530,6 +18711,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     return llm->res->get_gf();
 }
 
+
 //
 // interface implementation
 //
@@ -18724,6 +18906,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_LFM2:
         case LLM_ARCH_SMALLTHINKER:
         case LLM_ARCH_GLM4_MOE:
+        case LLM_ARCH_SEED_OSS:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:
diff --git a/src/llama-model.h b/src/llama-model.h
index f639fa1398..af4460cc01 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -76,6 +76,7 @@ enum llm_type {
     LLM_TYPE_32B,
     LLM_TYPE_34B,
     LLM_TYPE_35B,
+    LLM_TYPE_36B,
     LLM_TYPE_40B,
     LLM_TYPE_65B,
     LLM_TYPE_70B,
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index edfac3b08b..b863367db6 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -290,6 +290,14 @@ int main(void) {
             /* .bos_token= */ "",
             /* .eos_token= */ "",
         },
+        {
+            /* .name= */ "ByteDance-Seed/Seed-OSS-36B-Instruct",
+            /* .template_str */ "{# <seed:bos> #}{%- for message in messages %}{%- if message.role in [\"user\", \"system\"] %}{{ bos_token + message.role + \"\\n\" + message.content + eos_token }}{%- elif message.role == \"assistant\" %}{{ bos_token + message.role }}{%- if message.content is defined and message.content is string and message.content|trim|length > 0 %}{{ \"\\n\" + message.content|trim + eos_token }}{%- endif %}{%- else %}{{ bos_token + message.role + \"\\n\" + message.content + eos_token }}{%- endif %}{%- endfor %}{%- if add_generation_prompt %}{{ bos_token + \"assistant\\n\" }}{%- endif %}",
+            /* .expected_output= */ "<seed:bos>system\nYou are a helpful assistant<seed:eos><seed:bos>user\nHello<seed:eos><seed:bos>assistant\nHi there<seed:eos><seed:bos>user\nWho are you<seed:eos><seed:bos>assistant\nI am an assistant<seed:eos><seed:bos>user\nAnother question<seed:eos><seed:bos>assistant\n",
+            /* .expected_output_jinja= */ "<seed:bos>system\nYou are a helpful assistant<seed:eos><seed:bos>user\nHello<seed:eos><seed:bos>assistant\nHi there<seed:eos><seed:bos>user\nWho are you<seed:eos><seed:bos>assistant\nI am an assistant<seed:eos><seed:bos>user\nAnother question<seed:eos><seed:bos>assistant\n",
+            /* .bos_token= */ "<seed:bos>",
+            /* .eos_token= */ "<seed:eos>",
+        }
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;

From 611f419cff11e4952228162a1c44cb35dff2274a Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 23 Aug 2025 13:16:17 -0500
Subject: [PATCH 161/287] vulkan: optimize rms_norm, and allow the work to
 spread across multiple SMs (#15281)

* vulkan: optimize rms_norm, and allow the work to spread across multiple SMs

There are really two parts to this change:
(1) Some optimizations similar to what we have in soft_max, to unroll with
different numbers of iterations.
(2) A fusion optimization where we detect add followed by rms_norm, and make
the add shader atomically accumulate the values^2 into memory. Then the
rms_norm shader can just load that sum. This allows the rms_norm to be
parallelized across multiple workgroups, it just becomes a simple per-element
multiply.

The fusion optimization is currently only applied when the rms_norm is on a
single vector. This previously always ran on a single SM. It could apply more
broadly, but when there are other dimensions the work can already spread across
SMs, and there would be some complexity to tracking multiple atomic sums.

* Change add+rms_norm optimization to write out an array of partial sums
rather than using atomic add, to make it deterministic. The rms_norm
shader fetches a subgroup's worth in parallel and uses subgroupAdd to
add them up.

* complete rebase against fused adds - multi_add shader can also compute partial sums

* fix validation errors

* disable add_rms_fusion for Intel due to possible driver bug

* resolve against #15489, sync after clearing partial sums
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 193 +++++++++++++++---
 ggml/src/ggml-vulkan/vulkan-shaders/add.comp  |  42 +++-
 .../ggml-vulkan/vulkan-shaders/multi_add.comp |  42 +++-
 .../ggml-vulkan/vulkan-shaders/rms_norm.comp  |  60 +++++-
 .../vulkan-shaders/rms_norm_partials.comp     |  65 ++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  12 +-
 tests/test-backend-ops.cpp                    |  15 +-
 7 files changed, 379 insertions(+), 50 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c7cfb6473e..2c8d9ecaa0 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -102,9 +102,9 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 
 struct ggml_backend_vk_context;
 
-#define MAX_PARAMETER_COUNT 8
+#define MAX_PARAMETER_COUNT 12
 // Max number of adds that can be fused without exceeding MAX_PARAMETER_COUNT.
-#define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 2)
+#define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 3)
 
 struct vk_pipeline_struct {
     std::string name;
@@ -381,6 +381,9 @@ struct vk_device_struct {
     bool subgroup_shuffle;
     bool multi_add;
 
+    bool add_rms_fusion;
+    uint32_t partials_binding_alignment;
+
     bool integer_dot_product;
 
     bool subgroup_size_control;
@@ -460,9 +463,12 @@ struct vk_device_struct {
     vk_pipeline pipeline_mul_norepeat[2][2][2];
     vk_pipeline pipeline_div[2][2][2];
     vk_pipeline pipeline_div_norepeat[2][2][2];
+    vk_pipeline pipeline_add_rms[2][2][2];
+    vk_pipeline pipeline_add_rms_norepeat[2][2][2];
 
     // indexed by num_additional_fused_ops == num_adds - 1
     vk_pipeline pipeline_multi_add[MAX_FUSED_ADDS];
+    vk_pipeline pipeline_multi_add_rms[MAX_FUSED_ADDS];
 
     vk_pipeline pipeline_add_id_f32;
 
@@ -486,6 +492,8 @@ struct vk_device_struct {
     vk_pipeline pipeline_group_norm_f32;
     vk_pipeline pipeline_rms_norm_f32;
     vk_pipeline pipeline_rms_norm_mul_f32;
+    vk_pipeline pipeline_rms_norm_partials_f32;
+    vk_pipeline pipeline_rms_norm_mul_partials_f32;
     vk_pipeline pipeline_rms_norm_back_f32;
     vk_pipeline pipeline_l2_norm_f32;
 
@@ -823,8 +831,13 @@ struct vk_op_multi_add_push_constants {
     uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;
 
     // strides for srcs+dst
-    uint32_t nb[8][4];
+    uint32_t nb[MAX_PARAMETER_COUNT][4];
+
+    uint32_t rms_partials;
 };
+// update multi_add.comp if this changes
+static_assert(MAX_PARAMETER_COUNT == 12);
+static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);
 
 struct vk_op_add_id_push_constants {
     uint32_t ne0;
@@ -1208,6 +1221,12 @@ class vk_perf_logger {
             timings[name].push_back(time);
             return;
         }
+        if (node->op == GGML_OP_RMS_NORM) {
+            std::string   name    = ggml_op_name(node->op);
+            name += "(" + std::to_string(node->ne[0]) + "," + std::to_string(node->ne[1]) + "," + std::to_string(node->ne[2]) + "," + std::to_string(node->ne[3]) + ")";
+            timings[name].push_back(time);
+            return;
+        }
         timings[ggml_op_name(node->op)].push_back(time);
     }
   private:
@@ -1222,10 +1241,13 @@ struct ggml_backend_vk_context {
 
     size_t semaphore_idx, event_idx;
     ggml_vk_garbage_collector gc;
-    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
-    vk_buffer prealloc_x, prealloc_y, prealloc_split_k;
+    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k, prealloc_size_add_rms_partials, prealloc_size_add_rms_partials_offset;
+    vk_buffer prealloc_x, prealloc_y, prealloc_split_k, prealloc_add_rms_partials;
     vk::Fence fence, almost_ready_fence;
     bool almost_ready_fence_pending {};
+    // Set before op_add and unset after op_rms_norm to indicate that the add should
+    // write partial sums to accumulate the square of the vector components
+    bool do_add_rms_partials;
 
     // Cache most recent tensor that was converted into prealloc_y, and what pipeline it used to convert.
     vk_pipeline_struct * prealloc_y_last_pipeline_used {};
@@ -2987,8 +3009,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_f32, "rms_norm_mul_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_f32, "rms_norm_mul_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_partials_f32, "rms_norm_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_partials_f32, "rms_norm_mul_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true);
+
     ggml_vk_create_pipeline(device, device->pipeline_rms_norm_back_f32, "rms_norm_back_f32", rms_norm_back_f32_len, rms_norm_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_l2_norm_f32, "l2_norm_f32", l2_norm_f32_len, l2_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
 
@@ -3058,25 +3084,28 @@ static void ggml_vk_load_shaders(vk_device& device) {
     };
 
     bool rte = device->float_controls_rte_fp16;
-#define CREATE_BINARY(name, namemod, spec) \
+#define CREATE_BINARY(name, namemod, spec, bindings) \
     for (int s0 : {0,1}) for (int s1 : {0,1}) for (int d : {0,1}) \
         ggml_vk_create_pipeline(device, device->pipeline_ ## name ## namemod[s0][s1][d], \
                                 #name + get_suffix(s0, s1, d) + #namemod, name ## _len[s0][s1][d][rte], name ## _data[s0][s1][d][rte], \
-                                "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1);
+                                "main", (bindings), sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1);
 
-    CREATE_BINARY(add, , {0})
-    CREATE_BINARY(add, _norepeat, {1})
-    CREATE_BINARY(sub, , {0})
-    CREATE_BINARY(sub, _norepeat, {1})
-    CREATE_BINARY(mul, , {0})
-    CREATE_BINARY(mul, _norepeat, {1})
-    CREATE_BINARY(div, , {0})
-    CREATE_BINARY(div, _norepeat, {1})
+    CREATE_BINARY(add, , {0}, 4)
+    CREATE_BINARY(add, _norepeat, {1}, 4)
+    CREATE_BINARY(sub, , {0}, 3)
+    CREATE_BINARY(sub, _norepeat, {1}, 3)
+    CREATE_BINARY(mul, , {0}, 3)
+    CREATE_BINARY(mul, _norepeat, {1}, 3)
+    CREATE_BINARY(div, , {0}, 3)
+    CREATE_BINARY(div, _norepeat, {1}, 3)
+    CREATE_BINARY(add_rms, , {0}, 4)
+    CREATE_BINARY(add_rms, _norepeat, {1}, 4)
 #undef CREATE_BINARY
 
     if (device->multi_add) {
         for (uint32_t i = 0; i < MAX_FUSED_ADDS; ++i) {
-            ggml_vk_create_pipeline(device, device->pipeline_multi_add[i], "multi_add_f32_" + std::to_string(i+1), multi_add_f32_len, multi_add_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_multi_add[i],     "multi_add_f32_"     + std::to_string(i+1), multi_add_f32_len,     multi_add_f32_data,     "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
+            ggml_vk_create_pipeline(device, device->pipeline_multi_add_rms[i], "multi_add_rms_f32_" + std::to_string(i+1), multi_add_rms_f32_len, multi_add_rms_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
         }
     }
 
@@ -3944,6 +3973,12 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->disable_fusion = getenv("GGML_VK_DISABLE_FUSION") != nullptr;
 
+        device->add_rms_fusion = !device->disable_fusion &&
+                                 device->subgroup_add &&
+                                 device->vendor_id != VK_VENDOR_ID_INTEL;
+        device->partials_binding_alignment =
+            std::max(4u, (uint32_t)device->properties.limits.minStorageBufferOffsetAlignment);
+
         return device;
     }
 
@@ -7080,7 +7115,7 @@ static std::array<uint32_t, 3> ggml_vk_get_conv_elements(const ggml_tensor *dst)
     return elements;
 }
 
-static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
+static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * dst, ggml_op op) {
     switch (op) {
     case GGML_OP_GET_ROWS:
         GGML_ASSERT(src1->type == GGML_TYPE_I32);
@@ -7109,10 +7144,19 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         case GGML_OP_ADD:
         {
             if (ctx->num_additional_fused_ops > 0) {
-                return ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
+                if (ctx->do_add_rms_partials) {
+                    return ctx->device->pipeline_multi_add_rms[ctx->num_additional_fused_ops];
+                } else {
+                    return ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
+                }
+            }
+            if (ctx->do_add_rms_partials) {
+                auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_rms_norepeat : ctx->device->pipeline_add_rms;
+                return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
+            } else {
+                auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add;
+                return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
             }
-            auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add;
-            return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
         }
         case GGML_OP_SUB:
         {
@@ -7235,7 +7279,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         return nullptr;
     case GGML_OP_RMS_NORM:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_f32 : ctx->device->pipeline_rms_norm_f32;
+            if (ctx->do_add_rms_partials) {
+                return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_partials_f32 : ctx->device->pipeline_rms_norm_partials_f32;
+            } else {
+                return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_f32 : ctx->device->pipeline_rms_norm_f32;
+            }
         }
         return nullptr;
     case GGML_OP_RMS_NORM_BACK:
@@ -7748,7 +7796,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
             }
         } break;
     case GGML_OP_RMS_NORM:
-        elements = { (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne03 };
+        if (ctx->do_add_rms_partials) {
+            // Run one element per thread, 128 threads per workgroup
+            elements = { (uint32_t)CEIL_DIV(ne00, 128), 1, 1 };
+        } else {
+            elements = { (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne03 };
+        }
         break;
 
     case GGML_OP_SUM:
@@ -7897,7 +7950,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         }
     }
 
-    if (op == GGML_OP_GLU) {
+    if (op == GGML_OP_ADD || op == GGML_OP_RMS_NORM) {
+        vk_buffer d_A = ctx->do_add_rms_partials ? ctx->prealloc_add_rms_partials : d_X;
+        size_t a_buf_offset = ctx->do_add_rms_partials ? ctx->prealloc_size_add_rms_partials_offset : 0;
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
+            { vk_subbuffer{ d_X, x_buf_offset, x_sz },
+              vk_subbuffer{ d_Y, y_buf_offset, y_sz },
+              vk_subbuffer{ d_D, d_buf_offset, d_sz },
+              vk_subbuffer{ d_A, a_buf_offset, VK_WHOLE_SIZE },
+            }, pc, elements);
+    } else if (op == GGML_OP_GLU) {
         // Empty src1 is possible in glu, but the shader needs a buffer
         vk_subbuffer subbuf_y;
         if (use_src1) {
@@ -7998,7 +8060,7 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
     const ggml_tensor *tensors[MAX_PARAMETER_COUNT];
     uint32_t num_srcs = ctx->num_additional_fused_ops + 2;
     uint32_t num_tensors = num_srcs + 1;
-    GGML_ASSERT(num_tensors <= MAX_PARAMETER_COUNT);
+    GGML_ASSERT(num_tensors + ctx->do_add_rms_partials <= MAX_PARAMETER_COUNT);
 
     tensors[0] = first_node->src[0];
     tensors[1] = first_node->src[1];
@@ -8025,8 +8087,9 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
         pc.nb[i][2] = (uint32_t)t->nb[2] / sizeof(float);
         pc.nb[i][3] = (uint32_t)t->nb[3] / sizeof(float);
     }
+    pc.rms_partials = ctx->do_add_rms_partials;
 
-    vk_pipeline pipeline = ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, tensors[0], tensors[1], nullptr, dst, dst->op);
 
     if (pipeline == nullptr) {
         std::cerr << "ggml_vulkan: Error: Missing multi_add";
@@ -8064,6 +8127,10 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
         buf[i] = buf[0];
         offset[i] = 0;
     }
+    if (ctx->do_add_rms_partials) {
+        buf[num_tensors] = ctx->prealloc_add_rms_partials;
+        offset[num_tensors] = ctx->prealloc_size_add_rms_partials_offset;
+    }
 
     std::array<uint32_t, 3> elements;
 
@@ -8076,6 +8143,7 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
         elements = { ne, 1, 1 };
     }
 
+    static_assert(MAX_PARAMETER_COUNT == 12);
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
         {
             vk_subbuffer{ buf[0], offset[0], VK_WHOLE_SIZE },
@@ -8086,6 +8154,10 @@ static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx,
             vk_subbuffer{ buf[5], offset[5], VK_WHOLE_SIZE },
             vk_subbuffer{ buf[6], offset[6], VK_WHOLE_SIZE },
             vk_subbuffer{ buf[7], offset[7], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[8], offset[8], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[9], offset[9], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[10], offset[10], VK_WHOLE_SIZE },
+            vk_subbuffer{ buf[11], offset[11], VK_WHOLE_SIZE },
         }, pc, elements);
 }
 
@@ -8100,7 +8172,7 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const
         (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
         (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
         0,
-        0.0f, 0.0f, 0,
+        0.0f, 0.0f, ctx->do_add_rms_partials,
     }, dryrun);
 }
 
@@ -8558,19 +8630,39 @@ static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx
     ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f }, dryrun);
 }
 
+static uint32_t ggml_vk_rms_num_partials(ggml_backend_vk_context * ctx, const ggml_tensor *node) {
+    const uint32_t ne = (uint32_t)node->ne[0];
+    const uint32_t denom = ctx->device->pipeline_add_rms[0][0][0]->wg_denoms[0];
+    const uint32_t num_partials = CEIL_DIV(ne, denom);
+    return num_partials;
+}
+
+static uint32_t ggml_vk_rms_partials_size(ggml_backend_vk_context * ctx, const ggml_tensor *node) {
+    const uint32_t num_partials = ggml_vk_rms_num_partials(ctx, node);
+    const uint32_t num_bytes = ROUNDUP_POW2(num_partials * sizeof(uint32_t), ctx->device->partials_binding_alignment);
+    return num_bytes;
+}
+
 static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, float * op_params, bool dryrun = false) {
     const uint32_t src0_type_size = ggml_type_size(src0->type);
     const uint32_t src1_type_size = ggml_type_size(src1->type);
     const uint32_t dst_type_size = ggml_type_size(dst->type);
 
+    uint32_t param3 = ctx->do_add_rms_partials ? ggml_vk_rms_num_partials(ctx, dst) : 0;
+
     ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_RMS_NORM, {
         (uint32_t)ggml_nelements(src0),
         (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
         (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
         (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
         0,
-        op_params[0], 0.0f, 0,
+        op_params[0], 0.0f, (int32_t)param3,
     }, dryrun);
+
+    if (ctx->do_add_rms_partials) {
+        ctx->prealloc_size_add_rms_partials_offset += ggml_vk_rms_partials_size(ctx, src0);
+        ctx->do_add_rms_partials = false;
+    }
 }
 
 static void ggml_vk_rms_norm_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -9848,6 +9940,14 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
         }
         ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k);
     }
+    if (ctx->prealloc_add_rms_partials == nullptr || (ctx->prealloc_size_add_rms_partials > 0 && ctx->prealloc_add_rms_partials->size < ctx->prealloc_size_add_rms_partials)) {
+        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(add_partials_size: " << ctx->prealloc_add_rms_partials << ")");
+        // Resize buffer
+        if (ctx->prealloc_add_rms_partials != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials);
+        }
+        ctx->prealloc_add_rms_partials = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_add_rms_partials);
+    }
 }
 
 static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool use_fence, bool almost_ready);
@@ -9904,10 +10004,23 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
             return false;
         }
         break;
+    case GGML_OP_ADD:
+        {
+            int next_node_idx = node_idx + 1 + ctx->num_additional_fused_ops;
+            if (next_node_idx < cgraph->n_nodes &&
+                cgraph->nodes[next_node_idx]->op == GGML_OP_RMS_NORM &&
+                cgraph->nodes[next_node_idx]->src[0] == cgraph->nodes[next_node_idx - 1] &&
+                ggml_nrows(cgraph->nodes[next_node_idx]) == 1 &&
+                ctx->device->add_rms_fusion) {
+                if (dryrun) {
+                    ctx->prealloc_size_add_rms_partials += ggml_vk_rms_partials_size(ctx, cgraph->nodes[node_idx]);
+                }
+                ctx->do_add_rms_partials = true;
+            }
+        } break;
     case GGML_OP_REPEAT:
     case GGML_OP_REPEAT_BACK:
     case GGML_OP_GET_ROWS:
-    case GGML_OP_ADD:
     case GGML_OP_ADD_ID:
     case GGML_OP_ACC:
     case GGML_OP_SUB:
@@ -10029,6 +10142,9 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
                 // do the only thing needed for the dryrun.
                 vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, node, node->op);
                 ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
+                if (node->op == GGML_OP_RMS_NORM) {
+                    ctx->do_add_rms_partials = false;
+                }
                 return false;
             }
         default:
@@ -11098,6 +11214,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast<VkDebugUtilsLabelEXT*>(&dul));
     }
 
+    ctx->prealloc_size_add_rms_partials = 0;
+    ctx->prealloc_size_add_rms_partials_offset = 0;
+    ctx->do_add_rms_partials = false;
+
     uint64_t total_mat_mul_bytes = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         if (!ctx->device->disable_fusion) {
@@ -11166,6 +11286,19 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     ctx->prealloc_y_last_pipeline_used = nullptr;
     ctx->prealloc_y_last_tensor_used = nullptr;
 
+    if (ctx->prealloc_size_add_rms_partials) {
+        if (ctx->compute_ctx.expired()) {
+            compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
+            ctx->compute_ctx = compute_ctx;
+            ggml_vk_ctx_begin(ctx->device, compute_ctx);
+        } else {
+            compute_ctx = ctx->compute_ctx.lock();
+        }
+        // initialize partial sums to zero.
+        ggml_vk_buffer_memset_async(compute_ctx, ctx->prealloc_add_rms_partials, 0, 0, ctx->prealloc_size_add_rms_partials);
+        ggml_vk_sync_buffers(ctx, compute_ctx);
+    }
+
     // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
     // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
     // (and scaled down based on model size, so smaller models submit earlier).
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
index 2b4085c4f8..00cf2dd62f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp
@@ -1,20 +1,34 @@
 #version 450
 
 #extension GL_EXT_shader_16bit_storage : require
+#if ADD_RMS
+#extension GL_KHR_shader_subgroup_arithmetic : enable
+#extension GL_KHR_shader_subgroup_basic : enable
+#endif
 
 #include "types.comp"
 #include "generic_binary_head.comp"
 
 const uint num_threads = 256;
 
+layout (binding = 3, std430) buffer PartialBuf {float partial_sums[];};
+
 layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in;
 
+#if ADD_RMS
+// XXX TODO this could be sized based on number of subgroups, but that't not considered a constant
+shared FLOAT_TYPE sumsh[num_threads];
+#endif
+
 void main() {
     uint idx = get_idx();
+    uint orig_idx = idx;
 
     // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation
     const uint num_iter = 2;
 
+    FLOAT_TYPE sum_sq = 0;
+
     [[unroll]] for (uint i = 0; i < num_iter; ++i) {
         if (idx >= p.ne) {
             continue;
@@ -22,8 +36,34 @@ void main() {
         uint i00, i01, i02, i03;
         get_indices(idx, i00, i01, i02, i03);
 
-        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]));
+        FLOAT_TYPE sum = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]);
+        sum_sq += sum*sum;
+
+        data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(sum);
 
         idx += num_threads;
     }
+
+#if ADD_RMS
+    if (p.param3 != 0) {
+        // reduce the sum within each subgroup, then across subgroups
+        const uint NumSubgroups = num_threads / gl_SubgroupSize;
+        sum_sq = subgroupAdd(sum_sq);
+        if (gl_SubgroupInvocationID == 0) {
+            sumsh[gl_SubgroupID] = sum_sq;
+        }
+        barrier();
+        [[unroll]] for (uint s = NumSubgroups / 2; s > 0; s >>= 1) {
+            if (gl_SubgroupID < s && gl_SubgroupInvocationID == 0) {
+                sum_sq += sumsh[gl_SubgroupID + s];
+                sumsh[gl_SubgroupID] = sum_sq;
+            }
+            barrier();
+        }
+
+        if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) {
+            partial_sums[orig_idx / (num_iter * num_threads)] = sum_sq;
+        }
+    }
+#endif
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
index 0c7acb7060..f2f218b04a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
@@ -3,6 +3,10 @@
 #extension GL_EXT_shader_16bit_storage : require
 #extension GL_EXT_nonuniform_qualifier : enable
 #extension GL_EXT_control_flow_attributes : require
+#if ADD_RMS
+#extension GL_KHR_shader_subgroup_arithmetic : enable
+#extension GL_KHR_shader_subgroup_basic : enable
+#endif
 
 #include "rte.comp"
 #include "types.comp"
@@ -14,12 +18,16 @@ layout (push_constant) uniform parameter2
     uint ne20; uint ne21; uint ne22; uint ne23;
 
     // strides for srcs+dst
-    uint nb[8][4];
+    uint nb[12][4];
+
+    uint rms_partials;
 } p;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];} a[];
 layout (binding = 0) writeonly buffer D {D_TYPE data_d[];} d[];
 
+layout (binding = 0, std430) buffer PartialBuf {float partial_sums[];} partials[];
+
 layout(constant_id = 0) const uint num_srcs = 2;
 
 uint src_idx(uint s, uint i00, uint i01, uint i02, uint i03) {
@@ -42,14 +50,22 @@ const uint num_threads = 256;
 
 layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in;
 
+#if ADD_RMS
+// XXX TODO this could be sized based on number of subgroups, but that't not considered a constant
+shared FLOAT_TYPE sumsh[num_threads];
+#endif
+
 void main() {
     uint idx = get_idx();
+    uint orig_idx = idx;
 
     uint ne = p.ne20 * p.ne21 * p.ne22 * p.ne23;
 
     // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation
     const uint num_iter = 2;
 
+    FLOAT_TYPE sum_sq = 0;
+
     [[unroll]] for (uint i = 0; i < num_iter; ++i) {
         if (idx >= ne) {
             continue;
@@ -61,8 +77,32 @@ void main() {
         [[unroll]] for (uint s = 0; s < num_srcs; ++s) {
             sum += FLOAT_TYPE(a[s].data_a[src_idx(s, i00, i01, i02, i03)]);
         }
+        sum_sq += sum*sum;
         d[num_srcs].data_d[dst_idx(i00, i01, i02, i03)] = D_TYPE(sum);
 
         idx += num_threads;
     }
+
+#if ADD_RMS
+    if (p.rms_partials != 0) {
+        // reduce the sum within each subgroup, then across subgroups
+        const uint NumSubgroups = num_threads / gl_SubgroupSize;
+        sum_sq = subgroupAdd(sum_sq);
+        if (gl_SubgroupInvocationID == 0) {
+            sumsh[gl_SubgroupID] = sum_sq;
+        }
+        barrier();
+        [[unroll]] for (uint s = NumSubgroups / 2; s > 0; s >>= 1) {
+            if (gl_SubgroupID < s && gl_SubgroupInvocationID == 0) {
+                sum_sq += sumsh[gl_SubgroupID + s];
+                sumsh[gl_SubgroupID] = sum_sq;
+            }
+            barrier();
+        }
+
+        if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) {
+            partials[num_srcs + 1].partial_sums[orig_idx / (num_iter * num_threads)] = sum_sq;
+        }
+    }
+#endif
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
index bdd7db2d69..41197e9301 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
@@ -10,9 +10,9 @@ layout (constant_id = 1) const bool do_multiply = false;
 
 layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
 
-shared FLOAT_TYPE sum[BLOCK_SIZE];
+shared FLOAT_TYPE sumsh[BLOCK_SIZE];
 
-void main() {
+void rms_norm(uint num_iters) {
     const uint ncols     = p.ne00;
     const uint nrows     = gl_NumWorkGroups.x;
     const uint nchannels = gl_NumWorkGroups.y;
@@ -30,38 +30,76 @@ void main() {
     uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset();
     uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset();
 
-    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp
 
-    [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[a_offset + col]);
-        sum[tid] += xi * xi;
+    [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
+        FLOAT_TYPE xi = FLOAT_TYPE(0);
+        if (col < ncols) {
+            xi = FLOAT_TYPE(data_a[a_offset + col]);
+        }
+        sum += xi * xi;
     }
 
+    sumsh[tid] = sum;
     // sum up partial sums and write back result
     barrier();
     [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) {
-            sum[tid] += sum[tid + s];
+            sum += sumsh[tid + s];
+            sumsh[tid] = sum;
         }
         barrier();
     }
+    sum = sumsh[0];
 
-    const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(ncols);
+    const FLOAT_TYPE mean = sum / FLOAT_TYPE(ncols);
     const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
 
     if (do_multiply) {
         if (ncols > p.ne10) {
-            [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
+            [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
+                if (col >= ncols) {
+                    continue;
+                }
                 data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + fastmod(col, p.ne10)]));
             }
         } else {
-            [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
+            [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
+                if (col >= ncols) {
+                    continue;
+                }
                 data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + col]));
             }
         }
     } else {
-        [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
+        [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
+            if (col >= ncols) {
+                continue;
+            }
             data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]));
         }
     }
 }
+
+void main() {
+    // instantiate the rms_norm function for several different
+    // dimensions, to allow loop unrolling
+    uint num_blocks = (p.ne00 + BLOCK_SIZE - 1) / BLOCK_SIZE;
+    if (num_blocks > 32) {
+        rms_norm(num_blocks);
+    } else if (num_blocks > 16) {
+        rms_norm(32);
+    } else if (num_blocks > 8) {
+        rms_norm(16);
+    } else if (num_blocks > 4) {
+        rms_norm(8);
+    } else if (num_blocks == 4) {
+        rms_norm(4);
+    } else if (num_blocks == 3) {
+        rms_norm(3);
+    } else if (num_blocks == 2) {
+        rms_norm(2);
+    } else if (num_blocks == 1) {
+        rms_norm(1);
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp
new file mode 100644
index 0000000000..ba4677c293
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp
@@ -0,0 +1,65 @@
+#version 450
+
+#include "generic_binary_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_KHR_shader_subgroup_arithmetic : enable
+#extension GL_KHR_shader_subgroup_basic : enable
+
+#define BLOCK_SIZE 128
+
+layout (constant_id = 1) const bool do_multiply = false;
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 3, std430) readonly buffer PartialsBuf {float partial_sums[];};
+
+shared FLOAT_TYPE sumsh[BLOCK_SIZE];
+
+void main() {
+    const uint ncols     = p.ne00;
+    const uint nrows     = gl_NumWorkGroups.x;
+    const uint nchannels = gl_NumWorkGroups.y;
+
+    const uint row       = 0;
+    const uint channel   = gl_WorkGroupID.y;
+    const uint samp      = gl_WorkGroupID.z;
+    // The work is split across multiple workgroups in the x dimension. Each invocation
+    // processes one element
+    const uint tid       = gl_GlobalInvocationID.x;
+
+    const uint stride_row       = p.nb01;
+    const uint stride_channel   = p.nb02;
+    const uint stride_sample    = p.nb03;
+
+    uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset();
+    uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset();
+    uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset();
+
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp
+
+    uint32_t num_partials = p.param3;
+    for (uint32_t i = gl_SubgroupInvocationID; i < num_partials; i += gl_SubgroupSize) {
+        sum += partial_sums[i];
+    }
+    sum = subgroupAdd(sum);
+
+    uint col = tid;
+    if (col >= ncols) {
+        return;
+    }
+
+    const FLOAT_TYPE mean = sum / FLOAT_TYPE(ncols);
+    const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
+
+    if (do_multiply) {
+        if (ncols > p.ne10) {
+            data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + fastmod(col, p.ne10)]));
+        } else {
+            data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + col]));
+        }
+    } else {
+        data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]));
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 123ae04491..50a2774831 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -503,6 +503,7 @@ void process_shaders() {
     string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("rms_norm_partials_f32", "rms_norm_partials.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("rms_norm_back_f32", "rms_norm_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("l2_norm_f32", "l2_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 
@@ -538,13 +539,15 @@ void process_shaders() {
         s += std::string(dst_f16 ? "_f16" : "_f32");
         return s;
     };
-    for (std::string op : {"add", "sub", "mul", "div"}) {
+    for (std::string op : {"add", "sub", "mul", "div", "add_rms", }) {
     for (auto src0_f16 : {false, true}) {
     for (auto src1_f16 : {false, true}) {
     for (auto dst_f16  : {false, true}) {
     for (auto rte      : {false, true}) {
+        auto source = op == "add_rms" ? std::string("add") : op;
         auto name = op + get_suffix(src0_f16, src1_f16, dst_f16) + (rte ? "_rte" : "");
-        string_to_spv(name.c_str(), op + ".comp", {{"A_TYPE", get_type_str(src0_f16)}, {"B_TYPE", get_type_str(src1_f16)}, {"D_TYPE", get_type_str(dst_f16)}, {"FLOAT_TYPE", "float"}, {"RTE16", rte ? "1" : "0"}});
+        auto add_rms = op == "add_rms" ? "1" : "0";
+        string_to_spv(name.c_str(), source + ".comp", {{"A_TYPE", get_type_str(src0_f16)}, {"B_TYPE", get_type_str(src1_f16)}, {"D_TYPE", get_type_str(dst_f16)}, {"FLOAT_TYPE", "float"}, {"RTE16", rte ? "1" : "0"}, {"ADD_RMS" , add_rms}});
     }
     }
     }
@@ -687,7 +690,8 @@ void process_shaders() {
 
     string_to_spv("add_id_f32", "add_id.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
 
-    string_to_spv("multi_add_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}});
+    string_to_spv("multi_add_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}, {"ADD_RMS" , "0"}});
+    string_to_spv("multi_add_rms_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}, {"ADD_RMS" , "1"}});
 
     for (auto &c : compiles) {
         c.wait();
@@ -745,7 +749,7 @@ void write_output_files() {
     }
 
     std::string suffixes[2] = {"_f32", "_f16"};
-    for (const char *op : {"add", "sub", "mul", "div"}) {
+    for (const char *op : {"add", "sub", "mul", "div", "add_rms"}) {
         fprintf(hdr, "extern unsigned char *%s_data[2][2][2][2];\n", op);
         fprintf(hdr, "extern uint64_t %s_len[2][2][2][2];\n", op);
         std::string data = "unsigned char *" + std::string(op) + "_data[2][2][2][2] = ";
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 2e53f8e21a..1e1e43f505 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2858,6 +2858,7 @@ struct test_rms_norm_mul_add : public test_case {
     const std::array<int64_t, 4> ne;
     const float eps;
     const bool broadcast;
+    const bool multi_add; // test a sequence of adds feeding into rms_norm
 
     std::string op_desc(ggml_tensor * t) override {
         GGML_UNUSED(t);
@@ -2867,13 +2868,13 @@ struct test_rms_norm_mul_add : public test_case {
     bool run_whole_graph() override { return true; }
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, eps, broadcast);
+        return VARS_TO_STR5(type, ne, eps, broadcast, multi_add);
     }
 
     test_rms_norm_mul_add(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 5, 4, 3},
-            float eps = 1e-6f, bool broadcast = false)
-        : type(type), ne(ne), eps(eps), broadcast(broadcast) {}
+            float eps = 1e-6f, bool broadcast = false, bool multi_add = false)
+        : type(type), ne(ne), eps(eps), broadcast(broadcast), multi_add(multi_add) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         std::array<int64_t, 4> broadcast_dims = {ne[0]*2, ne[1]*3, ne[2]*3, ne[3]*4};
@@ -2891,6 +2892,9 @@ struct test_rms_norm_mul_add : public test_case {
 
         // Use a, b and c early, so we don't end up with an OP_NONE between rms_norm and mul
         a = ggml_add(ctx, ggml_add(ctx, a, b), c);
+        if (multi_add) {
+            a = ggml_add(ctx, ggml_add(ctx, a, b), c);
+        }
         ggml_tensor * out = ggml_add(ctx, ggml_mul(ctx, ggml_rms_norm(ctx, a, eps), b), c);
         ggml_set_name(out, "out");
 
@@ -5842,6 +5846,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_rms_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
         test_cases.emplace_back(new test_rms_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps, true));
     }
+    for (uint32_t n : {1, 511, 1025, 8192, 33*512}) {
+        for (bool multi_add : {false, true}) {
+            test_cases.emplace_back(new test_rms_norm_mul_add(GGML_TYPE_F32, {n, 1, 1, 1}, 1e-6f, false, multi_add));
+        }
+    }
 
     test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, {64, 5, 4, 3}, 1e-12f));
 

From 710dfc465a68f7443b87d9f792cffba00ed739fe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 23 Aug 2025 21:37:06 +0200
Subject: [PATCH 162/287] CUDA: fix half2 -> half conversion for HIP (#15529)

---
 ggml/src/ggml-cuda/fattn-tile-f16.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
index 6239d184d0..a900799a99 100644
--- a/ggml/src/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -258,7 +258,7 @@ static __global__ void flash_attn_tile_ext_f16(
             const half val = hexp(sink - kqmax[j0/nwarps]);
             kqsum[j0/nwarps] = kqsum[j0/nwarps] * KQ_max_scale;
             if (threadIdx.x == 0) {
-                kqsum[j0/nwarps].x = __hadd(kqsum[j0/nwarps].x, val);
+                kqsum[j0/nwarps].x = __hadd(__low2half(kqsum[j0/nwarps]), val);
             }
 
 #pragma unroll

From e78cf0d4b1bdbbc2479f11d58ce0c8f51f755875 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 24 Aug 2025 03:48:21 -0500
Subject: [PATCH 163/287] vulkan: workaround MoltenVK compile failure in
 multi_add (#15506)

* vulkan: workaround MoltenVK compile failure in multi_add

* Update ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp

Co-authored-by: 0cc4m <picard12@live.de>
---
 ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
index f2f218b04a..854a2ad818 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
@@ -23,8 +23,11 @@ layout (push_constant) uniform parameter2
     uint rms_partials;
 } p;
 
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];} a[];
-layout (binding = 0) writeonly buffer D {D_TYPE data_d[];} d[];
+// Workaround for MoltenVK Bug, see https://github.com/ggml-org/llama.cpp/issues/15498
+// layout (binding = 0) readonly buffer A {A_TYPE data_a[];} a[];
+// layout (binding = 0) writeonly buffer D {D_TYPE data_d[];} d[];
+layout (binding = 0) buffer A {A_TYPE data_a[];} a[];
+layout (binding = 0) buffer D {D_TYPE data_d[];} d[];
 
 layout (binding = 0, std430) buffer PartialBuf {float partial_sums[];} partials[];
 

From a9c6ffcbfacee092bfaaa400306fceda18199737 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Sun, 24 Aug 2025 10:48:53 +0200
Subject: [PATCH 164/287] vulkan: enable Conv2D for Apple after MoltenVK fixed
 the bug (#15526)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2c8d9ecaa0..c77d1d32a0 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -11853,14 +11853,13 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 // Op is disabled for Apple because it segfaults at pipeline create time on MoltenVK
                 ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
                 const vk_device& device = ggml_vk_get_device(ctx->device);
-                bool is_Apple = ggml_vk_get_device(ctx->device)->vendor_id == VK_VENDOR_ID_APPLE;
                 // Channel-contiguous format is not supported yet.
                 return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                     op->src[1]->type == GGML_TYPE_F32 &&
                     op->type == GGML_TYPE_F32 &&
                     ggml_is_contiguous(op->src[0]) &&
                     ggml_is_contiguous(op->src[1]) &&
-                    ggml_is_contiguous(op)) && !is_Apple;
+                    ggml_is_contiguous(op));
             }
         default:
             return false;

From c9a24fb93208fbbd3da6d903eb75431bfa97e59e Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 24 Aug 2025 04:24:25 -0500
Subject: [PATCH 165/287] vulkan: Support FA with any multiple of 8 head sizes
 (#15537)

The scalar FA shader already handled multiples of 8. The coopmat1 FA
shader assumed 16x16x16 and the shared memory allocations need the HSK
dimensions padded to a multiple of 16. NVIDIA's coopmat2 implementation
requires multiples of 16 for N and K, and needs the matrix dimensions
padded and loads clamped.

Store the FA pipelines in a map, indexed by the pipeline state.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 213 ++++++++----------
 .../vulkan-shaders/flash_attn_base.comp       |   4 +
 .../vulkan-shaders/flash_attn_cm1.comp        |  23 +-
 .../vulkan-shaders/flash_attn_cm2.comp        |  36 +--
 tests/test-backend-ops.cpp                    |   4 +-
 5 files changed, 143 insertions(+), 137 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c77d1d32a0..a5406f7612 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -115,6 +115,8 @@ struct vk_pipeline_struct {
     uint32_t parameter_count;
     std::array<uint32_t, 3> wg_denoms;
     uint32_t align;
+    // true if fields have been set by ggml_vk_create_pipeline
+    bool initialized {};
     // set to true to request the pipeline is compiled after the dryrun
     bool needed {};
     // set to true when the shader has been compiled
@@ -227,21 +229,6 @@ enum vk_device_architecture {
     NVIDIA_PRE_TURING,
 };
 
-// HSK x HSV
-enum FaHeadSizes {
-    FA_HEAD_SIZE_64,
-    FA_HEAD_SIZE_80,
-    FA_HEAD_SIZE_96,
-    FA_HEAD_SIZE_112,
-    FA_HEAD_SIZE_128,
-    FA_HEAD_SIZE_192,
-    FA_HEAD_SIZE_192_128,
-    FA_HEAD_SIZE_256,
-    FA_HEAD_SIZE_576_512,
-    FA_HEAD_SIZE_UNSUPPORTED,
-    FA_HEAD_SIZE_COUNT = FA_HEAD_SIZE_UNSUPPORTED,
-};
-
 static vk_device_architecture get_device_architecture(const vk::PhysicalDevice& device) {
     vk::PhysicalDeviceProperties props = device.getProperties();
 
@@ -351,6 +338,28 @@ enum dmmv_wg_sizes {
     DMMV_WG_SIZE_COUNT,
 };
 
+enum FaCodePath {
+    FA_SCALAR,
+    FA_COOPMAT1,
+    FA_COOPMAT2,
+};
+
+struct vk_fa_pipeline_state {
+    vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, FaCodePath path, bool aligned, bool f32acc)
+        : HSK(HSK), HSV(HSV), small_rows(small_rows), path(path), aligned(aligned), f32acc(f32acc) {}
+
+    uint32_t HSK, HSV;
+    bool small_rows;
+    FaCodePath path;
+    bool aligned;
+    bool f32acc;
+
+    bool operator<(const vk_fa_pipeline_state &b) const {
+        return std::tie(HSK, HSV, small_rows, path, aligned, f32acc) <
+               std::tie(b.HSK, b.HSV, b.small_rows, b.path, b.aligned, b.f32acc);
+    }
+};
+
 static constexpr uint32_t num_argsort_pipelines = 11;
 static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
 
@@ -541,16 +550,11 @@ struct vk_device_struct {
     vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
     vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;
 
-    // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
-    vk_pipeline pipeline_flash_attn_f32_f16_cm2[GGML_TYPE_COUNT][FA_HEAD_SIZE_COUNT][2][2][2];
-
-    vk_pipeline pipeline_flash_attn_f32_f16_cm1[GGML_TYPE_COUNT][FA_HEAD_SIZE_COUNT][2][2][2];
-
-    vk_pipeline pipeline_flash_attn_f32_f16[GGML_TYPE_COUNT][FA_HEAD_SIZE_COUNT][2][2][2];
+    std::map<vk_fa_pipeline_state, vk_pipeline> pipeline_flash_attn_f32_f16[GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_flash_attn_split_k_reduce;
 
-    std::unordered_map<std::string, vk_pipeline_ref> pipelines;
+    std::vector<vk_pipeline_ref> all_pipelines;
 
     std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
 
@@ -581,15 +585,15 @@ struct vk_device_struct {
         compute_queue.cmd_pool.destroy(device);
         transfer_queue.cmd_pool.destroy(device);
 
-        for (auto& pipeline : pipelines) {
-            if (pipeline.second.expired()) {
+        for (auto& pipeline : all_pipelines) {
+            if (pipeline.expired()) {
                 continue;
             }
 
-            vk_pipeline pl = pipeline.second.lock();
+            vk_pipeline pl = pipeline.lock();
             ggml_vk_destroy_pipeline(device, pl);
         }
-        pipelines.clear();
+        all_pipelines.clear();
 
         device.destroyDescriptorSetLayout(dsl);
 
@@ -1499,7 +1503,7 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
 
     {
         std::lock_guard<std::recursive_mutex> guard(device->mutex);
-        device->pipelines.insert({ pipeline->name, pipeline });
+        device->all_pipelines.push_back(pipeline);
     }
 
     {
@@ -1974,47 +1978,12 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events
     );
 }
 
-enum FaCodePath {
-    FA_SCALAR,
-    FA_COOPMAT1,
-    FA_COOPMAT2,
-};
-
-static FaHeadSizes fa_get_head_sizes(uint32_t hsk, uint32_t hsv) {
-    if (hsk != 192 && hsk != 576 && hsk != hsv) {
-        return FA_HEAD_SIZE_UNSUPPORTED;
-    }
-    switch (hsk) {
-    case 64: return FA_HEAD_SIZE_64;
-    case 80: return FA_HEAD_SIZE_80;
-    case 96: return FA_HEAD_SIZE_96;
-    case 112: return FA_HEAD_SIZE_112;
-    case 128: return FA_HEAD_SIZE_128;
-    case 192:
-        if (hsv == 192) {
-            return FA_HEAD_SIZE_192;
-        } else if (hsv == 128) {
-            return FA_HEAD_SIZE_192_128;
-        } else {
-            return FA_HEAD_SIZE_UNSUPPORTED;
-        }
-    case 256: return FA_HEAD_SIZE_256;
-    case 576:
-        if (hsv == 512) {
-            return FA_HEAD_SIZE_576_512;
-        } else {
-            return FA_HEAD_SIZE_UNSUPPORTED;
-        }
-    default: return FA_HEAD_SIZE_UNSUPPORTED;
-    }
-}
-
 // number of rows/cols for flash attention shader
 static constexpr uint32_t flash_attention_num_small_rows = 32;
 static constexpr uint32_t scalar_flash_attention_num_small_rows = 1;
 
 static uint32_t get_fa_scalar_num_large_rows(uint32_t hsv) {
-    if (hsv >= 512) {
+    if (hsv >= 192) {
         return 2;
     } else {
         return 8;
@@ -2044,7 +2013,13 @@ static std::array<uint32_t, 2> fa_rows_cols(FaCodePath path, uint32_t hsk, uint3
         if (small_rows) {
             return {scalar_flash_attention_num_small_rows, 64};
         } else {
-            return {get_fa_scalar_num_large_rows(hsv), 32};
+            if ((hsv | hsk) & 8) {
+                // HSV/HSK not being a multiple of 16 makes D_split smaller, which makes cols_per_iter
+                // larger, and Bc needs to be >= cols_per_thread. 64 is large enough, 32 is not.
+                return {get_fa_scalar_num_large_rows(hsv), 64};
+            } else {
+                return {get_fa_scalar_num_large_rows(hsv), 32};
+            }
         }
     }
 
@@ -2062,8 +2037,8 @@ static std::array<uint32_t, 2> fa_rows_cols(FaCodePath path, uint32_t hsk, uint3
     }
 
     // small cols to reduce register count
-    if (ggml_is_quantized(type) || hsk >= 256) {
-        if (hsk >= 512) {
+    if (ggml_is_quantized(type) || hsk >= 256 || hsv >= 256) {
+        if (hsk >= 512 || hsv >= 512) {
             return {32, 32};
         } else {
             return {64, 32};
@@ -2072,6 +2047,10 @@ static std::array<uint32_t, 2> fa_rows_cols(FaCodePath path, uint32_t hsk, uint3
     return {64, 64};
 }
 
+static uint32_t fa_align(FaCodePath path, uint32_t hsk, uint32_t hsv, ggml_type type, bool small_rows) {
+    return fa_rows_cols(path, hsk, hsv, 0, type, small_rows)[1];
+}
+
 static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {
 
     uint32_t lut_size = 0;
@@ -2337,11 +2316,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
         if (!pipeline) {
             pipeline = std::make_shared<vk_pipeline_struct>();
+        }
+        if (!pipeline->initialized) {
             pipeline->name = name;
             pipeline->parameter_count = parameter_count;
             pipeline->push_constant_size = push_constant_size;
             pipeline->wg_denoms = wg_denoms;
             pipeline->align = align;
+            pipeline->initialized = true;
         }
 
         if (!pipeline->needed || pipeline->compiled) {
@@ -2387,26 +2369,30 @@ static void ggml_vk_load_shaders(vk_device& device) {
         return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split};
     };
 
-#define CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, HSK, HSV, HEAD_SIZES) \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true),   1,                                            true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true),   fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
-
 #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 64, 64, 64) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 80, 80, 80) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 96, 96, 96) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 112, 112, 112) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 128, 128, 128) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 192, 192, 192) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 192, 128, 192_128) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 256, 256, 256) \
-        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 576, 512, 576_512)
+        for (auto &fa : device->pipeline_flash_attn_f32_f16[TYPE]) { \
+            uint32_t HSK = fa.first.HSK; \
+            uint32_t HSV = fa.first.HSV; \
+            bool small_rows = fa.first.small_rows; \
+            FaCodePath path = fa.first.path; \
+            bool aligned = fa.first.aligned; \
+            bool f32acc = fa.first.f32acc; \
+            if (path == FAPATH) { \
+                if (aligned) { \
+                    if (f32acc) { \
+                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_align(FAPATH,HSK,HSV,TYPE,small_rows), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+                    } else { \
+                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_align(FAPATH,HSK,HSV,TYPE,small_rows), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+                    } \
+                } else { \
+                    if (f32acc) { \
+                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc"         #NAMELC, flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+                    } else { \
+                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc"         #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+                    } \
+                } \
+            } \
+        }
 
     CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, )
     CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, )
@@ -2429,7 +2415,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_COOPMAT2, _cm2)
     }
 #endif
-#undef CREATE_FA2
 #undef CREATE_FA
 
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
@@ -6731,18 +6716,21 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
     const uint32_t Br = coopmat1_flash_attention_num_large_rows;
     const uint32_t Bc = scalar_flash_attention_Bc;
 
+    const uint32_t hsk_pad = ROUNDUP_POW2(hsk, 16);
+
     const uint32_t acctype = f32acc ? 4 : 2;
     const uint32_t f16vec4 = 8;
 
     const uint32_t tmpsh = wg_size * sizeof(float);
     const uint32_t tmpshv4 = wg_size * 4 * acctype;
 
-    const uint32_t Qf = Br * (hsk / 4 + 2) * f16vec4;
+    const uint32_t qstride = hsk_pad / 4 + 2;
+    const uint32_t Qf = Br * qstride * f16vec4;
 
     const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br;
     const uint32_t sfsh = Bc * sfshstride * acctype;
 
-    const uint32_t kshstride = hsk / 4 + 2;
+    const uint32_t kshstride = hsk_pad / 4 + 2;
     const uint32_t ksh = Bc * kshstride * f16vec4;
 
     const uint32_t slope = Br * sizeof(float);
@@ -6853,7 +6841,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         workgroups_y /= N;
     }
 
-    vk_pipeline *pipelines;
     bool small_rows = N <= get_fa_num_small_rows(path);
 
     // coopmat1 does not actually support "small rows" (it needs 16 rows).
@@ -6873,37 +6860,36 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         small_rows = true;
     }
 
-    bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
-
-    FaHeadSizes head_sizes = fa_get_head_sizes(k->ne[0], v->ne[0]);
-
-    switch (path) {
-    case FA_SCALAR:
-        pipelines = &ctx->device->pipeline_flash_attn_f32_f16[k->type][head_sizes][f32acc][small_rows][0];
-        break;
-    case FA_COOPMAT1:
-        pipelines = &ctx->device->pipeline_flash_attn_f32_f16_cm1[k->type][head_sizes][f32acc][small_rows][0];
-        break;
-    case FA_COOPMAT2:
-        pipelines = &ctx->device->pipeline_flash_attn_f32_f16_cm2[k->type][head_sizes][f32acc][small_rows][0];
-        break;
-    default:
-        GGML_ASSERT(0);
-    }
-    assert(pipelines);
-
     const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type));
     const uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type));
     const uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type));
 
-    bool aligned = (KV % pipelines[1]->align) == 0 &&
+    uint32_t alignment = fa_align(path, HSK, HSV, k->type, small_rows);
+    bool aligned = (KV % alignment) == 0 &&
                    // the "aligned" shader variant will forcibly align strides, for performance
                    (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0;
 
+    // Need to use the coopmat2 variant that clamps loads when HSK/HSV aren't sufficiently aligned.
+    if (((HSK | HSV) % 16) != 0 && path == FA_COOPMAT2) {
+        aligned = false;
+    }
     // mask dim1 is padded to 64, we rely on this to avoid clamping mask loads
     GGML_ASSERT((nem1 % GGML_KQ_MASK_PAD) == 0);
 
-    vk_pipeline pipeline = pipelines[aligned];
+    bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
+
+    vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, path, aligned, f32acc);
+
+    vk_pipeline pipeline = nullptr;
+
+    auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16[k->type];
+    auto it = pipelines.find(fa_pipeline_state);
+    if (it != pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        pipelines[fa_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
+    }
+
     assert(pipeline);
 
     uint32_t split_kv = KV;
@@ -6919,7 +6905,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         if (split_k > 1) {
             // Try to evenly split KV into split_k chunks, but it needs to be a multiple
             // of "align", so recompute split_k based on that.
-            split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), pipelines[1]->align);
+            split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment);
             split_k = CEIL_DIV(KV, split_kv);
             workgroups_x = split_k;
         }
@@ -11629,8 +11615,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
                 auto device = ggml_vk_get_device(ctx->device);
                 bool coopmat2 = device->coopmat2;
-                FaHeadSizes head_sizes = fa_get_head_sizes(op->src[1]->ne[0], op->src[2]->ne[0]);
-                if (head_sizes == FA_HEAD_SIZE_UNSUPPORTED) {
+                uint32_t HSK = op->src[1]->ne[0];
+                uint32_t HSV = op->src[2]->ne[0];
+                if ((HSK % 8) != 0 || (HSV % 8) != 0) {
                     return false;
                 }
                 if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
index b57c9dcfc4..f73e17e1fa 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp
@@ -9,6 +9,10 @@ layout (constant_id = 4) const uint32_t HSV = 32;
 layout (constant_id = 5) const uint32_t Clamp = 0;
 layout (constant_id = 6) const uint32_t D_split = 16;
 
+// Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths
+const uint32_t HSK_pad = (HSK + 15) & ~15;
+const uint32_t HSV_pad = (HSV + 15) & ~15;
+
 layout (push_constant) uniform parameter {
     uint32_t N;
     uint32_t KV;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 81cc3f81fc..97c2a54129 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -46,14 +46,14 @@ const uint32_t MatBc = 16;
 shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x];
 shared ACC_TYPEV4 tmpshv4[gl_WorkGroupSize.x];
 
-const uint32_t qstride = HSK / 4 + 2; // in units of f16vec4
+const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4
 shared f16vec4 Qf[Br * qstride];
 
 // Avoid padding for hsk==256 to make it fit in 48KB shmem.
 const uint32_t sfshstride = (HSK <= 128) ? (Br + 8) : Br;
 shared ACC_TYPE sfsh[Bc * sfshstride];
 
-const uint32_t kshstride = HSK / 4 + 2; // in units of f16vec4
+const uint32_t kshstride = HSK_pad / 4 + 2; // in units of f16vec4
 shared f16vec4 ksh[Bc * kshstride];
 
 shared float slope[Br];
@@ -74,6 +74,21 @@ void main() {
 
 #define tile_row(r) (row_tid * rows_per_thread + (r))
 
+    // Zero-initialize shared memory for Q/K when HSK is not a multiple of 16 (HSK_pad > HSK).
+    if ((HSK % 16) != 0) {
+        [[unroll]] for (uint i = 0; i < Br * qstride; i += gl_WorkGroupSize.x) {
+            if (i + tid < Br * qstride) {
+                Qf[i + tid] = f16vec4(0);
+            }
+        }
+        [[unroll]] for (uint i = 0; i < Bc * kshstride; i += gl_WorkGroupSize.x) {
+            if (i + tid < Bc * kshstride) {
+                ksh[i + tid] = f16vec4(0);
+            }
+        }
+        barrier();
+    }
+
     uint32_t q_offset = (iq2*p.nb02+iq3*p.nb03) / 4;
 
     [[unroll]] for (uint32_t idx = 0; idx < Br * HSK / 4; idx += gl_WorkGroupSize.x) {
@@ -151,14 +166,14 @@ void main() {
         }
         barrier();
 
-        // K * Q^T -> S^T: Bc x HSK * HSK x Br -> Bc x Br
+        // K * Q^T -> S^T: Bc x HSK_pad * HSK_pad x Br -> Bc x Br
         // Bc split across workgroup (four subgroups), loop over HSK in chunks of 16: 16 x 16 * 16 x 16 -> 16 x 16
         // This is written transposed in order to allow for N being 8 if implementations need it
         coopmat<ACC_TYPE, gl_ScopeSubgroup, MatBc, MatBr, gl_MatrixUseAccumulator> SfMat = coopmat<ACC_TYPE, gl_ScopeSubgroup, MatBc, MatBr, gl_MatrixUseAccumulator>(0);
         coopmat<float16_t, gl_ScopeSubgroup, MatBc, 16, gl_MatrixUseA> KMat;
         coopmat<float16_t, gl_ScopeSubgroup, 16, MatBr, gl_MatrixUseB> QMat;
 
-        for (uint32_t d = 0; d < HSK / 16; ++d) {
+        for (uint32_t d = 0; d < HSK_pad / 16; ++d) {
             coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor);
 
             uint coord = (gl_SubgroupID * MatBc) * kshstride + d * 16 / 4;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index b0564ca0bf..77ae5ff01d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -104,16 +104,16 @@ void main() {
     tensorLayoutK = setTensorLayoutStrideNV(tensorLayoutK, k_stride, 1);
     tensorLayoutV = setTensorLayoutStrideNV(tensorLayoutV, v_stride, 1);
 
-    coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, HSK, gl_MatrixUseAccumulator> Q;
-    coopmat<float16_t, gl_ScopeWorkgroup, Br, HSK, gl_MatrixUseA> Qf16;
+    coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, HSK_pad, gl_MatrixUseAccumulator> Q;
+    coopmat<float16_t, gl_ScopeWorkgroup, Br, HSK_pad, gl_MatrixUseA> Qf16;
 
     uint32_t q_offset = iq2*p.nb02+iq3*p.nb03;
-    coopMatLoadTensorNV(Q, data_q, q_offset, sliceTensorLayoutNV(tensorLayoutQ, i * Br, Br, 0, HSK));
+    coopMatLoadTensorNV(Q, data_q, q_offset, sliceTensorLayoutNV(tensorLayoutQ, i * Br, Br, 0, HSK_pad));
 
-    Qf16 = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSK, gl_MatrixUseA>(Q);
+    Qf16 = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSK_pad, gl_MatrixUseA>(Q);
     Qf16 *= float16_t(p.scale);
 
-    coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> O = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator>(0);
+    coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(0);
 
     coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> L, M;
 
@@ -140,10 +140,10 @@ void main() {
 
         coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> S = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0);
 
-        coopmat<float16_t, gl_ScopeWorkgroup, HSK, Bc, gl_MatrixUseB> K_T;
+        coopmat<float16_t, gl_ScopeWorkgroup, HSK_pad, Bc, gl_MatrixUseB> K_T;
 
         uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13;
-        coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK), tensorViewTranspose DECODEFUNC);
+        coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose DECODEFUNC);
         S = coopMatMulAdd(Qf16, K_T, S);
 
         if (p.logit_softcap != 0.0f) {
@@ -208,31 +208,31 @@ void main() {
         rowsum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0.0);
         rowsum = coopMatMulAdd(P_A, One, rowsum);
 
-        coopmat<float16_t, gl_ScopeWorkgroup, Bc, HSV, gl_MatrixUseB> V;
+        coopmat<float16_t, gl_ScopeWorkgroup, Bc, HSV_pad, gl_MatrixUseB> V;
         uint32_t v_offset = iv2*p.nb22 + iv3*p.nb23;
-        coopMatLoadTensorNV(V,  data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV) DECODEFUNC);
+        coopMatLoadTensorNV(V,  data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad) DECODEFUNC);
 
         L = eM*L + rowsum;
 
         // This is the "diagonal" matrix in the paper, but since we do componentwise
         // multiply rather than matrix multiply it has the diagonal element smeared
         // across the row
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> eMdiag;
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> eMdiag;
 
         // resize eM by using smear/reduce
         coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce);
 
         // multiply with fp16 accumulation, then add to O.
-        coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> PV = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator>(0);
+        coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> PV = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(0);
         PV = coopMatMulAdd(P_A, V, PV);
 
-        O = eMdiag * O + coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator>(PV);
+        O = eMdiag * O + coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(PV);
     }
 
     // If there is split_k, then the split_k resolve shader does the final
     // division by L. Store the intermediate O value and per-row m and L values.
     if (p.k_num > 1) {
-        coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator>(O);
+        coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(O);
 
         uint32_t o_offset = HSV * p.ne1 * (split_k_index + iq3 * p.k_num);
         coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
@@ -243,16 +243,16 @@ void main() {
         return;
     }
 
-    coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> Ldiag;
+    coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> Ldiag;
 
     // resize L by using smear/reduce
     coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce);
 
     if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) {
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> S;
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> S;
         coopMatPerElementNV(S, S, perElemOpGetSink, iq2);
 
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> Mr;
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> Mr;
 
         // resize M by using smear/reduce
         coopMatReduceNV(Mr, M, gl_CooperativeMatrixReduceRowNV, smearReduce);
@@ -285,7 +285,7 @@ void main() {
 
     uint32_t o_offset = iq3*p.ne2*p.ne1*HSV;
 
-    coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV, gl_MatrixUseAccumulator>(O);
+    coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(O);
     if (p.gqa_ratio > 1) {
         coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
     } else {
@@ -295,6 +295,6 @@ void main() {
         // permute dimensions
         tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2);
 
-        coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, N, 0, HSV), tensorViewPermute);
+        coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, N, 0, HSV_pad), tensorViewPermute);
     }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 1e1e43f505..74886b4549 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6239,8 +6239,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_timestep_embedding());
     test_cases.emplace_back(new test_leaky_relu());
 
-    for (int hsk : { 64, 80, 128, 192, 256, 576 }) {
-        for (int hsv : { 64, 80, 128, 192, 256, 512 }) {
+    for (int hsk : { 40, 64, 80, 128, 192, 256, 576 }) {
+        for (int hsv : { 40, 64, 80, 128, 192, 256, 512 }) {
             if (hsk != 192 && hsk != 576 && hsk != hsv) continue;
             if (hsk == 192 && (hsv != 128 && hsv != 192)) continue;
             if (hsk == 576 && hsv != 512) continue; // DeepSeek MLA

From b730706a49e576fb882dc34d9966345778b3ab0b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 24 Aug 2025 13:07:07 +0300
Subject: [PATCH 166/287] kv-cache : support layer reuse (#15504)

* kv-cache : support layer reuse

ggml-ci

* cont : update comments [no ci]
---
 src/llama-hparams.cpp          | 25 +++++++++++++
 src/llama-hparams.h            |  6 +++
 src/llama-kv-cache-iswa.cpp    | 31 ++++++++++++----
 src/llama-kv-cache-iswa.h      |  6 ++-
 src/llama-kv-cache.cpp         | 68 +++++++++++++++++-----------------
 src/llama-kv-cache.h           | 28 +++++++-------
 src/llama-memory-hybrid.cpp    | 57 ++++++++++++++--------------
 src/llama-memory-hybrid.h      | 40 +++++++++-----------
 src/llama-memory-recurrent.cpp | 14 +++----
 src/llama-memory-recurrent.h   | 18 ++++-----
 src/llama-memory.h             |  8 ++++
 src/llama-model.cpp            | 38 +++++++++++++------
 12 files changed, 203 insertions(+), 136 deletions(-)

diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 7a06368dcd..91636572da 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -153,3 +153,28 @@ bool llama_hparams::is_swa(uint32_t il) const {
 
     GGML_ABORT("fatal error");
 }
+
+bool llama_hparams::has_kv(uint32_t il) const {
+    if (n_layer_kv_from_start >= 0) {
+        if (il < (uint32_t) n_layer_kv_from_start) {
+            return true;
+        }
+
+        return false;
+    }
+
+    // by default, all layers have kv
+    return true;
+}
+
+uint32_t llama_hparams::n_layer_kv() const {
+    uint32_t res = 0;
+
+    for (uint32_t il = 0; il < n_layer; ++il) {
+        if (has_kv(il)) {
+            res++;
+        }
+    }
+
+    return res;
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index bd23122443..60415f0c20 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -41,6 +41,7 @@ struct llama_hparams {
     uint32_t n_embd;
     uint32_t n_embd_features = 0;
     uint32_t n_layer;
+     int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
     uint32_t n_rot;
     uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
     uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
@@ -221,6 +222,11 @@ struct llama_hparams {
     uint32_t n_pos_per_embd() const;
 
     bool is_swa(uint32_t il) const;
+
+    bool has_kv(uint32_t il) const;
+
+    // number of layers for which has_kv() returns true
+    uint32_t n_layer_kv() const;
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
diff --git a/src/llama-kv-cache-iswa.cpp b/src/llama-kv-cache-iswa.cpp
index a11ee5a5b1..d7342914c6 100644
--- a/src/llama-kv-cache-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -22,9 +22,26 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
                  uint32_t   kv_size,
                  uint32_t   n_seq_max,
                  uint32_t   n_ubatch,
-                 uint32_t   n_pad) : hparams(model.hparams), unified(unified) {
-    llama_kv_cache::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
-    llama_kv_cache::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
+                 uint32_t   n_pad,
+    const layer_filter_cb & filter,
+    const  layer_reuse_cb & reuse) : hparams(model.hparams), unified(unified) {
+
+    // chain filters
+    const layer_filter_cb filter_base = [&](int32_t il) {
+        if (filter && !filter(il)) {
+            return false;
+        }
+
+        return !model.hparams.is_swa(il);
+    };
+
+    const layer_filter_cb filter_swa  = [&](int32_t il) {
+        if (filter && !filter(il)) {
+            return false;
+        }
+
+        return  model.hparams.is_swa(il);
+    };
 
     const uint32_t size_base = kv_size;
 
@@ -41,16 +58,16 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
     LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
 
     kv_base = std::make_unique<llama_kv_cache>(
-            model, std::move(filter_base), type_k, type_v,
+            model, type_k, type_v,
             v_trans, offload, unified, size_base, n_seq_max, n_pad,
-            0, LLAMA_SWA_TYPE_NONE);
+            0, LLAMA_SWA_TYPE_NONE, filter_base, reuse);
 
     LLAMA_LOG_INFO("%s: creating     SWA KV cache, size = %u cells\n", __func__, size_swa);
 
     kv_swa = std::make_unique<llama_kv_cache>(
-            model, std::move(filter_swa), type_k, type_v,
+            model, type_k, type_v,
             v_trans, offload, unified, size_swa, n_seq_max, n_pad,
-            hparams.n_swa, hparams.swa_type);
+            hparams.n_swa, hparams.swa_type, filter_swa, reuse);
 }
 
 void llama_kv_cache_iswa::clear(bool data) {
diff --git a/src/llama-kv-cache-iswa.h b/src/llama-kv-cache-iswa.h
index dd673f18e7..5ed134b795 100644
--- a/src/llama-kv-cache-iswa.h
+++ b/src/llama-kv-cache-iswa.h
@@ -20,11 +20,13 @@ public:
                          bool   v_trans,
                          bool   offload,
                          bool   swa_full,
-                         bool  ,
+                         bool   unified,
                      uint32_t   kv_size,
                      uint32_t   n_seq_max,
                      uint32_t   n_ubatch,
-                     uint32_t   n_pad);
+                     uint32_t   n_pad,
+        const layer_filter_cb & filter,
+        const  layer_reuse_cb & reuse);
 
     ~llama_kv_cache_iswa() = default;
 
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 70ddd5f4b9..d7ab56ccd9 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -17,32 +17,25 @@
 //
 
 llama_kv_cache::llama_kv_cache(
-        const llama_model &  model,
-          layer_filter_cb && filter,
-                ggml_type    type_k,
-                ggml_type    type_v,
-                     bool    v_trans,
-                     bool    offload,
-                     bool    unified,
-                 uint32_t    kv_size,
-                 uint32_t    n_seq_max,
-                 uint32_t    n_pad,
-                 uint32_t    n_swa,
-           llama_swa_type    swa_type) :
+        const llama_model & model,
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   offload,
+                     bool   unified,
+                 uint32_t   kv_size,
+                 uint32_t   n_seq_max,
+                 uint32_t   n_pad,
+                 uint32_t   n_swa,
+           llama_swa_type   swa_type,
+    const layer_filter_cb & filter,
+    const  layer_reuse_cb & reuse) :
     model(model), hparams(model.hparams), v_trans(v_trans),
     n_seq_max(n_seq_max), n_stream(unified ? 1 : n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
 
     GGML_ASSERT(kv_size % n_pad == 0);
 
-    // TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
-    auto n_layer_cache = hparams.n_layer;
-    if (model.arch == LLM_ARCH_GEMMA3N) {
-        n_layer_cache = 20;
-    }
-    if (model.arch == LLM_ARCH_GLM4_MOE) {
-        // GLM-4.5: Only process up to last layer, skip final NextN layer
-        n_layer_cache = hparams.n_layer - hparams.nextn_predict_layers;
-    }
+    const uint32_t n_layer_kv = hparams.n_layer_kv();
 
     // create a context for each buffer type
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
@@ -50,7 +43,7 @@ llama_kv_cache::llama_kv_cache(
         auto it = ctx_map.find(buft);
         if (it == ctx_map.end()) {
             ggml_init_params params = {
-                /*.mem_size   =*/ size_t(2u*(1 + n_stream)*n_layer_cache*ggml_tensor_overhead()),
+                /*.mem_size   =*/ size_t(2u*(1 + n_stream)*n_layer_kv*ggml_tensor_overhead()),
                 /*.mem_buffer =*/ NULL,
                 /*.no_alloc   =*/ true,
             };
@@ -97,9 +90,14 @@ llama_kv_cache::llama_kv_cache(
                 __func__, hparams.n_embd_v_gqa_max());
     }
 
-    for (uint32_t il = 0; il < n_layer_cache; il++) {
+    for (uint32_t il = 0; il < hparams.n_layer; il++) {
+        if (!hparams.has_kv(il)) {
+            LLAMA_LOG_DEBUG("%s: layer %3d: does not have KV cache\n", __func__, il);
+            continue;
+        }
+
         if (filter && !filter(il)) {
-            LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
+            LLAMA_LOG_DEBUG("%s: layer %3d: filtered\n", __func__, il);
             continue;
         }
 
@@ -147,23 +145,27 @@ llama_kv_cache::llama_kv_cache(
         layers.push_back({ il, k, v, k_stream, v_stream, });
     }
 
-    // TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
-    if (model.arch == LLM_ARCH_GEMMA3N) {
-        LLAMA_LOG_DEBUG("%s: GEMMA3N: reuse layers [%d, %d]\n", __func__, n_layer_cache, hparams.n_layer - 1);
+    if (reuse) {
+        LLAMA_LOG_DEBUG("%s: reusing layers:\n", __func__);
 
-        for (uint32_t il = n_layer_cache; il < hparams.n_layer; il++) {
-            if (filter && !filter(il)) {
-                LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
+        for (uint32_t il = 0; il < hparams.n_layer; il++) {
+            const int32_t il_reuse = reuse(il);
+
+            if (il_reuse < 0) {
+                LLAMA_LOG_DEBUG("%s: - layer %3d: no reuse\n", __func__, il);
                 continue;
             }
 
-            const bool     is_swa   = hparams.is_swa(il);
-            const uint32_t il_reuse = n_layer_cache - (is_swa ? 2 : 1);
+            if (filter && !filter(il)) {
+                LLAMA_LOG_DEBUG("%s: - layer %3d: filtered\n", __func__, il);
+                continue;
+            }
 
             GGML_ASSERT(map_layer_ids.find(il_reuse) != map_layer_ids.end());
+
             map_layer_ids[il] = map_layer_ids[il_reuse];
 
-            LLAMA_LOG_DEBUG("%s: layer %3d: reuse layer %d, isw = %d\n", __func__, il, il_reuse, is_swa);
+            LLAMA_LOG_DEBUG("%s: - layer %3d: reuse layer %d, is_swa = %d\n", __func__, il, il_reuse, hparams.is_swa(il));
         }
     }
 
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 297a0973dd..76a5cb1e28 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -21,9 +21,6 @@ class llama_kv_cache : public llama_memory_i {
 public:
     static uint32_t get_padding(const llama_cparams & cparams);
 
-    // this callback is used to filter out layers that should not be included in the cache
-    using layer_filter_cb = std::function<bool(int32_t il)>;
-
     struct stream_copy_info {
         bool empty() const {
             assert(ssrc.size() == sdst.size());
@@ -82,18 +79,19 @@ public:
     using slot_info_vec_t = std::vector<slot_info>;
 
     llama_kv_cache(
-            const llama_model &  model,
-              layer_filter_cb && filter,
-                    ggml_type    type_k,
-                    ggml_type    type_v,
-                         bool    v_trans,
-                         bool    offload,
-                         bool    unified,
-                     uint32_t    kv_size,
-                     uint32_t    n_seq_max,
-                     uint32_t    n_pad,
-                     uint32_t    n_swa,
-               llama_swa_type    swa_type);
+            const llama_model & model,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                         bool   v_trans,
+                         bool   offload,
+                         bool   unified,
+                     uint32_t   kv_size,
+                     uint32_t   n_seq_max,
+                     uint32_t   n_pad,
+                     uint32_t   n_swa,
+               llama_swa_type   swa_type,
+        const layer_filter_cb & filter,
+        const  layer_reuse_cb & reuse);
 
     ~llama_kv_cache() = default;
 
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index f8303dacbf..ba61ebaa88 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -9,32 +9,29 @@
 //
 
 llama_memory_hybrid::llama_memory_hybrid(
-    const llama_model & model,
-                         /* attn */
-            ggml_type    type_k,
-            ggml_type    type_v,
-                 bool    v_trans,
-             uint32_t    kv_size,
-             uint32_t    n_pad,
-             uint32_t    n_swa,
-       llama_swa_type    swa_type,
-                         /* recurrent */
-            ggml_type    type_r,
-            ggml_type    type_s,
-             uint32_t    rs_size,
-                         /* common */
-             uint32_t    n_seq_max,
-                 bool    offload,
-                 bool    unified,
-                         /* layer filters */
-      layer_filter_cb && filter_attn,
-      layer_filter_cb && filter_recr) :
+        const llama_model & model,
+                            /* attn */
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                 uint32_t   kv_size,
+                 uint32_t   n_pad,
+                 uint32_t   n_swa,
+           llama_swa_type   swa_type,
+                            /* recurrent */
+                ggml_type   type_r,
+                ggml_type   type_s,
+                 uint32_t   rs_size,
+                            /* common */
+                 uint32_t   n_seq_max,
+                     bool   offload,
+                     bool   unified,
+                            /* layer filters */
+    const layer_filter_cb & filter_attn,
+    const layer_filter_cb & filter_recr) :
     hparams(model.hparams),
     mem_attn(new llama_kv_cache(
         model,
-        filter_attn == nullptr ?
-            [&](int32_t il) { return !hparams.is_recurrent(il); }
-            : filter_attn,
         type_k,
         type_v,
         v_trans,
@@ -44,18 +41,22 @@ llama_memory_hybrid::llama_memory_hybrid(
         n_seq_max,
         n_pad,
         n_swa,
-        swa_type
+        swa_type,
+        filter_attn == nullptr ?
+            [&](int32_t il) { return !hparams.is_recurrent(il); }
+            : filter_attn,
+        nullptr
     )),
     mem_recr(new llama_memory_recurrent(
         model,
-        filter_recr == nullptr ?
-            [&](int32_t il) { return hparams.is_recurrent(il); }
-            : filter_recr,
         type_r,
         type_s,
         offload,
         rs_size,
-        n_seq_max
+        n_seq_max,
+        filter_recr == nullptr ?
+            [&](int32_t il) { return hparams.is_recurrent(il); }
+            : filter_recr
     )) {}
 
 llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h
index e9c64ee40a..11a3565178 100644
--- a/src/llama-memory-hybrid.h
+++ b/src/llama-memory-hybrid.h
@@ -18,31 +18,27 @@
 
 class llama_memory_hybrid : public llama_memory_i {
 public:
-
-    // this callback is used to filter out layers that should not be included in the cache
-    using layer_filter_cb = std::function<bool(int32_t il)>;
-
     llama_memory_hybrid(
         const llama_model & model,
                             /* attn */
-                ggml_type    type_k,
-                ggml_type    type_v,
-                     bool    v_trans,
-                 uint32_t    kv_size,
-                 uint32_t    n_pad,
-                 uint32_t    n_swa,
-           llama_swa_type    swa_type,
-                             /* recurrent */
-                ggml_type    type_r,
-                ggml_type    type_s,
-                 uint32_t    rs_size,
-                             /* common */
-                 uint32_t    n_seq_max,
-                     bool    offload,
-                     bool    unified,
-                             /* layer filters */
-          layer_filter_cb && filter_attn = nullptr,
-          layer_filter_cb && filter_recr = nullptr);
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                 uint32_t   kv_size,
+                 uint32_t   n_pad,
+                 uint32_t   n_swa,
+           llama_swa_type   swa_type,
+                            /* recurrent */
+                ggml_type   type_r,
+                ggml_type   type_s,
+                 uint32_t   rs_size,
+                            /* common */
+                 uint32_t   n_seq_max,
+                     bool   offload,
+                     bool   unified,
+                            /* layer filters */
+    const layer_filter_cb & filter_attn = nullptr,
+    const layer_filter_cb & filter_recr = nullptr);
 
     ~llama_memory_hybrid() = default;
 
diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp
index 849675c418..08716ed91a 100644
--- a/src/llama-memory-recurrent.cpp
+++ b/src/llama-memory-recurrent.cpp
@@ -16,13 +16,13 @@
 //
 
 llama_memory_recurrent::llama_memory_recurrent(
-        const llama_model &  model,
-          layer_filter_cb && filter,
-                ggml_type    type_r,
-                ggml_type    type_s,
-                     bool    offload,
-                 uint32_t    mem_size,
-                 uint32_t    n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
+        const llama_model & model,
+                ggml_type   type_r,
+                ggml_type   type_s,
+                     bool   offload,
+                 uint32_t   mem_size,
+                 uint32_t   n_seq_max,
+    const layer_filter_cb & filter) : hparams(model.hparams), n_seq_max(n_seq_max) {
     const int32_t n_layer = hparams.n_layer;
 
     head = 0;
diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h
index c8e8623602..c4daf00495 100644
--- a/src/llama-memory-recurrent.h
+++ b/src/llama-memory-recurrent.h
@@ -15,18 +15,14 @@
 //       see the implementation of llama_kv_cache_context_i for an example how to do it
 class llama_memory_recurrent : public llama_memory_i {
 public:
-
-    // this callback is used to filter out layers that should not be included in the cache
-    using layer_filter_cb = std::function<bool(int32_t il)>;
-
     llama_memory_recurrent(
-            const llama_model &  model,
-              layer_filter_cb && filter,
-                    ggml_type    type_r,
-                    ggml_type    type_s,
-                         bool    offload,
-                     uint32_t    mem_size,
-                     uint32_t    n_seq_max);
+            const llama_model & model,
+                    ggml_type   type_r,
+                    ggml_type   type_s,
+                         bool   offload,
+                     uint32_t   mem_size,
+                     uint32_t   n_seq_max,
+        const layer_filter_cb & filter);
 
     ~llama_memory_recurrent() = default;
 
diff --git a/src/llama-memory.h b/src/llama-memory.h
index 94d858bccc..ccd1f073b0 100644
--- a/src/llama-memory.h
+++ b/src/llama-memory.h
@@ -3,6 +3,7 @@
 #include "llama.h"
 
 #include <memory>
+#include <functional>
 
 struct llama_ubatch;
 
@@ -64,6 +65,13 @@ using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
 // general concept of LLM memory
 // the KV cache is a type of LLM memory, but there can be other types
 struct llama_memory_i {
+    // this callback is used to filter out layers that should not be included in the cache
+    using layer_filter_cb = std::function<bool(int32_t il)>;
+
+    // this callback is used to specify which layers should reuse memory from other layers
+    // return negative value to indicate that the layer il should not reuse memory
+    using layer_reuse_cb = std::function<int32_t(int32_t il)>;
+
     virtual ~llama_memory_i() = default;
 
     // split the input batch into a set of ubatches and verify that they can fit into the cache
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index d5148f7df3..7d3429617b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1115,6 +1115,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
                 hparams.set_swa_pattern(5);
 
+                hparams.n_layer_kv_from_start     = 20;
                 hparams.rope_freq_base_train_swa  = 10000.0f;
                 hparams.rope_freq_scale_train_swa = 1.0f;
                 hparams.f_attention_scale         = 1.0f;
@@ -1474,12 +1475,15 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 // Expert gating function (GLM-4.5 uses sigmoid)
                 ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
                 if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+                    hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
                 }
 
                 // NextN/MTP parameters
                 ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
 
+                // TODO: when MTP is implemented, this should probably be updated if needed
+                hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
                 switch (hparams.n_layer) {
                     case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
                     case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
@@ -10524,7 +10528,6 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
     const int64_t n_embd_altup;
     const int64_t n_altup;
     const int     i_altup_act;
-    const int     n_layer_kv = 20; // number of layers having KV [KV_REUSE]
     const int     n_layer_sparsity = 10; // number of layers using activation sparsity
     const float   f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
 
@@ -10574,8 +10577,6 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
 
         for (int il = 0; il < n_layer; ++il) {
             // this block is made to be closely resemble Gemma3p5DecoderLayer on python code
-            const bool has_kv = (il < n_layer_kv);
-
             const float freq_base_l  = model.get_rope_freq_base (cparams, il);
             const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
 
@@ -10595,7 +10596,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
             ggml_tensor * laurel_out = laurel(cur, il); // [n_embd, n_tokens]
 
             // self-attention
-            if (has_kv) {
+            if (hparams.has_kv(il)) {
                 // compute Q and K and RoPE them
                 ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
@@ -10635,7 +10636,7 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
                         model.layers[il].wo, NULL,
                         Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
             } else {
-                // no KV layers
+                // reuse KV cache of earlier layers
                 ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
@@ -18256,12 +18257,12 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                 if (llm_arch_is_recurrent(arch)) {
                     res = new llama_memory_recurrent(
                             *this,
-                            nullptr,
                             GGML_TYPE_F32,
                             GGML_TYPE_F32,
                             cparams.offload_kqv,
                             std::max((uint32_t) 1, cparams.n_seq_max),
-                            cparams.n_seq_max);
+                            cparams.n_seq_max,
+                            nullptr);
                 } else if (llm_arch_is_hybrid(arch)) {
                     const auto padding = llama_kv_cache::get_padding(cparams);
 
@@ -18302,6 +18303,18 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
 
                     LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx);
 
+                    llama_memory_i::layer_reuse_cb reuse = nullptr;
+
+                    if (arch == LLM_ARCH_GEMMA3N) {
+                        reuse = [&](int32_t il) {
+                            if (il >= (int32_t) hparams.n_layer_kv_from_start) {
+                                return (int32_t) hparams.n_layer_kv_from_start - (hparams.is_swa(il) ? 2 : 1);
+                            }
+
+                            return -1;
+                        };
+                    }
+
                     if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
                         GGML_ASSERT(hparams.is_swa_any());
 
@@ -18316,13 +18329,14 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                                 n_ctx_per_stream,
                                 cparams.n_seq_max,
                                 cparams.n_ubatch,
-                                padding);
+                                padding,
+                                nullptr,
+                                reuse);
                     } else {
                         GGML_ASSERT(!hparams.is_swa_any());
 
                         res = new llama_kv_cache(
                                 *this,
-                                nullptr,
                                 params.type_k,
                                 params.type_v,
                                 !cparams.flash_attn,
@@ -18332,7 +18346,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                                 cparams.n_seq_max,
                                 padding,
                                 hparams.n_swa,
-                                hparams.swa_type);
+                                hparams.swa_type,
+                                nullptr,
+                                nullptr);
                     }
                 }
             }

From 043fb27d3808766d8ea8195bbd12359727264402 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Sun, 24 Aug 2025 19:36:36 +0200
Subject: [PATCH 167/287] vulkan: apply MUL_MAT_ID subgroup optimization to
 non-coopmat devices (#15524)

* vulkan: use subgroup function for mul_mat_id shader even without coopmat

* vulkan: fix compile warnings

* vulkan: properly check for subgroup size control and require full subgroups for subgroup mul_mat_id

* vulkan: disable subgroup mul_mat_id on devices with subgroups < 16
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 428 ++++++++++--------
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    |  11 +-
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  38 +-
 3 files changed, 282 insertions(+), 195 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a5406f7612..4b959d844f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -388,6 +388,7 @@ struct vk_device_struct {
     bool float_controls_rte_fp16;
     bool subgroup_add;
     bool subgroup_shuffle;
+    bool subgroup_ballot;
     bool multi_add;
 
     bool add_rms_fusion;
@@ -1044,7 +1045,7 @@ struct vk_op_sum_rows_push_constants
     uint32_t ne0_1mp, ne0_1L;
 };
 
-vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) {
+static vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) {
     uint32_t type_size = (uint32_t)ggml_type_size(src->type);
     vk_op_sum_rows_push_constants p = {};
     p.n_cols = (uint32_t)n_cols;
@@ -2176,8 +2177,17 @@ static void ggml_vk_load_shaders(vk_device& device) {
     const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
     const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);
 
+    const uint32_t mul_mat_subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
+    const uint32_t mul_mat_subgroup_size_8 = std::max(mul_mat_subgroup_size, 8u);
+    const uint32_t mul_mat_subgroup_size_16 = std::max(mul_mat_subgroup_size, 16u);
+    const uint32_t mul_mat_subgroup_size_32 = std::max(mul_mat_subgroup_size, 32u);
+
+    const bool subgroup_min_size_16 = (!device->subgroup_size_control && device->subgroup_size >= 16) ||
+                                      (device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16);
+
     // mulmat
     std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
+                          l_warptile_id, m_warptile_id, s_warptile_id,
                           l_warptile_mmq, m_warptile_mmq, s_warptile_mmq,
                           l_warptile_mmq_int, m_warptile_mmq_int, s_warptile_mmq_int,
                           l_warptile_mmq_k, m_warptile_mmq_k, s_warptile_mmq_k,
@@ -2248,9 +2258,18 @@ static void ggml_vk_load_shaders(vk_device& device) {
         m_warptile_mmq_int = { 128,  64,  64, 32, subgroup_size_8,     32, 2, 2, 2, 1, subgroup_size_8 };
         s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, 32,       32, 2, 2, 1, 1, subgroup_size_8 };
 
+        l_warptile_id = { 128, 128, 128, 16, mul_mat_subgroup_size_16 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_16 };
+        m_warptile_id = { 128,  64,  64, 16, mul_mat_subgroup_size_16, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_16 };
+        s_warptile_id = { mul_mat_subgroup_size_16, 32, 32, 16, 32, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_16 };
+
+        l_warptile_mmqid = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_8 };
+        m_warptile_mmqid = { 128,  64,  64, 32, mul_mat_subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_8 };
+        s_warptile_mmqid = { mul_mat_subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_8 };
+
         // chip specific tuning
         if ((device->architecture == AMD_GCN) && (device->driver_id != vk::DriverId::eAmdProprietary)) {
             m_warptile_mmq = m_warptile_mmq_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
+            m_warptile_mmqid = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
         }
 
         l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
@@ -2276,14 +2295,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
             }
 
             // Disable mul_mat_id if not enough shared memory is available
-            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, true, t)) {
+            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmqid, true, t)) {
                 device->mul_mat_id_s[i] = false;
                 device->mul_mat_id_m[i] = false;
                 device->mul_mat_id_l[i] = false;
-            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, true, t)) {
+            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmqid, true, t)) {
                 device->mul_mat_id_m[i] = false;
                 device->mul_mat_id_l[i] = false;
-            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, true, t)) {
+            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmqid, true, t)) {
                 device->mul_mat_id_l[i] = false;
             }
         }
@@ -2461,32 +2480,34 @@ static void ggml_vk_load_shaders(vk_device& device) {
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
         CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_MXFP4],   matmul_mxfp4_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
 
-        CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
+        GGML_ASSERT(device->subgroup_ballot);
+
+        CREATE_MM2(pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
 #if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
         if (device->coopmat_bf16_support) {
-            CREATE_MM(pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
+            CREATE_MM(pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
         }
 #endif
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
-        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
     } else
@@ -2573,55 +2594,56 @@ static void ggml_vk_load_shaders(vk_device& device) {
             CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
         }
 
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        GGML_ASSERT(device->subgroup_ballot);
+
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
 #if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
         if (device->coopmat_bf16_support) {
-            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
         }
 #endif
 
-        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-
-        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MM
     } else
 #endif  // defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     if (device->fp16) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
-#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l[TYPE]) { \
@@ -2638,38 +2660,38 @@ static void ggml_vk_load_shaders(vk_device& device) {
         } \
 
         // Create 2 variants, {f16,f32} accumulator
-#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
+        CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
+        CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
 
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S],   matmul_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M],   matmul_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS],  matmul_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S],   matmul_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S],   matmul_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M],   matmul_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS],  matmul_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S],   matmul_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
         if (device->integer_dot_product) {
@@ -2681,51 +2703,77 @@ static void ggml_vk_load_shaders(vk_device& device) {
         }
 #endif
 
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) {
+            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size_16);
 
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+        } else {
+            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id, 0);
 
-        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-
-        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+        }
 #undef CREATE_MM2
 #undef CREATE_MMQ
 #undef CREATE_MM
     } else {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
-#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
+#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
@@ -2735,34 +2783,34 @@ static void ggml_vk_load_shaders(vk_device& device) {
         if (device->mul_mat ## ID ## _s[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC "_s", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
 
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
 
-        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc,   matmul_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc,   matmul_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc,   matmul_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc,   matmul_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
         if (device->integer_dot_product) {
@@ -2774,33 +2822,59 @@ static void ggml_vk_load_shaders(vk_device& device) {
         }
 #endif
 
-        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
+        if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) {
+            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_subgroup_f16, , wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_subgroup_f16_f32, , wg_denoms, warptile_id, vk_mat_mat_push_constants, 4, _id, mul_mat_subgroup_size_16);
+            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size_16);
 
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_subgroup_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_subgroup_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_subgroup_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_subgroup_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_subgroup_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_subgroup_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_subgroup_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_subgroup_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_subgroup_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_subgroup_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc,   matmul_id_subgroup_iq1_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc,   matmul_id_subgroup_iq1_m_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_subgroup_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_subgroup_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_subgroup_iq2_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_subgroup_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_subgroup_iq3_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_subgroup_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_subgroup_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_subgroup_mxfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, mul_mat_subgroup_size);
+        } else {
+            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id, 0);
 
-        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-
-        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc,   matmul_id_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc,   matmul_id_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
-        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc,   matmul_id_iq1_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc,   matmul_id_iq1_m_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4, _id, 0);
+        }
     }
     // reusing CREATE_MM from the fp32 path
     if ((device->coopmat2 || device->coopmat_support)
@@ -2817,8 +2891,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
         m_wg_denoms = { 64,  64, 1 };
         s_wg_denoms = { 32,  32, 1 };
 
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
-        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
+        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4, _id, 0);
     }
 #undef CREATE_MM
 
@@ -3506,6 +3580,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device->subgroup_shuffle = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                    (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eShuffle);
 
+        device->subgroup_ballot = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
+                                  (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBallot);
+
         const bool force_disable_f16 = getenv("GGML_VK_DISABLE_F16") != nullptr;
 
         device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
@@ -3655,9 +3732,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 (subgroup_size_control_props.requiredSubgroupSizeStages & vk::ShaderStageFlagBits::eCompute) &&
                 subgroup_size_control_features.subgroupSizeControl;
 
-        if (device->subgroup_size_control) {
-            device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;
-        }
+        device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;
 
 #if defined(VK_KHR_cooperative_matrix)
         device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix;
@@ -10194,12 +10269,9 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
             }
         }
         if (need_sync) {
-            VK_LOG_DEBUG("node_idx=" << i << " sync");
             ctx->unsynced_nodes_written.clear();
             ctx->unsynced_nodes_read.clear();
             ggml_vk_sync_buffers(ctx, compute_ctx);
-        } else {
-            VK_LOG_DEBUG("node_idx=" << i << " unsynced");
         }
         // Add the last fused node and all fused source nodes to the unsynchronized list.
         const ggml_tensor * last_node = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
@@ -12241,7 +12313,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
     } else if (tensor->op == GGML_OP_CONCAT) {
         tensor_clone = ggml_concat(ggml_ctx, src_clone[0], src_clone[1], *(int *)tensor->op_params);
     } else if (tensor->op == GGML_OP_UPSCALE) {
-        tensor_clone = ggml_upscale_ext(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], (ggml_scale_mode) tensor->op_params[0]);
+        tensor_clone = ggml_interpolate(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], (ggml_scale_mode) tensor->op_params[0]);
     } else if (tensor->op == GGML_OP_SCALE) {
         const float * params = (const float *)tensor->op_params;
         tensor_clone = ggml_scale_bias(ggml_ctx, src_clone[0], params[0], params[1]);
@@ -12480,11 +12552,9 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph *
     if (tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_SET_ROWS) {
         return;
     }
-    bool fused_rms_norm_mul = false;
     if (ctx->num_additional_fused_ops == 1 &&
         tensor->op == GGML_OP_RMS_NORM &&
         cgraph->nodes[tensor_idx + 1]->op == GGML_OP_MUL) {
-        fused_rms_norm_mul = true;
         tensor = cgraph->nodes[tensor_idx + 1];
     }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index d57cc6bdec..40c0d9b0c5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -17,6 +17,9 @@
 #ifdef COOPMAT
 #extension GL_KHR_cooperative_matrix : enable
 #extension GL_KHR_memory_scope_semantics : enable
+#endif
+
+#if defined(COOPMAT) || defined(MUL_MAT_ID_USE_SUBGROUPS)
 #extension GL_KHR_shader_subgroup_basic : enable
 #extension GL_KHR_shader_subgroup_ballot : enable
 #endif
@@ -108,8 +111,10 @@ shared FLOAT_TYPE buf_b[BN * SHMEM_STRIDE];
 #ifdef MUL_MAT_ID
 shared u16vec2 row_ids[4096];
 uint _ne1;
-#ifdef COOPMAT
+
+#ifdef MUL_MAT_ID_USE_SUBGROUPS
 shared uvec4 ballots_sh[NUM_WARPS];
+
 void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
     _ne1 = 0;
     uint num_elements = p.nei1 * p.nei0;
@@ -168,7 +173,7 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
     }
     barrier();
 }
-#endif
+#endif // MUL_MAT_ID_USE_SUBGROUPS
 #endif // MUL_MAT_ID
 
 #ifdef COOPMAT
@@ -235,7 +240,7 @@ void main() {
     const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
 
 #ifdef MUL_MAT_ID
-#ifdef COOPMAT
+#ifdef MUL_MAT_ID_USE_SUBGROUPS
     if (bitCount(p.nei0) == 1) {
         load_row_ids(expert_idx, true);
     } else {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 50a2774831..a973625857 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -68,6 +68,12 @@ const std::vector<std::string> type_names = {
     "bf16",
 };
 
+enum MatMulIdType {
+    NONE,
+    DEFAULT,
+    SUBGROUP,
+};
+
 namespace {
 void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {
 #ifdef _WIN32
@@ -293,7 +299,7 @@ void string_to_spv(const std::string& _name, const std::string& in_fname, const
     compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16, coopmat, coopmat2, f16acc));
 }
 
-void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool f16acc) {
+void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool coopmat2, bool f16acc) {
     std::string load_vec = coopmat2 ? "1" : fp16 ? "8" : "4";
     std::string aligned_b_type_f32 = coopmat2 ? "float" : fp16 ? "mat2x4" : "vec4";
     std::string aligned_b_type_f16 = coopmat2 ? "float16_t" : fp16 ? "f16mat2x4" : "f16vec4";
@@ -303,9 +309,13 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool
     };
     std::string shader_name = "matmul";
 
-    if (matmul_id) {
+    if (matmul_id_type == MatMulIdType::DEFAULT) {
         base_dict["MUL_MAT_ID"] = "1";
         shader_name = "matmul_id";
+    } else if (matmul_id_type == MatMulIdType::SUBGROUP) {
+        base_dict["MUL_MAT_ID"] = "1";
+        base_dict["MUL_MAT_ID_USE_SUBGROUPS"] = "1";
+        shader_name = "matmul_id_subgroup";
     }
 
     if (fp16) {
@@ -389,7 +399,7 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool
         }
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-        if (!coopmat && !coopmat2 && !matmul_id && (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "q8_0")) {
+        if (!coopmat && !coopmat2 && matmul_id_type == MatMulIdType::NONE && (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "q8_0")) {
             string_to_spv(shader_name + "_" + tname + "_q8_1", "mul_mmq.comp", merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"D_TYPE", "float"},}), fp16, coopmat, coopmat2, f16acc);
         }
 #endif
@@ -401,26 +411,28 @@ void process_shaders() {
     std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", "float"}};
 
     // matmul
-    for (const auto& matmul_id : {false, true}) {
+    for (const MatMulIdType& matmul_id_type : {MatMulIdType::NONE, MatMulIdType::DEFAULT, MatMulIdType::SUBGROUP}) {
         // No coopmats
         // fp32
-        matmul_shaders(false, matmul_id, false, false, false);
+        matmul_shaders(false, matmul_id_type, false, false, false);
 
         // fp16, fp32acc and fp16acc
-        matmul_shaders(true, matmul_id, false, false, false);
-        matmul_shaders(true, matmul_id, false, false, true);
+        matmul_shaders(true, matmul_id_type, false, false, false);
+        matmul_shaders(true, matmul_id_type, false, false, true);
 
+        if (matmul_id_type != MatMulIdType::DEFAULT) {
 #if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
-        // Coopmat, fp32acc and fp16acc
-        matmul_shaders(true, matmul_id, true, false, false);
-        matmul_shaders(true, matmul_id, true, false, true);
+            // Coopmat, fp32acc and fp16acc
+            matmul_shaders(true, matmul_id_type, true, false, false);
+            matmul_shaders(true, matmul_id_type, true, false, true);
 #endif
 
 #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
-        // Coopmat2, fp32acc and fp16acc
-        matmul_shaders(true, matmul_id, false, true, false);
-        matmul_shaders(true, matmul_id, false, true, true);
+            // Coopmat2, fp32acc and fp16acc
+            matmul_shaders(true, matmul_id_type, false, true, false);
+            matmul_shaders(true, matmul_id_type, false, true, true);
 #endif
+        }
     }
 
     // flash attention

From c247d06f38fc09059c9607a28aa44f5ff6be208d Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Mon, 25 Aug 2025 10:32:21 +0800
Subject: [PATCH 168/287] CANN: ROPE cache sin/cos repeat (#15501)

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 195 ++++++++++++++++++-------------
 ggml/src/ggml-cann/common.h      |  28 +++--
 2 files changed, 135 insertions(+), 88 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 8f65904b8f..bc33b99d96 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1257,12 +1257,20 @@ static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) {
 
 void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src,
                       aclTensor* acl_dst) {
-    GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst);
+    if(acl_dst == nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCos, acl_src);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst);
+    }
 }
 
 void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src,
                       aclTensor* acl_dst) {
-    GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst);
+    if(acl_dst == nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSin, acl_src);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst);
+    }
 }
 
 void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx,
@@ -2221,13 +2229,54 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
     ggml_cann_release_resources(ctx, acl_index, acl_value);
 }
 
+/**
+ * @brief Initializes and caches sine/cosine positional encoding values
+ *        (used in RoPE, Rotary Position Embedding) for attention layers.
+ *
+ * This function computes and caches the sin/cos values of
+ * θ = position * theta_scale for RoPE encoding. The cache is shared
+ * across attention layers, and only the first attention layer will
+ * trigger initialization. The cache includes repeated sin/cos values
+ * with different repeat methods depending on the @param is_neox flag.
+ *
+ * Steps performed by this function:
+ *   1. Identify whether the target tensor belongs to Q/K in attention
+ *      and restrict computation to the first layer only.
+ *   2. Initialize the theta scale array (arange → power → freq scaling).
+ *   3. Allocate sin/cos caches if the max prompt length increases.
+ *   4. Compute θ = position * theta_scale.
+ *   5. Compute sin(θ), cos(θ) and optionally scale by attn_factor.
+ *   6. Expand sin/cos values by repeat or repeat_interleave depending
+ *      on whether @param is_neox is enabled.
+ *   7. Store the computed values into persistent buffers
+ *      (ctx.rope_sin_ptr / ctx.rope_cos_ptr).
+ *
+ * @param ctx         The CANN backend context, holding memory pool,
+ *                    stream, and persistent buffers for rope init/cache.
+ * @param dst         The destination ggml_tensor whose computation
+ *                    depends on the cached RoPE values (usually Qcur/Kcur).
+ * @param theta_scale Scalar exponent base for computing theta scale values.
+ * @param freq_scale  Frequency scaling factor, applied to theta scale.
+ * @param attn_factor Attention scaling factor, applied to sin/cos.
+ * @param is_neox     Whether to use Neox-style repeat strategy
+ *                    (dim expansion vs repeat_interleave).
+ */
 static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
-                             aclTensor* acl_cos_repeat_tensor,
-                             aclTensor* acl_sin_repeat_tensor,
                              float theta_scale, float freq_scale,
                              float attn_factor, bool is_neox) {
     // int sin/cos cache, cache has different repeat method depond on
     // @param.is_neox
+    bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
+    bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);
+
+    // used for accuracy testing
+    bool is_attention = is_q || is_k;
+
+    // just compute in first layer in attention
+    bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
+    if(is_attention && !is_fisrt_layer) {
+        return;
+    }
 
     ggml_tensor* src0 = dst->src[0];  // input
     ggml_tensor* src1 = dst->src[1];  // position
@@ -2253,21 +2302,16 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
     }
 
-    bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
-    bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);
-
-    // used for accuracy testing
-    bool is_attention = is_q || is_k;
-
-    if(ctx.init_ptr == nullptr || !is_attention) {
+    // init theta scale, just one time
+    if(ctx.rope_init_ptr == nullptr || !is_attention) {
         // theta_scale arange, [0,1,...,ne00/2 - 1]
-        if(ctx.init_ptr != nullptr){
-            ACL_CHECK(aclrtFree(ctx.init_ptr));
+        if(ctx.rope_init_ptr != nullptr){
+            ACL_CHECK(aclrtFree(ctx.rope_init_ptr));
         }
-        ACL_CHECK(aclrtMalloc(&ctx.init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
 
         aclTensor* acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
+            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
         float start = 0;
         float step = 1;
@@ -2297,67 +2341,55 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         ggml_cann_release_resources(ctx, acl_theta_scale_tensor,acl_theta_scale);
     }
 
-    if(ctx.sin_ptr == nullptr) {
-        int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
-        ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
-        ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
-    }
+    // init sin_repeat && cos_repeat, one token just init in 0 layer
     if(position_length > ctx.max_prompt_length) {
         ctx.max_prompt_length = position_length;
-        int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
-        ACL_CHECK(aclrtFree(ctx.sin_ptr));
-        ACL_CHECK(aclrtFree(ctx.cos_ptr));
-        ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
-        ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        int64_t repeat_theta_length = theta_scale_length * ctx.max_prompt_length * 2;
+        if(ctx.rope_sin_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_sin_ptr));
+            ACL_CHECK(aclrtFree(ctx.rope_cos_ptr));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.rope_sin_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cos_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
     }
 
-    bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
-
-    if(is_fisrt_layer || !is_attention) {
-
-        aclTensor* acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
+    aclTensor* acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
 
-        // position
-        aclTensor* acl_position_tensor = ggml_cann_create_tensor(
-            src1->data, ggml_cann_type_mapping(src1->type),
-            ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
+    // position
+    aclTensor* acl_position_tensor = ggml_cann_create_tensor(
+        src1->data, ggml_cann_type_mapping(src1->type),
+        ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
 
-        // power * position
-        int64_t theta_length = theta_scale_length * position_length;
-        ggml_cann_pool_alloc theta_allocator(ctx.pool(),
-                                            theta_length * sizeof(float_t));
-        void* theta_buffer = theta_allocator.get();
+    // power * position
+    int64_t theta_length = theta_scale_length * position_length;
+    ggml_cann_pool_alloc theta_allocator(ctx.pool(),
+                                        theta_length * sizeof(float_t));
+    void* theta_buffer = theta_allocator.get();
 
-        aclTensor* acl_theta_tensor =
-            ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
-                                    theta_ne, theta_nb, GGML_MAX_DIMS);
-        aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
-                acl_theta_tensor);
-
-        // sin/cos
-        aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-            ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-            GGML_MAX_DIMS, ACL_FORMAT_ND);
-        aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
-
-        aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-            ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-            GGML_MAX_DIMS, ACL_FORMAT_ND);
-        aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
-
-        // release
-        ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
-            acl_theta_tensor, acl_sin_tensor, acl_cos_tensor);
-    }
+    aclTensor* acl_theta_tensor =
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
+                                theta_ne, theta_nb, GGML_MAX_DIMS);
+    aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
+            acl_theta_tensor);
 
+    // sin/cos
+    ggml_cann_pool_alloc sin_allocator(ctx.pool(),
+                                    theta_length * sizeof(float_t));
+    void* sin_buffer = sin_allocator.get();
     aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-            ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-            GGML_MAX_DIMS, ACL_FORMAT_ND);
+        sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
+
+    ggml_cann_pool_alloc cos_allocator(ctx.pool(),
+                                    theta_length * sizeof(float_t));
+    void* cos_buffer = cos_allocator.get();
     aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-            ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
-            GGML_MAX_DIMS, ACL_FORMAT_ND);
+        cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
 
     // attn_factor
     if (attn_factor != 1) {
@@ -2365,6 +2397,19 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true);
     }
 
+    int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
+    size_t sin_reshape_nb[GGML_MAX_DIMS];
+    sin_reshape_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
+    }
+    aclTensor* acl_sin_repeat_tensor =
+        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    aclTensor* acl_cos_repeat_tensor =
+        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+
     // repeat
     if (is_neox) {
         int64_t repeatsArray[] = {1, 1, 1, 2};
@@ -2380,8 +2425,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
                                 num_repeats, output_size);
     }
 
-    // release
-    ggml_cann_release_resources(ctx, acl_sin_tensor, acl_cos_tensor);
+    ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
+        acl_theta_tensor, acl_sin_tensor, acl_sin_repeat_tensor, acl_cos_tensor,
+        acl_cos_repeat_tensor);
 }
 
 #ifdef __cplusplus
@@ -2435,13 +2481,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
 
-    // init cos/sin cache
-    ggml_cann_pool_alloc sin_allocator(
-        ctx.pool(), ne00 * ne02 * sizeof(float_t));
-    ggml_cann_pool_alloc cos_allocator(
-        ctx.pool(), ne00 * ne02 * sizeof(float_t));
-    void* sin_buffer = sin_allocator.get();
-    void* cos_buffer = cos_allocator.get();
+    // init ctx.rope_cos/rope_sin cache
+    aclnn_cache_init(ctx, dst, theta_scale, freq_scale, attn_factor, is_neox);
 
     int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
@@ -2450,13 +2491,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_reshape_tensor =
-        ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_reshape_tensor =
-        ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
-    aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor,
-                    theta_scale, freq_scale, attn_factor, is_neox);
 
     aclTensor* acl_src = ggml_cann_create_tensor(src0);
     aclTensor* acl_dst = ggml_cann_create_tensor(dst);
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 5858bd3f6a..33794062f5 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -368,10 +368,6 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
-    void* init_ptr = nullptr;
-    void* sin_ptr = nullptr;
-    void* cos_ptr = nullptr;
-    int64_t max_prompt_length = 65536;
 #ifdef USE_ACL_GRAPH
     /// Cached CANN ACL graph used for executing the current ggml computation graph.
     std::unique_ptr<ggml_cann_graph> cann_graph;
@@ -379,6 +375,12 @@ struct ggml_backend_cann_context {
     cann_task_queue task_queue;
     bool async_mode;
     bool support_set_rows;
+    // Rope Cache
+    void* rope_init_ptr = nullptr;
+    void* rope_sin_ptr = nullptr;
+    void* rope_cos_ptr = nullptr;
+    int64_t max_prompt_length = 0;
+    // Constant Pool
     void* f32_zero_cache = nullptr;
     void* f32_one_cache = nullptr;
     int64_t f32_zero_cache_element = 0;
@@ -422,14 +424,20 @@ struct ggml_backend_cann_context {
                 ACL_CHECK(aclrtDestroyStream(streams[i]));
             }
         }
-        if(init_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(init_ptr));
+        if(rope_init_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_init_ptr));
         }
-        if(sin_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(sin_ptr));
+        if(rope_sin_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_sin_ptr));
         }
-        if(cos_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(cos_ptr));
+        if(rope_cos_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_cos_ptr));
+        }
+        if(f32_zero_cache != nullptr) {
+            ACL_CHECK(aclrtFree(f32_zero_cache));
+        }
+        if(f32_one_cache != nullptr) {
+            ACL_CHECK(aclrtFree(f32_one_cache));
         }
     }
 

From 7da9fed0d6f1750a8783436f6f313b87e76e6378 Mon Sep 17 00:00:00 2001
From: RunningLeon <mnsheng@yeah.net>
Date: Mon, 25 Aug 2025 14:32:16 +0800
Subject: [PATCH 169/287] convert : support interns1-mini (#15412)

* support interns1-mini

* fix comment

* update
---
 convert_hf_to_gguf.py | 133 +++++++++++++++++++++---------------------
 1 file changed, 65 insertions(+), 68 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 35fadbc83e..12ecd64515 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1216,6 +1216,55 @@ class TextModel(ModelBase):
                 raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
             self.gguf_writer.add_pooling_type(pooling_type)
 
+    def _set_vocab_interns1(self):
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
+        vocab_size = self.hparams.get("vocab_size", len(vocab))
+        assert max(vocab.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab._set_special_token("bos", 151643)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
 
 class MmprojModel(ModelBase):
     model_type = ModelType.MMPROJ
@@ -2932,7 +2981,8 @@ class Qwen2Model(TextModel):
         if "language_model." in name:
             name = name.replace("language_model.", "") # for InternVL
         if name.startswith("mlp") or name.startswith("multi_modal_projector") \
-                or name.startswith("vision_model") or name.startswith("audio_tower"):
+                or name.startswith("vision_model") or name.startswith("audio_tower") \
+                or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
             # skip vision and audio tensors
             return []
         yield from super().modify_tensors(data_torch, name, bid)
@@ -3604,6 +3654,19 @@ class Qwen2MoeModel(TextModel):
 class Qwen3Model(Qwen2Model):
     model_arch = gguf.MODEL_ARCH.QWEN3
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+    def set_vocab(self):
+        # deal with intern-s1-mini
+        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
+            self._set_vocab_interns1()
+            return
+
+        super().set_vocab()
+
 
 @ModelBase.register("Qwen3MoeForCausalLM")
 class Qwen3MoeModel(Qwen2MoeModel):
@@ -3620,73 +3683,7 @@ class Qwen3MoeModel(Qwen2MoeModel):
             self._set_vocab_interns1()
             return
 
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def _set_vocab_interns1(self):
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
-        vocab_size = self.hparams.get("vocab_size", len(vocab))
-        assert max(vocab.values()) < vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
-        additional_special_tokens = []
-        if special_tokens_map_file.is_file():
-            with open(special_tokens_map_file, encoding = 'utf-8') as f:
-                additional_special_tokens = json.load(f).get('additional_special_tokens', [])
-        tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
-        if tokenizer_cfg_file.is_file():
-            with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
-                added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
-                token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
-                for token in additional_special_tokens:
-                    if token in token2ids_map:
-                        special_vocab._set_special_token(token, token2ids_map[token])
-        special_vocab._set_special_token('eos', 151645)
-        special_vocab._set_special_token("bos", 151643)
-        special_vocab.add_to_gguf(self.gguf_writer)
+        super().set_vocab()
 
 
 @ModelBase.register("GPT2LMHeadModel")

From b0ba31f525a2ad7fb539660be0c8f088c9869f6a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 25 Aug 2025 10:14:48 +0300
Subject: [PATCH 170/287] metal : add FA kernels for HS=40 (#15559)

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal.m     | 53 ++++++++++++++++++++++++++++
 ggml/src/ggml-metal/ggml-metal.metal | 17 +++++++++
 2 files changed, 70 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 7c70d352df..b2ec7a263f 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -443,6 +443,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
     GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
@@ -452,6 +453,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96,
@@ -461,6 +463,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96,
@@ -470,6 +473,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96,
@@ -479,6 +483,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96,
@@ -488,6 +493,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96,
@@ -497,6 +503,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96,
@@ -506,6 +513,13 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,
@@ -1459,6 +1473,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,             argsort_f32_i32_asc,             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,            argsort_f32_i32_desc,            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,                  leaky_relu_f32,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H40,          flash_attn_ext_f16_h40,          has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,          flash_attn_ext_f16_h64,          has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,          flash_attn_ext_f16_h80,          has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,          flash_attn_ext_f16_h96,          has_simdgroup_mm);
@@ -1468,6 +1483,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK192_HV128,  flash_attn_ext_f16_hk192_hv128,  has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,         flash_attn_ext_f16_h256,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512,  flash_attn_ext_f16_hk576_hv512,  has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H40,         flash_attn_ext_bf16_h40,         has_simdgroup_mm && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64,         flash_attn_ext_bf16_h64,         has_simdgroup_mm && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80,         flash_attn_ext_bf16_h80,         has_simdgroup_mm && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96,         flash_attn_ext_bf16_h96,         has_simdgroup_mm && use_bfloat);
@@ -1477,6 +1493,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK192_HV128, flash_attn_ext_bf16_hk192_hv128, has_simdgroup_mm && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256,        flash_attn_ext_bf16_h256,        has_simdgroup_mm && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512, flash_attn_ext_bf16_hk576_hv512, has_simdgroup_mm && use_bfloat);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H40,         flash_attn_ext_q4_0_h40,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64,         flash_attn_ext_q4_0_h64,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80,         flash_attn_ext_q4_0_h80,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96,         flash_attn_ext_q4_0_h96,         has_simdgroup_mm);
@@ -1486,6 +1503,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK192_HV128, flash_attn_ext_q4_0_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256,        flash_attn_ext_q4_0_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512, flash_attn_ext_q4_0_hk576_hv512, has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H40,         flash_attn_ext_q4_1_h40,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64,         flash_attn_ext_q4_1_h64,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80,         flash_attn_ext_q4_1_h80,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96,         flash_attn_ext_q4_1_h96,         has_simdgroup_mm);
@@ -1495,6 +1513,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK192_HV128, flash_attn_ext_q4_1_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256,        flash_attn_ext_q4_1_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512, flash_attn_ext_q4_1_hk576_hv512, has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H40,         flash_attn_ext_q5_0_h40,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64,         flash_attn_ext_q5_0_h64,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80,         flash_attn_ext_q5_0_h80,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96,         flash_attn_ext_q5_0_h96,         has_simdgroup_mm);
@@ -1504,6 +1523,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK192_HV128, flash_attn_ext_q5_0_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256,        flash_attn_ext_q5_0_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512, flash_attn_ext_q5_0_hk576_hv512, has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H40,         flash_attn_ext_q5_1_h40,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64,         flash_attn_ext_q5_1_h64,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80,         flash_attn_ext_q5_1_h80,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96,         flash_attn_ext_q5_1_h96,         has_simdgroup_mm);
@@ -1513,6 +1533,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK192_HV128, flash_attn_ext_q5_1_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256,        flash_attn_ext_q5_1_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512, flash_attn_ext_q5_1_hk576_hv512, has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H40,         flash_attn_ext_q8_0_h40,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64,         flash_attn_ext_q8_0_h64,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80,         flash_attn_ext_q8_0_h80,         has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96,         flash_attn_ext_q8_0_h96,         has_simdgroup_mm);
@@ -1522,6 +1543,13 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128, flash_attn_ext_q8_0_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,        flash_attn_ext_q8_0_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512, flash_attn_ext_q8_0_hk576_hv512, has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40,      flash_attn_ext_vec_f16_h40,      has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40,     flash_attn_ext_vec_bf16_h40,     has_simdgroup_reduction && use_bfloat);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40,     flash_attn_ext_vec_q4_0_h40,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40,     flash_attn_ext_vec_q4_1_h40,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40,     flash_attn_ext_vec_q5_0_h40,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40,     flash_attn_ext_vec_q5_1_h40,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40,     flash_attn_ext_vec_q8_0_h40,     has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,      flash_attn_ext_vec_f16_h64,      has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,     flash_attn_ext_vec_bf16_h64,     has_simdgroup_reduction && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,     flash_attn_ext_vec_q4_0_h64,     has_simdgroup_reduction);
@@ -5130,6 +5158,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
@@ -5154,6 +5183,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break;
@@ -5178,6 +5208,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96 ].pipeline; break;
@@ -5202,6 +5233,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96 ].pipeline; break;
@@ -5226,6 +5258,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96 ].pipeline; break;
@@ -5250,6 +5283,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96 ].pipeline; break;
@@ -5274,6 +5308,7 @@ static int ggml_metal_encode_node(
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512].pipeline;
                                 } else {
                                     switch (ne00) {
+                                        case 40:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H40 ].pipeline; break;
                                         case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64 ].pipeline; break;
                                         case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80 ].pipeline; break;
                                         case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96 ].pipeline; break;
@@ -5301,6 +5336,24 @@ static int ggml_metal_encode_node(
                     use_vec_kernel = true;
 
                     switch (ne00) {
+                        case 40:
+                            {
+                                switch (src1->type) {
+                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40].pipeline; break;
+                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40].pipeline; break;
+                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40].pipeline; break;
+                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40].pipeline; break;
+                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40].pipeline; break;
+                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40].pipeline; break;
+                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40].pipeline; break;
+                                    default:
+                                        {
+                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
+                                            GGML_LOG_ERROR("add template specialization for this type\n");
+                                            GGML_ABORT("add template specialization for this type");
+                                        }
+                                }
+                            } break;
                         case 64:
                             {
                                 switch (src1->type) {
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index b35a3bbdc3..3dd55fd325 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -4663,6 +4663,7 @@ kernel void kernel_flash_attn_ext(
 
 typedef decltype(kernel_flash_attn_ext<FA_TYPES, half4x4, 1, dequantize_f16, half4x4, 1, dequantize_f16, 64, 64>) flash_attn_ext_t;
 
+template [[host_name("kernel_flash_attn_ext_f16_h40" )]]         kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  40,  40>;
 template [[host_name("kernel_flash_attn_ext_f16_h64" )]]         kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  64,  64>;
 template [[host_name("kernel_flash_attn_ext_f16_h80" )]]         kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  80,  80>;
 template [[host_name("kernel_flash_attn_ext_f16_h96" )]]         kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  96,  96>;
@@ -4674,6 +4675,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h256")]]         kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f16_hk576_hv512")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  576, 512>;
 
 #if defined(GGML_METAL_USE_BF16)
+template [[host_name("kernel_flash_attn_ext_bf16_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_bf16_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_bf16_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_bf16_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 96,  96>;
@@ -4685,6 +4687,7 @@ template [[host_name("kernel_flash_attn_ext_bf16_h256")]]        kernel flash_at
 template [[host_name("kernel_flash_attn_ext_bf16_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 576, 512>;
 #endif
 
+template [[host_name("kernel_flash_attn_ext_q4_0_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_q4_0_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_q4_0_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q4_0_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 96,  96>;
@@ -4695,6 +4698,7 @@ template [[host_name("kernel_flash_attn_ext_q4_0_hk192_hv128")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_0_h256")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q4_0_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 576, 512>;
 
+template [[host_name("kernel_flash_attn_ext_q4_1_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_q4_1_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_q4_1_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q4_1_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 96,  96>;
@@ -4705,6 +4709,7 @@ template [[host_name("kernel_flash_attn_ext_q4_1_hk192_hv128")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_1_h256")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q4_1_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 576, 512>;
 
+template [[host_name("kernel_flash_attn_ext_q5_0_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_q5_0_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_q5_0_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q5_0_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 96,  96>;
@@ -4715,6 +4720,7 @@ template [[host_name("kernel_flash_attn_ext_q5_0_hk192_hv128")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_0_h256")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q5_0_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 576, 512>;
 
+template [[host_name("kernel_flash_attn_ext_q5_1_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_q5_1_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_q5_1_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q5_1_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 96,  96>;
@@ -4725,6 +4731,7 @@ template [[host_name("kernel_flash_attn_ext_q5_1_hk192_hv128")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_1_h256")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q5_1_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 576, 512>;
 
+template [[host_name("kernel_flash_attn_ext_q8_0_h40" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 40,  40>;
 template [[host_name("kernel_flash_attn_ext_q8_0_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 64,  64>;
 template [[host_name("kernel_flash_attn_ext_q8_0_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q8_0_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 96,  96>;
@@ -5115,6 +5122,16 @@ kernel void kernel_flash_attn_ext_vec(
 
 typedef decltype(kernel_flash_attn_ext_vec<FA_TYPES, half4, 1, dequantize_f16_t4, half4, 1, dequantize_f16_t4, 128, 128, 4>) flash_attn_ext_vec_t;
 
+template [[host_name("kernel_flash_attn_ext_vec_f16_h40")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  40, 40, 8>;
+#if defined(GGML_METAL_USE_BF16)
+template [[host_name("kernel_flash_attn_ext_vec_bf16_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 40, 40, 8>;
+#endif
+template [[host_name("kernel_flash_attn_ext_vec_q4_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_0,        8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 40, 40, 8>;
+template [[host_name("kernel_flash_attn_ext_vec_q4_1_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_1,        8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 40, 40, 8>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_0,        8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 40, 40, 8>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_1_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_1,        8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 40, 40, 8>;
+template [[host_name("kernel_flash_attn_ext_vec_q8_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q8_0,        8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 40, 40, 8>;
+
 template [[host_name("kernel_flash_attn_ext_vec_f16_h64")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  64, 64, 8>;
 #if defined(GGML_METAL_USE_BF16)
 template [[host_name("kernel_flash_attn_ext_vec_bf16_h64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 64, 64, 8>;

From 0d5a470223fc90b6b6807921d68011ff06ae7f9e Mon Sep 17 00:00:00 2001
From: Weizhao Ouyang <o451686892@gmail.com>
Date: Mon, 25 Aug 2025 17:15:06 +0800
Subject: [PATCH 171/287] convert : update Ernie 4.5 dense architecture name
 (#15555)

Signed-off-by: Weizhao Ouyang <o451686892@gmail.com>
---
 convert_hf_to_gguf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 12ecd64515..9fa35e8b11 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3159,7 +3159,7 @@ class LLaDAModel(TextModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Ernie4_5_ForCausalLM")
+@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
 class Ernie4_5Model(TextModel):
     model_arch = gguf.MODEL_ARCH.ERNIE4_5
 

From 6b64f74b55628e4193f4fb00313f07dbd8556528 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 25 Aug 2025 13:56:43 +0300
Subject: [PATCH 172/287] batched-bench : fix unified KV cache handling + pp
 timing (#15562)

* batched-bench : fix unified KV cache handling + pp timing

* cont : run dummy token only with split KV cache
---
 tools/batched-bench/batched-bench.cpp | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp
index c6c601add3..93efad3280 100644
--- a/tools/batched-bench/batched-bench.cpp
+++ b/tools/batched-bench/batched-bench.cpp
@@ -124,7 +124,7 @@ int main(int argc, char ** argv) {
                 const int tg = n_tg[i_tg];
                 const int pl = n_pl[i_pl];
 
-                const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg);
+                const int n_ctx_req = is_pp_shared ? (params.kv_unified ? pp : pl*pp) + pl*tg : pl*(pp + tg);
 
                 if (n_ctx_req > n_kv_max) {
                     continue;
@@ -147,13 +147,24 @@ int main(int argc, char ** argv) {
                     return 1;
                 }
 
+                const auto t_pp_end = ggml_time_us();
+
                 if (is_pp_shared) {
                     for (int32_t i = 1; i < pl; ++i) {
                         llama_memory_seq_cp(mem, 0, i, -1, -1);
                     }
-                }
 
-                const auto t_pp_end = ggml_time_us();
+                    if (!params.kv_unified) {
+                        // run one dummy token to apply the memory copy
+                        common_batch_clear(batch);
+                        common_batch_add(batch, get_token_rand(), pp + 0, { 0 }, true);
+                        if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
+                            LOG_ERR("%s: llama_decode() failed\n", __func__);
+                            return 1;
+                        }
+                        llama_memory_seq_rm(mem, 0, pp, -1);
+                    }
+                }
 
                 const auto t_tg_start = ggml_time_us();
 

From 5a6bc6b1a6cb665a944426c2055794950e524bf5 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 25 Aug 2025 14:25:25 +0200
Subject: [PATCH 173/287] model-conversion : add model card template for
 embeddings [no ci] (#15557)

* model-conversion: add model card template for embeddings [no ci]

This commit adds a separate model card template (model repository
README.md template) for embedding models.

The motivation for this is that there server command for the embedding
model is a little different and some addition information can be useful
in the model card for embedding models which might not be directly
relevant for causal models.

* squash! model-conversion: add model card template for embeddings [no ci]

Fix pyright lint error.

* remove --pooling override and clarify embd_normalize usage
---
 examples/model-conversion/Makefile            |  9 ++++
 examples/model-conversion/README.md           | 10 +++-
 .../modelcard.template}                       |  0
 .../scripts/embedding/modelcard.template      | 48 +++++++++++++++++++
 .../scripts/utils/hf-create-model.py          | 47 +++++++++++-------
 5 files changed, 97 insertions(+), 17 deletions(-)
 rename examples/model-conversion/scripts/{readme.md.template => causal/modelcard.template} (100%)
 create mode 100644 examples/model-conversion/scripts/embedding/modelcard.template

diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
index 27d95b4f2b..2f1c3eb903 100644
--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -144,6 +144,15 @@ perplexity-run:
 hf-create-model:
 	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}"
 
+hf-create-model-dry-run:
+	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -d
+
+hf-create-model-embedding:
+	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e
+
+hf-create-model-embedding-dry-run:
+	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e -d
+
 hf-create-model-private:
 	@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -p
 
diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md
index c924a6be3c..424c4e5655 100644
--- a/examples/model-conversion/README.md
+++ b/examples/model-conversion/README.md
@@ -285,13 +285,21 @@ For the following targets a `HF_TOKEN` environment variable is required.
 This will create a new model repsository on Hugging Face with the specified
 model name.
 ```console
-(venv) $ make hf-create-model MODEL_NAME='TestModel' NAMESPACE="danbev"
+(venv) $ make hf-create-model MODEL_NAME='TestModel' NAMESPACE="danbev" ORIGINAL_BASE_MODEL="some-base-model"
 Repository ID:  danbev/TestModel-GGUF
 Repository created: https://huggingface.co/danbev/TestModel-GGUF
 ```
 Note that we append a `-GGUF` suffix to the model name to ensure a consistent
 naming convention for GGUF models.
 
+An embedding model can be created using the following command:
+```console
+(venv) $ make hf-create-model-embedding MODEL_NAME='TestEmbeddingModel' NAMESPACE="danbev" ORIGINAL_BASE_MODEL="some-base-model"
+```
+The only difference is that the model card for an embedding model will be different
+with regards to the llama-server command and also how to access/call the embedding
+endpoint.
+
 ### Upload a GGUF model to model repository
 The following target uploads a model to an existing Hugging Face model repository.
 ```console
diff --git a/examples/model-conversion/scripts/readme.md.template b/examples/model-conversion/scripts/causal/modelcard.template
similarity index 100%
rename from examples/model-conversion/scripts/readme.md.template
rename to examples/model-conversion/scripts/causal/modelcard.template
diff --git a/examples/model-conversion/scripts/embedding/modelcard.template b/examples/model-conversion/scripts/embedding/modelcard.template
new file mode 100644
index 0000000000..75c580524f
--- /dev/null
+++ b/examples/model-conversion/scripts/embedding/modelcard.template
@@ -0,0 +1,48 @@
+---
+base_model:
+- {base_model}
+---
+# {model_name} GGUF
+
+Recommended way to run this model:
+
+```sh
+llama-server -hf {namespace}/{model_name}-GGUF
+```
+
+Then the endpoint can be accessed at http://localhost:8080/embedding, for
+example using `curl`:
+```console
+curl --request POST \
+    --url http://localhost:8080/embedding \
+    --header "Content-Type: application/json" \
+    --data '{{"input": "Hello embeddings"}}' \
+    --silent
+```
+
+Alternatively, the `llama-embedding` command line tool can be used:
+```sh
+llama-embedding -hf {namespace}/{model_name}-GGUF --verbose-prompt -p "Hello embeddings"
+```
+
+#### embd_normalize
+When a model uses pooling, or the pooling method is specified using `--pooling`,
+the normalization can be controlled by the `embd_normalize` parameter.
+
+The default value is `2` which means that the embeddings are normalized using
+the Euclidean norm (L2). Other options are:
+* -1 No normalization
+*  0 Max absolute
+*  1 Taxicab
+*  2 Euclidean/L2
+* \>2 P-Norm
+
+This can be passed in the request body to `llama-server`, for example:
+```sh
+    --data '{{"input": "Hello embeddings", "embd_normalize": -1}}' \
+```
+
+And for `llama-embedding`, by passing `--embd-normalize <value>`, for example:
+```sh
+llama-embedding -hf {namespace}/{model_name}-GGUF  --embd-normalize -1 -p "Hello embeddings"
+```
diff --git a/examples/model-conversion/scripts/utils/hf-create-model.py b/examples/model-conversion/scripts/utils/hf-create-model.py
index 09bb8511ef..ea99bd886f 100755
--- a/examples/model-conversion/scripts/utils/hf-create-model.py
+++ b/examples/model-conversion/scripts/utils/hf-create-model.py
@@ -26,21 +26,31 @@ parser.add_argument('--namespace', '-ns', help='Namespace to add the model to',
 parser.add_argument('--org-base-model', '-b', help='Original Base model name', default="")
 parser.add_argument('--no-card', action='store_true', help='Skip creating model card')
 parser.add_argument('--private', '-p', action='store_true', help='Create private model')
+parser.add_argument('--embedding', '-e', action='store_true', help='Use embedding model card template')
+parser.add_argument('--dry-run', '-d', action='store_true', help='Print repository info and template without creating repository')
 
 args = parser.parse_args()
 
 repo_id = f"{args.namespace}/{args.model_name}-GGUF"
 print("Repository ID: ", repo_id)
 
-repo_url = api.create_repo(
-    repo_id=repo_id,
-    repo_type="model",
-    private=args.private,
-    exist_ok=False
-)
+repo_url = None
+if not args.dry_run:
+    repo_url = api.create_repo(
+        repo_id=repo_id,
+        repo_type="model",
+        private=args.private,
+        exist_ok=False
+    )
 
 if not args.no_card:
-    template_path = "scripts/readme.md.template"
+    if args.embedding:
+        template_path = "scripts/embedding/modelcard.template"
+    else:
+        template_path = "scripts/causal/modelcard.template"
+
+    print("Template path: ", template_path)
+
     model_card_content = load_template_and_substitute(
         template_path,
         model_name=args.model_name,
@@ -48,16 +58,21 @@ if not args.no_card:
         base_model=args.org_base_model,
     )
 
-    if model_card_content:
-        api.upload_file(
-            path_or_fileobj=model_card_content.encode('utf-8'),
-            path_in_repo="README.md",
-            repo_id=repo_id
-        )
-        print("Model card created successfully.")
+    if args.dry_run:
+        print("\nTemplate Content:\n")
+        print(model_card_content)
     else:
-        print("Failed to create model card.")
+        if model_card_content:
+            api.upload_file(
+                path_or_fileobj=model_card_content.encode('utf-8'),
+                path_in_repo="README.md",
+                repo_id=repo_id
+            )
+            print("Model card created successfully.")
+        else:
+            print("Failed to create model card.")
 
-print(f"Repository created: {repo_url}")
+if not args.dry_run and repo_url:
+    print(f"Repository created: {repo_url}")
 
 

From dfd9b5f6c7586c88588f06a644c131bec071a0a1 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 25 Aug 2025 15:00:43 +0200
Subject: [PATCH 174/287] model-conversion : set pooling type to none in
 logits.cpp (#15564)

This commit explicitly sets the pooling type to 'none' in the logits.cpp
to support models that have a pooling type specified.

The motivation for this is that some models may have a pooling type set
in the model file (.gguf file) and for this specific case where we only
want to extract logits, we need to ensure that no pooling is used to
so that we are comparing raw logits and not pooled embeddings.
---
 examples/model-conversion/logits.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/model-conversion/logits.cpp b/examples/model-conversion/logits.cpp
index 2cac6a3b3e..ddc5e9005f 100644
--- a/examples/model-conversion/logits.cpp
+++ b/examples/model-conversion/logits.cpp
@@ -112,6 +112,7 @@ int main(int argc, char ** argv) {
     ctx_params.no_perf = false;
     if (embedding_mode) {
         ctx_params.embeddings = true;
+        ctx_params.pooling_type = LLAMA_POOLING_TYPE_NONE;
         ctx_params.n_ubatch = ctx_params.n_batch;
     }
 

From 5eff6ec9b1220b599a43b594b1110487ab6aca08 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 25 Aug 2025 17:23:40 +0200
Subject: [PATCH 175/287] CUDA: MoE helper in device code, better tile sizes
 (#15525)

* CUDA: MoE helper in device code, better tile sizes

* reduce superfluous CUDA blocks
---
 ggml/src/ggml-cuda/common.cuh    |  28 ++--
 ggml/src/ggml-cuda/mmq.cu        | 224 ++++++++++++++++++++++++-------
 ggml/src/ggml-cuda/mmq.cuh       |  34 +++--
 ggml/src/ggml-cuda/vendors/hip.h |   3 +
 4 files changed, 221 insertions(+), 68 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 767ad83f60..48de1649cf 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -420,16 +420,28 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_all(int x) {
-#ifdef GGML_USE_HIP
+    if (width == ggml_cuda_get_physical_warp_size()) {
+        return __all_sync(0xffffffff, x);
+    } else {
 #pragma unroll
-    for (int offset = width/2; offset > 0; offset >>= 1) {
-        x = x && __shfl_xor_sync(0xffffffff, x, offset, width);
+        for (int offset = width/2; offset > 0; offset >>= 1) {
+            x = __shfl_xor_sync(0xffffffff, x, offset, width) && x;
+        }
+        return x;
+    }
+}
+
+template<int width = WARP_SIZE>
+static __device__ __forceinline__ int warp_reduce_any(int x) {
+    if (width == ggml_cuda_get_physical_warp_size()) {
+        return __any_sync(0xffffffff, x);
+    } else {
+#pragma unroll
+        for (int offset = width/2; offset > 0; offset >>= 1) {
+            x = __shfl_xor_sync(0xffffffff, x, offset, width) || x;
+        }
+        return x;
     }
-    return x;
-#else
-    static_assert(width == WARP_SIZE, "width != WARP_SIZE not implemented");
-    return __all_sync(0xffffffff, x);
-#endif // GGML_USE_HIP
 }
 
 template<int width = WARP_SIZE>
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 576032a0ce..714b23f9f4 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -3,6 +3,140 @@
 
 #include <vector>
 
+// To reduce shared memory use, store "it" and "iex_used" with 22/10 bits each.
+struct mmq_ids_helper_store {
+    uint32_t data;
+
+    __device__ mmq_ids_helper_store(const uint32_t it, const uint32_t iex_used) {
+        data = (it & 0x003FFFFF) | (iex_used << 22);
+    }
+
+    __device__ uint32_t it() const {
+        return data & 0x003FFFFF;
+    }
+
+    __device__ uint32_t iex_used() const {
+        return data >> 22;
+    }
+};
+static_assert(sizeof(mmq_ids_helper_store) == 4, "unexpected size for mmq_ids_helper_store");
+
+// Helper function for mul_mat_id, converts ids to a more convenient format.
+// ids_src1 describes how to permute the flattened column indices of src1 in order to get a compact src1 tensor sorted by expert.
+// ids_dst describes the same mapping but for the dst tensor.
+// The upper and lower bounds for the ith expert in the compact src1 tensor are stored in expert_bounds[i:i+1].
+template <int n_expert_used_template>
+__launch_bounds__(ggml_cuda_get_physical_warp_size(), 1)
+static __global__ void mmq_ids_helper(
+        const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds,
+        const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) {
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+    const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template;
+    const int expert = blockIdx.x;
+
+    extern __shared__ char data_mmq_ids_helper[];
+    mmq_ids_helper_store * store = (mmq_ids_helper_store *) data_mmq_ids_helper;
+
+    int nex_prev   = 0; // Number of columns for experts with a lower index.
+    int it_compact = 0; // Running index for the compact slice of this expert.
+
+    if constexpr (n_expert_used_template == 0) {
+        // Generic implementation:
+        for (int it = 0; it < n_tokens; ++it) {
+            int iex_used = -1; // The index at which the expert is used, if any.
+            for (int iex = threadIdx.x; iex < n_expert_used; iex += warp_size) {
+                const int expert_used = ids[it*si1 + iex];
+                nex_prev += expert_used < expert;
+                if (expert_used == expert) {
+                    iex_used = iex;
+                }
+            }
+
+            if (iex_used != -1) {
+                store[it_compact] = mmq_ids_helper_store(it, iex_used);
+            }
+
+            if (warp_reduce_any<warp_size>(iex_used != -1)) {
+                it_compact++;
+            }
+        }
+    } else {
+        // Implementation optimized for specific numbers of experts used:
+        static_assert(n_expert_used == 6 || warp_size % n_expert_used == 0, "bad n_expert_used");
+        const int neu_padded = n_expert_used == 6 ? 8 : n_expert_used; // Padded to next higher power of 2.
+        for (int it0 = 0; it0 < n_tokens; it0 += warp_size/neu_padded) {
+            const int it = it0 + threadIdx.x / neu_padded;
+
+            const int iex = threadIdx.x % neu_padded; // The index at which the expert is used, if any.
+            const int expert_used = (neu_padded == n_expert_used || iex < n_expert_used) && it < n_tokens ?
+                ids[it*si1 + iex] : INT_MAX;
+            const int iex_used = expert_used == expert ? iex : -1;
+            nex_prev += expert_used < expert;
+
+            // Whether the threads at this token position have used the expert:
+            const int it_compact_add_self = warp_reduce_any<neu_padded>(iex_used != -1);
+
+            // Do a scan over threads at lower token positions in warp to get the correct index for writing data:
+            int it_compact_add_lower = 0;
+#pragma unroll
+            for (int offset = neu_padded; offset < warp_size; offset += neu_padded) {
+                const int tmp = __shfl_up_sync(0xFFFFFFFF, it_compact_add_self, offset, warp_size);
+                if (threadIdx.x >= offset) {
+                    it_compact_add_lower += tmp;
+                }
+            }
+
+            if (iex_used != -1) {
+                store[it_compact + it_compact_add_lower] = mmq_ids_helper_store(it, iex_used);
+            }
+
+            // The thread with the highest index in the warp always has the sum over the whole warp, use it to increment all threads:
+            it_compact += __shfl_sync(0xFFFFFFFF, it_compact_add_lower + it_compact_add_self, warp_size - 1, warp_size);
+        }
+    }
+    nex_prev = warp_reduce_sum<warp_size>(nex_prev);
+
+    for (int itc = threadIdx.x; itc < it_compact; itc += warp_size) {
+        const mmq_ids_helper_store store_it = store[itc];
+        const int it       = store_it.it();
+        const int iex_used = store_it.iex_used();
+        ids_src1[nex_prev + itc] = it*sis1          + iex_used % nchannels_y;
+        ids_dst [nex_prev + itc] = it*n_expert_used + iex_used;
+    }
+
+    if (threadIdx.x != 0) {
+        return;
+    }
+
+    expert_bounds[expert] = nex_prev;
+
+    if (expert < gridDim.x - 1) {
+        return;
+    }
+
+    expert_bounds[gridDim.x] = nex_prev + it_compact;
+}
+
+template <int n_expert_used_template>
+static void launch_mmq_ids_helper(
+        const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds,
+        const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) {
+    GGML_ASSERT(n_tokens          < (1 << 22) && "too few bits in mmq_ids_helper_store");
+    GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mmq_ids_helper_store");
+
+    const int id = ggml_cuda_get_device();
+    const int warp_size = ggml_cuda_info().devices[id].warp_size;
+    const size_t smpbo = ggml_cuda_info().devices[id].smpbo;
+    CUDA_SET_SHARED_MEMORY_LIMIT(mmq_ids_helper<n_expert_used_template>, smpbo);
+
+    const dim3 num_blocks(n_experts, 1, 1);
+    const dim3 block_size(warp_size, 1, 1);
+    const size_t nbytes_shared = n_tokens*sizeof(mmq_ids_helper_store);
+    GGML_ASSERT(nbytes_shared <= smpbo);
+    mmq_ids_helper<n_expert_used_template><<<num_blocks, block_size, nbytes_shared, stream>>>
+        (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1);
+}
+
 static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
     switch (args.type_x) {
         case GGML_TYPE_Q4_0:
@@ -137,7 +271,7 @@ void ggml_cuda_mul_mat_q(
             ne00, ne01, ne1, s01, ne11, s1,
             ne02, ne12, s02, s12, s2,
             ne03, ne13, s03, s13, s3,
-            use_stream_k};
+            use_stream_k, ne1};
         ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
         return;
     }
@@ -148,54 +282,50 @@ void ggml_cuda_mul_mat_q(
 
     const int64_t n_expert_used = ids->ne[0];
     const int64_t ne_get_rows = ne12 * n_expert_used;
+    GGML_ASSERT(ne1 == n_expert_used);
 
-    std::vector<char> ids_host(ggml_nbytes(ids));
-    std::vector<int32_t> ids_src1_host;
-    ids_src1_host.reserve(ne_get_rows);
-    std::vector<int32_t> ids_dst_host;
-    ids_dst_host.reserve(ne_get_rows);
-    std::vector<int32_t> tokens_per_expert_host(ne02);
-    std::vector<int32_t> expert_bounds_host(ne02 + 1);
-    ggml_cuda_pool_alloc<int32_t> ids_buf_dev(ctx.pool());
+    ggml_cuda_pool_alloc<int32_t> ids_src1(ctx.pool(), ne_get_rows);
+    ggml_cuda_pool_alloc<int32_t> ids_dst(ctx.pool(), ne_get_rows);
+    ggml_cuda_pool_alloc<int32_t> expert_bounds(ctx.pool(), ne02 + 1);
 
-    CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids->data, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
-    CUDA_CHECK(cudaStreamSynchronize(stream));
+    {
+        GGML_ASSERT(ids->nb[0] == ggml_element_size(ids));
+        const int si1  = ids->nb[1] / ggml_element_size(ids);
+        const int sis1 = nb12 / nb11;
 
-    for (int64_t i02 = 0; i02 < ne02; ++i02) { // expert matrices
-        for (int64_t i12 = 0; i12 < ne12; ++i12) { // tokens
-            for (int64_t iex = 0; iex < n_expert_used; ++iex) {
-                const int32_t expert_to_use = *(const int32_t *)(ids_host.data() + i12*ids->nb[1] + iex*ids->nb[0]);
-                assert(expert_to_use >= 0 && expert_to_use < ne02);
-                if (expert_to_use == i02) {
-                    ids_src1_host.push_back(i12*(nb12/nb11) + iex % ne11);
-                    ids_dst_host.push_back(i12*ne1 + iex);
-                    tokens_per_expert_host[i02]++;
-                    break;
-                }
-            }
+        switch (n_expert_used) {
+            case  2:
+                launch_mmq_ids_helper< 2> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            case  4:
+                launch_mmq_ids_helper< 4> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            case  6:
+                launch_mmq_ids_helper< 6> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            case  8:
+                launch_mmq_ids_helper< 8> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            case 16:
+                launch_mmq_ids_helper<16> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            case 32:
+                launch_mmq_ids_helper<32> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
+            default:
+                launch_mmq_ids_helper< 0> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
+                    ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
+                break;
         }
+        CUDA_CHECK(cudaGetLastError());
     }
 
-    int32_t cumsum = 0;
-    for (int64_t i = 0; i < ne02; ++i) {
-        expert_bounds_host[i] = cumsum;
-        cumsum += tokens_per_expert_host[i];
-    }
-    expert_bounds_host[ne02] = cumsum;
-
-    std::vector<int32_t> ids_buf_host;
-    ids_buf_host.reserve(ids_src1_host.size() + ids_dst_host.size() + expert_bounds_host.size());
-    ids_buf_host.insert(ids_buf_host.end(), ids_src1_host.begin(), ids_src1_host.end());
-    ids_buf_host.insert(ids_buf_host.end(), ids_dst_host.begin(), ids_dst_host.end());
-    ids_buf_host.insert(ids_buf_host.end(), expert_bounds_host.begin(), expert_bounds_host.end());
-    ids_buf_dev.alloc(ids_buf_host.size() + get_mmq_x_max_host(cc)); // Expert bounds are padded on device.
-    CUDA_CHECK(cudaMemcpyAsync(ids_buf_dev.ptr, ids_buf_host.data(), ids_buf_host.size()*sizeof(int32_t), cudaMemcpyHostToDevice, stream));
-    CUDA_CHECK(cudaStreamSynchronize(stream));
-
-    const int32_t * ids_src1_dev      = ids_buf_dev.ptr;
-    const int32_t * ids_dst_dev       = ids_src1_dev + ids_src1_host.size();
-    const int32_t * expert_bounds_dev = ids_dst_dev + ids_dst_host.size();
-
     const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * sizeof(block_q8_1)/QK8_1 +
         get_mmq_x_max_host(cc)*sizeof(block_q8_1_mmq);
     ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
@@ -208,7 +338,7 @@ void ggml_cuda_mul_mat_q(
         const int64_t s11 = src1->nb[1] / ts_src1;
         const int64_t s12 = src1->nb[2] / ts_src1;
         const int64_t s13 = src1->nb[2] / ts_src1;
-        quantize_mmq_q8_1_cuda(src1_d, ids_src1_dev, src1_q8_1.get(), src0->type,
+        quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type,
             ne10, s11, s12, s13, ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
         CUDA_CHECK(cudaGetLastError());
     }
@@ -218,11 +348,11 @@ void ggml_cuda_mul_mat_q(
 
     // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.
     const mmq_args args = {
-        src0_d, src0->type, (const int *) src1_q8_1.ptr, ids_dst_dev, expert_bounds_dev, dst_d,
+        src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d,
         ne00, ne01, ne_get_rows, s01, ne_get_rows, s1,
         ne02, ne02, s02, s12, s2,
         ne03, ne13, s03, s13, s3,
-        use_stream_k};
+        use_stream_k, ne12};
 
     ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
 }
@@ -262,7 +392,7 @@ void ggml_cuda_op_mul_mat_q(
         ne00, row_diff, src1_ncols, stride01, ne11, nrows_dst,
         1, 1, 0, 0, 0,
         1, 1, 0, 0, 0,
-        use_stream_k};
+        use_stream_k, src1_ncols};
 
     ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
 
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 650f708067..c9a07e82fe 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -3138,7 +3138,8 @@ static __global__ void mul_mat_q(
         const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup,
         const int ncols_x, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst,
         const int channel_ratio, const int nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
-        const int sample_ratio, const int nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
+        const int sample_ratio, const int nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
+        const int ncols_max) {
 
     // Skip unused template specializations for faster compilation:
     if (mmq_x > get_mmq_x_max_device() || mmq_x % mmq_get_granularity_device(mmq_x) != 0) {
@@ -3152,7 +3153,7 @@ static __global__ void mul_mat_q(
     constexpr int qk    = ggml_cuda_type_traits<type>::qk;
     constexpr int mmq_y = get_mmq_y_device();
 
-    const int ntx = (ncols_dst + mmq_x - 1) / mmq_x; // Number of tiles x
+    const int ntx = (ncols_max + mmq_x - 1) / mmq_x; // Number of tiles x
     const int nty = (nrows_x   + mmq_y - 1) / mmq_y; // Number of tiles y
 
     // Initialize the ids for writing back data with just the index.
@@ -3376,7 +3377,8 @@ template <ggml_type type, int mmq_x, bool need_check>
 static __global__ void mul_mat_q_stream_k_fixup(
         const int32_t * ids_dst, const int32_t * expert_bounds, float * __restrict__ dst, const float * __restrict__ tmp_last_tile,
         const int ncols_x, const int nrows_x, const int ncols_dst, const int stride_col_dst,
-        const int nchannels_y, const int stride_channel_dst, const int nsamples_y, const int stride_sample_dst) {
+        const int nchannels_y, const int stride_channel_dst, const int nsamples_y, const int stride_sample_dst,
+        const int ncols_max) {
     constexpr int     mmq_y           = get_mmq_y_device();
     constexpr int     qk              = ggml_cuda_type_traits<type>::qk;
     constexpr int     blocks_per_iter = MMQ_ITER_K / qk;
@@ -3387,7 +3389,7 @@ static __global__ void mul_mat_q_stream_k_fixup(
 
     float sum[mmq_x*mmq_y / (nwarps*warp_size)] = {0.0f};
 
-    const int ntx  = (ncols_dst + mmq_x - 1) / mmq_x;
+    const int ntx  = (ncols_max + mmq_x - 1) / mmq_x;
     const int nty  = (nrows_x   + mmq_y - 1) / mmq_y;
 
     const int bidx0 = blockIdx.x;
@@ -3528,7 +3530,7 @@ struct mmq_args {
     int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst;
     int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst;
     int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst;
-    bool use_stream_k;
+    bool use_stream_k; int64_t ncols_max;
 };
 
 template<ggml_type type>
@@ -3558,7 +3560,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     CUDA_SET_SHARED_MEMORY_LIMIT((mul_mat_q<type, mmq_x,  true>), nbytes_shared);
 
     const int nty  = (args.nrows_x   + mmq_y - 1) / mmq_y;
-    const int ntx  = (args.ncols_dst + mmq_x - 1) / mmq_x;
+    const int ntx  = (args.ncols_max + mmq_x - 1) / mmq_x;
     const int ntzw = args.nchannels_y * args.nsamples_y;
     const dim3 block_nums_xy_tiling(nty, ntx, ntzw);
 
@@ -3574,14 +3576,16 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
                 (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
                  args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
                  channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst);
+                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+                 args.ncols_max);
         } else {
             constexpr bool need_check = true;
             mul_mat_q<type, mmq_x, need_check><<<block_nums_xy_tiling, block_dims, nbytes_shared, stream>>>
                 (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
                  args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
                  channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst);
+                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+                 args.ncols_max);
         }
         return;
     }
@@ -3601,7 +3605,8 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
             (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
              args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
              channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst);
+             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+             args.ncols_max);
 
         if (!fixup_needed) {
             return;
@@ -3609,14 +3614,16 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
 
         mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, 0, stream>>>
             (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst,
-             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst);
+             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst,
+             args.ncols_max);
     } else {
         constexpr bool need_check = true;
         mul_mat_q<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, nbytes_shared, stream>>>
             (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
              args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
              channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst);
+             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+             args.ncols_max);
 
         if (!fixup_needed) {
             return;
@@ -3624,7 +3631,8 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
 
         mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, 0, stream>>>
             (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst,
-             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst);
+             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst,
+             args.ncols_max);
     }
 }
 
@@ -3649,7 +3657,7 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
             continue;
         }
 
-        const int ntiles_x = (args.ncols_y + mmq_x - 1) / mmq_x;
+        const int ntiles_x = (args.ncols_max + mmq_x - 1) / mmq_x;
 
         if (ntiles_x < ntiles_x_best) {
             mmq_x_best = mmq_x;
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 6e9c67aca0..c6a33d5de3 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -22,7 +22,10 @@
 #define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
 #define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
 #define __shfl_sync(mask, var, laneMask, width) __shfl(var, laneMask, width)
+#define __shfl_up_sync(mask, var, laneMask, width) __shfl_up(var, laneMask, width)
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define __all_sync(mask, var) __all(var)
+#define __any_sync(mask, var) __any(var)
 #define cublasCreate hipblasCreate
 #define cublasDestroy hipblasDestroy
 #define cublasGemmEx hipblasGemmEx

From 111f8d06f0b9169059779a35f655b343242ccbb6 Mon Sep 17 00:00:00 2001
From: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Date: Mon, 25 Aug 2025 11:27:34 -0400
Subject: [PATCH 176/287] metal: fix regression when no metal devices are
 present (#15531)

---
 ggml/src/ggml-metal/ggml-metal.m | 38 +++++++++++++++++---------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index b2ec7a263f..de52b3a4f4 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -93,35 +93,37 @@ static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_dev
     if (ctx->mtl_device == nil) {
         ctx->mtl_device = MTLCreateSystemDefaultDevice();
 
-        ctx->has_simdgroup_reduction  = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
-        ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
+        if (ctx->mtl_device) {
+            ctx->has_simdgroup_reduction  = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
+            ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
 
-        ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
+            ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
 
 #if defined(GGML_METAL_HAS_RESIDENCY_SETS)
-        ctx->has_residency_sets = getenv("GGML_METAL_NO_RESIDENCY") == nil;
+            ctx->has_residency_sets = getenv("GGML_METAL_NO_RESIDENCY") == nil;
 #endif
 
-        ctx->has_bfloat  = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
-        ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6];
+            ctx->has_bfloat  = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
+            ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6];
 
 #if defined(GGML_METAL_USE_BF16)
-        ctx->use_bfloat = ctx->has_bfloat;
+            ctx->use_bfloat = ctx->has_bfloat;
 #else
-        ctx->use_bfloat = false;
+            ctx->use_bfloat = false;
 #endif
-        ctx->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;
+            ctx->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;
 
-        {
-            const char * val = getenv("GGML_METAL_FUSION_DEBUG");
-            ctx->debug_fusion = val ? atoi(val) : 0;
+            {
+                const char * val = getenv("GGML_METAL_FUSION_DEBUG");
+                ctx->debug_fusion = val ? atoi(val) : 0;
+            }
+
+            memset(ctx->fuse_cnt, 0, sizeof(ctx->fuse_cnt));
+
+            ctx->max_size = ctx->mtl_device.maxBufferLength;
+
+            strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1);
         }
-
-        memset(ctx->fuse_cnt, 0, sizeof(ctx->fuse_cnt));
-
-        ctx->max_size = ctx->mtl_device.maxBufferLength;
-
-        strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1);
     }
 
     ctx->mtl_device_ref_count++;

From 886b97a5d693550c2da470c091d9d27bf38398f8 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 25 Aug 2025 10:47:16 -0500
Subject: [PATCH 177/287] tests: Generate unique input values for count_equal
 (#15487)

This avoids backend-dependent behavior for argmax that leads to intermittent failures.
---
 tests/test-backend-ops.cpp | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 74886b4549..ef6f452195 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2209,6 +2209,26 @@ struct test_count_equal : public test_case {
     double max_nmse_err() override {
         return 0.0;
     }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        std::random_device rd;
+        std::default_random_engine rng(rd());
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->type == GGML_TYPE_F32) {
+                // initialize with unique values to avoid ties
+                for (int64_t r = 0; r < ggml_nrows(t); r++) {
+                    std::vector<float> data(t->ne[0]);
+                    for (int i = 0; i < t->ne[0]; i++) {
+                        data[i] = i;
+                    }
+                    std::shuffle(data.begin(), data.end(), rng);
+                    ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float));
+                }
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
 };
 
 // GGML_OP_REPEAT

From 4d917cd4f64cc37744e76d084659475819fb0728 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Mon, 25 Aug 2025 17:56:59 +0200
Subject: [PATCH 178/287] vulkan: fix min subgroup 16 condition for mmid
 subgroup optimization (#15565)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4b959d844f..30e5317504 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2183,7 +2183,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     const uint32_t mul_mat_subgroup_size_32 = std::max(mul_mat_subgroup_size, 32u);
 
     const bool subgroup_min_size_16 = (!device->subgroup_size_control && device->subgroup_size >= 16) ||
-                                      (device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16);
+                                      (device->subgroup_size_control && device->subgroup_max_size >= 16);
 
     // mulmat
     std::vector<uint32_t> l_warptile, m_warptile, s_warptile,

From f7207b0415986dd7f48447149da7de3a82338276 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Mon, 25 Aug 2025 14:18:09 -0700
Subject: [PATCH 179/287] opencl: fix support ops condition for `rms_norm`
 (#15560)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index df27501361..36b18ddb8a 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2647,8 +2647,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SOFT_MAX:
         case GGML_OP_NORM:
-        case GGML_OP_RMS_NORM:
             return true;
+        case GGML_OP_RMS_NORM:
+            return op->ne[0] % 4 == 0 && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_REPEAT:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
         case GGML_OP_PAD:

From 74f52f77f28a5ad6d6075231afcb8d1ad763ca32 Mon Sep 17 00:00:00 2001
From: Qeeweew <68716978+Qeeweew@users.noreply.github.com>
Date: Tue, 26 Aug 2025 05:21:22 +0800
Subject: [PATCH 180/287] CUDA: Accelerate MXFP4 table lookup using
 `__byte_perm` (#15451)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CUDA: optimize get_int_from_table_16

* CUDA: use v_perm_b32 to replace byte_perm on AMD GPUs

* revise documentation

---------

Co-authored-by: xix <xiapc@outlook.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/vecdotq.cuh | 52 ++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
index d60292b83b..6baab1176f 100644
--- a/ggml/src/ggml-cuda/vecdotq.cuh
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -28,7 +28,58 @@ static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32
     return ((const int *) x)[i32]; // assume at least 4 byte alignment
 }
 
+// q4 contains 8 indices with 4 bit each.
+// This function selects those bytes from table that are at those indices and returns them as int2.
+// The first int contains the bytes with even indices in q4, the second int contains the bytes with odd indices in q4.
 static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * table) {
+#if defined(GGML_USE_HIP)
+    // Load the 16-byte table into four 32-bit unsigned integers.
+    const uint32_t *values = (const uint32_t *)table;
+
+    const uint32_t q_even = q4;
+    const uint32_t q_odd  = (q4 >> 4);
+
+    // Perform lookups in the lower half of the table (indices 0-7).
+    uint32_t v_even_low = __builtin_amdgcn_perm(values[1], values[0], q_even & 0x07070707);
+    uint32_t v_odd_low = __builtin_amdgcn_perm(values[1], values[0], q_odd & 0x07070707);
+
+    // Perform lookups in the upper half of the table (indices 8-15).
+    uint32_t v_even_high = __builtin_amdgcn_perm(values[3], values[2], q_even & 0x07070707);
+    uint32_t v_odd_high = __builtin_amdgcn_perm(values[3], values[2], q_odd & 0x07070707);
+
+    // Select between the low and high results based on the MSB of each index nibble.
+    uint32_t mask_even = 0x03020100 | ((q_even & 0x08080808) >> 1);
+    uint32_t res_x = __builtin_amdgcn_perm(v_even_high, v_even_low, mask_even);
+    uint32_t mask_odd = 0x03020100 | ((q_odd & 0x08080808) >> 1);
+    uint32_t res_y = __builtin_amdgcn_perm(v_odd_high, v_odd_low, mask_odd);
+
+    return make_int2(res_x, res_y);
+#elif !defined(GGML_USE_MUSA)
+    // CUDA does not have an instruction for selecting bytes with 4 bit indices.
+    // However, __byte_perm is an instruction that selects bytes with 3 bit indices that can be used instead.
+    const uint32_t * table32 = (const uint32_t *) table;
+
+    // __byte_perm selects bytes based on the lower 16 bits in its third argument.
+    // Therefore, do 2 iterations over the 32 bits in q4 with 0 and 16 shift.
+    // To handle the fourth bit, first call _byte_perm both for the low and the high 64 bit of table, using the low 3 bits.
+    // Then, call __byte_perm again to select from the low and high bytes based on the fourth bit.
+    uint32_t tmp[2];
+    const uint32_t low_high_selection_indices = (0x32103210 | ((q4 & 0x88888888) >> 1));
+#pragma unroll
+    for (uint32_t i = 0; i < 2; ++i) {
+        const uint32_t shift = 16 * i;
+
+        const uint32_t low  = __byte_perm(table32[0], table32[1], q4 >> shift);
+        const uint32_t high = __byte_perm(table32[2], table32[3], q4 >> shift);
+        tmp[i] = __byte_perm(low, high, low_high_selection_indices >> shift);
+    }
+
+    // tmp contains the bytes from tyble in the same order as the 4 bit indices in q4.
+    // However, for the result we need ints with all even/odd 4 bit indices in q4.
+    // Therefore, 2 more calls to __byte_perm to put the bytes in the correct order.
+    return make_int2(__byte_perm(tmp[0], tmp[1], 0x6420), __byte_perm(tmp[0], tmp[1], 0x7531));
+#else
+    // Generic implementation.
     const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
     const int8_t * q0_8   = (const int8_t *) &q0_32;
     const char4    val0_8 = make_char4(
@@ -40,6 +91,7 @@ static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, con
         table[q1_8[0]], table[q1_8[1]], table[q1_8[2]], table[q1_8[3]]);
 
     return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
+#endif
 }
 
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called

From 34bdbbd7c2b70b848718e95bc48010f6aecd2816 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 25 Aug 2025 23:42:44 -0500
Subject: [PATCH 181/287] vulkan: Remove splitting for mul_mat_id (#15568)

row_ids only needs to hold the BN rows for the current tile.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 38 ++-----------------
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    | 33 +++++++++-------
 .../vulkan-shaders/mul_mm_cm2.comp            | 19 ++++++----
 tests/test-backend-ops.cpp                    |  1 +
 4 files changed, 35 insertions(+), 56 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 30e5317504..04ad664e61 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2090,10 +2090,11 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     const uint32_t warps = warptile[0] / warptile[10];
 
     const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size;
-    const uint32_t mmid_row_ids = mul_mat_id ? (4096 * sizeof(uint32_t) + 4/*_ne1*/) : 0;
+    const uint32_t mmid_row_ids = mul_mat_id ? (warptile[2] * 2 * sizeof(uint16_t)) : 0;
     const uint32_t coopmat_stage = device->coopmat_support ? warptile[7] * warptile[8] / warps * sizeof(float) : 0;
+    const uint32_t ballots_sh = mul_mat_id ? (warps * 4 * sizeof(uint32_t)) : 0;
 
-    const uint32_t total_size = load_bufs + mmid_row_ids + coopmat_stage + lut_size;
+    const uint32_t total_size = load_bufs + mmid_row_ids + coopmat_stage + lut_size + ballots_sh;
     const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
 
     VK_LOG_DEBUG("ggml_vk_matmul_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), "
@@ -6288,7 +6289,6 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
 
     const uint64_t nei0 = ids->ne[0];
     const uint64_t nei1 = ids->ne[1];
-    GGML_ASSERT(nei0 * nei1 <= 4096);
 
     const uint32_t nbi1 = ids->nb[1];
     const uint32_t nbi2 = ids->nb[2];
@@ -6728,37 +6728,7 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
     if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
         ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
     } else {
-        // Split based on number of ids, to fit in shared memory
-        const uint32_t nei0 = (uint32_t)src2->ne[0];
-        const uint32_t nei1 = (uint32_t)src2->ne[1];
-
-        GGML_ASSERT(nei0 <= 4096);
-        const uint32_t split_size = std::min(nei1, 4096u / nei0);
-
-        if (split_size == nei1) {
-            ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
-        } else {
-            ggml_tensor src1_copy = *src1;
-            ggml_tensor src2_copy = *src2;
-            ggml_tensor dst_copy = *dst;
-
-            for (uint32_t token_start = 0; token_start < nei1; token_start += split_size) {
-                const uint32_t n_tokens = std::min(split_size, nei1 - token_start);
-
-                src1_copy.view_offs = src1->view_offs + token_start * src1_copy.nb[2];
-                src2_copy.view_offs = src2->view_offs + token_start * src2_copy.nb[1];
-                dst_copy.view_offs = dst->view_offs + token_start * dst_copy.nb[2];
-
-                src1_copy.ne[2] = n_tokens;
-                src2_copy.ne[1] = n_tokens;
-                dst_copy.ne[2] = n_tokens;
-
-                ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, &src1_copy, &src2_copy, &dst_copy, dryrun);
-                // invalidate cached prealloc_y, can't cache based on the copy of the ggml_tensor
-                ctx->prealloc_y_last_pipeline_used = {};
-                ctx->prealloc_y_last_tensor_used = nullptr;
-            }
-        }
+        ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
     }
 }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index 40c0d9b0c5..5ecf68a643 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -109,13 +109,13 @@ shared FLOAT_TYPE buf_b[BN * SHMEM_STRIDE];
 #define NUM_WARPS (BLOCK_SIZE / WARP)
 
 #ifdef MUL_MAT_ID
-shared u16vec2 row_ids[4096];
+shared u16vec2 row_ids[BN];
 uint _ne1;
 
 #ifdef MUL_MAT_ID_USE_SUBGROUPS
 shared uvec4 ballots_sh[NUM_WARPS];
 
-void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
+void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) {
     _ne1 = 0;
     uint num_elements = p.nei1 * p.nei0;
     uint nei0shift = findLSB(p.nei0);
@@ -165,11 +165,14 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
         barrier();
 
         uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot);
-        if (in_range && id == expert_idx) {
-            row_ids[_ne1 + idx] = u16vec2(ii0, ii1);
+        if (in_range && id == expert_idx && _ne1 + idx >= ic * BN && _ne1 + idx < (ic + 1) * BN) {
+            row_ids[_ne1 + idx - ic * BN] = u16vec2(ii0, ii1);
         }
         _ne1 += total;
         iter &= 15;
+        if (_ne1 >= (ic + 1) * BN) {
+            break;
+        }
     }
     barrier();
 }
@@ -242,16 +245,18 @@ void main() {
 #ifdef MUL_MAT_ID
 #ifdef MUL_MAT_ID_USE_SUBGROUPS
     if (bitCount(p.nei0) == 1) {
-        load_row_ids(expert_idx, true);
+        load_row_ids(expert_idx, true, ic);
     } else {
-        load_row_ids(expert_idx, false);
+        load_row_ids(expert_idx, false, ic);
     }
 #else
     _ne1 = 0;
-    for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
-        for (uint ii0 = 0; ii0 < p.nei0; ii0++) {
+    for (uint ii1 = 0; ii1 < p.nei1 && _ne1 < (ic + 1) * BN; ii1++) {
+        for (uint ii0 = 0; ii0 < p.nei0 && _ne1 < (ic + 1) * BN; ii0++) {
             if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) {
-                row_ids[_ne1] = u16vec2(ii0, ii1);
+                if (_ne1 >= ic * BN) {
+                    row_ids[_ne1 - ic * BN] = u16vec2(ii0, ii1);
+                }
                 _ne1++;
             }
         }
@@ -797,7 +802,7 @@ void main() {
         [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
 #if LOAD_VEC_B == 8
 #ifdef MUL_MAT_ID
-            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const u16vec2 row_idx = row_ids[loadc_b + l];
             const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
 #else
             const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
@@ -813,7 +818,7 @@ void main() {
             buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
 #elif LOAD_VEC_B == 4
 #ifdef MUL_MAT_ID
-            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const u16vec2 row_idx = row_ids[loadc_b + l];
             const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
 #else
             const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
@@ -832,7 +837,7 @@ void main() {
 #else
             const uint row_i = ic * BN + loadc_b + l;
             if (row_i < _ne1 && block + loadr_b < end_k) {
-                const u16vec2 row_idx = row_ids[row_i];
+                const u16vec2 row_idx = row_ids[loadc_b + l];
                 buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]);
             } else {
                 buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(0.0f);
@@ -903,7 +908,7 @@ void main() {
                 const uint row_i = dc + cm_col * TN + col + store_c;
                 if (row_i >= _ne1) break;
 
-                const u16vec2 row_idx = row_ids[row_i];
+                const u16vec2 row_idx = row_ids[row_i - ic * BN];
 
                 if (dr + cm_row * TM + store_r < p.M) {
                     data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]);
@@ -953,7 +958,7 @@ void main() {
                 const uint row_i = dc_warp + cc;
                 if (row_i >= _ne1) break;
 
-                const u16vec2 row_idx = row_ids[row_i];
+                const u16vec2 row_idx = row_ids[row_i - ic * BN];
 #endif // MUL_MAT_ID
                 [[unroll]] for (uint cr = 0; cr < TM; cr++) {
 #ifdef MUL_MAT_ID
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 4d16eb0791..f5aebf6e93 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -93,7 +93,7 @@ layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 #ifdef MUL_MAT_ID
 layout (binding = 3) readonly buffer IDS {int data_ids[];};
 
-shared u16vec4 row_ids[4096];
+shared u16vec4 row_ids[BN];
 
 layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB {
    B_TYPE b[];
@@ -111,7 +111,7 @@ B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const i
         return B_TYPE(0.0);
     }
 
-    const u16vec4 row_idx = row_ids[row_i];
+    const u16vec4 row_idx = row_ids[row_i & (BN - 1)];
     B_TYPE ret = data_b[row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + blockCoords[1]];
 
     return ret;
@@ -123,14 +123,14 @@ D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem
     uint dc = ic * BN + c;
 
     if (dr < p.M && dc < _ne1) {
-        uint row_i = dc;
+        uint row_i = c;
         const u16vec4 row_idx = row_ids[row_i];
         data_d[row_idx.y * p.batch_stride_d + row_idx.z * p.stride_d + dr] = elem;
     }
     return elem;
 }
 
-void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
+void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) {
     _ne1 = 0;
     uint num_elements = p.nei1 * p.nei0;
     uint nei0shift = findLSB(p.nei0);
@@ -180,11 +180,14 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2) {
         barrier();
 
         uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot);
-        if (in_range && id == expert_idx) {
-            row_ids[_ne1 + idx] = u16vec4(fastmod(ii0, p.ne11), ii1, ii0, 0);
+        if (in_range && id == expert_idx && _ne1 + idx >= ic * BN && _ne1 + idx < (ic + 1) * BN) {
+            row_ids[_ne1 + idx - ic * BN] = u16vec4(fastmod(ii0, p.ne11), ii1, ii0, 0);
         }
         _ne1 += total;
         iter &= 15;
+        if (_ne1 >= (ic + 1) * BN) {
+            break;
+        }
     }
     barrier();
 }
@@ -218,9 +221,9 @@ void main() {
 
 #ifdef MUL_MAT_ID
     if (bitCount(p.nei0) == 1) {
-        load_row_ids(expert_idx, true);
+        load_row_ids(expert_idx, true, ic);
     } else {
-        load_row_ids(expert_idx, false);
+        load_row_ids(expert_idx, false, ic);
     }
 
     // Workgroup has no work
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index ef6f452195..765521ffeb 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6017,6 +6017,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     // test large experts*tokens
     for (bool b : {false, true}) {
         test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, b, 32, 1024, 16));
+        test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 2, 2, b, 32, 8192, 64));
     }
 
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));

From 4c37636b3ea96f2574eeb7668b93fcc0e64b05dd Mon Sep 17 00:00:00 2001
From: Yoshi_likes_e4 <104140648+pt13762104@users.noreply.github.com>
Date: Tue, 26 Aug 2025 13:15:33 +0700
Subject: [PATCH 182/287] Add a warning for special devices (#15563)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add warning

* Print the devices names

* Add newlines

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Fix vector names

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/ggml-cuda.cu | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index aa45ab39ed..4494883415 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -204,6 +204,8 @@ static ggml_cuda_device_info ggml_cuda_init() {
     GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__);
 #endif // GGML_CUDA_FORCE_CUBLAS
     GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
+
+    std::vector<std::pair<int, std::string>> turing_devices_without_mma;
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
@@ -261,7 +263,25 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
         GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
                         id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
-#endif // defined(GGML_USE_HIP)
+        std::string device_name(prop.name);
+        if (device_name == "NVIDIA GeForce MX450") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        } else if (device_name == "NVIDIA GeForce MX550") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        } else if (device_name.substr(0, 21) == "NVIDIA GeForce GTX 16") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        }
+#endif  // defined(GGML_USE_HIP)
+    }
+
+    if (ggml_cuda_highest_compiled_arch(GGML_CUDA_CC_TURING) >= GGML_CUDA_CC_TURING && !turing_devices_without_mma.empty()) {
+        GGML_LOG_INFO("The following devices will have suboptimal performance due to a lack of tensor cores:\n");
+        for (size_t device_pos = 0; device_pos < turing_devices_without_mma.size(); device_pos++) {
+            GGML_LOG_INFO(
+                "  Device %d: %s\n", turing_devices_without_mma[device_pos].first, turing_devices_without_mma[device_pos].second.c_str());
+        }
+        GGML_LOG_INFO(
+            "Consider compiling with CMAKE_CUDA_ARCHITECTURES=61-virtual;80-virtual and DGGML_CUDA_FORCE_MMQ to force the use of the Pascal code for Turing.\n");
     }
 
     for (int id = 0; id < info.device_count; ++id) {

From 0fd90db5858e325358a5fbdaa4e327ee2a79d0d4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 26 Aug 2025 08:51:43 +0200
Subject: [PATCH 183/287] metal : remove contiguous assertion for src0 in
 IM2COL (#15577)

* remove contiguous assertion for src0 in IM2COL

* add contiguous check in supports_op
---
 ggml/src/ggml-metal/ggml-metal.m | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index de52b3a4f4..dcd816a430 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1876,7 +1876,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_ROPE:
             return true;
         case GGML_OP_IM2COL:
-            return op->src[1]->type == GGML_TYPE_F32 && (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
+            return ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32 && (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
         case GGML_OP_POOL_1D:
             return false;
         case GGML_OP_UPSCALE:
@@ -4731,7 +4731,6 @@ static int ggml_metal_encode_node(
             } break;
         case GGML_OP_IM2COL:
             {
-                GGML_ASSERT(ggml_is_contiguous(src0));
                 GGML_ASSERT(ggml_is_contiguous(src1));
                 GGML_ASSERT(src1->type == GGML_TYPE_F32);
                 GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);

From 39842a7f73012eb42816ca4f26411782bd3da7c5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 26 Aug 2025 09:08:08 +0200
Subject: [PATCH 184/287] gguf-py : remove erroneous FFN_GATE entry (#15583)

---
 gguf-py/gguf/tensor_mapping.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 87edaa3232..38bbd6e310 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -427,7 +427,6 @@ class TensorNameMap:
             "model.layers.{bid}.residual_mlp.w1",         # arctic
             "transformer.h.{bid}.mlp.c_fc_0",             # exaone
             "model.layers.{bid}.feed_forward.gate_proj",  # llama4 jamba granite-hybrid
-            "model.layers.{bid}.block_sparse_moe.gate",   # smallthinker
             "model.transformer.blocks.{bid}.ff_proj",     # llada
             "layers.{bid}.mlp.gate_proj",                 # qwen3-embedding
         ),

From c4e9239064a564de7b94ee2b401ae907235a8fca Mon Sep 17 00:00:00 2001
From: tc-mb <157115220+tc-mb@users.noreply.github.com>
Date: Tue, 26 Aug 2025 16:05:55 +0800
Subject: [PATCH 185/287] model : support MiniCPM-V 4.5 (#15575)

---
 docs/multimodal/minicpmv4.0.md                |  2 +-
 docs/multimodal/minicpmv4.5.md                | 47 +++++++++++++++++++
 tools/mtmd/clip.cpp                           |  5 ++
 .../minicpmv-convert-image-encoder-to-gguf.py |  7 +++
 tools/mtmd/mtmd.cpp                           |  2 +-
 5 files changed, 61 insertions(+), 2 deletions(-)
 create mode 100644 docs/multimodal/minicpmv4.5.md

diff --git a/docs/multimodal/minicpmv4.0.md b/docs/multimodal/minicpmv4.0.md
index 65887d9601..d04cb338ce 100644
--- a/docs/multimodal/minicpmv4.0.md
+++ b/docs/multimodal/minicpmv4.0.md
@@ -6,7 +6,7 @@ Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model
 
 
 ### Build llama.cpp
-Readme modification time: 20250206
+Readme modification time: 20250731
 
 If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
 
diff --git a/docs/multimodal/minicpmv4.5.md b/docs/multimodal/minicpmv4.5.md
new file mode 100644
index 0000000000..8fea5e611d
--- /dev/null
+++ b/docs/multimodal/minicpmv4.5.md
@@ -0,0 +1,47 @@
+## MiniCPM-V 4.5
+
+### Prepare models and code
+
+Download [MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) PyTorch model from huggingface to "MiniCPM-V-4_5" folder.
+
+
+### Build llama.cpp
+Readme modification time: 20250826
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) by us)
+
+```bash
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-4_5
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-4_5 --minicpmv-projector ../MiniCPM-V-4_5/minicpmv.projector --output-dir ../MiniCPM-V-4_5/ --minicpmv_version 6
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_5/model
+
+# quantize int4 version
+./build/bin/llama-quantize ../MiniCPM-V-4_5/model/ggml-model-f16.gguf ../MiniCPM-V-4_5/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_5/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4_5/mmproj-model-f16.gguf
+```
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index b3628db64f..0e76b9c590 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -2202,6 +2202,8 @@ struct clip_model_loader {
                         hparams.minicpmv_query_num = 64;
                     } else if (hparams.minicpmv_version == 5) {
                         hparams.minicpmv_query_num = 64;
+                    } else if (hparams.minicpmv_version == 6) {
+                        hparams.minicpmv_query_num = 64;
                     } else {
                         hparams.minicpmv_query_num = 96;
                     }
@@ -3685,6 +3687,9 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                     } else if (params.minicpmv_version == 5) {
                         // MiniCPM-V 4.0
                         n_patches = 64;
+                    } else if (params.minicpmv_version == 6) {
+                        // MiniCPM-V 4.5
+                        n_patches = 64;
                     } else {
                         GGML_ABORT("Unknown minicpmv version");
                     }
diff --git a/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py b/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
index 4dda60a211..f34d858d67 100644
--- a/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
+++ b/tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py
@@ -607,6 +607,9 @@ else:
     elif minicpmv_version == 5:
         emb_dim = 2560
         block_count = 27
+    elif minicpmv_version == 6:
+        emb_dim = 4096
+        block_count = 27
 
     default_vision_config = {
             "hidden_size": 1152,
@@ -630,6 +633,10 @@ elif minicpmv_version == 5:
     default_vision_config["model_type"] = "siglip_vision_model"
     vision_config = SiglipVisionConfig(**default_vision_config)
     model = SiglipVisionTransformer(vision_config)
+elif minicpmv_version == 6:
+    default_vision_config["model_type"] = "siglip_vision_model"
+    vision_config = SiglipVisionConfig(**default_vision_config)
+    model = SiglipVisionTransformer(vision_config)
 
 processor = None
 # if model.attn_pool is not None:
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index a05373d5b3..cd022c5e24 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -207,7 +207,7 @@ struct mtmd_context {
             tok_row_end_trail = false; // no trailing end-of-row token
             ov_img_first      = true;
 
-        } else if (minicpmv_version == 3 || minicpmv_version == 4 || minicpmv_version == 5) {
+        } else if (minicpmv_version == 3 || minicpmv_version == 4 || minicpmv_version == 5 || minicpmv_version == 6) {
             // minicpmv 2.6 format:
             // <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
             slice_tmpl        = MTMD_SLICE_TMPL_MINICPMV_2_6;

From 1d8d83deaa48d4a5491820d58ff1c0d8cf9d196c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 26 Aug 2025 12:46:15 +0300
Subject: [PATCH 186/287] metal : improve `MUL_MAT_ID` (#15541)

* metal : mul_mm_id remove hdst

* metal : remove mul_mm_id hsrc1

* metal : mul_mm_id simplify + add test

* metal : opt mul_mm_id map0

* metal : optimize mul_mm_id id gathering

* metal : mul/div opt

* metal : optimize mul_mm_id_map0

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal-impl.h |  31 ++--
 ggml/src/ggml-metal/ggml-metal.m      | 141 ++++++----------
 ggml/src/ggml-metal/ggml-metal.metal  | 235 +++++++++++++-------------
 tests/test-backend-ops.cpp            |   1 +
 4 files changed, 180 insertions(+), 228 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index fc6526d6d5..82c1ac1da0 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -320,40 +320,31 @@ typedef struct {
 } ggml_metal_kargs_mul_mv_ext;
 
 typedef struct {
+    int32_t  ne02;
     int32_t  ne10;
     int32_t  ne11;  // n_expert_used (bcast)
     uint64_t nb11;
     uint64_t nb12;
-    int32_t  neh11; // n_tokens
-    uint64_t nbh11;
+    int32_t  ne21; // n_tokens
     int32_t  ne20;  // n_expert_used
     uint64_t nb21;
 } ggml_metal_kargs_mul_mm_id_map0;
 
-typedef struct {
-    int32_t  ne20; // n_expert_used
-    int32_t  neh0;
-    int32_t  neh1;
-    uint64_t nbh1;
-    uint64_t nbh2;
-    int32_t  ne0;
-    uint64_t nb1;
-    uint64_t nb2;
-} ggml_metal_kargs_mul_mm_id_map1;
-
 typedef struct {
     int32_t  ne00;
     int32_t  ne02;
     uint64_t nb01;
     uint64_t nb02;
     uint64_t nb03;
-    int32_t  neh12;
-    uint64_t nbh10;
-    uint64_t nbh11;
-    uint64_t nbh12;
-    uint64_t nbh13;
-    int32_t  neh0;
-    int32_t  neh1;
+    int32_t  ne11;
+    uint64_t nb10;
+    uint64_t nb11;
+    uint64_t nb12;
+    uint64_t nb13;
+    int32_t  ne20;
+    int32_t  ne21;
+    int32_t  ne0;
+    int32_t  ne1;
     int16_t  r2;
     int16_t  r3;
 } ggml_metal_kargs_mul_mm_id;
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index dcd816a430..7a05a98278 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -398,8 +398,12 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
-    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16,
-    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_1,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_2,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F16,
@@ -1428,8 +1432,12 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,                mul_mm_iq1_m_f32,                has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,               mul_mm_iq4_nl_f32,               has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,               mul_mm_iq4_xs_f32,               has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16,              mul_mm_id_map0_f16,              has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32,              mul_mm_id_map1_f32,              has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_1,       mul_mm_id_map0_f16_ne20_1,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_2,       mul_mm_id_map0_f16_ne20_2,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4,       mul_mm_id_map0_f16_ne20_4,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6,       mul_mm_id_map0_f16_ne20_6,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8,       mul_mm_id_map0_f16_ne20_8,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16,      mul_mm_id_map0_f16_ne20_16,      has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,               mul_mm_id_f32_f16,               has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,               mul_mm_id_f16_f16,               has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F16,              mul_mm_id_bf16_f16,              has_simdgroup_mm && use_bfloat);
@@ -3908,38 +3916,6 @@ static int ggml_metal_encode_node(
                         default: break;
                     }
 
-                    const int64_t neh10 = ne10; // n_embd
-                    const int64_t neh11 = ne21; // n_tokens
-                    const int64_t neh12 = ne02; // n_expert
-
-                    const uint64_t nbh10 = ggml_type_size(GGML_TYPE_F16);
-                    const uint64_t nbh11 = nbh10*neh10;
-                    const uint64_t nbh12 = nbh11*neh11;
-                    const uint64_t nbh13 = nbh12*neh12;
-
-                    const size_t s_src1 = ggml_type_size(GGML_TYPE_F16)*neh10*neh11*neh12;
-                    id<MTLBuffer> h_src1 = ggml_metal_mem_pool_alloc(mem_pool, s_src1);
-                    if (!h_src1) {
-                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_src1);
-                        return 0;
-                    }
-
-                    const int64_t neh0 = ne0;
-                    const int64_t neh1 = ne21;
-                    const int64_t neh2 = ne02;
-
-                    const uint64_t nbh0 = ggml_type_size(GGML_TYPE_F32);
-                    const uint64_t nbh1 = nbh0*neh0;
-                    const uint64_t nbh2 = nbh1*neh1;
-                  //const uint64_t nbh3 = nbh2*neh2;
-
-                    const size_t s_dst = ggml_type_size(GGML_TYPE_F32)*neh0*neh1*neh2;
-                    id<MTLBuffer> h_dst = ggml_metal_mem_pool_alloc(mem_pool, s_dst);
-                    if (!h_dst) {
-                        GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_dst);
-                        return 0;
-                    }
-
                     // tokens per expert
                     const size_t s_tpe = ggml_type_size(GGML_TYPE_I32)*ne02;
                     id<MTLBuffer> h_tpe = ggml_metal_mem_pool_alloc(mem_pool, s_tpe);
@@ -3949,8 +3925,8 @@ static int ggml_metal_encode_node(
                     }
 
                     // id map
-                    // [n_expert_used, n_tokens]
-                    const size_t s_ids = ggml_type_size(GGML_TYPE_I32)*ne20*ne21;
+                    // [n_tokens, n_expert]
+                    const size_t s_ids = ggml_type_size(GGML_TYPE_I32)*ne21*ne02;
                     id<MTLBuffer> h_ids = ggml_metal_mem_pool_alloc(mem_pool, s_ids);
                     if (!h_ids) {
                         GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_ids);
@@ -3958,32 +3934,45 @@ static int ggml_metal_encode_node(
                     }
 
                     {
-                        const int nth = MIN(1024, ne10/4);
-
                         ggml_metal_kargs_mul_mm_id_map0 args = {
+                            ne02,
                             ne10,
-                            ne11,  // n_expert_used (bcast)
+                            ne11, // n_expert_used (bcast)
                             nb11,
                             nb12,
-                            neh11, // n_tokens
-                            nbh11,
-                            ne20,  // n_expert_used
+                            ne21, // n_tokens
+                            ne20, // n_expert_used
                             nb21,
                         };
 
                         id<MTLComputePipelineState> pipeline = nil;
 
-                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16].pipeline;
+                        pipeline = nil;
+
+                        switch (ne20) {
+                            case 1:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_1 ].pipeline; break;
+                            case 2:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_2 ].pipeline; break;
+                            case 4:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4 ].pipeline; break;
+                            case 6:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6 ].pipeline; break;
+                            case 8:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8 ].pipeline; break;
+                            case 16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16].pipeline; break;
+                            default: GGML_ABORT("missing specialization for ne20 = %d", (int) ne20);
+                        }
+
+                        GGML_ASSERT(ne02 <= (int) pipeline.maxTotalThreadsPerThreadgroup);
+
+                        const size_t smem = ne02*ne20*sizeof(uint16_t);
+
+                        GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
 
                         [encoder setComputePipelineState:pipeline];
                         [encoder setBytes:&args    length:sizeof(args) atIndex:0];
-                        [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
-                        [encoder setBuffer:id_src2 offset:offs_src2    atIndex:2];
-                        [encoder setBuffer: h_src1 offset:0            atIndex:3];
-                        [encoder setBuffer: h_tpe  offset:0            atIndex:4];
-                        [encoder setBuffer: h_ids  offset:0            atIndex:5];
+                        [encoder setBuffer:id_src2 offset:offs_src2    atIndex:1];
+                        [encoder setBuffer: h_tpe  offset:0            atIndex:2];
+                        [encoder setBuffer: h_ids  offset:0            atIndex:3];
+                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
 
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne02, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(ne02, 1, 1)];
                     }
 
                     {
@@ -4022,13 +4011,15 @@ static int ggml_metal_encode_node(
                             /*.nb01  =*/ nb01,
                             /*.nb02  =*/ nb02,
                             /*.nb03  =*/ nb03,
-                            /*.neh12 =*/ neh12,
-                            /*.nbh10 =*/ nbh10,
-                            /*.nbh11 =*/ nbh11,
-                            /*.nbh12 =*/ nbh12,
-                            /*.nbh13 =*/ nbh13,
-                            /*.neh0  =*/ neh0,
-                            /*.neh1  =*/ neh1,
+                            /*.ne11  =*/ ne11, // n_expert_used (bcast)
+                            /*.nb10  =*/ nb10,
+                            /*.nb11  =*/ nb11,
+                            /*.nb12  =*/ nb12,
+                            /*.nb13  =*/ nb13,
+                            /*.ne20  =*/ ne20, // n_expert_used
+                            /*.ne21  =*/ ne21, // n_tokens
+                            /*.ne0   =*/ ne0,
+                            /*.ne1   =*/ ne1,
                             /*.r2    =*/ r2,
                             /*.r3    =*/ r3,
                         };
@@ -4036,42 +4027,14 @@ static int ggml_metal_encode_node(
                         [encoder setComputePipelineState:pipeline];
                         [encoder setBytes:&args    length:sizeof(args) atIndex:0];
                         [encoder setBuffer:id_src0 offset:offs_src0    atIndex:1];
-                        [encoder setBuffer: h_src1 offset:0            atIndex:2];
+                        [encoder setBuffer:id_src1 offset:offs_src1    atIndex:2];
                         [encoder setBuffer: h_tpe  offset:0            atIndex:3];
-                        [encoder setBuffer: h_dst  offset:0            atIndex:4];
+                        [encoder setBuffer: h_ids  offset:0            atIndex:4];
+                        [encoder setBuffer:id_dst  offset:offs_dst     atIndex:5];
 
                         [encoder setThreadgroupMemoryLength:8192 atIndex:0];
                         [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, ne02) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                     }
-
-                    {
-                        GGML_ASSERT(ne0 % 4 == 0);
-
-                        const int nth = MIN(1024, ne0/4);
-
-                        ggml_metal_kargs_mul_mm_id_map1 args = {
-                            ne20, // n_expert_used
-                            neh0,
-                            neh1,
-                            nbh1,
-                            nbh2,
-                            ne0,
-                            nb1,
-                            nb2,
-                        };
-
-                        id<MTLComputePipelineState> pipeline = nil;
-
-                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP1_F32].pipeline;
-
-                        [encoder setComputePipelineState:pipeline];
-                        [encoder setBytes:&args   length:sizeof(args) atIndex:0];
-                        [encoder setBuffer: h_dst offset:0            atIndex:1];
-                        [encoder setBuffer: h_ids offset:0            atIndex:2];
-                        [encoder setBuffer:id_dst offset:offs_dst     atIndex:3];
-
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne20, ne21, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                    }
                 } else {
                     id<MTLComputePipelineState> pipeline = nil;
 
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 3dd55fd325..7037c1aa0d 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -974,9 +974,16 @@ kernel void kernel_mul(
     device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
     device       char * dst_ptr  = dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs;
 
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        const int i10 = i0%args.ne10;
-        *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10));
+    if (args.ne10 == 1) {
+        const float x = *((device float *)(src1_ptr));
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
+        }
+    } else {
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            const int i10 = i0%args.ne10;
+            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10));
+        }
     }
 }
 
@@ -1000,9 +1007,16 @@ kernel void kernel_div(
     device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
     device       char * dst_ptr  = dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs;
 
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        const int i10 = i0%args.ne10;
-        *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10));
+    if (args.ne10 == 1) {
+        const float x = 1.0f / *((device float *)(src1_ptr));
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
+        }
+    } else {
+        for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+            const int i10 = i0%args.ne10;
+            *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10));
+        }
     }
 }
 
@@ -7491,97 +7505,81 @@ kernel void kernel_mul_mm(
     }
 }
 
-template<typename T4>
+template<short ne20> // n_expert_used
 kernel void kernel_mul_mm_id_map0(
         constant ggml_metal_kargs_mul_mm_id_map0 & args,
-        device  const char * src1,
         device  const char * src2,
-        device        char * hsrc1,
         device        char * htpe,
         device        char * hids,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int ide = tgpig[0]; // expert id
+        threadgroup   char * shmem [[threadgroup(0)]],
+        ushort tpitg[[thread_position_in_threadgroup]],
+        ushort   ntg[[threads_per_threadgroup]]) {
+    const short ide = tpitg; // expert id
 
-    int n_all = 0;
+    uint32_t n_all = 0;
 
-    device int32_t * ids_i32 = (device int32_t *) (hids);
+    device int32_t * ids_i32 = (device int32_t *) hids + ide*args.ne21;
 
-    for (int i21 = 0; i21 < args.neh11; i21++) { // n_tokens
-        device const int32_t * src2_i32 = (device const int32_t *) (src2 + i21*args.nb21);
+    for (int i21 = 0; i21 < args.ne21; i21 += ntg) { // n_tokens
+        if (i21 + tpitg < args.ne21) {
+            device const int32_t * src2_i32 = (device const int32_t *) (src2 + (i21 + tpitg)*args.nb21);
 
-        for (int i20 = 0; i20 < args.ne20; i20++) { // n_expert_used
-            if (src2_i32[i20] != ide) {
-                continue;
+            threadgroup uint16_t * sids = (threadgroup uint16_t *) shmem + tpitg*ne20;
+
+            #pragma unroll(ne20)
+            for (short i20 = 0; i20 < ne20; i20++) {
+                sids[i20] = src2_i32[i20];
             }
-
-            device const float4 *  src1_f32x4 = (device const float4 *) ( src1 + i21*args.nb12 + (i20%args.ne11)*args.nb11);
-            device       T4     * hsrc1_f32x4 = (device       T4     *) (hsrc1 + (ide*args.neh11 + n_all)*args.nbh11);
-
-            for (int64_t i00 = tpitg.x; i00 < args.ne10/4; i00 += ntg.x) {
-                hsrc1_f32x4[i00] = (T4) (src1_f32x4[i00]);
-            }
-
-            if (tpitg.x == 0) {
-                ids_i32[i21*args.ne20 + i20] = ide*args.neh11 + n_all;
-            }
-
-            ++n_all;
         }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        for (short t = 0; t < ntg; t++) {
+            if (i21 + t >= args.ne21) {
+                break;
+            }
+
+            threadgroup const uint16_t * sids = (threadgroup const uint16_t *) shmem + t*ne20;
+
+            short sel = 0;
+            #pragma unroll(ne20)
+            for (short i20 = 0; i20 < ne20; i20++) {
+                sel += (sids[i20] == ide)*(i20 + 1);
+            }
+
+            ids_i32[n_all] = (i21 + t)*ne20 + sel - 1;
+
+            n_all += sel > 0;
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-    if (tpitg.x == 0) {
-        device int32_t * tpe_i32 = (device int32_t *) (htpe);
-        tpe_i32[ide] = n_all;
-    }
+    device uint32_t * tpe_u32 = (device uint32_t *) (htpe);
+    tpe_u32[ide] = n_all;
 }
 
-typedef decltype(kernel_mul_mm_id_map0<half4>) kernel_mul_mm_id_map0_t;
+typedef decltype(kernel_mul_mm_id_map0<1>) kernel_mul_mm_id_map0_t;
 
-template [[host_name("kernel_mul_mm_id_map0_f16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<half4>;
-
-template<typename T>
-kernel void kernel_mul_mm_id_map1(
-        constant ggml_metal_kargs_mul_mm_id_map1 & args,
-        device  const char * hdst,
-        device  const char * hids,
-        device        char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i20 = tgpig[0]; // used expert
-    const int i21 = tgpig[1]; // token
-
-    device const int32_t * ids_i32    = (device const int32_t *) (hids);
-    device       float4  * dst_f32x4  = (device       float4  *) (dst + i20*args.nb1 + i21*args.nb2);
-
-    const int id = ids_i32[i21*args.ne20 + i20];
-
-    const int ide = id / args.neh1;
-    const int idt = id % args.neh1;
-
-    device const float4 * hdst_f32x4 = (device const float4 *) (hdst + idt*args.nbh1 + ide*args.nbh2);
-
-    for (int64_t i0 = tpitg.x; i0 < args.neh0/4; i0 += ntg.x) {
-        dst_f32x4[i0] = hdst_f32x4[i0];
-    }
-}
-
-typedef decltype(kernel_mul_mm_id_map1<float>) kernel_mul_mm_id_map1_t;
-
-template [[host_name("kernel_mul_mm_id_map1_f32")]] kernel kernel_mul_mm_id_map1_t kernel_mul_mm_id_map1<float>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_1" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<1>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_2" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<2>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_4" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<4>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_6" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<6>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>;
 
 template<typename T, typename T4x4, typename simdgroup_T8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
 kernel void kernel_mul_mm_id(
         constant ggml_metal_kargs_mul_mm_id & args,
         device const char * src0,
         device const char * src1,
-        device const char * tpe,
+        device const char * htpe,
+        device const char * hids,
         device       char * dst,
         threadgroup  char * shmem [[threadgroup(0)]],
         uint3  tgpig[[threadgroup_position_in_grid]],
         ushort tiitg[[thread_index_in_threadgroup]],
+        ushort tiisg[[thread_index_in_simdgroup]],
         ushort sgitg[[simdgroup_index_in_threadgroup]]) {
 
     threadgroup T    * sa = (threadgroup T    *)(shmem);
@@ -7589,19 +7587,20 @@ kernel void kernel_mul_mm_id(
 
     const int r0 = tgpig.y;
     const int r1 = tgpig.x;
-    const int im = tgpig.z;
+    const int im = tgpig.z; // expert
 
-    device const int32_t * tpe_i32 = (device const int32_t *) (tpe);
+    device const uint32_t * tpe_u32 = (device const uint32_t *) (htpe);
+    device const int32_t  * ids_i32 = (device const int32_t  *) (hids);
 
-    const int neh1 = tpe_i32[im];
+    const int32_t neh1 = tpe_u32[im];
 
     if (r1*BLOCK_SIZE_N >= neh1) {
         return;
     }
 
     // if this block is of 64x32 shape or smaller
-    const short n_rows = (args.neh0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.neh0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M;
-    const short n_cols = (     neh1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (     neh1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N;
+    const short n_rows = (args.ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M;
+    const short n_cols = (    neh1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (    neh1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N;
 
     // a thread shouldn't load data outside of the matrix
     const short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
@@ -7617,20 +7616,23 @@ kernel void kernel_mul_mm_id(
 
     short il = (tiitg % THREAD_PER_ROW);
 
-    const int i12 = im%args.neh12;
-    const int i13 = im/args.neh12;
+    const int id = ids_i32[im*args.ne21 + r1*BLOCK_SIZE_N + thread_col];
 
-    const uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const short i11 = (id % args.ne20) % args.ne11;
+    const short i12 = (id / args.ne20);
+    const short i13 = 0;
+
+    const uint64_t offset0 = im*args.nb02 + i13*args.nb03;
     const short    offset1 = il/nl;
 
     device const block_q * x = (device const block_q *)(src0
         + args.nb01*(r0*BLOCK_SIZE_M + thread_row) + offset0) + offset1;
 
-    device const half   * y = (device const half   *)(src1
-        + args.nbh13*i13
-        + args.nbh12*i12
-        + args.nbh11*(r1*BLOCK_SIZE_N + thread_col)
-        + args.nbh10*(BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
+    device const float   * y = (device const float   *)(src1
+        + args.nb13*i13
+        + args.nb12*i12
+        + args.nb11*i11
+        + args.nb10*(BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
 
     for (int loop_k = 0; loop_k < args.ne00; loop_k += BLOCK_SIZE_K) {
         // load data and store to threadgroup memory
@@ -7646,7 +7648,7 @@ kernel void kernel_mul_mm_id(
             +                     (tiitg/THREAD_PER_ROW)%8  + (i&7)*8) = temp_a[i/4][i%4];
         }
 
-        *(threadgroup half2x4 *)(sb + 32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL)) = *((device half2x4 *) y);
+        *(threadgroup half2x4 *)(sb + 32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL)) = (half2x4)(*((device float2x4 *) y));
 
         il = (il + 2 < nl) ? il + 2 : il % 2;
         x  = (il < 2) ? x + (2 + nl - 1)/nl : x;
@@ -7682,43 +7684,38 @@ kernel void kernel_mul_mm_id(
         }
     }
 
-    if ((r0 + 1) * BLOCK_SIZE_M <= args.neh0 && (r1 + 1) * BLOCK_SIZE_N <= neh1) {
-        device float * C = (device float *) dst +
-            (BLOCK_SIZE_M * r0 + 32*(sgitg &  1)) + \
-            (BLOCK_SIZE_N * r1 + 16*(sgitg >> 1)) * args.neh0 + im*args.neh1*args.neh0;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        for (short i = 0; i < 8; i++) {
-            simdgroup_store(mc[i], C + 8 * (i%4) + 8 * args.neh0 * (i/4), args.neh0);
-        }
-    } else {
-        // block is smaller than 64x32, we should avoid writing data outside of the matrix
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        threadgroup float * temp_str = ((threadgroup float *) shmem) \
-                                     + 32*(sgitg&1) + (16*(sgitg >> 1))*BLOCK_SIZE_M;
-        for (short i = 0; i < 8; i++) {
-            simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M);
+    threadgroup float * temp_str = ((threadgroup float *) shmem) \
+                                 + 32*(sgitg&1) + (16*(sgitg >> 1))*BLOCK_SIZE_M;
+
+    #pragma unroll(8)
+    for (short i = 0; i < 8; i++) {
+        simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M);
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    for (short j = sgitg; j < n_cols; j += 4) {
+        const int id = ids_i32[im*args.ne21 + r1*BLOCK_SIZE_N + j];
+
+        const short ide = id % args.ne20;
+        const short idt = id / args.ne20;
+
+        device float  * D  = (device float  *) dst + (r0*BLOCK_SIZE_M) + ide*args.ne0 + idt*args.ne1*args.ne0;
+        device float4 * D4 = (device float4 *) D;
+
+        threadgroup float  * C  = (threadgroup float  *) shmem + (j*BLOCK_SIZE_M);
+        threadgroup float4 * C4 = (threadgroup float4 *) C;
+
+        int i = tiisg;
+        for (; i < n_rows/4; i += 32) {
+            *(D4 + i) = *(C4 + i);
         }
 
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (sgitg == 0) {
-            for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
-                device float  * D  = (device float  *) dst + (r0*BLOCK_SIZE_M) + (r1*BLOCK_SIZE_N + j)*args.neh0 + im*args.neh1*args.neh0;
-                device float4 * D4 = (device float4 *) D;
-
-                threadgroup float  * C  = temp_str + (j*BLOCK_SIZE_M);
-                threadgroup float4 * C4 = (threadgroup float4 *) C;
-
-                int i = 0;
-                for (; i < n_rows/4; i++) {
-                    *(D4 + i) = *(C4 + i);
-                }
-
-                i *= 4;
-                for (; i < n_rows; i++) {
-                    *(D + i) = *(C + i);
-                }
-            }
+        i = (4*(n_rows/4)) + tiisg;
+        for (; i < n_rows; i += 32) {
+            *(D + i) = *(C + i);
         }
     }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 765521ffeb..4b4299d49d 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6018,6 +6018,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     for (bool b : {false, true}) {
         test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, b, 32, 1024, 16));
         test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 2, 2, b, 32, 8192, 64));
+        test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, b, 50, 200, 64));
     }
 
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));

From 85cc1ae998e4898d9fa992cb9b8620338cee97bf Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 26 Aug 2025 12:47:00 +0300
Subject: [PATCH 187/287] context : print graph stats for memory-less contexts
 (#15586)

ggml-ci
---
 src/llama-context.cpp | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 18cf25079d..99bfed7513 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -280,7 +280,7 @@ llama_context::llama_context(
     }
 
     // reserve worst-case graph
-    if (!hparams.vocab_only && memory) {
+    if (!hparams.vocab_only) {
         const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
 
@@ -292,11 +292,13 @@ llama_context::llama_context(
         int n_splits_tg = -1;
         int n_nodes_tg  = -1;
 
-        // simulate full KV cache
-
-        const auto mctx = memory->init_full();
-        if (!mctx) {
-            throw std::runtime_error("failed to initialize KV cache");
+        llama_memory_context_ptr mctx;
+        if (memory) {
+            LLAMA_LOG_DEBUG("%s: reserving full memory module\n", __func__);
+            mctx = memory->init_full();
+            if (!mctx) {
+                throw std::runtime_error("failed to initialize memory module");
+            }
         }
 
         cross.v_embd.clear();
@@ -1056,7 +1058,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
         const auto * res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status);
 
         if (!res) {
-            // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
+            // the last ubatch failed or was aborted -> remove all positions of that ubatch from the memory module
             llama_pos pos_min[LLAMA_MAX_SEQ];
             for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
                 pos_min[s] = std::numeric_limits<llama_pos>::max();
@@ -1073,7 +1075,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
                     continue;
                 }
 
-                LLAMA_LOG_WARN("%s: removing KV cache entries for seq_id = %d, pos = [%d, +inf)\n", __func__, s, pos_min[s]);
+                LLAMA_LOG_WARN("%s: removing memory module entries for seq_id = %d, pos = [%d, +inf)\n", __func__, s, pos_min[s]);
 
                 memory->seq_rm(s, pos_min[s], -1);
             }
@@ -1857,7 +1859,7 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     }
 
     if (memory != nullptr) {
-        LLAMA_LOG_DEBUG("%s: - writing KV self\n", __func__);
+        LLAMA_LOG_DEBUG("%s: - writing memory module\n", __func__);
         memory->state_write(io);
     }
 
@@ -1943,7 +1945,7 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
     }
 
     if (memory) {
-        LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__);
+        LLAMA_LOG_DEBUG("%s: - reading memory module\n", __func__);
 
         memory->state_read(io);
     }

From 79a546220c719e6a70627b243a478ab8d84dc9e1 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 26 Aug 2025 12:54:19 +0200
Subject: [PATCH 188/287] mtmd : support Kimi VL model (#15458)

* convert : fix tensor naming conflict for llama 4 vision

* convert ok

* support kimi vision model

* clean up

* fix style

* fix calc number of output tokens

* refactor resize_position_embeddings

* add test case

* rename build fn

* correct a small bug
---
 convert_hf_to_gguf.py          |  45 ++++++-
 gguf-py/gguf/constants.py      |   1 +
 gguf-py/gguf/tensor_mapping.py |  12 ++
 tools/mtmd/clip-impl.h         |   2 +
 tools/mtmd/clip.cpp            | 211 ++++++++++++++++++++++++---------
 tools/mtmd/tests.sh            |   1 +
 6 files changed, 211 insertions(+), 61 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 9fa35e8b11..31a11cbec0 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -6254,9 +6254,11 @@ class DeepseekModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register("DeepseekV2ForCausalLM")
-@ModelBase.register("DeepseekV3ForCausalLM")
-@ModelBase.register("KimiVLForConditionalGeneration")
+@ModelBase.register(
+    "DeepseekV2ForCausalLM",
+    "DeepseekV3ForCausalLM",
+    "KimiVLForConditionalGeneration",
+)
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -8507,6 +8509,43 @@ class PixtralModel(LlavaVisionModel):
             return "mm.2.weight"
         return super().map_tensor_name(name, try_suffixes)
 
+
+@ModelBase.register("KimiVLForConditionalGeneration")
+class KimiVLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 64 * 14 # for compatibility
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_projector_scale_factor(2)
+        # eps is the same as pytorch's default value
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+
+        if is_vision_tensor:
+            if "pos_emb.weight" in name:
+                data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
+            elif "wqkv" in name:
+                split_dim = 0 if "weight" in name else -1
+                wq, wk, wv = data_torch.chunk(3, dim=split_dim)
+                return [
+                    (self.map_tensor_name(name.replace("wqkv", "wq")), wq),
+                    (self.map_tensor_name(name.replace("wqkv", "wk")), wk),
+                    (self.map_tensor_name(name.replace("wqkv", "wv")), wv)
+                ]
+
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return [] # skip other tensors
+
 ###### CONVERSION LOGIC ######
 
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index d03a02c7bf..b9d1235d17 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -2850,6 +2850,7 @@ class VisionProjectorType:
     QWEN25O = "qwen2.5o" # omni
     VOXTRAL = "voxtral"
     LFM2 = "lfm2"
+    KIMIVL = "kimivl"
 
 
 # Items here are (block size, type size)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 38bbd6e310..abb21fa821 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1122,6 +1122,7 @@ class TensorNameMap:
             "vision_encoder.patch_conv", # pixtral
             "vision_model.patch_embedding.linear", # llama 4
             "visual.patch_embed.proj", # qwen2vl
+            "vision_tower.patch_embed.proj", # kimi-vl
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_POS: (
@@ -1130,6 +1131,7 @@ class TensorNameMap:
             "vpm.embeddings.position_embedding",
             "model.vision_model.embeddings.position_embedding", # SmolVLM
             "vision_model.positional_embedding_vlm", # llama 4
+            "vision_tower.patch_embed.pos_emb", # kimi-vl
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
@@ -1141,6 +1143,7 @@ class TensorNameMap:
             "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral-hf
             "vision_encoder.transformer.layers.{bid}.attention.wq", # pixtral
             "visual.blocks.{bid}.attn.q", # qwen2vl, generated
+            "vision_tower.encoder.blocks.{bid}.wq", # kimi-vl, generated
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
@@ -1157,6 +1160,7 @@ class TensorNameMap:
             "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral-hf
             "vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral
             "visual.blocks.{bid}.attn.k", # qwen2vl, generated
+            "vision_tower.encoder.blocks.{bid}.wk", # kimi-vl, generated
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
@@ -1173,6 +1177,7 @@ class TensorNameMap:
             "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral-hf
             "vision_encoder.transformer.layers.{bid}.attention.wv", # pixtral
             "visual.blocks.{bid}.attn.v", # qwen2vl, generated
+            "vision_tower.encoder.blocks.{bid}.wv", # kimi-vl, generated
         ),
 
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
@@ -1185,6 +1190,7 @@ class TensorNameMap:
             "vision_encoder.transformer.layers.{bid}.attention_norm", # pixtral
             "vision_model.model.layers.{bid}.input_layernorm", # llama4
             "visual.blocks.{bid}.norm1", # qwen2vl
+            "vision_tower.encoder.blocks.{bid}.norm0", # kimi-vl (norm0/norm1)
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_O: (
@@ -1197,6 +1203,7 @@ class TensorNameMap:
             "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral-hf
             "vision_encoder.transformer.layers.{bid}.attention.wo", # pixtral
             "visual.blocks.{bid}.attn.proj", # qwen2vl
+            "vision_tower.encoder.blocks.{bid}.wo", # kimi-vl
         ),
 
         MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
@@ -1209,6 +1216,7 @@ class TensorNameMap:
             "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral-hf
             "vision_encoder.transformer.layers.{bid}.ffn_norm", # pixtral
             "visual.blocks.{bid}.norm2", # qwen2vl
+            "vision_tower.encoder.blocks.{bid}.norm1", # kimi-vl (norm0/norm1)
         ),
 
         MODEL_TENSOR.V_ENC_FFN_UP: (
@@ -1221,6 +1229,7 @@ class TensorNameMap:
             "vision_model.model.layers.{bid}.mlp.fc1", # llama4
             "visual.blocks.{bid}.mlp.fc1", # qwen2vl
             "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl
+            "vision_tower.encoder.blocks.{bid}.mlp.fc0", # kimi-vl (fc0/fc1)
         ),
 
         MODEL_TENSOR.V_ENC_FFN_GATE: (
@@ -1239,6 +1248,7 @@ class TensorNameMap:
             "vision_model.model.layers.{bid}.mlp.fc2", # llama4
             "visual.blocks.{bid}.mlp.fc2", # qwen2vl
             "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl
+            "vision_tower.encoder.blocks.{bid}.mlp.fc1", # kimi-vl (fc0/fc1)
         ),
 
         MODEL_TENSOR.V_LAYER_SCALE_1: (
@@ -1263,6 +1273,7 @@ class TensorNameMap:
             "model.vision_model.post_layernorm", # SmolVLM
             "vision_model.layernorm_post", # llama4
             "visual.merger.ln_q", # qwen2vl
+            "vision_tower.encoder.final_layernorm", # kimi-vl
         ),
 
         MODEL_TENSOR.V_MM_INP_PROJ: (
@@ -1272,6 +1283,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_MM_INP_NORM: (
             "multi_modal_projector.norm",
             "multi_modal_projector.layer_norm",
+            "multi_modal_projector.pre_norm",
             "pre_mm_projector_norm",
         ),
 
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 706ed2e3b5..664b0c9ac6 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -135,6 +135,7 @@ enum projector_type {
     PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
     PROJECTOR_TYPE_VOXTRAL,
     PROJECTOR_TYPE_LFM2,
+    PROJECTOR_TYPE_KIMIVL,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -156,6 +157,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
     { PROJECTOR_TYPE_VOXTRAL,   "voxtral"},
     { PROJECTOR_TYPE_LFM2,      "lfm2"},
+    { PROJECTOR_TYPE_KIMIVL,    "kimivl"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 0e76b9c590..e7c516d2de 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -526,57 +526,16 @@ struct clip_graph {
                 cur);
 
         } else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) {
+            // pixel_shuffle
             // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578
-
             const int scale_factor = model.hparams.proj_scale_factor;
-            const int n_embd = cur->ne[0];
-            const int seq    = cur->ne[1];
-            const int bsz    = 1; // batch size, always 1 for now since we don't support batching
-            const int height = std::sqrt(seq);
-            const int width  = std::sqrt(seq);
-            GGML_ASSERT(scale_factor != 0);
-            cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height, bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_cont_4d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                height / scale_factor,
-                width / scale_factor,
-                bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_cont_3d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                seq / (scale_factor * scale_factor),
-                bsz);
-
+            cur = build_patch_merge_permute(cur, scale_factor);
             cur = ggml_mul_mat(ctx0, model.projection, cur);
+
         } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
             // pixel unshuffle block
             const int scale_factor = model.hparams.proj_scale_factor;
-            GGML_ASSERT(scale_factor > 1);
-
-            const int n_embd = cur->ne[0];
-            int width  = img.nx / patch_size;
-            int height = img.ny / patch_size;
-
-            // pad width and height to factor
-            const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width;
-            const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
-            cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
-            if (pad_width || pad_height) {
-                cur     = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
-                width  += pad_width;
-                height += pad_height;
-            }
-
-            // unshuffle h
-            cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-
-            // unshuffle w
-            cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-
-            cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+            cur = build_patch_merge_permute(cur, scale_factor);
 
             // projection
             cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
@@ -1086,7 +1045,7 @@ struct clip_graph {
                 n_patches_x / scale_factor,
                 n_patches_y / scale_factor,
                 bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+            //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
             // flatten to 2D
             cur = ggml_cont_2d(ctx0, cur,
                 n_embd * scale_factor * scale_factor,
@@ -1113,6 +1072,67 @@ struct clip_graph {
         return gf;
     }
 
+    ggml_cgraph * build_kimivl() {
+        // 2D input positions
+        ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+        ggml_set_name(pos_h, "pos_h");
+        ggml_set_input(pos_h);
+
+        ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+        ggml_set_name(pos_w, "pos_w");
+        ggml_set_input(pos_w);
+
+        ggml_tensor * learned_pos_embd = resize_position_embeddings();
+
+        // build ViT with 2D position embeddings
+        auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+            // first half is X axis and second half is Y axis
+            return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
+        };
+
+        ggml_tensor * inp = build_inp();
+        ggml_tensor * cur = build_vit(
+                                inp, n_patches,
+                                NORM_TYPE_NORMAL,
+                                hparams.ffn_op,
+                                learned_pos_embd,
+                                add_pos);
+
+        cb(cur, "vit_out", -1);
+
+        {
+            // patch_merger
+            const int scale_factor = model.hparams.proj_scale_factor;
+            cur = build_patch_merge_permute(cur, scale_factor);
+
+            // projection norm
+            int proj_inp_dim = cur->ne[0];
+            cur = ggml_view_2d(ctx0, cur,
+                n_embd, cur->ne[1] * scale_factor * scale_factor,
+                ggml_row_size(cur->type, n_embd), 0);
+            cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
+            cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
+            cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
+            cur = ggml_view_2d(ctx0, cur,
+                proj_inp_dim, cur->ne[1] / scale_factor / scale_factor,
+                ggml_row_size(cur->type, proj_inp_dim), 0);
+            cb(cur, "proj_inp_normed", -1);
+
+            // projection mlp
+            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
+            cur = ggml_add(ctx0, cur, model.mm_1_b);
+            cur = ggml_gelu(ctx0, cur);
+            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
+            cur = ggml_add(ctx0, cur, model.mm_2_b);
+            cb(cur, "proj_out", -1);
+        }
+
+        // build the graph
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     // this graph is used by llava, granite and glm
     // due to having embedding_stack (used by granite), we cannot reuse build_vit
     ggml_cgraph * build_llava() {
@@ -1611,18 +1631,20 @@ private:
         ggml_tensor * pos_embd = model.position_embeddings;
         const int height       = img.ny / patch_size;
         const int width        = img.nx / patch_size;
+        const uint32_t mode    = GGML_SCALE_MODE_BILINEAR;
+        const int n_per_side   = (int)std::sqrt(pos_embd->ne[1]);
 
-        if (!pos_embd || height * width == pos_embd->ne[1]) {
+        GGML_ASSERT(pos_embd);
+
+        if (height == n_per_side && width == n_per_side) {
             return pos_embd;
         }
 
-        const int n_pos_embd = std::sqrt(pos_embd->ne[1]);
-        pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_pos_embd, n_pos_embd);  // -> (n_embd, n_pos_embd, n_pos_embd)
-        pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3);                         // -> (n_pos_embd, n_pos_embd, n_embd)
-        pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, 1);    // -> (width, height, n_embd)
-        pos_embd = ggml_reshape_2d(ctx0, pos_embd, height * width, n_embd);          // -> (height * width, n_embd)
-        pos_embd = ggml_transpose(ctx0, pos_embd);                                   // -> (n_embd, height * width)
-        pos_embd = ggml_cont(ctx0, pos_embd);
+        pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side);  // -> (n_embd, n_per_side, n_per_side)
+        pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3);                         // -> (n_per_side, n_per_side, n_embd)
+        pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd)
+        pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3);                         // -> (n_embd, width, height)
+        pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);             // -> (n_embd, width * height)
 
         return pos_embd;
     }
@@ -2021,6 +2043,39 @@ private:
         return cur;
     }
 
+    // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
+    // support dynamic resolution
+    ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor) {
+        GGML_ASSERT(scale_factor > 1);
+
+        const int n_embd = cur->ne[0];
+        int width  = img.nx / patch_size;
+        int height = img.ny / patch_size;
+
+        // pad width and height to factor
+        const int64_t pad_width  = CLIP_ALIGN(width,  scale_factor) - width;
+        const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
+        if (pad_width || pad_height) {
+            cur     = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
+            width  += pad_width;
+            height += pad_height;
+        }
+
+        // unshuffle h
+        cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
+        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+
+        // unshuffle w
+        cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
+        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+
+        cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+        cb(cur, "pixel_shuffle", -1);
+
+        return cur;
+    }
+
 };
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
@@ -2063,6 +2118,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 res = graph.build_whisper_enc();
             } break;
+        case PROJECTOR_TYPE_KIMIVL:
+            {
+                res = graph.build_kimivl();
+            } break;
         default:
             {
                 res = graph.build_llava();
@@ -2313,6 +2372,12 @@ struct clip_model_loader {
                         hparams.image_size = 1024;
                         get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false);
                     } break;
+                case PROJECTOR_TYPE_KIMIVL:
+                    {
+                        hparams.rope_theta = 10000.0f;
+                        hparams.warmup_image_size = hparams.patch_size * 8;
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
+                    } break;
                 case PROJECTOR_TYPE_GEMMA3:
                     {
                         // default value (used by all model sizes in gemma 3 family)
@@ -2477,7 +2542,20 @@ struct clip_model_loader {
 
             // some models already exported with legacy (incorrect) naming which is quite messy, let's fix it here
             // note: Qwen model converted from the old surgery script has n_ff = 0, so we cannot use n_ff to check!
-            if (layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd) {
+            bool is_ffn_swapped = (
+                    // only old models need this fix
+                    model.proj_type == PROJECTOR_TYPE_MLP
+                    || model.proj_type == PROJECTOR_TYPE_MLP_NORM
+                    || model.proj_type == PROJECTOR_TYPE_LDP
+                    || model.proj_type == PROJECTOR_TYPE_LDPV2
+                    || model.proj_type == PROJECTOR_TYPE_QWEN2VL
+                    || model.proj_type == PROJECTOR_TYPE_QWEN25VL
+                    || model.proj_type == PROJECTOR_TYPE_GLM_EDGE
+                    || model.proj_type == PROJECTOR_TYPE_GEMMA3
+                    || model.proj_type == PROJECTOR_TYPE_IDEFICS3
+                    || model.proj_type == PROJECTOR_TYPE_MINICPMV
+                ) && layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd;
+            if (is_ffn_swapped) {
                 // swap up and down weights
                 ggml_tensor * tmp = layer.ff_up_w;
                 layer.ff_up_w = layer.ff_down_w;
@@ -2486,6 +2564,9 @@ struct clip_model_loader {
                 tmp = layer.ff_up_b;
                 layer.ff_up_b = layer.ff_down_b;
                 layer.ff_down_b = tmp;
+                if (il == 0) {
+                    LOG_WRN("%s: ffn up/down are swapped\n", __func__);
+                }
             }
         }
 
@@ -2604,6 +2685,7 @@ struct clip_model_loader {
                     model.projection = get_tensor(TN_MM_PROJECTOR);
                 } break;
             case PROJECTOR_TYPE_LFM2:
+            case PROJECTOR_TYPE_KIMIVL:
                 {
                     model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
                     model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B);
@@ -3507,7 +3589,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         res_imgs->grid_y = inst.grid_size.height;
         return true;
 
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
+    } else if ( ctx->proj_type() == PROJECTOR_TYPE_LFM2
+             || ctx->proj_type() == PROJECTOR_TYPE_KIMIVL
+    ) {
         GGML_ASSERT(params.proj_scale_factor);
 
         // smart resize
@@ -3708,12 +3792,21 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_IDEFICS3:
         case PROJECTOR_TYPE_INTERNVL:
         case PROJECTOR_TYPE_LLAMA4:
-        case PROJECTOR_TYPE_LFM2:
             {
-                // both W and H are divided by proj_scale_factor
+                // both X and Y are downscaled by the scale factor
                 int scale_factor = ctx->model.hparams.proj_scale_factor;
                 n_patches /= (scale_factor * scale_factor);
             } break;
+        case PROJECTOR_TYPE_LFM2:
+        case PROJECTOR_TYPE_KIMIVL:
+            {
+                // dynamic size
+                int scale_factor = ctx->model.hparams.proj_scale_factor;
+                int out_patch_size = params.patch_size * scale_factor;
+                int x_patch = CLIP_ALIGN(img->nx, out_patch_size) / out_patch_size;
+                int y_patch = CLIP_ALIGN(img->ny, out_patch_size) / out_patch_size;
+                n_patches = x_patch * y_patch;
+            } break;
         case PROJECTOR_TYPE_PIXTRAL:
             {
                 // dynamic size
@@ -4096,6 +4189,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 set_input_i32("positions", positions);
             } break;
         case PROJECTOR_TYPE_PIXTRAL:
+        case PROJECTOR_TYPE_KIMIVL:
             {
                 // set the 2D positions
                 int n_patches_per_col = image_size_width / patch_size;
@@ -4250,6 +4344,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         case PROJECTOR_TYPE_QWEN2A:
             return ctx->model.mm_fc_w->ne[1];
         case PROJECTOR_TYPE_LFM2:
+        case PROJECTOR_TYPE_KIMIVL:
             return ctx->model.mm_2_w->ne[1];
         default:
             GGML_ABORT("Unknown projector type");
diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh
index 6f8a5f86ac..c64be03630 100755
--- a/tools/mtmd/tests.sh
+++ b/tools/mtmd/tests.sh
@@ -86,6 +86,7 @@ if [ "$RUN_BIG_TESTS" = true ]; then
     add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
     add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
     # add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
+    add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M"
 
     add_test_audio  "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
     add_test_audio  "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"

From b3964c1e890ef8c947afb36a5124ce6fcb2136d4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 26 Aug 2025 14:22:14 +0300
Subject: [PATCH 189/287] metal : optimize FA vec for large sequences and BS <=
 8 (#15566)

* metal : optmize FA vec for large heads and sequences

* metal : adjust small-batch mul mv kernels

ggml-ci

* batched-bench : fix total speed computation

ggml-ci

* cont : add comments

ggml-ci
---
 ggml/src/ggml-metal/ggml-metal-impl.h |   6 ++
 ggml/src/ggml-metal/ggml-metal.m      | 127 ++++++++++++++++++++++----
 ggml/src/ggml-metal/ggml-metal.metal  |  73 +++++++++++++--
 tools/batched-bench/batched-bench.cpp |   2 +-
 4 files changed, 183 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 82c1ac1da0..b9d3639448 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -249,6 +249,7 @@ typedef struct {
     uint64_t nb33;
     int32_t  ne1;
     int32_t  ne2;
+    int32_t  ne3;
     float    scale;
     float    max_bias;
     float    m0;
@@ -257,6 +258,11 @@ typedef struct {
     float    logit_softcap;
 } ggml_metal_kargs_flash_attn_ext;
 
+typedef struct {
+    int32_t  nrows;
+    int32_t  ne20;
+} ggml_metal_kargs_flash_attn_ext_reduce;
+
 typedef struct {
     int32_t  ne00;
     int32_t  ne02;
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 7a05a98278..1f93633d91 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -291,6 +291,10 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_2,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_3,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_4,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_5,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,
     GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,
@@ -575,6 +579,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK576_HV512,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK576_HV512,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK576_HV512,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_REDUCE,
     GGML_METAL_KERNEL_TYPE_SET_I32,
     GGML_METAL_KERNEL_TYPE_SET_F32,
     GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
@@ -1324,6 +1329,10 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,                 mul_mv_q5_1_f32,                 has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,                 mul_mv_q8_0_f32,                 has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,                mul_mv_mxfp4_f32,                has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_2,         mul_mv_ext_f32_f32_r1_2,         has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_3,         mul_mv_ext_f32_f32_r1_3,         has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_4,         mul_mv_ext_f32_f32_r1_4,         has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_5,         mul_mv_ext_f32_f32_r1_5,         has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,         mul_mv_ext_f16_f32_r1_2,         has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,         mul_mv_ext_f16_f32_r1_3,         has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,         mul_mv_ext_f16_f32_r1_4,         has_simdgroup_reduction);
@@ -1609,6 +1618,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_HK576_HV512,    flash_attn_ext_vec_q5_0_hk576_hv512,    has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_HK576_HV512,    flash_attn_ext_vec_q5_1_hk576_hv512,    has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_HK576_HV512,    flash_attn_ext_vec_q8_0_hk576_hv512,    has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_REDUCE,           flash_attn_ext_reduce,           has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_F32,                         set_f32,                         true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_I32,                         set_i32,                         true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                     cpy_f32_f32,                     true);
@@ -3385,15 +3395,16 @@ static int ggml_metal_encode_node(
 
                 // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                 // to the matrix-vector kernel
-                const int ne11_mm_min = 4;
+                const int ne11_mm_min = 8;
 
                 // first try to use small-batch mat-mv kernels
                 // these should be efficient for BS [2, ~8]
-                if (src1t == GGML_TYPE_F32 && (ne00%256 == 0) &&
+                if (src1t == GGML_TYPE_F32 && (ne00%128 == 0) &&
                     (
                      (
                       (
-                       src0t == GGML_TYPE_F16  || // TODO: helper function
+                       src0t == GGML_TYPE_F32  || // TODO: helper function
+                       src0t == GGML_TYPE_F16  ||
                        src0t == GGML_TYPE_Q4_0 ||
                        src0t == GGML_TYPE_Q4_1 ||
                        src0t == GGML_TYPE_Q5_0 ||
@@ -3421,7 +3432,17 @@ static int ggml_metal_encode_node(
                     //       values and there can be some tail effects when nsg is high. need to confirm this
                     //
                     const int nsg    = 2;                 // num simdgroups per threadgroup
-                    const int nxpsg  = ne11 < 3 ? 16 : 8; // num threads along row per simdgroup
+
+                    // num threads along row per simdgroup
+                    int nxpsg = 0;
+                    if (ne00 % 256 == 0 && ne11 < 3) {
+                        nxpsg = 16;
+                    } else if (ne00 % 128 == 0) {
+                        nxpsg = 8;
+                    } else {
+                        nxpsg = 4;
+                    }
+
                     const int nypsg  = 32/nxpsg;          // num threads along col per simdgroup (i.e. a simdgroup processes that many src0 rows at a time)
                     const int r0ptg  = nypsg*nsg;         // num src0 rows per threadgroup
                           int r1ptg  = 4;                 // num src1 rows per threadgroup
@@ -3444,6 +3465,14 @@ static int ggml_metal_encode_node(
                     id<MTLComputePipelineState> pipeline = nil;
 
                     switch (src0->type) {
+                        case GGML_TYPE_F32:
+                            switch (r1ptg) {
+                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_2].pipeline; break;
+                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_3].pipeline; break;
+                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_4].pipeline; break;
+                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F32_F32_R1_5].pipeline; break;
+                                default: GGML_ABORT("not implemented");
+                            } break;
                         case GGML_TYPE_F16:
                             switch (r1ptg) {
                                 case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2].pipeline; break;
@@ -3598,7 +3627,7 @@ static int ggml_metal_encode_node(
                         case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32   ].pipeline; break;
                         case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32   ].pipeline; break;
                         case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32   ].pipeline; break;
-                        case GGML_TYPE_MXFP4:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32   ].pipeline; break;
+                        case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32  ].pipeline; break;
                         case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32   ].pipeline; break;
                         case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32   ].pipeline; break;
                         case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32   ].pipeline; break;
@@ -5482,6 +5511,7 @@ static int ggml_metal_encode_node(
                     /*.nb33          =*/ nb33,
                     /*.ne1           =*/ ne1,
                     /*.ne2           =*/ ne2,
+                    /*.ne3           =*/ ne3,
                     /*.scale         =*/ scale,
                     /*.max_bias      =*/ max_bias,
                     /*.m0            =*/ m0,
@@ -5505,7 +5535,6 @@ static int ggml_metal_encode_node(
                 } else {
                     [encoder setBuffer:id_src0 offset:offs_src0 atIndex:5];
                 }
-                [encoder setBuffer:id_dst offset:offs_dst       atIndex:6];
 
                 if (!use_vec_kernel) {
                     // half8x8 kernel
@@ -5531,7 +5560,7 @@ static int ggml_metal_encode_node(
 
                     while (true) {
                         const size_t smem = FATTN_SMEM(nsgmax);
-                        if (smem > device.maxThreadgroupMemoryLength) {
+                        if (smem > device.maxThreadgroupMemoryLength/2) {
                             break;
                         }
                         nsgmax *= 2;
@@ -5543,15 +5572,18 @@ static int ggml_metal_encode_node(
 
                     const size_t smem = FATTN_SMEM(nsg);
 
+                    [encoder setBuffer:id_dst offset:offs_dst atIndex:6];
+
                     //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg);
                     GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
                     [encoder setThreadgroupMemoryLength:smem atIndex:0];
-#undef FATTN_SMEM
                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+#undef FATTN_SMEM
                 } else {
                     // half4x4 kernel
                     const int64_t nqptg = 1;  // queries per threadgroup    !! sync with kernel template arguments !!
                     const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
+                    const int64_t nkpsg = 1*ncpsg; // TODO: make adjustable
 
                     GGML_ASSERT(nqptg <= 32);
                     GGML_ASSERT(nqptg  % 1  == 0);
@@ -5561,15 +5593,17 @@ static int ggml_metal_encode_node(
                     // for each query, we load it as f16 in shared memory (ne00)
                     // and store the soft_max values and the mask
                     //
-                    // ne00*(nsg)
+                    // ne20*(nsg)
                     // each simdgroup has a full f32 head vector in shared mem to accumulate results
                     //
 #define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(GGML_PAD(ne00, 128) + 4*ncpsg*(nsg)) + 2*ne20*(nsg))*(sizeof(float)/2), 16))
+//#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(GGML_PAD(ne00, 128) + 4*ncpsg*(nsg)))*(sizeof(float)/2), 16))
 
                     int64_t nsgmax = 2;
                     while (true) {
                         const size_t smem = FATTN_SMEM(nsgmax);
-                        if (smem > device.maxThreadgroupMemoryLength) {
+                        // avoid using more than half of the threadgroup memory - can cause slow downs especially for large head sizes
+                        if (smem > device.maxThreadgroupMemoryLength/2) {
                             break;
                         }
                         nsgmax *= 2;
@@ -5577,7 +5611,7 @@ static int ggml_metal_encode_node(
                     nsgmax /= 2;
 
                     // simdgroups per threadgroup (a.k.a. warps)
-                    const int64_t nsgt = MAX(2, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)));
+                    const int64_t nsgt = MAX(2, MIN(nsgmax, MIN((ne11 + nkpsg - 1)/(nkpsg), (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)));
 
                     int64_t nsg = 1;
                     while (nsg <= nsgt) {
@@ -5585,13 +5619,74 @@ static int ggml_metal_encode_node(
                     }
                     nsg /= 2;
 
-                    const size_t smem = FATTN_SMEM(nsg);
+                    // workgroups
+                    // each workgroup handles nsg*nkpsg cache values
+                    uint16_t nwg = 1;
+                    if (4*nsg*nkpsg >= ne11) {
+                        const size_t smem = FATTN_SMEM(nsg);
 
-                    //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg);
-                    GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
-                    [encoder setThreadgroupMemoryLength:smem atIndex:0];
+                        //printf("smem: %zu, max: %zu, nsg = %d, nsgmax = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg, (int) nsgmax);
+                        GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
+
+                        // using 1 workgroup -> write the result directly into dst
+                        [encoder setBuffer:id_dst offset:offs_dst      atIndex:6];
+                        [encoder setBytes:&nwg length:sizeof(uint16_t) atIndex:7];
+
+                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
+                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+                    } else {
+                        nwg = 32;
+                        nsg = MIN(4, nsg);
+
+                        const size_t smem = FATTN_SMEM(nsg);
+
+                        //printf("smem: %zu, max: %zu, nsg = %d, nsgmax = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg, (int) nsgmax);
+                        GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength);
+
+                        // sanity checks
+                        GGML_ASSERT(ne01*ne02*ne03 == ne1*ne2*ne3);
+                        GGML_ASSERT(ne1*ne2*ne3 <= (1u << 31));
+
+                        const int32_t nrows = ne1*ne2*ne3;
+
+                        // temp buffer for writing the results from each workgroup
+                        // - ne20: the size of the head vector
+                        // -  + 2: the S and M values for each intermediate result
+                        const size_t s_tmp = ggml_type_size(GGML_TYPE_F32)*(nrows*nwg*(ne20 + 2));
+                        id<MTLBuffer> h_tmp = ggml_metal_mem_pool_alloc(mem_pool, s_tmp);
+                        if (!h_tmp) {
+                            GGML_LOG_ERROR("%s: failed to allocate buffer from memory pool, size = %zu\n", __func__, s_tmp);
+                            return 0;
+                        }
+
+                        //printf("ne01 = %d, ne02 = %d, ne03 = %d, ne20 = %d\n", ne01, ne02, ne03, ne20);
+                        //printf("needed memory: %.3f MiB\n", (float) (ne01*ne02*ne03*ne20*sizeof(float))/1024.0f/1024.0f);
+
+                        [encoder setBuffer:h_tmp  offset:0             atIndex:6];
+                        [encoder setBytes:&nwg length:sizeof(uint16_t) atIndex:7];
+
+                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
+                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+
+                        // reduce the results from the workgroups
+                        {
+                            ggml_metal_kargs_flash_attn_ext_reduce args0 = {
+                                nrows,
+                                ne20,
+                            };
+
+                            id<MTLComputePipelineState> pipeline0 = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_REDUCE].pipeline;
+
+                            [encoder setComputePipelineState:pipeline0];
+                            [encoder setBytes:&args0   length:sizeof(args0) atIndex:0];
+                            [encoder setBuffer:h_tmp   offset:0             atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst      atIndex:2];
+
+                            //printf("ne1 = %d, ne2 = %d, ne3 = %d, ne20 = %d\n", ne1, ne2, ne3, ne20);
+                            [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(32*32, 1, 1)];
+                        }
+                    }
 #undef FATTN_SMEM
-                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                 }
             } break;
         case GGML_OP_DUP:
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 7037c1aa0d..fa80d6e405 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -68,6 +68,11 @@ void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg)
     reg = (type4x4)(*src);
 }
 
+template <typename type4>
+void dequantize_f32_t4(device const float4 * src, short il, thread type4 & reg) {
+    reg = (type4)(*src);
+}
+
 template <typename type4x4>
 void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
     reg = (type4x4)(*src);
@@ -3015,7 +3020,6 @@ void kernel_mul_mv_ext_q4_f32_impl(
 #pragma unroll(r1ptg)
             for (short ir1 = 0; ir1 < r1ptg; ++ir1) {
                 sumf[ir1] += dot(lx[ch], y4[ir1][ch*nxpsg]);
-
             }
         }
 
@@ -3200,6 +3204,11 @@ kernel void kernel_mul_mv_ext_q4x4_f32_disp(
 typedef decltype(kernel_mul_mv_ext_q4_f32_disp  <2, block_q8_0, 32,  dequantize_q8_0_t4>) mul_mv_ext_q4_f32_t;
 typedef decltype(kernel_mul_mv_ext_q4x4_f32_disp<2, block_q4_K, 256, dequantize_q4_K>)    mul_mv_ext_q4x4_f32_t;
 
+template [[host_name("kernel_mul_mv_ext_f32_f32_r1_2")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, float4,       4,  dequantize_f32_t4>;
+template [[host_name("kernel_mul_mv_ext_f32_f32_r1_3")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, float4,       4,  dequantize_f32_t4>;
+template [[host_name("kernel_mul_mv_ext_f32_f32_r1_4")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, float4,       4,  dequantize_f32_t4>;
+template [[host_name("kernel_mul_mv_ext_f32_f32_r1_5")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, float4,       4,  dequantize_f32_t4>;
+
 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_2")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, half4,        4,  dequantize_f16_t4>;
 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_3")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, half4,        4,  dequantize_f16_t4>;
 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_4")]]    kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, half4,        4,  dequantize_f16_t4>;
@@ -4786,14 +4795,16 @@ kernel void kernel_flash_attn_ext_vec(
         device const char * mask,
         device const char * sinks,
         device       char * dst,
+        constant uint16_t & nwg,
         threadgroup  half * shmem_f16 [[threadgroup(0)]],
         uint3   tgpig[[threadgroup_position_in_grid]],
         ushort3   ntg[[threads_per_threadgroup]],
         ushort  tiisg[[thread_index_in_simdgroup]],
         ushort  sgitg[[simdgroup_index_in_threadgroup]]) {
     const short nsg = ntg.y; // number of simdgroups
+    const short iwg = tgpig[2]%nwg;
 
-    const int iq3 = tgpig[2];
+    const int iq3 = tgpig[2]/nwg;
     const int iq2 = tgpig[1];
     const int iq1 = tgpig[0];
 
@@ -4872,7 +4883,7 @@ kernel void kernel_flash_attn_ext_vec(
 
         // loop over the KV cache
         // each simdgroup handles blocks of Q rows and C columns
-        for (int ic0 = 0; ic0 < args.ne11; ic0 += C*nsg) {
+        for (int ic0 = (int) iwg*C*nsg; ic0 < args.ne11; ic0 += (int) nwg*C*nsg) {
             const int ic = ic0 + C*sgitg;
             if (ic >= args.ne11) {
                 break;
@@ -5002,7 +5013,7 @@ kernel void kernel_flash_attn_ext_vec(
             }
         }
 
-        if (sinks != q && sgitg == 0) {
+        if (sinks != q && sgitg == 0 && iwg == 0) {
             const float m = M;
             const float s = tiisg == 0 ? ((device const float *) sinks)[iq2] : -FLT_MAX/2;
 
@@ -5111,14 +5122,25 @@ kernel void kernel_flash_attn_ext_vec(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-    device float4 * dst4 = (device float4 *) dst;
-
     // final rescale with 1/S and store to global memory
     if (sgitg == 0) {
-        const float S = ss[0];
+        const int64_t nrows = args.ne3*args.ne2*args.ne1;
+        const int64_t rid   = iq3*args.ne2*args.ne1 + iq2 + iq1*args.ne1;
 
+        device float4 * dst4 = (device float4 *) dst;
+        device float  * dst1 = (device float  *) dst + nrows*DV*nwg; // the S and M are stored after the results
+
+        const float S = nwg == 1 ? 1.0f/ss[0] : 1.0f;
+
+        // interleave the workgroup data
         for (short i = tiisg; i < DV4; i += NW) {
-            dst4[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)iq1*args.ne1)*DV4 + i] = (float4) sr4[i]/S;
+            dst4[rid*DV4*nwg + nwg*i + iwg] = (float4) sr4[i]*S;
+        }
+
+        // store S and M
+        if (nwg > 1 && tiisg == 0) {
+            dst1[rid*(2*nwg) + 2*iwg + 0] = ss[0];
+            dst1[rid*(2*nwg) + 2*iwg + 1] = ss[1];
         }
     }
 }
@@ -5218,6 +5240,41 @@ template [[host_name("kernel_flash_attn_ext_vec_q8_0_hk576_hv512")]] kernel flas
 
 #undef FA_TYPES
 
+kernel void kernel_flash_attn_ext_reduce(
+        constant ggml_metal_kargs_flash_attn_ext_reduce & args,
+        device  const char * htmp,
+        device        char * dst,
+        uint   tgpig[[threadgroup_position_in_grid]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+    const uint64_t rid = tgpig;
+
+    const short nwg = 32;
+    const short iwg = tiisg;
+    const short DV  = args.ne20;
+    const short DV4 = DV/4;
+
+    device const float4 * htmp4 = (device const float4 *) htmp + rid*DV4*nwg;
+    device const float  * ss    = (device const float  *) htmp + (uint64_t)args.nrows*DV*nwg;
+    device       float4 * dst4  = (device       float4 *) dst  + rid*DV4;
+
+    float S = ss[rid*(2*nwg) + 2*iwg + 0];
+    float M = ss[rid*(2*nwg) + 2*iwg + 1];
+
+    const float m  = simd_max(M);
+    const float ms = exp(M - m);
+
+    S = 1.0f/simd_sum(S*ms);
+
+    for (int i = sgitg; i < DV4; i += nwg) {
+        const float4 v = simd_sum(htmp4[i*nwg + iwg]*ms);
+
+        if (iwg == 0) {
+            dst4[i] = v*S;
+        }
+    }
+}
+
 template<typename T>
 kernel void kernel_set(
     constant ggml_metal_kargs_set & args,
diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp
index 93efad3280..23d03039dc 100644
--- a/tools/batched-bench/batched-bench.cpp
+++ b/tools/batched-bench/batched-bench.cpp
@@ -191,7 +191,7 @@ int main(int argc, char ** argv) {
 
                 const float speed_pp = is_pp_shared ? pp / t_pp : pl*pp / t_pp;
                 const float speed_tg = pl*tg / t_tg;
-                const float speed    = n_kv / t;
+                const float speed    = ((is_pp_shared ? pp : pl*pp) + pl*tg) / t;
 
                 if(params.batched_bench_output_jsonl) {
                     LOG(

From 8f5afa94c4f929da71f560db7c9f38ef6a783d95 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 26 Aug 2025 16:01:20 +0200
Subject: [PATCH 190/287] CUDA: return -1 for nonexistent compiled arch
 (#15587)

---
 ggml/src/ggml-cuda/common.cuh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 48de1649cf..85bc9e933b 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -107,9 +107,9 @@ constexpr bool ggml_cuda_has_arch(const int arch) {
     return ggml_cuda_has_arch_impl(arch, __CUDA_ARCH_LIST__);
 }
 
-constexpr int ggml_cuda_highest_compiled_arch_impl(const int arch, const int cur) {
+constexpr int ggml_cuda_highest_compiled_arch_impl(const int /*arch*/, const int cur) {
     if (cur == 0) {
-        GGML_ABORT("ggml was not compiled with any CUDA arch <= %d", arch);
+        return -1;
     }
     return cur;
 }

From 62cef26ac5b6b7acb635d3dc963813b43952dc2b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 26 Aug 2025 16:12:29 +0200
Subject: [PATCH 191/287] model-conversion : add qat-q4 quantization targets
 (#15588)

This commit adds two targets to the Makefile for quantizing of
Quantization Aware Trained (QAT) models to Q4_0 format.

The motivation for this is that this sets the token embedding and the
output tensors data types to Q8_0 instead of the default Q6_K. This is
someting that we wish to enforce for QAT Q4_0 models that are to be
uploaded to ggml-org on Huggingface to guarantee the best quality.
---
 examples/model-conversion/Makefile            | 30 +++++++++++++++----
 examples/model-conversion/README.md           | 24 +++++++++++++++
 .../scripts/utils/quantize.sh                 | 18 +++++++++--
 3 files changed, 65 insertions(+), 7 deletions(-)

diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
index 2f1c3eb903..37982495b2 100644
--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -1,4 +1,5 @@
-# Validation functions
+MAKEFLAGS += --no-print-directory
+
 define validate_model_path
 	@if [ -z "$(MODEL_PATH)" ]; then \
 		echo "Error: MODEL_PATH must be provided either as:"; \
@@ -17,6 +18,13 @@ define validate_embedding_model_path
 	fi
 endef
 
+define quantize_model
+	@CONVERTED_MODEL="$(1)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" \
+	TOKEN_EMBD_TYPE="$(TOKEN_EMBD_TYPE)" OUTPUT_TYPE="$(OUTPUT_TYPE)" \
+	./scripts/utils/quantize.sh "$(1)" "$(QUANTIZED_TYPE)" "$(TOKEN_EMBD_TYPE)" "$(OUTPUT_TYPE)"
+	@echo "Export the quantized model path to $(2) variable in your environment"
+endef
+
 ###
 ### Casual Model targets/recipes
 ###
@@ -67,9 +75,15 @@ causal-quantize-Q8_0: causal-quantize-model
 causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
 causal-quantize-Q4_0: causal-quantize-model
 
+# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
+# token embedding and output types to Q8_0 instead of the default Q6_K.
+causal-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
+causal-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
+causal-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
+causal-quantize-qat-Q4_0: causal-quantize-model
+
 causal-quantize-model:
-	@CONVERTED_MODEL="$(CONVERTED_MODEL)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" ./scripts/utils/quantize.sh ${CONVERTED_MODEL} ${QUANTIZED_TYPE}
-	@echo "Export the quantized model path to QUANTIZED_MODEL variable in your environment"
+	$(call quantize_model,$(CONVERTED_MODEL),QUANTIZED_MODEL)
 
 causal-run-quantized-model:
 	@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
@@ -117,9 +131,15 @@ embedding-quantize-Q8_0: embedding-quantize-model
 embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
 embedding-quantize-Q4_0: embedding-quantize-model
 
+# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
+# token embedding and output types to Q8_0 instead of the default Q6_K.
+embedding-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
+embedding-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
+embedding-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
+embedding-quantize-qat-Q4_0: embedding-quantize-model
+
 embedding-quantize-model:
-	@./scripts/utils/quantize.sh ${CONVERTED_EMBEDDING_MODEL} ${QUANTIZED_TYPE}
-	@echo "Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment"
+	$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
 
 embedding-run-quantized-model:
 	@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}
diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md
index 424c4e5655..5e5992d964 100644
--- a/examples/model-conversion/README.md
+++ b/examples/model-conversion/README.md
@@ -137,6 +137,18 @@ Then the quantized model can be run using the following command:
 (venv) $ make causal-run-quantized-model
 ```
 
+### Quantizing QAT (Quantization Aware Training) models
+When quantizing to `Q4_0`, the default data type for the token embedding weights
+will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
+recommended to use `Q8_0` instead for the embeddings and output tensors.
+The reason is that although `Q6_K` is smaller in size, it requires more compute
+to unpack, which can hurt performance during output generation when the entire
+embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
+provides practically full quality with better computational efficiency.
+```console
+(venv) $ make causal-quantize-qat-Q4_0
+```
+
 
 ## Embedding Language Model Conversion
 
@@ -238,6 +250,18 @@ Then the quantized model can be run using the following command:
 (venv) $ make embedding-run-quantized-model
 ```
 
+### Quantizing QAT (Quantization Aware Training) models
+When quantizing to `Q4_0`, the default data type for the token embedding weights
+will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
+recommended to use `Q8_0` instead for the embeddings and output tensors.
+The reason is that although `Q6_K` is smaller in size, it requires more compute
+to unpack, which can hurt performance during output generation when the entire
+embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
+provides practically full quality with better computational efficiency.
+```console
+(venv) $ make embedding-quantize-qat-Q4_0
+```
+
 ## Perplexity Evaluation
 
 ### Simple perplexity evaluation
diff --git a/examples/model-conversion/scripts/utils/quantize.sh b/examples/model-conversion/scripts/utils/quantize.sh
index bcb8775754..90460aa6b0 100755
--- a/examples/model-conversion/scripts/utils/quantize.sh
+++ b/examples/model-conversion/scripts/utils/quantize.sh
@@ -4,6 +4,8 @@ set -e
 
 CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
 QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
+TOKEN_EMBD_TYPE="${3:-"${TOKEN_EMBD_TYPE}"}"
+OUTPUT_TYPE="${4:-"${OUTPUT_TYPE}"}"
 QUANTIZED_MODEL=$CONVERTED_MODEL
 
 # Final check if we have a model path
@@ -14,6 +16,11 @@ if [ -z "$CONVERTED_MODEL" ]; then
     exit 1
 fi
 
+if [ -z "$QUANTIZED_TYPE" ]; then
+    echo "Error: QUANTIZED_TYPE is required" >&2
+    exit 1
+fi
+
 echo $CONVERTED_MODEL
 
 # Process the quantized model filename
@@ -26,9 +33,16 @@ else
     exit 1
 fi
 
-
 cmake --build ../../build --target llama-quantize -j8
 
-../../build/bin/llama-quantize $CONVERTED_MODEL $QUANTIZED_MODEL $QUANTIZED_TYPE
+echo $TOKEN_EMBD_TYPE
+echo $OUTPUT_TYPE
+
+CMD_ARGS=("../../build/bin/llama-quantize")
+[[ -n "$TOKEN_EMBD_TYPE" ]] && CMD_ARGS+=("--token-embedding-type" "$TOKEN_EMBD_TYPE")
+[[ -n "$OUTPUT_TYPE" ]]     && CMD_ARGS+=("--output-tensor-type" "$OUTPUT_TYPE")
+CMD_ARGS+=("$CONVERTED_MODEL" "$QUANTIZED_MODEL" "$QUANTIZED_TYPE")
+
+"${CMD_ARGS[@]}"
 
 echo "Quantized model saved to: $QUANTIZED_MODEL"

From 0373486dbc0dccbdcb3b5fdd65759d88cec06196 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 26 Aug 2025 17:45:17 +0300
Subject: [PATCH 192/287] graph : fix assert in memory-less build_attn (#15590)

ggml-ci
---
 src/llama-graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 6419d739bd..b928e9e16e 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1376,7 +1376,7 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // [TAG_NO_CACHE_PAD]
     // TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams
-    assert(!ubatch.equal_seqs());
+    assert(!ubatch.equal_seqs() || (k_cur->ne[3] == 1 && k_cur->ne[3] == ubatch.n_seqs_unq));
 
     ggml_tensor * q = q_cur;
     ggml_tensor * k = k_cur;

From a6a58d64785cb458ed9de52f391aa38142d38d64 Mon Sep 17 00:00:00 2001
From: shalinib-ibm <Shalini.Salomi.Bodapati@ibm.com>
Date: Tue, 26 Aug 2025 21:05:25 +0530
Subject: [PATCH 193/287] llamafile: PowerPC Sgemm Optimization (#15558)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This patch improves GEMM for FP32 Data Type on PowerPC

Implements GEMM on large blocks with configurable block size mc, nc, kc
(default: 256, 256, 256).
Packing Function optimized to access blocks as per memory layout.
GEMM Optimized to work on larger blocks.
Isolated Packing from GEMM Operations for better MMA utilization.

Verified functionality and correctness uing llama-cli and stand alone
test case (performs matmul and compares final mattrix C result with base).

Minor code refactoring changes:
Replace macro with inline function
Code Indent made consistent with 4 spaces

Performance Testing:

Observed 50% ~ 70% improvement in Prompt Processing Speed mesured using
llama-bench with Meta-Llama3-8B FP32 Model.  Similar gains observed with
Mistral-7b-Instruct-v0.3 Model.

model                   Size                Params     Backend       Threads   Test    Patch   Base
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp512   98.58   60.3
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp1024  95.88   57.36
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp2048  85.46   53.26
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp4096  68.66   45.78
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp6144  57.35   40.44

25 ~ 30% improvement in llama-batched-bench with Metla-Llama3-8B in
Prompt Processing Speed for large prompts (256, 512, 1024, 2048, 4096)tokens with various batch
sizes ( 1, 2, 4, 8, 16)

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
---
 ggml/src/ggml-cpu/llamafile/sgemm.cpp | 373 +++++++++++++++++---------
 1 file changed, 241 insertions(+), 132 deletions(-)

diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index 2be54c31b5..2c4ad9d58b 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -2169,94 +2169,117 @@ class tinyBLAS_Q0_PPC {
 class tinyBLAS_PPC {
   public:
     tinyBLAS_PPC(int64_t k,
-                const float *A, int64_t lda,
-                const float *B, int64_t ldb,
-                float *C, int64_t ldc,
+                const float * A, int64_t lda,
+                const float * B, int64_t ldb,
+                float * C, int64_t ldc,
                 int ith, int nth)
         : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
     }
 
     void matmul(int64_t m, int64_t n) {
-       mnpack(0, m, 0, n);
+        int64_t mc = 256; int64_t nc = 256; int64_t kc = 256;
+        if (m % mc == 0 && n % nc == 0 && k % kc == 0) {
+            matmul_tiled(m, n, mc, nc, kc);
+        } else {
+            mnpack(0, m, 0, n);
+        }
     }
 
   private:
 
-    void (tinyBLAS_PPC::*kernel)(int64_t, int64_t);
-
-    inline void vector_permute_store_4(vector float *src, float *vecOffset) {
-       vector float t1, t2, t3, t4, t5, t6, t7, t8;
-           t1 = vec_mergeh(src[0], src[1]);
-           t2 = vec_mergeh(src[2], src[3]);
-           t3 = vec_mergel(src[0], src[1]);
-           t4 = vec_mergel(src[2], src[3]);
-
-           t5 = vec_xxpermdi(t1, t2, 0);
-           t6 = vec_xxpermdi(t1, t2, 3);
-           t7 = vec_xxpermdi(t3, t4, 0);
-           t8 = vec_xxpermdi(t3, t4, 3);
-
-           vec_xst(t5, 0, vecOffset);
-           vec_xst(t6, 0, vecOffset + 4);
-           vec_xst(t7, 0, vecOffset + 8);
-           vec_xst(t8, 0, vecOffset + 12);
-       }
-
-    inline void vector_permute_store_8(vector float *src, float *vecOffset) {
-       vector float t1, t2, t3, t4, t5, t6, t7, t8;
-          t1 = vec_mergeh(src[0], src[1]);
-          t2 = vec_mergeh(src[2], src[3]);
-          t3 = vec_mergeh(src[4], src[5]);
-          t4 = vec_mergeh(src[6], src[7]);
-
-          t5 = vec_xxpermdi(t1, t2, 0);
-          t6 = vec_xxpermdi(t3, t4, 0);
-          t7 = vec_xxpermdi(t1, t2, 3);
-          t8 = vec_xxpermdi(t3, t4, 3);
-
-          vec_xst(t5, 0, vecOffset);
-          vec_xst(t6, 0, vecOffset + 4);
-          vec_xst(t7, 0, vecOffset + 8);
-          vec_xst(t8, 0, vecOffset + 12);
-
-          t1 = vec_mergel(src[0], src[1]);
-          t2 = vec_mergel(src[2], src[3]);
-          t3 = vec_mergel(src[4], src[5]);
-          t4 = vec_mergel(src[6], src[7]);
-
-          t5 = vec_xxpermdi(t1, t2, 0);
-          t6 = vec_xxpermdi(t3, t4, 0);
-          t7 = vec_xxpermdi(t1, t2, 3);
-          t8 = vec_xxpermdi(t3, t4, 3);
-
-          vec_xst(t5, 0, vecOffset + 16);
-          vec_xst(t6, 0, vecOffset + 20);
-          vec_xst(t7, 0, vecOffset + 24);
-          vec_xst(t8, 0, vecOffset + 28);
+    inline void save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
+        vec_t vec_C[4];
+        __builtin_mma_disassemble_acc(vec_C, ACC);
+        for (int I = 0; I < 4; I++) {
+            for (int J = 0; J < 4; J++) {
+                *((float *)(C+ii+((jj+J)*ldc)+I)) = *((float *)&vec_C[I]+J);
+            }
+        }
     }
 
- void packTranspose(const float* a, int64_t lda, int rows, int cols, float* vec) {
+    inline void add_save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
+        vec_t vec_C[4];
+        __builtin_mma_disassemble_acc(vec_C, ACC);
+        for (int I = 0; I < 4; I++) {
+            for (int J = 0; J < 4; J++) {
+                float * c_ptr = (float *)(C+ii+((jj+J)*ldc)+I);
+                *c_ptr += *((float *)&vec_C[I]+J);
+            }
+        }
+    }
+
+    inline void vector_permute_store_4(vector float * src, float * vecOffset) {
+        vector float t1, t2, t3, t4, t5, t6, t7, t8;
+        t1 = vec_mergeh(src[0], src[1]);
+        t2 = vec_mergeh(src[2], src[3]);
+        t3 = vec_mergel(src[0], src[1]);
+        t4 = vec_mergel(src[2], src[3]);
+
+        t5 = vec_xxpermdi(t1, t2, 0);
+        t6 = vec_xxpermdi(t1, t2, 3);
+        t7 = vec_xxpermdi(t3, t4, 0);
+        t8 = vec_xxpermdi(t3, t4, 3);
+
+        vec_xst(t5, 0, vecOffset);
+        vec_xst(t6, 0, vecOffset + 4);
+        vec_xst(t7, 0, vecOffset + 8);
+        vec_xst(t8, 0, vecOffset + 12);
+    }
+
+    inline void vector_permute_store_8(vector float * src, float * vecOffset) {
+        vector float t1, t2, t3, t4, t5, t6, t7, t8;
+        t1 = vec_mergeh(src[0], src[1]);
+        t2 = vec_mergeh(src[2], src[3]);
+        t3 = vec_mergeh(src[4], src[5]);
+        t4 = vec_mergeh(src[6], src[7]);
+
+        t5 = vec_xxpermdi(t1, t2, 0);
+        t6 = vec_xxpermdi(t3, t4, 0);
+        t7 = vec_xxpermdi(t1, t2, 3);
+        t8 = vec_xxpermdi(t3, t4, 3);
+
+        vec_xst(t5, 0, vecOffset);
+        vec_xst(t6, 0, vecOffset + 4);
+        vec_xst(t7, 0, vecOffset + 8);
+        vec_xst(t8, 0, vecOffset + 12);
+
+        t1 = vec_mergel(src[0], src[1]);
+        t2 = vec_mergel(src[2], src[3]);
+        t3 = vec_mergel(src[4], src[5]);
+        t4 = vec_mergel(src[6], src[7]);
+
+        t5 = vec_xxpermdi(t1, t2, 0);
+        t6 = vec_xxpermdi(t3, t4, 0);
+        t7 = vec_xxpermdi(t1, t2, 3);
+        t8 = vec_xxpermdi(t3, t4, 3);
+
+        vec_xst(t5, 0, vecOffset + 16);
+        vec_xst(t6, 0, vecOffset + 20);
+        vec_xst(t7, 0, vecOffset + 24);
+        vec_xst(t8, 0, vecOffset + 28);
+    }
+
+    void packTranspose(const float * a, int64_t lda, int rows, int cols, float * vec) {
         int64_t i, j;
         float * aoffsets[8];
-        float *aoffset = NULL, *boffset = NULL;
+        float * aoffset = NULL, * boffset = NULL;
         __vector_pair arr[8];
         vector float c[8][2] = {0};
         vector float c1[8] = {0};
         vector float c2[8] = {0};
-        aoffset = const_cast<float*>(a);
+        aoffset = const_cast<float *>(a);
         boffset = vec;
         j = (rows >> 3);
         if (j > 0) {
-
             do {
                 aoffsets[0] = aoffset;
-                for (int it = 1; it< 8; it++)
+                for (int it = 1; it < 8; it++)
                     aoffsets[it] = aoffsets[it-1] + lda;
                 aoffset += 8 * lda;
                 i = (cols >> 3);
                 if (i > 0) {
                     do {
-                        for (int it = 0; it< 8; it++) {
+                        for (int it = 0; it < 8; it++) {
                             arr[it] = __builtin_vsx_lxvp(0, (__vector_pair*)aoffsets[it]);
                             __builtin_vsx_disassemble_pair(c[it], &arr[it]);
                             c1[it] = c[it][0];
@@ -2264,11 +2287,14 @@ class tinyBLAS_PPC {
                         }
 
                         vector_permute_store_8(c1, boffset);
-                        vector_permute_store_8(c2, boffset+32);
-                        for (int it = 0; it < 4; it++)
-                            aoffsets[it] = aoffsets[it] + 8*lda;
+                        vector_permute_store_8(c2, boffset + 32);
                         boffset += 64;
                         i--;
+                        if (i > 0) {
+                           for (int it = 0; it < 8; it++) {
+                               aoffsets[it] = aoffsets[it] + 8;
+                           }
+                        }
                     } while(i > 0);
                 }
                 if (cols & 4) {
@@ -2295,9 +2321,9 @@ class tinyBLAS_PPC {
                         c2[it] = c[it][1];
                     }
                     vector_permute_store_4(c1, boffset);
-                    vector_permute_store_4(c2, boffset+16);
+                    vector_permute_store_4(c2, boffset + 16);
                     for (int it = 0; it < 4; it++)
-                        aoffsets[it] += 8*lda;
+                        aoffsets[it] += 8 * lda;
                     boffset += 32;
                     i--;
                 } while(i > 0);
@@ -2325,15 +2351,15 @@ class tinyBLAS_PPC {
         vec_t vec_A[4], vec_B[4], vec_C[4];
         acc_t acc_0;
         __builtin_mma_xxsetaccz(&acc_0);
-        for (int l = 0; l < k; l+=4) {
-            packTranspose(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
-            packTranspose(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
+        for (int l = 0; l < k; l += 4) {
+            packTranspose(A + (ii * lda) + l, lda, 4, 4, (float *)vec_A);
+            packTranspose(B + (jj * ldb) + l, ldb, 4, 4, (float *)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]);
         }
-        SAVE_ACC(&acc_0, ii, jj);
+        save_acc(&acc_0, ii, jj);
     }
 
     void KERNEL_4x8(int64_t ii, int64_t jj) {
@@ -2341,9 +2367,9 @@ class tinyBLAS_PPC {
         acc_t acc_0, acc_1;
         __builtin_mma_xxsetaccz(&acc_0);
         __builtin_mma_xxsetaccz(&acc_1);
-        for (int64_t l = 0; l < k; l+=4) {
-            packTranspose(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
-            packTranspose(B+(jj*ldb)+l, ldb, 8, 4, (float*)vec_B);
+        for (int64_t l = 0; l < k; l += 4) {
+            packTranspose(A + (ii * lda) + l, lda, 4, 4, (float *)vec_A);
+            packTranspose(B + (jj * ldb) + l, ldb, 8, 4, (float *)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], (vec_t)vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_1, vec_A[0], (vec_t)vec_B[1]);
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], (vec_t)vec_B[2]);
@@ -2353,8 +2379,8 @@ class tinyBLAS_PPC {
             __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], (vec_t)vec_B[6]);
             __builtin_mma_xvf32gerpp(&acc_1, vec_A[3], (vec_t)vec_B[7]);
         }
-        SAVE_ACC(&acc_0, ii, jj);
-        SAVE_ACC(&acc_1, ii, jj+4);
+        save_acc(&acc_0, ii, jj);
+        save_acc(&acc_1, ii, jj + 4);
     }
 
     void KERNEL_8x4(int64_t ii, int64_t jj) {
@@ -2362,9 +2388,9 @@ class tinyBLAS_PPC {
         acc_t acc_0, acc_1;
         __builtin_mma_xxsetaccz(&acc_0);
         __builtin_mma_xxsetaccz(&acc_1);
-        for (int64_t l = 0; l < k; l+=4) {
-            packTranspose(A+(ii*lda)+l, lda, 8, 4, (float*)vec_A);
-            packTranspose(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
+        for (int64_t l = 0; l < k; l += 4) {
+            packTranspose(A + (ii * lda) + l, lda, 8, 4, (float *)vec_A);
+            packTranspose(B + (jj * ldb) + l, ldb, 4, 4, (float *)vec_B);
             __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[0], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[1], vec_B[0]);
             __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[2], vec_B[1]);
@@ -2374,8 +2400,8 @@ class tinyBLAS_PPC {
             __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[6], vec_B[3]);
             __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[7], vec_B[3]);
         }
-        SAVE_ACC(&acc_0, ii, jj);
-        SAVE_ACC(&acc_1, ii+4, jj);
+        save_acc(&acc_0, ii, jj);
+        save_acc(&acc_1, ii + 4, jj);
     }
 
     void KERNEL_8x8(int64_t ii, int64_t jj) {
@@ -2386,19 +2412,96 @@ class tinyBLAS_PPC {
         __builtin_mma_xxsetaccz(&acc_2);
         __builtin_mma_xxsetaccz(&acc_3);
         for (int l = 0; l < k; l+=8) {
-            packTranspose(A+(ii*lda)+l, lda, 8, 8, (float*)vec_A);
-            packTranspose(B+(jj*ldb)+l, ldb, 8, 8, (float*)vec_B);
+            packTranspose(A + (ii * lda) + l, lda, 8, 8, (float *)vec_A);
+            packTranspose(B + (jj * ldb) + l, ldb, 8, 8, (float *)vec_B);
             for(int x = 0; x < 16; x+=2) {
                 __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[x], vec_B[x]);
-                __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x+1]);
-                __builtin_mma_xvf32gerpp(&acc_2, (vec_t)vec_A[x+1], vec_B[x]);
-                __builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x+1], vec_B[x+1]);
+                __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x + 1]);
+                __builtin_mma_xvf32gerpp(&acc_2, (vec_t)vec_A[x + 1], vec_B[x]);
+                __builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x + 1], vec_B[x + 1]);
+            }
+        }
+        save_acc(&acc_0, ii, jj);
+        save_acc(&acc_1, ii, jj + 4);
+        save_acc(&acc_2, ii + 4, jj);
+        save_acc(&acc_3, ii + 4, jj + 4);
+    }
+
+    inline void MMA_16x8(vec_t * vec_A0, vec_t * vec_A1, vec_t * vec_B, acc_t * acc) {
+        for (int x = 0; x < 16; x += 2) {
+            __builtin_mma_xvf32gerpp(&acc[0], vec_A0[x + 0], vec_B[x]);
+            __builtin_mma_xvf32gerpp(&acc[1], vec_A0[x + 0], vec_B[x + 1]);
+            __builtin_mma_xvf32gerpp(&acc[2], vec_A0[x + 1], vec_B[x]);
+            __builtin_mma_xvf32gerpp(&acc[3], vec_A0[x + 1], vec_B[x + 1]);
+            __builtin_mma_xvf32gerpp(&acc[4], vec_A1[x + 0], vec_B[x]);
+            __builtin_mma_xvf32gerpp(&acc[5], vec_A1[x + 0], vec_B[x + 1]);
+            __builtin_mma_xvf32gerpp(&acc[6], vec_A1[x + 1], vec_B[x]);
+            __builtin_mma_xvf32gerpp(&acc[7], vec_A1[x + 1], vec_B[x + 1]);
+        }
+    }
+
+    void KERNEL(int64_t ii, int64_t jj, int64_t mc, int64_t nc, int64_t kc, vec_t * vec_A, vec_t * vec_B, int64_t kk) {
+        for (int64_t i = 0; i < mc; i += 16) {
+            int A_base_addr = (mc / 8) * (i / 8) * 16;
+            for (int64_t j = 0; j < nc; j += 8) {
+                 int B_base_addr = (nc / 8) * (j / 8) * 16;
+                 acc_t acc[8];
+                 vec_t A0_block[16]; vec_t A1_block[16];
+                 for (int x = 0; x < 8; x++)
+                     __builtin_mma_xxsetaccz(&acc[x]);
+                 for (int64_t l = 0; l < kc; l += 8) {
+                     int A0_block_idx = A_base_addr + (l / 8) * 16;
+                     int A1_block_idx = A0_block_idx + (mc / 8) * 16;
+                     int B_block_idx = B_base_addr + (l / 8) * 16;
+                     vec_t* A0_block = &vec_A[A0_block_idx];
+                     vec_t* A1_block = &vec_A[A1_block_idx];
+                     vec_t* B_block = &vec_B[B_block_idx];
+                     MMA_16x8(A0_block, A1_block, B_block, acc);
+                 }
+                 if (kk == 0) {
+                     save_acc(&acc[0], ii + i, jj + j);
+                     save_acc(&acc[1], ii + i, jj + j + 4);
+                     save_acc(&acc[2], ii + i + 4, jj + j);
+                     save_acc(&acc[3], ii + i + 4, jj + j + 4);
+                     save_acc(&acc[4], ii + i + 8, jj + j);
+                     save_acc(&acc[5], ii + i + 8, jj + j + 4);
+                     save_acc(&acc[6], ii + i + 12, jj + j);
+                     save_acc(&acc[7], ii + i + 12, jj + j + 4);
+                 } else {
+                     add_save_acc(&acc[0], ii + i, jj + j);
+                     add_save_acc(&acc[1], ii + i, jj + j + 4);
+                     add_save_acc(&acc[2], ii + i + 4, jj + j);
+                     add_save_acc(&acc[3], ii + i + 4, jj + j + 4);
+                     add_save_acc(&acc[4], ii + i + 8, jj + j);
+                     add_save_acc(&acc[5], ii + i + 8, jj + j + 4);
+                     add_save_acc(&acc[6], ii + i + 12, jj + j);
+                     add_save_acc(&acc[7], ii + i + 12, jj + j + 4);
+                 }
+            }
+        }
+    }
+
+    void matmul_tiled(int64_t m , int64_t n, int64_t mc, int64_t nc, int64_t kc) {
+        int64_t ytiles = m / mc;
+        int64_t xtiles = n / nc;
+        int64_t tiles = xtiles * ytiles;
+        int64_t duty = (tiles + nth - 1) / nth;
+        int64_t start = duty * ith;
+        int64_t end = start + duty;
+        if (end > tiles) {
+            end = tiles;
+        }
+        for (int64_t job = start; job < end; ++job) {
+            int64_t ii = (job / xtiles) * mc;
+            int64_t jj = (job % xtiles) * nc;
+            for (int64_t kk = 0; kk < k; kk += kc) {
+                 vec_t A_pack[kc * mc / 4];
+                 vec_t B_pack[kc * nc / 4];
+                 packTranspose(A + (ii * lda) + kk, lda, kc, mc, (float *)A_pack);
+                 packTranspose(B + (jj * ldb) + kk, ldb, kc, nc, (float *)B_pack);
+                 KERNEL(ii, jj, mc, nc, kc, A_pack, B_pack, kk);
             }
         }
-        SAVE_ACC(&acc_0, ii, jj);
-        SAVE_ACC(&acc_1, ii, jj+4);
-        SAVE_ACC(&acc_2, ii+4, jj);
-        SAVE_ACC(&acc_3, ii+4, jj+4);
     }
 
     void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
@@ -2406,35 +2509,35 @@ class tinyBLAS_PPC {
         int n_rem = MIN(n - n0, 8);
         int mc = 0, nc = 0;
         if (m_rem >= 8 && n_rem >= 8) {
-           mc = 8;
-           nc = 8;
-           gemm<8, 8>(m0, m, n0, n);
+            mc = 8;
+            nc = 8;
+            gemm<8, 8>(m0, m, n0, n);
         } else if (m_rem >= 4 && n_rem >= 8) {
-                mc = 4;
-                nc = 8;
-                gemm<4, 8>(m0, m, n0, n);
+            mc = 4;
+            nc = 8;
+            gemm<4, 8>(m0, m, n0, n);
         } else if (m_rem >= 8 && n_rem >= 4) {
-                mc = 8;
-                nc = 4;
-                gemm<8, 4>(m0, m, n0, n);
+            mc = 8;
+            nc = 4;
+            gemm<8, 4>(m0, m, n0, n);
         } else if (m_rem >= 4 && n_rem >= 4) {
-                mc = 4;
-                nc = 4;
-                gemm<4, 4>(m0, m, n0, n);
+            mc = 4;
+            nc = 4;
+            gemm<4, 4>(m0, m, n0, n);
         } else {
             mc = (m_rem >= 4) ? 4 : m_rem;
             nc = (n_rem >= 4) ? 4 : n_rem;
             if (mc == 0 || nc == 0)
-               return;
+                return;
             gemm_small(m0, m, n0, n, mc, nc);
         }
         int64_t mp = m0 + ((m - m0) / mc) * mc;
         int64_t np = n0 + ((n - n0) / nc) * nc;
         mnpack(mp, m, n0, np);
         mnpack(m0, m, np, n);
-     }
+    }
 
-     void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n, int RM, int RN) {
+    void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n, int RM, int RN) {
         int64_t ytiles = (m - m0) / RM;
         int64_t xtiles = (n - n0) / RN;
         int64_t tiles = xtiles * ytiles;
@@ -2449,30 +2552,30 @@ class tinyBLAS_PPC {
             vec_t vec_C[4];
             acc_t acc_0;
             __builtin_mma_xxsetaccz(&acc_0);
-            vec_t vec_A[4] {0}, vec_B[4] = {0};
-            for (int l=0; l<k; l+=4) {
+            vec_t vec_A[4] = {0}, vec_B[4] = {0};
+            for (int l = 0; l < k; l += 4) {
                 /* 'GEMV Forwarding' concept is used in first two conditional loops.
                  * when one of the matrix has a single row/column, the elements are
                  * broadcasted, instead of using packing routine to prepack the
                  * matrix elements.
                  */
                 if (RM == 1) {
-                    float* a = const_cast<float*>(A+(ii)*lda+l);
-                    packTranspose(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B);
+                    float * a = const_cast<float *>(A + (ii) * lda + l);
+                    packTranspose(B + (jj * ldb) + l, ldb, RN, 4, (float *)vec_B);
                     vec_A[0] = (vec_t)vec_xl(0,a);
-                    vec_A[1] = (vec_t)vec_splats(*((float*)&vec_A+1));
-                    vec_A[2] = (vec_t)vec_splats(*((float*)&vec_A+2));
-                    vec_A[3] = (vec_t)vec_splats(*((float*)&vec_A+3));
+                    vec_A[1] = (vec_t)vec_splats(*((float *)&vec_A+1));
+                    vec_A[2] = (vec_t)vec_splats(*((float *)&vec_A+2));
+                    vec_A[3] = (vec_t)vec_splats(*((float *)&vec_A+3));
                 } else if (RN == 1) {
-                    packTranspose(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A);
-                    float* b = const_cast<float*>(B+(jj)*ldb+l);
+                    packTranspose(A + (ii * lda) + l, lda, RM, 4, (float *)vec_A);
+                    float * b = const_cast<float *>(B + (jj) * ldb + l);
                     vec_B[0] = (vec_t)vec_xl(0,b);
-                    vec_B[1] = (vec_t)vec_splats(*((float*)&vec_B+1));
-                    vec_B[2] = (vec_t)vec_splats(*((float*)&vec_B+2));
-                    vec_B[3] = (vec_t)vec_splats(*((float*)&vec_B+3));
+                    vec_B[1] = (vec_t)vec_splats(*((float *)&vec_B+1));
+                    vec_B[2] = (vec_t)vec_splats(*((float *)&vec_B+2));
+                    vec_B[3] = (vec_t)vec_splats(*((float *)&vec_B+3));
                 } else {
-                    packTranspose(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A);
-                    packTranspose(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B);
+                    packTranspose(A + (ii * lda) + l, lda, RM, 4, (float *)vec_A);
+                    packTranspose(B + (jj * ldb) + l, ldb, RN, 4, (float *)vec_B);
                 }
                 __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
                 __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
@@ -2482,12 +2585,27 @@ class tinyBLAS_PPC {
             __builtin_mma_disassemble_acc(vec_C, &acc_0);
             for (int I = 0; I < RM; I++) {
                 for (int J = 0; J < RN; J++) {
-                    *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J);
+                    *((float *)(C+ii+((jj+J)*ldc)+I)) = *((float *)&vec_C[I]+J);
                 }
             }
        }
     }
 
+    template<int RM, int RN>
+    inline void kernel(int64_t ii, int64_t jj) {
+        if constexpr(RM == 4 && RN == 4) {
+            KERNEL_4x4(ii, jj);
+        } else if constexpr(RM == 4 && RN == 8) {
+            KERNEL_4x8(ii, jj);
+        } else if constexpr(RM == 8 && RN == 4) {
+            KERNEL_8x4(ii, jj);
+        } else if constexpr(RM == 8 && RN == 8) {
+            KERNEL_8x8(ii, jj);
+        } else {
+            static_assert(false, "RN/RM values not supported");
+        }
+    }
+
     template <int RM, int RN>
     NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
         int64_t ytiles = (m - m0) / RM;
@@ -2496,27 +2614,18 @@ class tinyBLAS_PPC {
         int64_t duty = (tiles + nth - 1) / nth;
         int64_t start = duty * ith;
         int64_t end = start + duty;
-        if (RM == 4 && RN == 4) {
-            kernel = &tinyBLAS_PPC::KERNEL_4x4;
-        } else if (RM == 4 && RN == 8) {
-            kernel = &tinyBLAS_PPC::KERNEL_4x8;
-        } else if (RM == 8 && RN == 4) {
-            kernel = &tinyBLAS_PPC::KERNEL_8x4;
-        } else if (RM == 8 && RN == 8) {
-            kernel = &tinyBLAS_PPC::KERNEL_8x8;
-        }
         if (end > tiles)
             end = tiles;
         for (int64_t job = start; job < end; ++job) {
             int64_t ii = m0 + job / xtiles * RM;
             int64_t jj = n0 + job % xtiles * RN;
-            (this->*kernel)(ii, jj);
+            kernel<RM, RN>(ii, jj);
         }
     }
 
-    const float *const A;
-    const float *const B;
-    float *C;
+    const float * const A;
+    const float * const B;
+    float * C;
     const int64_t k;
     const int64_t lda;
     const int64_t ldb;

From 44b1efa41acd4df3f56ee0e46f898135ecd1a054 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Tue, 26 Aug 2025 15:42:49 +0000
Subject: [PATCH 194/287] tests: add performance test for mul mat id (#15543)

---
 tests/test-backend-ops.cpp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 4b4299d49d..c84023e05e 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6400,6 +6400,24 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         }
     }
 
+    // qwen3-30b-a3b
+    for (int bs : {1, 4, 8, 512}) {
+        for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) {
+            for (ggml_type type_b : {GGML_TYPE_F32}) {
+                test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 128, 8, false, 768, bs, 2048, 1));
+            }
+        }
+    }
+
+    // gpt-oss-20b
+    for (int bs : {1, 4, 8, 512}) {
+        for (ggml_type type_a : {GGML_TYPE_MXFP4}) {
+            for (ggml_type type_b : {GGML_TYPE_F32}) {
+                test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1));
+            }
+        }
+    }
+
     for (int K : {3, 5}) {
         for (int IC : {256, 2560}) {
             for (int IW_IH : {32, 64, 256}) {

From 8ce3ff1d91245e158d98d8062cd64b0dd98dcfe3 Mon Sep 17 00:00:00 2001
From: fidoriel <49869342+fidoriel@users.noreply.github.com>
Date: Tue, 26 Aug 2025 20:05:50 +0200
Subject: [PATCH 195/287] mtmd : fix mtmd ios build (#15579)

---
 tools/mtmd/CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 4baa15b960..0979488560 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -55,6 +55,8 @@ add_executable(llama-qwen2vl-cli  deprecation-warning.cpp)
 set(TARGET llama-mtmd-cli)
 add_executable         (${TARGET} mtmd-cli.cpp)
 set_target_properties  (${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
-install                (TARGETS ${TARGET} RUNTIME)
+if(NOT CMAKE_SYSTEM_NAME STREQUAL "iOS")
+    install(TARGETS ${TARGET} RUNTIME)
+endif()
 target_link_libraries  (${TARGET} PRIVATE common mtmd Threads::Threads)
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

From 8b696861364360770e9f61a3422d32941a477824 Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan@menlo.ai>
Date: Wed, 27 Aug 2025 00:27:49 +0530
Subject: [PATCH 196/287] SYCL: fix rms_norm_mul_add for tensor dim not a
 multiple of sg_size (#15592)

The original implementation unconditionally returned true for this operation, leading to a failure when the tensor's first dimension (ne[0]) was not a multiple of WARP_SIZE. This caused an GGML_ASSERT(ncols % WARP_SIZE == 0) failure in ggml-sycl/norm.cpp.

This change updates the ggml_backend_sycl_device_supports_op check to correctly return true for GGML_OP_RMS_NORM only when the first dimension of the tensor is a multiple of WARP_SIZE, ensuring the operation can be performed without error.
---
 ggml/src/ggml-sycl/ggml-sycl.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 12dd5dd2e6..18ff4e0b0c 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4364,11 +4364,12 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             return (op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32) && (op->type == op->src[0]->type);
 #endif
         case GGML_OP_NORM:
-        case GGML_OP_RMS_NORM:
             return true;
         case GGML_OP_L2_NORM:
         case GGML_OP_GROUP_NORM:
             return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_RMS_NORM:
+            return ((op->src[0]->ne[0] % WARP_SIZE) == 0);
         case GGML_OP_SCALE:
             return true;
         case GGML_OP_CONT:

From bcbddcd54f0d5c22eab180831fdea6484107112f Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Tue, 26 Aug 2025 13:14:38 -0700
Subject: [PATCH 197/287] tests : fix test-opt with GGML_BACKEND_DL (#15599)

---
 tests/test-opt.cpp | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index 18d3fcf2cb..8dcb4a7dbf 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -3,7 +3,6 @@
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
-#include "ggml-cpu.h"
 #include "ggml-opt.h"
 
 #include <cmath>
@@ -899,6 +898,7 @@ static std::pair<int, int> test_backend(
 
 int main(void) {
     ggml_log_set(nullptr, nullptr);
+    ggml_backend_load_all();
     const size_t dev_count = ggml_backend_dev_count();
     printf("Testing %zu devices\n\n", dev_count);
     size_t n_ok = 0;
@@ -911,11 +911,12 @@ int main(void) {
 
         ggml_backend_t backend = ggml_backend_dev_init(devs[i], NULL);
         GGML_ASSERT(backend != NULL);
-#ifndef _MSC_VER
-        if (ggml_backend_is_cpu(backend)) {
-            ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2);
+
+        auto * reg = ggml_backend_dev_backend_reg(devs[i]);
+        auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
+        if (ggml_backend_set_n_threads_fn) {
+            ggml_backend_set_n_threads_fn(backend, std::thread::hardware_concurrency() / 2);
         }
-#endif
         backends.push_back(backend);
     }
 

From 86076f92de1a547ef87c304facca3b4b9fae6c21 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Wed, 27 Aug 2025 08:36:05 +0200
Subject: [PATCH 198/287] OpenCL: add fused group_norm/norm, mul, add (#15314)

* add fused group_norm/norm, mul, add

* fix spacing

* revert rms_norm logic

* fix trailing whitespace
---
 ggml/src/ggml-opencl/ggml-opencl.cpp       | 189 ++++++++++++++++++++-
 ggml/src/ggml-opencl/kernels/group_norm.cl |  49 ++++++
 ggml/src/ggml-opencl/kernels/norm.cl       |  80 +++++++++
 tests/test-backend-ops.cpp                 |  85 +++++++++
 4 files changed, 399 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 36b18ddb8a..c25c2daaf6 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -420,9 +420,9 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_clamp;
     cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_swiglu_oai, kernel_geglu_erf, kernel_geglu_quick,
               kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
-    cl_kernel kernel_norm;
+    cl_kernel kernel_norm, kernel_norm_mul_add;
     cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
-    cl_kernel kernel_group_norm;
+    cl_kernel kernel_group_norm, kernel_group_norm_mul_add;
     cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
     cl_kernel kernel_soft_max, kernel_soft_max_4;
     cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
@@ -1161,7 +1161,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         backend_ctx->program_norm =
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
 
-        CL_CHECK((backend_ctx->kernel_norm = clCreateKernel(backend_ctx->program_norm, "kernel_norm", &err), err));
+        CL_CHECK((backend_ctx->kernel_norm         = clCreateKernel(backend_ctx->program_norm, "kernel_norm", &err), err));
+        CL_CHECK((backend_ctx->kernel_norm_mul_add = clCreateKernel(backend_ctx->program_norm, "kernel_norm_mul_add", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -1487,7 +1488,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         backend_ctx->program_group_norm =
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
 
-        CL_CHECK((backend_ctx->kernel_group_norm = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm", &err), err));
+        CL_CHECK((backend_ctx->kernel_group_norm         = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm", &err), err));
+        CL_CHECK((backend_ctx->kernel_group_norm_mul_add = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm_mul_add", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -2498,12 +2500,47 @@ static bool ggml_opencl_can_fuse(const struct ggml_cgraph * cgraph, int node_idx
         if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
             return false;
         }
+    } else if (ops.size() == 3 && ops.begin()[0] == GGML_OP_NORM && ops.begin()[1] == GGML_OP_MUL && ops.begin()[2] == GGML_OP_ADD) {
+        const ggml_tensor *norm = cgraph->nodes[node_idx];
+        const ggml_tensor *mul  = cgraph->nodes[node_idx+1];
+        const ggml_tensor *add  = cgraph->nodes[node_idx+2];
+        const ggml_tensor *w    = mul->src[0] == norm ? mul->src[1] : mul->src[0];
+        const ggml_tensor *b    = add->src[0] == mul  ? add->src[1] : add->src[0];
+
+        // norm fusion only supports F32
+        if (norm->src[0]->type != GGML_TYPE_F32 || w->type != GGML_TYPE_F32 || b->type != GGML_TYPE_F32) {
+            return false;
+        }
+
+        if (norm->src[0]->ne[0] % 4 != 0) {
+            return false;
+        }
+
+        if (!ggml_is_contiguous(norm->src[0]) || !ggml_is_contiguous(w) || !ggml_is_contiguous(b)) {
+            return false;
+        }
+    } else if (ops.size() == 3 && ops.begin()[0] == GGML_OP_GROUP_NORM && ops.begin()[1] == GGML_OP_MUL && ops.begin()[2] == GGML_OP_ADD) {
+        const ggml_tensor *gn = cgraph->nodes[node_idx];
+        const ggml_tensor *mul = cgraph->nodes[node_idx+1];
+        const ggml_tensor *add = cgraph->nodes[node_idx+2];
+        const ggml_tensor *w   = mul->src[0] == gn ? mul->src[1] : mul->src[0];
+        const ggml_tensor *b   = add->src[0] == mul ? add->src[1] : add->src[0];
+
+        if (gn->src[0]->type != GGML_TYPE_F32 || w->type != GGML_TYPE_F32 || b->type != GGML_TYPE_F32) {
+            return false;
+        }
+
+        if (!ggml_is_contiguous(gn->src[0]) || !ggml_is_contiguous(w) || !ggml_is_contiguous(b)) {
+            return false;
+        }
     }
 
     return true;
 }
 
 static void ggml_opencl_op_rms_norm_fused(ggml_backend_t backend, ggml_tensor * rms_norm_tensor, ggml_tensor * mul_tensor);
+static void ggml_opencl_op_norm_fused(ggml_backend_t backend, ggml_tensor * norm_tensor, ggml_tensor * mul_tensor, ggml_tensor * add_tensor);
+static void ggml_opencl_op_group_norm_fused(ggml_backend_t backend, ggml_tensor * gn_tensor, ggml_tensor * mul_tensor, ggml_tensor * add_tensor);
 
 static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
@@ -2520,6 +2557,16 @@ static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggm
             continue;
         }
 
+        if (!backend_ctx->disable_fusion && ggml_opencl_can_fuse(cgraph, i, { GGML_OP_NORM, GGML_OP_MUL, GGML_OP_ADD })) {
+            ggml_opencl_op_norm_fused(backend, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
+            i += 2;
+            continue;
+        }
+        if (!backend_ctx->disable_fusion && ggml_opencl_can_fuse(cgraph, i, { GGML_OP_GROUP_NORM, GGML_OP_MUL, GGML_OP_ADD })) {
+            ggml_opencl_op_group_norm_fused(backend, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
+            i += 2;
+            continue;
+        }
         if (!backend_ctx->disable_fusion && ggml_opencl_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
             ggml_opencl_op_rms_norm_fused(backend, node, cgraph->nodes[i+1]);
             i++;
@@ -5039,6 +5086,140 @@ static void ggml_opencl_op_rms_norm_fused(ggml_backend_t backend, ggml_tensor *
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 }
 
+static void ggml_opencl_op_norm_fused(ggml_backend_t backend, ggml_tensor * norm_tensor, ggml_tensor * mul_tensor, ggml_tensor * add_tensor) {
+    GGML_ASSERT(norm_tensor && mul_tensor && add_tensor);
+
+    const ggml_tensor * src0 = norm_tensor->src[0];
+    const ggml_tensor * src1 = mul_tensor->src[0] == norm_tensor ? mul_tensor->src[1] : mul_tensor->src[0];
+    const ggml_tensor * src2 = add_tensor->src[0] == mul_tensor ? add_tensor->src[1] : add_tensor->src[0];
+    const ggml_tensor * dst = add_tensor;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offset2 = extra2->offset + src2->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    float eps;
+    memcpy(&eps, norm_tensor->op_params, sizeof(float));
+
+    const int ne00 = src0->ne[0], ne01 = src0->ne[1], ne02 = src0->ne[2], ne03 = src0->ne[3];
+    const cl_ulong nb01 = src0->nb[1], nb02 = src0->nb[2], nb03 = src0->nb[3];
+    const int ne10 = src1->ne[0], ne11 = src1->ne[1], ne12 = src1->ne[2], ne13 = src1->ne[3];
+    const cl_ulong nb11 = src1->nb[1], nb12 = src1->nb[2], nb13 = src1->nb[3];
+    const int ne20 = src2->ne[0], ne21 = src2->ne[1], ne22 = src2->ne[2], ne23 = src2->ne[3];
+    const cl_ulong nb21 = src2->nb[1], nb22 = src2->nb[2], nb23 = src2->nb[3];
+    const cl_ulong nbd1 = dst->nb[1], nbd2 = dst->nb[2], nbd3 = dst->nb[3];
+
+    size_t sgs;
+    if (backend_ctx->gpu_family == ADRENO) sgs = 64;
+    else if (backend_ctx->gpu_family == INTEL) sgs = 32;
+    else GGML_ASSERT(false && "Unsupported GPU");
+
+    cl_kernel kernel = backend_ctx->kernel_norm_mul_add;
+
+    int nth = sgs;
+    int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel);
+    while (nth < ne00/4 && nth < max_workgroup_size) nth *= 2;
+    nth = MIN(nth, max_workgroup_size);
+    nth = MIN(nth, ne00/4);
+
+    size_t gws[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
+    size_t lws[] = {(size_t)nth, 1, 1};
+    size_t num_subgroups = (nth + sgs - 1) / sgs;
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra2->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne03));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne10));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne11));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &ne12));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &ne13));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
+    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int), &ne20));
+    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int), &ne21));
+    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int), &ne22));
+    CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int), &ne23));
+    CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb21));
+    CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb22));
+    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb23));
+    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nbd1));
+    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(cl_ulong), &nbd2));
+    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(cl_ulong), &nbd3));
+    CL_CHECK(clSetKernelArg(kernel, 32, sizeof(float), &eps));
+    CL_CHECK(clSetKernelArg(kernel, 33, sizeof(cl_float2) * num_subgroups, NULL));
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, gws, lws, dst);
+}
+
+static void ggml_opencl_op_group_norm_fused(ggml_backend_t backend, ggml_tensor * gn_tensor, ggml_tensor * mul_tensor, ggml_tensor * add_tensor) {
+    GGML_ASSERT(gn_tensor && mul_tensor && add_tensor);
+
+    const ggml_tensor * src0 = gn_tensor->src[0];
+    const ggml_tensor * src1 = mul_tensor->src[0] == gn_tensor ? mul_tensor->src[1] : mul_tensor->src[0];
+    const ggml_tensor * src2 = add_tensor->src[0] == mul_tensor ? add_tensor->src[1] : add_tensor->src[0];
+    const ggml_tensor * dst = add_tensor;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offset2 = extra2->offset + src2->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    int groups;
+    float eps;
+    memcpy(&groups, gn_tensor->op_params, sizeof(int));
+    memcpy(&eps, (char *)gn_tensor->op_params + sizeof(int), sizeof(float));
+
+    cl_kernel kernel = backend_ctx->kernel_group_norm_mul_add;
+    int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel);
+    int ne = ggml_nelements(src0);
+    int group_size = ne / groups;
+
+    size_t lws[] = { (size_t)MIN(max_workgroup_size, group_size) };
+    size_t gws[] = { (size_t)groups * lws[0] };
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra2->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &group_size));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(float), &eps));
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 1, gws, lws, dst);
+}
+
 static void ggml_cl_group_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
diff --git a/ggml/src/ggml-opencl/kernels/group_norm.cl b/ggml/src/ggml-opencl/kernels/group_norm.cl
index 57c9df4d35..8e4fa0ed12 100644
--- a/ggml/src/ggml-opencl/kernels/group_norm.cl
+++ b/ggml/src/ggml-opencl/kernels/group_norm.cl
@@ -70,3 +70,52 @@ kernel void kernel_group_norm(
         dst[j] *= scale;
     }
 }
+
+//------------------------------------------------------------------------------
+// group_norm_mul_add
+//------------------------------------------------------------------------------
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_32
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_group_norm_mul_add(
+        global float * src0, ulong offset0,
+        global float * src1, ulong offset1,
+        global float * src2, ulong offset2,
+        global float * dst, ulong offsetd,
+        int ne,
+        int group_size,
+        float eps
+) {
+    src0 = (global float *)((global char *)src0 + offset0);
+    src1 = (global float *)((global char *)src1 + offset1);
+    src2 = (global float *)((global char *)src2 + offset2);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    int start = get_group_id(0) * group_size;
+    int end = start + group_size;
+    if (end > ne) {
+        end = ne;
+    }
+
+    float sum = 0.0f;
+    float sum_sq = 0.0f;
+
+    for (int j = start + get_local_id(0); j < end; j += get_local_size(0)) {
+        float val = src0[j];
+        sum += val;
+        sum_sq += val*val;
+    }
+
+    sum = sub_group_reduce_add(sum);
+    sum_sq = sub_group_reduce_add(sum_sq);
+
+    const float mean = sum / group_size;
+    const float var = sum_sq / group_size - mean * mean;
+    const float scale = rsqrt(var + eps);
+
+    for (int j = start + get_local_id(0); j < end; j += get_local_size(0)) {
+        dst[j] = ((src0[j] - mean) * scale) * src1[j] + src2[j];
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/norm.cl b/ggml/src/ggml-opencl/kernels/norm.cl
index 43167ba4d2..170f822787 100644
--- a/ggml/src/ggml-opencl/kernels/norm.cl
+++ b/ggml/src/ggml-opencl/kernels/norm.cl
@@ -79,3 +79,83 @@ kernel void kernel_norm(
         y[i00] = y[i00] * scale;
     }
 }
+
+//------------------------------------------------------------------------------
+// norm_mul_add
+//------------------------------------------------------------------------------
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_32
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_norm_mul_add(
+        global char * src0_ptr, ulong src0_offset,
+        global char * src1_ptr, ulong src1_offset,
+        global char * src2_ptr, ulong src2_offset,
+        global char * dst_ptr,  ulong dst_offset,
+        int ne00, int ne01, int ne02, int ne03,
+        ulong nb01, ulong nb02, ulong nb03,
+        int ne10, int ne11, int ne12, int ne13,
+        ulong nb11, ulong nb12, ulong nb13,
+        int ne20, int ne21, int ne22, int ne23,
+        ulong nb21, ulong nb22, ulong nb23,
+        ulong nbd1, ulong nbd2, ulong nbd3,
+        float eps,
+        local float2 * sums
+) {
+    const int i03 = get_group_id(2);
+    const int i02 = get_group_id(1);
+    const int i01 = get_group_id(0);
+
+    global float4 * x = (global float4 *)(src0_ptr + src0_offset + i01*nb01 + i02*nb02 + i03*nb03);
+    global float4 * w = (global float4 *)(src1_ptr + src1_offset + (i01%ne11)*nb11 + (i02%ne12)*nb12 + (i03%ne13)*nb13);
+    global float4 * b = (global float4 *)(src2_ptr + src2_offset + (i01%ne21)*nb21 + (i02%ne22)*nb22 + (i03%ne23)*nb23);
+    global float4 * y = (global float4 *)(dst_ptr  + dst_offset  + i01*nbd1 + i02*nbd2 + i03*nbd3);
+
+    float p_sum = 0.0f;
+    float p_sum_sq = 0.0f;
+
+    const int n_chunks = ne00 / 4;
+    for (int i00 = get_local_id(0); i00 < n_chunks; i00 += get_local_size(0)) {
+        float4 val = x[i00];
+        p_sum += val.x + val.y + val.z + val.w;
+        p_sum_sq += dot(val, val);
+    }
+
+    p_sum = sub_group_reduce_add(p_sum);
+    p_sum_sq = sub_group_reduce_add(p_sum_sq);
+
+    if (get_sub_group_local_id() == 0) {
+        sums[get_sub_group_id()] = (float2)(p_sum, p_sum_sq);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (get_local_id(0) == 0) {
+        float sum = 0.0f;
+        float sum_sq = 0.0f;
+        for (uint i = 0; i < get_num_sub_groups(); ++i) {
+            float2 s = sums[i];
+            sum += s.x;
+            sum_sq += s.y;
+        }
+
+        const float inv_ne00 = 1.0f / (float)ne00;
+        const float mean = sum * inv_ne00;
+        const float variance = mad(-mean, mean, sum_sq * inv_ne00);
+
+        sums[0] = (float2)(mean, rsqrt(variance + eps));
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float2 mean_scale = sums[0];
+    const float mean = mean_scale.x;
+    const float scale = mean_scale.y;
+    const float neg_mean_scale = -mean * scale;
+
+    for (int i00 = get_local_id(0); i00 < n_chunks; i00 += get_local_size(0)) {
+        const int w_idx = ne10 > 1 ? i00 : 0;
+        const int b_idx = ne20 > 1 ? i00 : 0;
+        const float4 norm_x = mad(x[i00], (float4)scale, (float4)neg_mean_scale);
+        y[i00] = mad(norm_x, w[w_idx], b[b_idx]);
+    }
+}
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index c84023e05e..3a58621094 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2789,6 +2789,49 @@ struct test_norm : public test_case {
     }
 };
 
+// GGML_OP_NORM + GGML_OP_MUL + GGML_OP_ADD
+struct test_norm_mul_add : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    float eps;
+    const bool broadcast;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "NORM_MUL_ADD";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne, eps, broadcast);
+    }
+
+    test_norm_mul_add(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {128, 2, 1, 1},
+            float eps = 1e-5f,
+            bool broadcast = false)
+        : type(type), ne(ne), eps(eps), broadcast(broadcast) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        std::array<int64_t, 4> broadcast_dims = {ne[0], ne[1] * 2, ne[2] * 2, ne[3] * 2};
+
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, broadcast ? broadcast_dims.data() : ne.data());
+        ggml_tensor * w = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_param(a); ggml_set_param(w); ggml_set_param(b);
+        ggml_set_name(a, "a"); ggml_set_name(w, "w"); ggml_set_name(b, "b");
+
+        // Use a, w and b early to avoid OP_NONE in graph
+        a = ggml_add(ctx, ggml_add(ctx, a, w), b);
+
+        ggml_tensor * n = ggml_norm(ctx, a, eps);
+        ggml_tensor * m = ggml_mul(ctx, n, w);
+        ggml_tensor * out = ggml_add(ctx, m, b);
+        ggml_set_name(out, "out");
+        return out;
+    }
+};
 // GGML_OP_RMS_NORM
 struct test_rms_norm : public test_case {
     const ggml_type type;
@@ -4475,6 +4518,44 @@ struct test_group_norm : public test_case {
     }
 };
 
+// GGML_OP_GROUP_NORM + GGML_OP_MUL + GGML_OP_ADD
+struct test_group_norm_mul_add : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    int num_groups;
+    float eps;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "GROUP_NORM_MUL_ADD";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne, num_groups, eps);
+    }
+
+    test_group_norm_mul_add(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {128, 1, 1, 1},
+            int num_groups = 4,
+            float eps = 1e-5f)
+        : type(type), ne(ne), num_groups(num_groups), eps(eps) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * w = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_param(a); ggml_set_param(w); ggml_set_param(b);
+        ggml_set_name(a, "a"); ggml_set_name(w, "w"); ggml_set_name(b, "b");
+        ggml_tensor * n = ggml_group_norm(ctx, a, num_groups, eps);
+        ggml_tensor * m = ggml_mul(ctx, n, w);
+        ggml_tensor * out = ggml_add(ctx, m, b);
+        ggml_set_name(out, "out");
+        return out;
+    }
+};
+
 // GGML_OP_L2_NORM
 struct test_l2_norm : public test_case {
     const ggml_type type;
@@ -5865,6 +5946,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     for (float eps : {0.0f, 1e-6f, 1e-4f, 1e-1f, 1.0f}) {
         test_cases.emplace_back(new test_rms_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps));
         test_cases.emplace_back(new test_rms_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps, true));
+        test_cases.emplace_back(new test_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps, false));
+        test_cases.emplace_back(new test_norm_mul_add(GGML_TYPE_F32, {64, 5, 4, 3}, eps, true));
     }
     for (uint32_t n : {1, 511, 1025, 8192, 33*512}) {
         for (bool multi_add : {false, true}) {
@@ -6253,6 +6336,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32769, 1, 1, 1 }));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1}));
     test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
+    test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {64, 64, 320, 1}));
+    test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {9, 9, 1280, 1}));
     test_cases.emplace_back(new test_acc());
     test_cases.emplace_back(new test_pad());
     test_cases.emplace_back(new test_pad_reflect_1d());

From fcca2182a18c786c57d02d1d1927204b133f1fdc Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 27 Aug 2025 10:28:53 +0200
Subject: [PATCH 199/287] common : add -m to bash completion for --model [no
 ci] (#15591)

This commit updates the bash completion script to include the -m
short option for the --model argument.

The motivation for this is that currently tab completion only works the
full --model option, and it is nice to have it work for the short option
as well.
---
 common/arg.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 81c4005c5e..1ae3fdbf4a 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1106,7 +1106,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
     printf("\"\n\n");
 
     printf("    case \"$prev\" in\n");
-    printf("        --model)\n");
+    printf("        --model|-m)\n");
     printf("            COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n");
     printf("            return 0\n");
     printf("            ;;\n");

From 1cf123a343ab7ca5586aacb9e0a1d2de7fe33be4 Mon Sep 17 00:00:00 2001
From: xctan <xc-tan@outlook.com>
Date: Wed, 27 Aug 2025 16:44:22 +0800
Subject: [PATCH 200/287] ggml-cpu : add basic RVV support for vector f32 ops
 (#15057)

* ggml-cpu : add basic RVV support for vector f32 ops

* ggml-cpu : add RVV support for f32 softmax
---
 ggml/src/ggml-cpu/CMakeLists.txt  |   2 +-
 ggml/src/ggml-cpu/ops.cpp         |   7 +-
 ggml/src/ggml-cpu/simd-mappings.h |  53 +++++++++++----
 ggml/src/ggml-cpu/vec.cpp         |  21 +++++-
 ggml/src/ggml-cpu/vec.h           | 104 +++++++++++++++++++++++++++++-
 5 files changed, 168 insertions(+), 19 deletions(-)

diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index ce0a3e1285..b70302ec8c 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -435,7 +435,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             )
         if (GGML_RVV)
             if (GGML_XTHEADVECTOR)
-                list(APPEND ARCH_FLAGS -march=rv64gc_xtheadvector -mabi=lp64d)
+                list(APPEND ARCH_FLAGS -march=rv64gc_zfhmin_xtheadvector -mabi=lp64d)
             elseif (GGML_RV_ZFH)
                 list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
             else()
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 460367cca0..93330b43a9 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -9072,6 +9072,9 @@ static void ggml_compute_forward_ssm_scan_f32(
                         }
 
                         sumf = GGML_F32xt_REDUCE_ONE(sum);
+    #elif defined(__riscv_v_intrinsic)
+                        // todo: RVV implementation
+                        const int np = 0;
     #else
                         const int np = (nc & ~(GGML_F32_STEP - 1));
 
@@ -10023,8 +10026,8 @@ static void ggml_compute_forward_rwkv_wkv7_f32(
     int64_t h_stride_2d = head_size * head_size;
 
     #if defined(GGML_SIMD)
-        #if defined(__ARM_FEATURE_SVE)
-            // scalar Route to scalar implementation       //TODO: Write SVE code
+        #if defined(__ARM_FEATURE_SVE) || defined(__riscv_v_intrinsic)
+            // scalar Route to scalar implementation       //TODO: Write SVE code and RVV code
             for (int64_t t = 0; t < T; t++) {
                 int64_t t_offset = t * t_stride;
                 int64_t state_offset = head_size * C * (t / (T / n_seqs));
diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h
index b4ad68c9fd..f71ce58079 100644
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -18,6 +18,10 @@
 #include <immintrin.h>
 #endif
 
+#if defined(__riscv_v_intrinsic)
+#include <riscv_vector.h>
+#endif
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -94,24 +98,15 @@ extern "C" {
     }
 #elif defined(__riscv) && defined(__riscv_zfhmin)
     static inline float riscv_compute_fp16_to_fp32(ggml_fp16_t h) {
-        float f;
-        __asm__(
-            "fmv.h.x %[f], %[h]\n\t"
-            "fcvt.s.h %[f], %[f]"
-            : [f] "=&f" (f)
-            : [h] "r" (h)
-        );
-        return f;
+        _Float16 hf;
+        memcpy(&hf, &h, sizeof(ggml_fp16_t));
+        return hf;
     }
 
     static inline ggml_fp16_t riscv_compute_fp32_to_fp16(float f) {
         ggml_fp16_t res;
-        __asm__(
-            "fcvt.h.s %[f], %[f]\n\t"
-            "fmv.x.h %[h], %[f]"
-            : [h] "=&r" (res)
-            : [f] "f" (f)
-        );
+        _Float16 hf = (_Float16)f;
+        memcpy(&res, &hf, sizeof(ggml_fp16_t));
         return res;
     }
 
@@ -1170,6 +1165,36 @@ static inline void __lzs_f16cx4_store(ggml_fp16_t * x, float32x4_t v_y) {
 #define GGML_F16_VEC_MUL            GGML_F32x4_MUL
 #define GGML_F16_VEC_REDUCE         GGML_F32x4_REDUCE
 
+#elif defined(__riscv_v_intrinsic)
+
+// compatible with vlen >= 128
+
+#define GGML_SIMD
+
+// F32
+
+#define GGML_F32_STEP 16
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4              vfloat32m1_t
+#define GGML_F32x4_ZERO         __riscv_vfmv_v_f_f32m1(0.0f, GGML_F32_EPR)
+#define GGML_F32x4_SET1(x)      __riscv_vfmv_v_f_f32m1(x, GGML_F32_EPR)
+#define GGML_F32x4_LOAD(x)      __riscv_vle32_v_f32m1(x, GGML_F32_EPR)
+#define GGML_F32x4_STORE(b, v)  __riscv_vse32_v_f32m1(b, v, GGML_F32_EPR)
+#define GGML_F32x4_FMA(a, b, c) __riscv_vfmacc_vv_f32m1(a, b, c, GGML_F32_EPR)
+#define GGML_F32x4_ADD(a, b)    __riscv_vfadd_vv_f32m1(a, b, GGML_F32_EPR)
+#define GGML_F32x4_MUL(a, b)    __riscv_vfmul_vv_f32m1(a, b, GGML_F32_EPR)
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
 #endif
 
 // GGML_F32_ARR / GGML_F16_ARR
diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp
index 07b377bdd8..d8ec3b81d2 100644
--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -84,6 +84,16 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
         }
         // reduce sum1,sum2 to sum1
         GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
+    #elif defined(__riscv_v_intrinsic)
+        vfloat32m1_t vsum = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
+            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
+            vfloat32m8_t prod = __riscv_vfmul_vv_f32m8(ax, ay, avl);
+            vsum = __riscv_vfredusum_vs_f32m8_f32m1(prod, vsum, avl);
+        }
+        sumf += __riscv_vfmv_f_s_f32m1_f32(vsum);
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -197,7 +207,7 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
 
     ggml_float sumf = 0.0;
 
-#if defined(GGML_SIMD)
+#if defined(GGML_SIMD) && !defined(__riscv_v_intrinsic)
     const int np = (n & ~(GGML_F16_STEP - 1));
 
     GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
@@ -325,6 +335,15 @@ ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float
         vst1q_f32(y + i, val);
         sum += (ggml_float)vaddvq_f32(val);
     }
+#elif defined(__riscv_v_intrinsic)
+    vfloat64m1_t vsum = __riscv_vfmv_v_f_f64m1(0, 1);
+    for (int avl; i < n; i += avl) {
+        avl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t val = ggml_v_expf_m2(__riscv_vfsub_vf_f32m2(__riscv_vle32_v_f32m2(&x[i], avl), max, avl), avl);
+        __riscv_vse32_v_f32m2(&y[i], val, avl);
+        vsum = __riscv_vfwredusum_vs_f32m2_f64m1(val, vsum, avl);
+    }
+    return (ggml_float)__riscv_vfmv_f_s_f64m1_f64(vsum);
 #endif
     for (; i < n; ++i) {
         float val = expf(x[i] - max);
diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h
index 2250d93cb0..8ccf340d47 100644
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -119,6 +119,14 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
     }
 
 #if defined(GGML_SIMD)
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+            sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
+        }
+    }
+#else
     const int np = (n & ~(GGML_F16_STEP - 1));
 
     GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
@@ -149,6 +157,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
             sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
         }
     }
+#endif
 #else
     for (int i = 0; i < n; ++i) {
         for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
@@ -243,6 +252,14 @@ inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const
 
             svst1_f32(pg, y + np2, ay1);
         }
+    #elif defined(__riscv_v_intrinsic)
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
+            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
+            vfloat32m8_t ny = __riscv_vfmadd_vf_f32m8(ax, v, ay, avl);
+            __riscv_vse32_v_f32m8(&y[i], ny, avl);
+        }
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -276,6 +293,13 @@ inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const
 
 inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y, const ggml_fp16_t * GGML_RESTRICT x, const float v) {
 #if defined(GGML_SIMD)
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
+    }
+#else
     const int np = (n & ~(GGML_F16_STEP - 1));
 
     GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
@@ -297,6 +321,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y,
     for (int i = np; i < n; ++i) {
         y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
     }
+#endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -324,6 +349,16 @@ inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int
                 y[i] += x[k][i]*v[k][0];
             }
         }
+    #elif defined(__riscv_v_intrinsic)
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
+            for (int k = 0; k < GGML_VEC_MAD_UNROLL; k++) {
+                vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[k][i], avl);
+                ay = __riscv_vfmadd_vf_f32m8(ax, v[k][0], ay, avl);
+            }
+            __riscv_vse32_v_f32m8(&y[i], ay, avl);
+        }
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -375,6 +410,14 @@ inline static void ggml_vec_mad1_f32(const int n, float * y, const float * x, co
         for (int i = 0; i < n; ++i) {
             y[i] = x[i]*s + b;
         }
+    #elif defined(__riscv_v_intrinsic)
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
+            vfloat32m8_t vb = __riscv_vfmv_v_f_f32m8(b, avl);
+            vfloat32m8_t ny = __riscv_vfmadd_vf_f32m8(ax, s, vb, avl);
+            __riscv_vse32_v_f32m8(&y[i], ny, avl);
+        }
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -436,6 +479,13 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
             ay1 = svmul_f32_m(pg, ay1, vx);
             svst1_f32(pg, y + np, ay1);
         }
+    #elif defined(__riscv_v_intrinsic)
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
+            vfloat32m8_t ny = __riscv_vfmul_vf_f32m8(ay, v, avl);
+            __riscv_vse32_v_f32m8(&y[i], ny, avl);
+        }
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -467,6 +517,13 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 
 inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
 #if defined(GGML_SIMD)
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
+    }
+#else
     const int np = (n & ~(GGML_F16_STEP - 1));
 
     GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
@@ -486,6 +543,7 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
     for (int i = np; i < n; ++i) {
         y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
     }
+#endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -928,7 +986,51 @@ inline static __m128 ggml_v_silu(__m128 x) {
     return _mm_div_ps(x, one_plus_exp_neg_x);
 }
 
-#endif // __ARM_NEON / __AVX2__ / __SSE2__
+#elif defined(__riscv_v_intrinsic)
+
+// adapted from arm limited optimized routine
+// the maximum error is 1.45358 plus 0.5 ulps
+// numbers above 88.38 will flush to infinity
+// numbers beneath -103.97 will flush to zero
+inline static vfloat32m2_t ggml_v_expf_m2(vfloat32m2_t x, int vl) {
+    const vfloat32m2_t r = __riscv_vfmv_v_f_f32m2(0x1.8p23f, vl);
+#ifdef __riscv_xtheadvector
+    // workaround for compiler bug (gcc 14.3.0: Error: unrecognized opcode `th.vmv1r.v v2,v4')
+    vfloat32m2_t z = __riscv_vfadd_vf_f32m2(r, 0.0f, vl);
+    z = __riscv_vfmacc_vf_f32m2(z, 0x1.715476p+0f, x, vl);
+#else
+    const vfloat32m2_t z = __riscv_vfmacc_vf_f32m2(r, 0x1.715476p+0f, x, vl);
+#endif
+    const vfloat32m2_t n = __riscv_vfsub_vv_f32m2(z, r, vl);
+    const vfloat32m2_t b = __riscv_vfnmsac_vf_f32m2(__riscv_vfnmsac_vf_f32m2(x, 0x1.62e4p-1f, n, vl),
+                                                    0x1.7f7d1cp-20f, n, vl);
+    const vuint32m2_t e = __riscv_vsll_vx_u32m2(__riscv_vreinterpret_v_f32m2_u32m2(z), 23, vl);
+    const vfloat32m2_t k = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vadd_vx_u32m2(e, 0x3f800000, vl)); // 1.0f
+    const vbool16_t c = __riscv_vmfgt_vf_f32m2_b16(__riscv_vfabs_v_f32m2(n, vl), 126.0f, vl);
+    const vfloat32m2_t u = __riscv_vfmul_vv_f32m2(b, b, vl);
+    const vfloat32m2_t j = __riscv_vfmacc_vv_f32m2(
+        __riscv_vfmul_vf_f32m2(b, 0x1.ffffecp-1f, vl),
+        __riscv_vfmacc_vv_f32m2(
+            __riscv_vfmacc_vf_f32m2(__riscv_vfmv_v_f_f32m2(0x1.fffdb6p-2f, vl), 0x1.555e66p-3f, b, vl),
+            __riscv_vfmacc_vf_f32m2(__riscv_vfmv_v_f_f32m2(0x1.573e2ep-5f, vl), 0x1.0e4020p-7f, b, vl),
+            u, vl), u, vl);
+    if (!__riscv_vcpop_m_b16(c, vl))
+        return __riscv_vfmacc_vv_f32m2(k, j, k, vl);
+    const vbool16_t  dm = __riscv_vmfle_vf_f32m2_b16(n, 0.0f, vl);
+    const vuint32m2_t d = __riscv_vmerge_vxm_u32m2(__riscv_vmv_v_x_u32m2(0, vl), 0x82000000, dm, vl);
+    const vfloat32m2_t s1 = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vadd_vx_u32m2(d, 0x7f000000, vl));
+    const vfloat32m2_t s2 = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vsub_vv_u32m2(e, d, vl));
+    const vfloat32m2_t r1 = __riscv_vmerge_vvm_f32m2(
+        __riscv_vfmacc_vv_f32m2(k, k, j, vl),
+        __riscv_vfmul_vv_f32m2(__riscv_vfmacc_vv_f32m2(s2, s2, j, vl), s1, vl),
+        c, vl);
+    return __riscv_vmerge_vvm_f32m2(
+        r1, __riscv_vfmul_vv_f32m2(s1, s1, vl),
+        __riscv_vmfgt_vf_f32m2_b16(__riscv_vfabs_v_f32m2(n, vl), 192.0f, vl),
+        vl);
+}
+
+#endif // __ARM_NEON / __AVX2__ / __SSE2__ / __riscv_v_intrinsic
 
 inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
     for (int i = 0; i < n; ++i) {

From 1e7489745a74996fc36e8fd05b73aa16bc184e0c Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Wed, 27 Aug 2025 17:21:41 +0800
Subject: [PATCH 201/287] CANN: refactor mask handling and improve performance
 in FA (#15561)

* CANN(flash-attn): refactor mask handling and improve performance

1. Refactored the mask computation in Flash Attention, unified the logic without separating prefill and decode.
2. Optimized performance in non-alibi scenarios by reducing one repeat operation.
3. Updated operator management to explicitly mark unsupported cases on 310P devices and when dim is not divisible by 16.

Signed-off-by: noemotiovon <757486878@qq.com>

* [CANN]: fix review

Signed-off-by: noemotiovon <757486878@qq.com>

* [CANN]: Optimization FA BNSD to BSND

Signed-off-by: noemotiovon <757486878@qq.com>

---------

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 173 ++++++++++++++++---------------
 ggml/src/ggml-cann/ggml-cann.cpp |  12 ++-
 2 files changed, 97 insertions(+), 88 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index bc33b99d96..c42871c575 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1427,17 +1427,17 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
 static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer,
     float m, int64_t size, float start, float stop, float step){
     int64_t ne[] = {size};
-    size_t nb[] = {sizeof(float)};
+    size_t nb[] = {sizeof(uint16_t)};
 
-    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * sizeof(float));
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * sizeof(uint16_t));
     void* arange_buffer = arange_allocator.get();
 
     aclTensor* arange_tensor = ggml_cann_create_tensor(
-        arange_buffer, ACL_FLOAT, sizeof(float), ne, nb, 1);
+        arange_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
     aclnn_arange(ctx, arange_tensor, start, stop, step, size);
 
     aclTensor* slope_tensor = ggml_cann_create_tensor(
-        slope_buffer, ACL_FLOAT, sizeof(float), ne, nb, 1);
+        slope_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
 
     aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT);
 
@@ -3180,11 +3180,38 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
 void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
 
-    ggml_tensor* src0 = dst->src[0]; // q, fp32
-    ggml_tensor* src1 = dst->src[1]; // k, fp16
-    ggml_tensor* src2 = dst->src[2]; // v, fp16
+    ggml_tensor* src0 = dst->src[0]; // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont)
+    ggml_tensor* src1 = dst->src[1]; // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont)
+    ggml_tensor* src2 = dst->src[2]; // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont)
     ggml_tensor* src3 = dst->src[3]; // mask, fp16
 
+    // B, N, S, D (uncont) -> B, S, N, D (cont)
+    int64_t src0_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src0_bsnd_ne, src0->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src0_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src0_bsnd_nb, src0->nb, GGML_MAX_DIMS * sizeof(size_t));
+    int64_t src1_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src1_bsnd_ne, src1->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src1_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src1_bsnd_nb, src1->nb, GGML_MAX_DIMS * sizeof(size_t));
+    int64_t src2_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src2_bsnd_ne, src2->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src2_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src2_bsnd_nb, src2->nb, GGML_MAX_DIMS * sizeof(size_t));
+
+    auto transpose12 = [](int64_t* ne, size_t* nb) {
+        int64_t ne_tmp = ne[1];
+        size_t  nb_tmp = nb[1];
+        ne[1] = ne[2];
+        nb[1] = nb[2];
+        ne[2] = ne_tmp;
+        nb[2] = nb_tmp;
+    };
+
+    transpose12(src0_bsnd_ne, src0_bsnd_nb);
+    transpose12(src1_bsnd_ne, src1_bsnd_nb);
+    transpose12(src2_bsnd_ne, src2_bsnd_nb);
+
     float maxBias = 0.0f;
     float scaleValue = 1.0f;
     float logitSoftcap = 0.0f;
@@ -3206,11 +3233,12 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
         void* src0_f16_buffer = nullptr;
 
         if(ggml_cann_type_mapping(src0->type) != faDataType){
-            aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0);
+            aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne,
+                src0_bsnd_nb, GGML_MAX_DIMS);
             src0_f16_buffer = src0_f16_allocator.alloc(
                                     ggml_nelements(src0) * faElemSize);
 
-            int64_t* src0_f16_ne = src0->ne;
+            int64_t* src0_f16_ne = src0_bsnd_ne;
             size_t   src0_f16_nb[GGML_MAX_DIMS];
             src0_f16_nb[0] = sizeof(uint16_t);
             for(int i = 1; i < GGML_MAX_DIMS; ++i){
@@ -3224,20 +3252,23 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
             aclnn_cast(ctx, acl_src0_f32_tensor, acl_src0_f16_tensor, faDataType);
             ggml_cann_release_resources(ctx, acl_src0_f32_tensor);
         }else{
-            acl_src0_f16_tensor = ggml_cann_create_tensor(src0);
+            acl_src0_f16_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne,
+                src0_bsnd_nb, GGML_MAX_DIMS);
         }
 
         // Step 2: create the acl tensors for src1 (Key), src2 (Value),
         //         and the direct output from FusedInferAttention
 
-        acl_src1_f16_tensor = ggml_cann_create_tensor(src1);
-        acl_src2_f16_tensor = ggml_cann_create_tensor(src2);
+        acl_src1_f16_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne,
+            src1_bsnd_nb, GGML_MAX_DIMS);
+        acl_src2_f16_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne,
+            src2_bsnd_nb, GGML_MAX_DIMS);
 
         ggml_cann_pool_alloc out_f16_allocator(ctx.pool());
         void* out_f16_buffer = out_f16_allocator.alloc(
                                     ggml_nelements(dst) * faElemSize);
 
-        int64_t* out_f16_ne = src0->ne;
+        int64_t* out_f16_ne = src0_bsnd_ne;
         size_t out_f16_nb[GGML_MAX_DIMS];
         out_f16_nb[0] = faElemSize;
         for(int i = 1; i < GGML_MAX_DIMS; ++i){
@@ -3251,88 +3282,81 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
 
         // Step 3: create the PSEShift tensor if needed
         //         this tensor is considered as mask (f16) in the llama.cpp
-
         aclTensor* bcast_pse_tensor = nullptr;
-        int64_t bcast_pse_ne[GGML_MAX_DIMS];
-        size_t bcast_pse_nb[GGML_MAX_DIMS];
         ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool());
-        void* bcast_pse_buffer = nullptr;
-
         if(src3 != nullptr){
-            bcast_pse_buffer = bcast_pse_allocator.alloc(
-                            ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t));
+            // Construct the truncated pse tensor (common for prefill/decode)
+            int64_t trunc_pse_ne[GGML_MAX_DIMS] = {
+                src3->ne[0],        // D
+                src0->ne[1],        // S (number of Q tokens)
+                src3->ne[2],        // mask N
+                src3->ne[3]         // B
+            };
+            size_t* trunc_pse_nb = src3->nb;
 
-            if(src0->ne[1] > 1){
-                // Case 1: broadcast pse for prefill stage with multiple head
-                aclTensor* acl_mask_f16_tensor = ggml_cann_create_tensor(src3);
-                bcast_pse_ne[0] = src3->ne[0];
-                bcast_pse_ne[1] = src3->ne[1];
-                bcast_pse_ne[2] = src0->ne[2];
-                bcast_pse_ne[3] = src3->ne[3];
+            aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(
+                src3->data, ACL_FLOAT16, sizeof(uint16_t),
+                trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS
+            );
 
+            int64_t bcast_pse_ne[GGML_MAX_DIMS];
+            size_t bcast_pse_nb[GGML_MAX_DIMS];
+            bcast_pse_ne[0] = src3->ne[0];      // D
+            bcast_pse_ne[1] = src0->ne[1];      // S
+            bcast_pse_ne[2] = src0->ne[2];      // N (num_heads)
+            bcast_pse_ne[3] = src3->ne[3];      // B
+            if (maxBias == 0.0f) {
+                // When maxBias == 0.0f, use nb = 0 reduce once repeat (Qwen2)
+                // Construct the bcast tensor (simulate repeat on the head dimension using stride=0)
                 bcast_pse_nb[0] = sizeof(uint16_t);
-                for(int i = 1; i < GGML_MAX_DIMS; ++i){
-                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
-                }
+                bcast_pse_nb[1] = bcast_pse_nb[0] * bcast_pse_ne[0];
+                bcast_pse_nb[2] = 0;                // <---- the head dimension shares the same data
+                bcast_pse_nb[3] = src3->nb[3];
 
                 bcast_pse_tensor = ggml_cann_create_tensor(
-                    bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
-                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
-
-                int64_t repeats[] = {1, src0->ne[2], 1, 1};
-                aclnn_repeat(ctx, acl_mask_f16_tensor, bcast_pse_tensor, repeats);
-
-                ggml_cann_release_resources(ctx, acl_mask_f16_tensor);
-            }else{
-                // Case 2: trunc the first row and broadcast pse for decode stage with multiple head
-                int64_t trunc_pse_ne[GGML_MAX_DIMS] = {src3->ne[0], src0->ne[1], src3->ne[2], src3->ne[3]};
-                size_t* trunc_pse_nb = src3->nb;
-
-                aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(
                     src3->data, ACL_FLOAT16, sizeof(uint16_t),
-                    trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS);
-
-                bcast_pse_ne[0] = src3->ne[0];
-                bcast_pse_ne[1] = src0->ne[1];
-                bcast_pse_ne[2] = src0->ne[2];
-                bcast_pse_ne[3] = src3->ne[3];
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS
+                );
 
+                ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor);
+            } else {
                 bcast_pse_nb[0] = sizeof(uint16_t);
-                for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
                     bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
                 }
 
+                void* bcast_pse_buffer = bcast_pse_allocator.alloc(
+                    ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t)
+                );
+
                 bcast_pse_tensor = ggml_cann_create_tensor(
                     bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
-                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS
+                );
 
                 int64_t repeats[] = {1, src0->ne[2], 1, 1};
                 aclnn_repeat(ctx, acl_mask_f16_trunc_tensor, bcast_pse_tensor, repeats);
 
-                ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor);
-            }
-
-            // Compute the slope if needed. Derived from ggml_cann_softmax().
-            if(maxBias != 0.0f){
                 // alibi
+                // Compute the slope if needed. Derived from ggml_cann_softmax().
                 const int64_t n_heads = src0->ne[2];
-                ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float));
+                ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t));
                 void* slope_buffer = slope_allocator.get();
                 aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias);
 
                 int64_t slope_ne[] = {1, 1, n_heads, 1};
                 size_t slope_nb[GGML_MAX_DIMS];
-                slope_nb[0] = sizeof(float);
+                slope_nb[0] = sizeof(uint16_t);
                 for(int i = 1;i<GGML_MAX_DIMS;i++) {
                     slope_nb[i] = slope_nb[i-1] * slope_ne[0];
                 }
 
                 aclTensor* slope_tensor = ggml_cann_create_tensor(
-                    slope_buffer, ACL_FLOAT, sizeof(float),
+                    slope_buffer, ACL_FLOAT16, sizeof(uint16_t),
                     slope_ne, slope_nb, GGML_MAX_DIMS);
                 GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor, slope_tensor);
 
-                ggml_cann_release_resources(ctx, slope_tensor);
+                ggml_cann_release_resources(ctx, slope_tensor, acl_mask_f16_trunc_tensor);
             }
         }
 
@@ -3349,7 +3373,7 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
         // double  scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d)
         int64_t preTokens = 65535;
         int64_t nextTokens = 65535;
-        char layout[5] = {'B', 'N', 'S', 'D', 0};
+        char layout[5] = {'B', 'S', 'N', 'D', 0};
         int64_t sparseMode = 0;
         int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2;
         int64_t blockSize = 0;
@@ -3386,32 +3410,9 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
         );
 
         // Step 6: post-processing, permute and cast to f32
-
-        int64_t new_dim[] = {0, 2, 1, 3};
         aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
-
-        if(ggml_cann_type_mapping(dst->type) != faDataType){
-            ggml_cann_pool_alloc perm_out_f16_allocator(ctx.pool());
-            perm_out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize);
-            void* perm_out_f16_buffer = perm_out_f16_allocator.get();
-
-            int64_t* perm_out_f16_ne = dst->ne;
-            size_t  perm_out_f16_nb[GGML_MAX_DIMS];
-            perm_out_f16_nb[0] = faElemSize;
-            for(int i = 1; i < GGML_MAX_DIMS; ++i){
-                perm_out_f16_nb[i] = perm_out_f16_nb[i - 1] * perm_out_f16_ne[i - 1];
-            }
-            aclTensor* acl_perm_out_f16_tensor = ggml_cann_create_tensor(
-                perm_out_f16_buffer, faDataType, faElemSize,
-                perm_out_f16_ne, perm_out_f16_nb, GGML_MAX_DIMS);
-            aclnn_permute(ctx, acl_dst_f16_tensor, acl_perm_out_f16_tensor, new_dim, GGML_MAX_DIMS);
-            aclnn_cast(ctx,
-                acl_perm_out_f16_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
-            ggml_cann_release_resources(ctx, acl_perm_out_f16_tensor);
-        }else{
-            // only need to permute
-            aclnn_permute(ctx, acl_dst_f16_tensor, acl_dst_tensor, new_dim, GGML_MAX_DIMS);
-        }
+        // TODO: when dst is fp16, don't need cast
+        aclnn_cast(ctx, acl_dst_f16_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
         ggml_cann_release_resources(ctx, acl_src0_f16_tensor,
                                          acl_src1_f16_tensor,
                                          acl_src2_f16_tensor,
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index cb8af42ebf..8121542561 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2336,7 +2336,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 case GGML_TYPE_Q8_0:
                 case GGML_TYPE_Q4_0:
 #ifdef ASCEND_310P
-                    // Q4 && Q8 per group is not suppor on 310p device
+                    // Q4 && Q8 per group is not support on 310p device
                     return false;
 #endif
                     // only support contiguous for quantized types.
@@ -2354,7 +2354,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 case GGML_TYPE_Q8_0:
                 case GGML_TYPE_Q4_0:
 #ifdef ASCEND_310P
-                    // Q4 && Q8 per group is not suppor on 310p device
+                    // Q4 && Q8 per group is not support on 310p device
                     return false;
 #endif
                     // only support contiguous for quantized types.
@@ -2505,6 +2505,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             }
             return true;
         case GGML_OP_FLASH_ATTN_EXT:{
+#ifdef ASCEND_310P
+            // FA not support on 310p device
+            return false;
+#endif
             // derived from [ggml-cuda.cu]
             if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){
                 return false;
@@ -2530,6 +2534,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // DeepSeek MLA
                 return false;
             }
+            if (op->src[0]->ne[0] % 16 != 0) {
+                // TODO: padding to support
+                return false;
+            }
             float logitSoftcap = 0.0f;
             memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
             if(logitSoftcap != 0.0f) {

From 1bded5a3b3376b2aa3ba2f11ade5910c550f354b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 27 Aug 2025 13:55:12 +0300
Subject: [PATCH 202/287] kv-cache : better estimate of n_kv for multi-sequence
 batches (#15610)

ggml-ci
---
 src/llama-kv-cache.cpp | 25 ++++++++++++-------------
 src/llama-kv-cache.h   |  6 +++---
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index d7ab56ccd9..920c1d0dbd 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -771,8 +771,8 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
             GGML_ASSERT(ubatch.seq_id  [s*n_tokens][0] == seq_id);
         }
 
-        res.s0 = std::min<llama_seq_id>(res.s0, seq_to_stream[seq_id]);
-        res.s1 = std::max<llama_seq_id>(res.s1, seq_to_stream[seq_id]);
+        res.s0 = std::min<uint32_t>(res.s0, seq_to_stream[seq_id]);
+        res.s1 = std::max<uint32_t>(res.s1, seq_to_stream[seq_id]);
 
         res.strm[s] = seq_to_stream[seq_id];
         res.idxs[s].reserve(n_tokens);
@@ -964,11 +964,11 @@ bool llama_kv_cache::get_has_shift() const {
     return result;
 }
 
-uint32_t llama_kv_cache::get_n_kv() const {
+uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const {
     uint32_t result = 0;
 
-    for (uint32_t s = 0; s < n_stream; ++s) {
-        const auto & cells = v_cells[s];
+    for (uint32_t s = 0; s < sinfo.n_stream(); ++s) {
+        const auto & cells = v_cells[sinfo.strm[s]];
 
         result = std::max(std::min(cells.size(), std::max(n_pad, GGML_PAD(cells.used_max_p1(), n_pad))), result);
     }
@@ -1017,18 +1017,18 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
         // note: v->nb[1] <= v->nb[2]
         return ggml_view_4d(ctx, v,
                 hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv, ns,
-                ggml_row_size(v->type, hparams.n_embd_head_v),            // v->nb[1]
-                ggml_row_size(v->type, n_embd_v_gqa),         // v->nb[2]
-                ggml_row_size(v->type, n_embd_v_gqa*kv_size), // v->nb[3]
+                ggml_row_size(v->type, hparams.n_embd_head_v),          // v->nb[1]
+                ggml_row_size(v->type, n_embd_v_gqa),                   // v->nb[2]
+                ggml_row_size(v->type, n_embd_v_gqa*kv_size),           // v->nb[3]
                 ggml_row_size(v->type, n_embd_v_gqa*kv_size)*sinfo.s0);
     }
 
     // note: v->nb[1] > v->nb[2]
     return ggml_view_4d(ctx, v,
             n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v, ns,
-            ggml_row_size(v->type, kv_size*hparams.n_embd_head_v),    // v->nb[1]
-            ggml_row_size(v->type, kv_size),                          // v->nb[2]
-            ggml_row_size(v->type, kv_size*n_embd_v_gqa), // v->nb[3]
+            ggml_row_size(v->type, kv_size*hparams.n_embd_head_v),  // v->nb[1]
+            ggml_row_size(v->type, kv_size),                        // v->nb[2]
+            ggml_row_size(v->type, kv_size*n_embd_v_gqa),           // v->nb[3]
             ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0);
 }
 
@@ -1985,8 +1985,7 @@ bool llama_kv_cache_context::apply() {
     }
 
     kv->apply_ubatch(sinfos[i_cur], ubatches[i_cur]);
-
-    n_kv = kv->get_n_kv();
+    n_kv = kv->get_n_kv(sinfos[i_cur]);
 
     return true;
 }
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 76a5cb1e28..3ca82917d3 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -38,8 +38,8 @@ public:
         using idx_vec_t = std::vector<uint32_t>;
 
         // number of streams: ns = s1 - s0 + 1
-        llama_seq_id s0;
-        llama_seq_id s1;
+        uint32_t s0;
+        uint32_t s1;
 
         std::vector<llama_seq_id> strm; // [ns]
         std::vector<idx_vec_t>    idxs; // [ns]
@@ -139,7 +139,7 @@ public:
     // graph_build API
     //
 
-    uint32_t get_n_kv() const;
+    uint32_t get_n_kv(const slot_info & sinfo) const;
 
     // TODO: temporary
     bool get_supports_set_rows() const;

From 47373271f971aa5a0a6462b286f4a7b5bd4ba644 Mon Sep 17 00:00:00 2001
From: uvos <carl@uvos.xyz>
Date: Wed, 27 Aug 2025 13:58:54 +0200
Subject: [PATCH 203/287] HIP: Enable support for
 ggml_backend_cuda_register_host_buffer (#15615)

---
 ggml/src/ggml-cuda/ggml-cuda.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 4494883415..3a50527248 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3106,7 +3106,7 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
         return false;
     }
 
-#if CUDART_VERSION >= 11010 || defined(GGML_USE_MUSA)
+#if CUDART_VERSION >= 11010 || defined(GGML_USE_MUSA) || defined(GGML_USE_HIP)
     cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
     if (err != cudaSuccess) {
         // clear the error

From da54f9f1a2db07aaae390024ac466e7867685d94 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 27 Aug 2025 15:48:07 +0300
Subject: [PATCH 204/287] presets : add qwen3-30B-a3b FIM (#15616)

---
 common/arg.cpp | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/common/arg.cpp b/common/arg.cpp
index 1ae3fdbf4a..d82f55890d 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -3538,6 +3538,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
 
+    add_opt(common_arg(
+        {"--fim-qwen-30b-default"},
+        string_format("use default Qwen 3 Coder 30B A3B Instruct (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.model.hf_repo = "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF";
+            params.model.hf_file = "qwen3-coder-30b-a3b-instruct-q8_0.gguf";
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
     add_opt(common_arg(
         { "--diffusion-steps" }, "N",
         string_format("number of diffusion steps (default: %d)", params.diffusion.steps),

From fbef0fad7a7c765939f6c9e322fa05cd52cf0c15 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 27 Aug 2025 20:58:09 +0200
Subject: [PATCH 205/287] server: higher timeout for tests (#15621)

---
 tools/server/tests/utils.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index 5f42bcae61..f55a539471 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -26,10 +26,7 @@ from re import RegexFlag
 import wget
 
 
-DEFAULT_HTTP_TIMEOUT = 12
-
-if "LLAMA_SANITIZE" in os.environ or "GITHUB_ACTION" in os.environ:
-    DEFAULT_HTTP_TIMEOUT = 30
+DEFAULT_HTTP_TIMEOUT = 30
 
 
 class ServerResponse:

From 5a0e3ef6f00c658fbae53797f02d5a360ebf8fec Mon Sep 17 00:00:00 2001
From: matiaslin <45382001+matiaslin@users.noreply.github.com>
Date: Wed, 27 Aug 2025 17:32:36 -0700
Subject: [PATCH 206/287] cuda: Add cublasLt_static linking when GGML_STATIC is
 enabled (#15622)

Prior to this change, we faced undefined cublasLt references when
attempting to compile 'llama-cli' with GGML_STATIC=ON on Linux.

We add linking with CUDA::cublasLt_static when CUDA version is greater
than 10.1.
---
 ggml/src/ggml-cuda/CMakeLists.txt | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index ea824965aa..d3dfc7807d 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -94,7 +94,11 @@ if (CUDAToolkit_FOUND)
             # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
             target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas)
         else ()
-            target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static)
+            if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "10.1")
+                target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+            else()
+                target_link_libraries(ggml-cuda PRIVATE  CUDA::cudart_static CUDA::cublas_static)
+            endif()
         endif()
     else()
         target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas)

From 46d9caa27a0281150e8cf082308c0f9e7576ebe5 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 28 Aug 2025 09:26:48 +0200
Subject: [PATCH 207/287] model-conversion : add mmproj conversion target
 (#15628)

This commit adds a new target to the Makefile for converting models that
are multimodal. This target will convert the original model and in
addition also create the mmproj GGUF model.

The motivation for this change is that for models that are multimodal,
for example those that contain a vision encoders, we will often want to
upload both the quantized model and the vision encoder model to
HuggingFace.

Example usage:
```console
$ make causal-convert-mm-model MODEL_PATH=~/work/ai/models/gemma-3-4b-it-qat-q4_0-unquantized/
...
The environment variable CONVERTED_MODEL can be set to this path using:
export CONVERTED_MODEL=/home/danbev/work/ai/llama.cpp/models/gemma-3-4b-it-qat-q4_0-unquantized.gguf
The mmproj model was created in /home/danbev/work/ai/llama.cpp/models/mmproj-gemma-3-4b-it-qat-q4_0-unquantized.gguf
```
The converted original model can then be quantized, and after that both
the quantized model and the mmproj file can then be uploaded to
HuggingFace.

Refs: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF/tree/main
---
 examples/model-conversion/Makefile            | 14 ++++++++
 .../scripts/causal/convert-model.sh           | 34 ++++++++++++++++---
 2 files changed, 43 insertions(+), 5 deletions(-)

diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
index 37982495b2..03b928afda 100644
--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -37,6 +37,20 @@ causal-convert-model:
 	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
 	./scripts/causal/convert-model.sh
 
+causal-convert-mm-model-bf16: OUTTYPE=bf16
+causal-convert-mm-model-bf16: MM_OUTTYPE=f16
+causal-convert-mm-model-bf16: causal-convert-mm-model
+
+causal-convert-mm-model:
+	$(call validate_model_path,causal-convert-mm-model)
+	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
+	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
+	./scripts/causal/convert-model.sh
+
+	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(MM_OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
+	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
+	./scripts/causal/convert-model.sh --mmproj
+
 causal-run-original-model:
 	$(call validate_model_path,causal-run-original-model)
 	@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py
diff --git a/examples/model-conversion/scripts/causal/convert-model.sh b/examples/model-conversion/scripts/causal/convert-model.sh
index 56b21f9baa..9d95025950 100755
--- a/examples/model-conversion/scripts/causal/convert-model.sh
+++ b/examples/model-conversion/scripts/causal/convert-model.sh
@@ -1,5 +1,21 @@
 #!/bin/bash
 
+set -e
+
+# Parse command line arguments
+MMPROJ=""
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --mmproj)
+            MMPROJ="--mmproj"
+            shift
+            ;;
+        *)
+            shift
+            ;;
+    esac
+done
+
 MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
 OUTPUT_DIR="${OUTPUT_DIR:-../../models}"
 TYPE="${OUTTYPE:-f16}"
@@ -11,12 +27,20 @@ echo "Model name: ${MODEL_NAME}"
 echo "Data  type: ${TYPE}"
 echo "Converted model path:: ${CONVERTED_MODEL}"
 echo "Metadata override: ${METADATA_OVERRIDE}"
-python ../../convert_hf_to_gguf.py --verbose \
-    ${MODEL_PATH} \
-    --outfile ${CONVERTED_MODEL} \
-    --outtype ${TYPE} \
-    --metadata "${METADATA_OVERRIDE}"
+
+CMD_ARGS=("python" "../../convert_hf_to_gguf.py" "--verbose")
+CMD_ARGS+=("${MODEL_PATH}")
+CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}")
+CMD_ARGS+=("--outtype" "${TYPE}")
+[[ -n "$METADATA_OVERRIDE" ]] && CMD_ARGS+=("--metadata" "${METADATA_OVERRIDE}")
+[[ -n "$MMPROJ" ]] && CMD_ARGS+=("${MMPROJ}")
+
+"${CMD_ARGS[@]}"
 
 echo ""
 echo "The environment variable CONVERTED_MODEL can be set to this path using:"
 echo "export CONVERTED_MODEL=$(realpath ${CONVERTED_MODEL})"
+if [[ -n "$MMPROJ" ]]; then
+    mmproj_file="${OUTPUT_DIR}/mmproj-$(basename "${CONVERTED_MODEL}")"
+    echo "The mmproj model was created in $(realpath "$mmproj_file")"
+fi

From d35a1e8c41f747548775225973a99507896a8c61 Mon Sep 17 00:00:00 2001
From: Joshua Cogliati <jrincayc@users.noreply.github.com>
Date: Thu, 28 Aug 2025 01:48:20 -0600
Subject: [PATCH 208/287] cli : change log to warning to explain reason for
 stopping (#15604)

* Change to warn instead of debug, to explain reason for stopping.

* Update tools/main/main.cpp

Fix printing --2

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tools/main/main.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/main/main.cpp b/tools/main/main.cpp
index dc776f59e9..865ea4a2f7 100644
--- a/tools/main/main.cpp
+++ b/tools/main/main.cpp
@@ -587,12 +587,12 @@ int main(int argc, char ** argv) {
 
                 if (n_past + (int) embd.size() >= n_ctx) {
                     if (!params.ctx_shift){
-                        LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
+                        LOG_WRN("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
                         break;
                     }
 
                     if (params.n_predict == -2) {
-                        LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
+                        LOG_WRN("\n\n%s: context full and n_predict == %d => stopping\n", __func__, params.n_predict);
                         break;
                     }
 

From 64387f6e95434b393ac3df285864692b7fd9c4d2 Mon Sep 17 00:00:00 2001
From: Aleksei Nikiforov
 <103434461+AlekseiNikiforovIBM@users.noreply.github.com>
Date: Thu, 28 Aug 2025 10:56:41 +0200
Subject: [PATCH 209/287] gguf-py: byteswapping improvements (#12851)

* gguf-py: implement byteswapping for Q4_0

This is needed to byteswap Mistral model.

Also restore original shapes after byteswapping tensors.
It is not needed at the moment, but do it in case
they'd be used in future.

* Rework byteswapping code in gguf-py

Move out details from byteswapping tensor blocks code
---
 gguf-py/gguf/scripts/gguf_convert_endian.py | 132 ++++++++++----------
 1 file changed, 68 insertions(+), 64 deletions(-)

diff --git a/gguf-py/gguf/scripts/gguf_convert_endian.py b/gguf-py/gguf/scripts/gguf_convert_endian.py
index 0e0febaa79..211a3f536a 100755
--- a/gguf-py/gguf/scripts/gguf_convert_endian.py
+++ b/gguf-py/gguf/scripts/gguf_convert_endian.py
@@ -19,6 +19,61 @@ import gguf
 logger = logging.getLogger("gguf-convert-endian")
 
 
+def byteswap_q4_0(tensor, block_offs):
+    # Each block_q4_0 consists of an f16 delta (scaling factor) followed by 16 int8 quantizations.
+
+    # Byte-Swap f16 sized delta field
+    delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
+def byteswap_q8_0(tensor, block_offs):
+    # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations.
+
+    # Byte-Swap f16 sized delta field
+    delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
+def byteswap_q4_k(tensor, block_offs):
+    # Each block_q4_k consists of 2 f16 values followed by 140 int8 values.
+
+    # Byte-Swap f16 sized fields
+    delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+    delta = tensor.data[block_offs + 2:block_offs + 4].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
+def byteswap_q6_k(tensor, block_offs):
+    # Each block_q6_k consists of 208 int8 values followed by 1 f16 value.
+
+    # Byte-Swap f16 sized field
+    delta = tensor.data[block_offs + 208:block_offs + 210].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
+byteswap_tensors = {
+    gguf.GGMLQuantizationType.Q4_0: {
+        "block_size": 18, # 18 bytes = <f16 delta scaling factor> + 16 * <int8 quant>
+        "byteswap_func": byteswap_q4_0,
+    },
+    gguf.GGMLQuantizationType.Q8_0: {
+        "block_size": 34, # 34 bytes = <f16 delta scaling factor> + 32 * <int8 quant>
+        "byteswap_func": byteswap_q8_0,
+    },
+    gguf.GGMLQuantizationType.Q4_K: {
+        "block_size": 144, # 144 bytes = 2 * <f16 delta scaling factor> + 140 * <int8 quant>
+        "byteswap_func": byteswap_q4_k,
+    },
+    gguf.GGMLQuantizationType.Q6_K: {
+        "block_size": 210, # 210 bytes = <f16 delta scaling factor> + 208 * <int8 quant>
+        "byteswap_func": byteswap_q6_k,
+    },
+}
+
+
 def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None:
     file_endian = reader.endianess.name
     if reader.byte_order == 'S':
@@ -32,13 +87,11 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None
         sys.exit(0)
     logger.info("* Checking tensors for conversion compatibility")
     for tensor in reader.tensors:
-        if tensor.tensor_type not in (
-            gguf.GGMLQuantizationType.F32,
-            gguf.GGMLQuantizationType.F16,
-            gguf.GGMLQuantizationType.Q8_0,
-            gguf.GGMLQuantizationType.Q4_K,
-            gguf.GGMLQuantizationType.Q6_K,
-        ):
+        if tensor.tensor_type not in byteswap_tensors and \
+           tensor.tensor_type not in (
+                gguf.GGMLQuantizationType.F32,
+                gguf.GGMLQuantizationType.F16,
+           ):
             raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}")
     logger.info(f"* Preparing to convert from {file_endian} to {order}")
     if args.dry_run:
@@ -72,78 +125,29 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None
             part.byteswap(inplace=True)
 
         # Byte-swap tensor data if necessary
-        if tensor.tensor_type == gguf.GGMLQuantizationType.Q8_0:
-            # Handle Q8_0 tensor blocks (block_q8_0)
-            # Specific handling of block_q8_0 is required.
-            # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations.
-
-            block_size = 34 # 34 bytes = <f16 delta scaling factor> + 32 * <int8 quant>
-
-            n_blocks = len(tensor.data) // block_size
-            for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)):
-                block_offs = block_num * block_size
-
-                # Byte-Swap f16 sized delta field
-                delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
-                delta.byteswap(inplace=True)
-
-                # Byte-Swap Q8 weights
-                if block_num % 100000 == 0:
-                    inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]")
-
-        elif tensor.tensor_type == gguf.GGMLQuantizationType.Q4_K:
-            # Handle Q4_K tensor blocks (block_q4_k)
-            # Specific handling of block_q4_k is required.
-            # Each block_q4_k consists of 2 f16 values followed by 140 int8 values.
-
+        if tensor.tensor_type in byteswap_tensors:
             # first flatten structure
+            oldshape = tensor.data.shape
             newshape = 1
             for i in tensor.data.shape:
                 newshape *= i
 
             tensor.data.resize(newshape)
 
-            block_size = 144
+            block_size    = byteswap_tensors[tensor.tensor_type]["block_size"]
+            byteswap_func = byteswap_tensors[tensor.tensor_type]["byteswap_func"]
+
             n_blocks = len(tensor.data) // block_size
             for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)):
                 block_offs = block_num * block_size
 
-                # Byte-Swap f16 sized fields
-                delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
-                delta.byteswap(inplace=True)
+                byteswap_func(tensor, block_offs)
 
-                delta = tensor.data[block_offs + 2:block_offs + 4].view(dtype=np.uint16)
-                delta.byteswap(inplace=True)
-
-                # Byte-Swap
-                if block_num % 100000 == 0:
-                    inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]")
-
-        elif tensor.tensor_type == gguf.GGMLQuantizationType.Q6_K:
-            # Handle Q6_K tensor blocks (block_q6_k)
-            # Specific handling of block_q6_k is required.
-            # Each block_q6_k consists of 208 int8 values followed by 1 f16 value.
-
-            # first flatten structure
-            newshape = 1
-            for i in tensor.data.shape:
-                newshape *= i
-
-            tensor.data.resize(newshape)
-
-            block_size = 210
-            n_blocks = len(tensor.data) // block_size
-            for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)):
-                block_offs = block_num * block_size
-
-                # Byte-Swap f16 sized field
-                delta = tensor.data[block_offs + 208:block_offs + 210].view(dtype=np.uint16)
-                delta.byteswap(inplace=True)
-
-                # Byte-Swap
                 if block_num % 100000 == 0:
                     inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]")
 
+            # restore old shape in case it's ever used
+            tensor.data.resize(oldshape)
         else:
             # Handle other tensor types
             tensor.data.byteswap(inplace=True)

From 8a4280ce431da6b33e5a95ae1fd61472c8c3f8cc Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 28 Aug 2025 12:27:02 +0300
Subject: [PATCH 210/287] kv-cache : remove LLAMA_SET_ROWS checks (#15505)

ggml-ci
---
 ggml/src/ggml-cann/common.h      |   9 ---
 ggml/src/ggml-cann/ggml-cann.cpp |   5 --
 src/llama-context.cpp            |  22 -------
 src/llama-context.h              |   4 --
 src/llama-graph.cpp              |   4 --
 src/llama-kv-cache.cpp           | 110 +++++++------------------------
 src/llama-kv-cache.h             |  10 ---
 7 files changed, 23 insertions(+), 141 deletions(-)

diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 33794062f5..88cc3f481e 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -374,7 +374,6 @@ struct ggml_backend_cann_context {
 #endif
     cann_task_queue task_queue;
     bool async_mode;
-    bool support_set_rows;
     // Rope Cache
     void* rope_init_ptr = nullptr;
     void* rope_sin_ptr = nullptr;
@@ -400,14 +399,6 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
-
-        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
-        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
-
-        if (!support_set_rows) {
-            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
-                    "Falling back to eager mode.\n", __func__);
-        }
     }
 
     /**
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 8121542561..558121dff7 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2251,11 +2251,6 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     bool use_cann_graph = true;
     bool cann_graph_update_required = false;
 
-    // check environment LLAMA_SET_ROWS
-    if (!cann_ctx->support_set_rows) {
-        use_cann_graph = false;
-    }
-
     if (use_cann_graph) {
         if (cann_ctx->cann_graph == nullptr) {
             cann_ctx->cann_graph.reset(new ggml_cann_graph());
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 99bfed7513..6b20161a38 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -102,16 +102,6 @@ llama_context::llama_context(
     cparams.op_offload = params.op_offload;
     cparams.kv_unified = params.kv_unified;
 
-    {
-        const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
-        supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : supports_set_rows;
-
-        if (!supports_set_rows && !cparams.kv_unified) {
-            LLAMA_LOG_WARN("%s: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache\n", __func__);
-            cparams.kv_unified = true;
-        }
-    }
-
     {
         const char * LLAMA_GRAPH_REUSE_DISABLE = getenv("LLAMA_GRAPH_REUSE_DISABLE");
         graph_reuse_disable = LLAMA_GRAPH_REUSE_DISABLE ? (atoi(LLAMA_GRAPH_REUSE_DISABLE) != 0) : graph_reuse_disable;
@@ -890,12 +880,6 @@ int llama_context::encode(const llama_batch & batch_inp) {
         }
     }
 
-    if (!supports_set_rows) {
-        // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
-        // overlap with device computation.
-        ggml_backend_sched_reset(sched.get());
-    }
-
     // TODO: hacky solution
     if (model.arch == LLM_ARCH_T5 && t_embd) {
         //cross.t_embd = t_embd;
@@ -1226,12 +1210,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
     // wait for the computation to finish (automatically done when obtaining the model output)
     //synchronize();
 
-    if (!supports_set_rows) {
-        // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
-        // overlap with device computation.
-        ggml_backend_sched_reset(sched.get());
-    }
-
     return 0;
 }
 
diff --git a/src/llama-context.h b/src/llama-context.h
index 3dd9205446..a372bcfbe4 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -283,10 +283,6 @@ private:
 
     bool has_evaluated_once = false;
 
-    // env: LLAMA_SET_ROWS (temporary)
-    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
-    bool supports_set_rows = true;
-
     // env: LLAMA_GRAPH_REUSE_DISABLE
     bool graph_reuse_disable = false;
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index b928e9e16e..1f2fc3ab62 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -314,8 +314,6 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
     res &= self_kq_mask->ne[0] == mctx->get_n_kv();
     res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
 
-    res &= mctx->get_supports_set_rows(); // TODO: tmp
-
     return res;
 }
 
@@ -350,8 +348,6 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
     res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
     res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
 
-    res &= mctx->get_base()->get_supports_set_rows(); // TODO: tmp
-
     return res;
 }
 
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 920c1d0dbd..4485f78d5f 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -197,18 +197,6 @@ llama_kv_cache::llama_kv_cache(
 
     const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
     debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
-
-    const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
-    supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) != 0 : supports_set_rows;
-
-    if (!supports_set_rows) {
-        // ref: https://github.com/ggml-org/llama.cpp/pull/14363
-        GGML_ASSERT(unified && "cannot use non-unified KV cache without ggml_set_rows() support");
-    }
-
-    if (!supports_set_rows) {
-        LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
-    }
 }
 
 void llama_kv_cache::clear(bool data) {
@@ -551,11 +539,8 @@ llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_
     bool success = true;
 
     for (const auto & ubatch : ubatches) {
-        // non-continuous slots require support for ggml_set_rows()
-        const bool cont = supports_set_rows ? false : true;
-
         // only find a suitable slot for the ubatch. don't modify the cells yet
-        const auto sinfo_new = find_slot(ubatch, cont);
+        const auto sinfo_new = find_slot(ubatch, true);
         if (sinfo_new.empty()) {
             success = false;
             break;
@@ -976,10 +961,6 @@ uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const {
     return result;
 }
 
-bool llama_kv_cache::get_supports_set_rows() const {
-    return supports_set_rows;
-}
-
 ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
@@ -1033,36 +1014,26 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
 }
 
 ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
+    GGML_UNUSED(sinfo);
+
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * k = layers[ikv].k;
 
-    const int64_t n_embd_k_gqa = k->ne[0];
     const int64_t n_tokens = k_cur->ne[2];
 
     k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
 
-    if (k_idxs && supports_set_rows) {
-        if (k->ne[2] > 1) {
-            k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
-        }
-
-        return ggml_set_rows(ctx, k, k_cur, k_idxs);
+    if (k->ne[2] > 1) {
+        k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
     }
 
-    // TODO: fallback to old ggml_cpy() method for backwards compatibility
-    //       will be removed when ggml_set_rows() is adopted by all backends
-
-    GGML_ASSERT(n_stream == 1 && "n_stream > 1 not supported without LLAMA_SET_ROWS");
-
-    ggml_tensor * k_view = ggml_view_1d(ctx, k,
-            n_tokens*n_embd_k_gqa,
-            ggml_row_size(k->type, n_embd_k_gqa)*sinfo.head());
-
-    return ggml_cpy(ctx, k_cur, k_view);
+    return ggml_set_rows(ctx, k, k_cur, k_idxs);
 }
 
 ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
+    GGML_UNUSED(sinfo);
+
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * v = layers[ikv].v;
@@ -1072,48 +1043,25 @@ ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggm
 
     v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
 
-    if (v_idxs && supports_set_rows) {
-        if (!v_trans) {
-            if (v->ne[2] > 1) {
-                v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
-            }
-
-            return ggml_set_rows(ctx, v, v_cur, v_idxs);
-        }
-
-        // [TAG_V_CACHE_VARIABLE]
-        if (n_embd_v_gqa < v->ne[0]) {
-            v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_v_gqa, 0, 0, 0);
-        }
-
-        // the row becomes a single element
-        ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, v->ne[0]*v->ne[1]*v->ne[2]);
-
-        v_cur = ggml_reshape_2d(ctx, v_cur, 1, v_cur->ne[0]*v_cur->ne[1]);
-
-        return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
-    }
-
-    // TODO: fallback to old ggml_cpy() method for backwards compatibility
-    //       will be removed when ggml_set_rows() is adopted by all backends
-
-    GGML_ASSERT(n_stream == 1 && "n_stream > 1 not supported without LLAMA_SET_ROWS");
-
-    ggml_tensor * v_view = nullptr;
-
     if (!v_trans) {
-        v_view = ggml_view_1d(ctx, v,
-                n_tokens*n_embd_v_gqa,
-                ggml_row_size(v->type, n_embd_v_gqa)*sinfo.head());
-    } else {
-        v_cur = ggml_transpose(ctx, v_cur);
+        if (v->ne[2] > 1) {
+            v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
+        }
 
-        v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
-                (v->ne[1]    )*ggml_element_size(v),
-                (sinfo.head())*ggml_element_size(v));
+        return ggml_set_rows(ctx, v, v_cur, v_idxs);
     }
 
-    return ggml_cpy(ctx, v_cur, v_view);
+    // [TAG_V_CACHE_VARIABLE]
+    if (n_embd_v_gqa < v->ne[0]) {
+        v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_v_gqa, 0, 0, 0);
+    }
+
+    // the row becomes a single element
+    ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, v->ne[0]*v->ne[1]*v->ne[2]);
+
+    v_cur = ggml_reshape_2d(ctx, v_cur, 1, v_cur->ne[0]*v_cur->ne[1]);
+
+    return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
 }
 
 ggml_tensor * llama_kv_cache::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
@@ -1143,10 +1091,6 @@ ggml_tensor * llama_kv_cache::build_input_v_idxs(ggml_context * ctx, const llama
 }
 
 void llama_kv_cache::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
-    if (!supports_set_rows) {
-        return;
-    }
-
     const uint32_t n_tokens = ubatch->n_tokens;
     GGML_ASSERT(n_tokens == (int64_t) sinfo.size()*sinfo.n_stream());
 
@@ -1163,10 +1107,6 @@ void llama_kv_cache::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ub
 }
 
 void llama_kv_cache::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
-    if (!supports_set_rows) {
-        return;
-    }
-
     const uint32_t n_tokens = ubatch->n_tokens;
     GGML_ASSERT(n_tokens == (int64_t) sinfo.size()*sinfo.n_stream());
 
@@ -2004,10 +1944,6 @@ uint32_t llama_kv_cache_context::get_n_kv() const {
     return n_kv;
 }
 
-bool llama_kv_cache_context::get_supports_set_rows() const {
-    return kv->get_supports_set_rows();
-}
-
 ggml_tensor * llama_kv_cache_context::get_k(ggml_context * ctx, int32_t il) const {
     return kv->get_k(ctx, il, n_kv, sinfos[i_cur]);
 }
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 3ca82917d3..07d29bb818 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -141,9 +141,6 @@ public:
 
     uint32_t get_n_kv(const slot_info & sinfo) const;
 
-    // TODO: temporary
-    bool get_supports_set_rows() const;
-
     // get views of the current state of the cache
     ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
     ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
@@ -215,10 +212,6 @@ private:
     // env: LLAMA_KV_CACHE_DEBUG
     int debug = 0;
 
-    // env: LLAMA_SET_ROWS (temporary)
-    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
-    bool supports_set_rows = true;
-
     const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
 
     std::vector<ggml_context_ptr>        ctxs;
@@ -318,9 +311,6 @@ public:
 
     uint32_t get_n_kv() const;
 
-    // TODO: temporary
-    bool get_supports_set_rows() const;
-
     // get views of the current state of the cache
     ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;

From 55042b3692cb1467c9ee15c62c4a9fbf180f89e3 Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Thu, 28 Aug 2025 19:23:22 +0800
Subject: [PATCH 211/287] scripts: add sqlite3 check for compare-commits.sh
 (#15633)

---
 scripts/compare-commits.sh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
index a28cd5e535..1802d6e5ef 100755
--- a/scripts/compare-commits.sh
+++ b/scripts/compare-commits.sh
@@ -25,6 +25,12 @@ fi
 # verify at the start that the compare script has all the necessary dependencies installed
 ./scripts/compare-llama-bench.py --check
 
+if ! command -v sqlite3 >/dev/null 2>&1; then
+    echo "Error: sqlite3 is not installed or not in PATH"
+    echo "Please install sqlite3 to use this script"
+    exit 1
+fi
+
 if [ "$tool" = "llama-bench" ]; then
     db_file="llama-bench.sqlite"
     target="llama-bench"

From 84ab83cc0b4b7e769451ee48e4c7d1acef91ef25 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Thu, 28 Aug 2025 15:49:50 +0200
Subject: [PATCH 212/287] model : jina-embeddings-v3 support (#13693)

* initial jina-embeddings-v3 support

* initial jina-embeddings-v3 support

* initial jina-embeddings-v3 support

* fix vocab parsing with only tokenizer.json

* set mask token lstrip attribute

* additional unk_token_id fallback just in case [no ci]

* revert vocab_size() change [no ci]

* merge tensor loading into general bert

* rope

* add lora embedding and loading (non-functional)

* export separate lora ggufs instead

* add adapter metadata api

* use std::string

* convert_hf_to_lora compatibility

* fix assert

* apply suggestions from review

* apply suggestion from review
---
 common/arg.cpp            |  4 +-
 common/common.cpp         |  5 +++
 common/common.h           |  3 ++
 convert_hf_to_gguf.py     | 79 ++++++++++++++++++++++++++++++++++++++-
 gguf-py/gguf/constants.py | 20 +++++++++-
 include/llama.h           | 18 +++++++++
 src/llama-adapter.cpp     | 72 +++++++++++++++++++++++++++++++++--
 src/llama-adapter.h       |  3 ++
 src/llama-arch.cpp        | 21 ++++++++++-
 src/llama-arch.h          |  3 ++
 src/llama-model.cpp       | 35 ++++++++++++-----
 src/llama-model.h         |  1 +
 src/llama-vocab.cpp       |  2 +-
 tools/server/server.cpp   |  2 +
 14 files changed, 245 insertions(+), 23 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index d82f55890d..93f0108b2b 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2555,7 +2555,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0, nullptr });
+            params.lora_adapters.push_back({ std::string(value), 1.0, "", "", nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -2563,7 +2563,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora-scaled"}, "FNAME", "SCALE",
         "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale), nullptr });
+            params.lora_adapters.push_back({ fname, std::stof(scale), "", "", nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
diff --git a/common/common.cpp b/common/common.cpp
index fdce1dcdec..054b43be77 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -988,7 +988,12 @@ struct common_init_result common_init_from_params(common_params & params) {
             return iparams;
         }
 
+        char buf[1024];
         la.ptr = lora.get();
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
+        la.task_name = buf;
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
+        la.prompt_prefix = buf;
         iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
 
diff --git a/common/common.h b/common/common.h
index 390dda5e53..87ea060695 100644
--- a/common/common.h
+++ b/common/common.h
@@ -34,6 +34,9 @@ struct common_adapter_lora_info {
     std::string path;
     float scale;
 
+    std::string task_name;
+    std::string prompt_prefix;
+
     struct llama_adapter_lora * ptr;
 };
 
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 31a11cbec0..6c8a034060 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -72,6 +72,7 @@ class ModelBase:
     endianess: gguf.GGUFEndian
     use_temp_file: bool
     lazy: bool
+    dry_run: bool
     part_names: list[str]
     is_safetensors: bool
     hparams: dict[str, Any]
@@ -111,6 +112,7 @@ class ModelBase:
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
         self.lazy = not eager or (remote_hf_model_id is not None)
+        self.dry_run = dry_run
         self.remote_hf_model_id = remote_hf_model_id
         if remote_hf_model_id is not None:
             self.is_safetensors = True
@@ -4871,11 +4873,35 @@ class NeoBert(BertModel):
 @ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
 class XLMRobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
+    _lora_files = {}
+    _lora_names = []
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, False)
+
+        if lora_names := hparams.get("lora_adaptations"):
+            self._lora_names = lora_names
+            self.model_arch = gguf.MODEL_ARCH.JINA_BERT_V3
+
+        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
         self._xlmroberta_tokenizer_init()
 
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self._lora_names:
+            for name in self._lora_names:
+                fname = self.add_prefix_to_filename(self.fname_out, f"lora-{name}-")
+                self._lora_files[name] = gguf.GGUFWriter(fname, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, dry_run=self.dry_run)
+
+        return super().generate_extra_tensors()
+
+    def set_type(self):
+        for lora_writer in self._lora_files.values():
+            lora_writer.add_type(gguf.GGUFType.ADAPTER)
+            lora_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
+        super().set_type()
+
     def set_vocab(self):
         self._xlmroberta_set_vocab()
 
@@ -4885,13 +4911,62 @@ class XLMRobertaModel(BertModel):
         if name.startswith("roberta."):
             name = name[8:]
 
+        # jina-embeddings-v3
+        if ".parametrizations." in name:
+            name = name.replace(".parametrizations.", ".")
+            if name.endswith(".original"):
+                name = name[:-9]
+
         # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
         if name == "embeddings.position_embeddings.weight":
             if self._position_offset is not None:
                 data_torch = data_torch[self._position_offset:,:]
 
+        if name.endswith(".0.lora_A") or name.endswith(".0.lora_B"):
+            if name.startswith("pooler.dense"):
+                return []
+
+            num_loras = data_torch.size(0)
+            assert num_loras == len(self._lora_names)
+
+            # Split out each LoRA in their own GGUF
+            for i, lora_writer in enumerate(self._lora_files.values()):
+                new_name = self.map_tensor_name(name[:-9]) + name[-7:].lower()
+                data = data_torch[i, :, :]
+                # Transpose/flip token_embd/types into correct shape
+                if new_name == "token_embd.weight.lora_b":
+                    data = data.T
+                elif new_name.startswith("token_types.weight."):
+                    new_name = new_name[:-1] + ("a" if new_name[-1:] == "b" else "b")
+                lora_writer.add_tensor(new_name, data.float().numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+
+            return []
+
         return super().modify_tensors(data_torch, name, bid)
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # jina-embeddings-v3
+        if rotary_emb_base := self.hparams.get("rotary_emb_base"):
+            self.gguf_writer.add_rope_freq_base(rotary_emb_base)
+        lora_alpha = self.hparams.get("lora_alpha")
+        if lora_prompt_prefixes := self.hparams.get("task_instructions"):
+            assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
+        for lora_name, lora_writer in self._lora_files.items():
+            lora_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, lora_alpha if lora_alpha is not None else 1.0)
+            lora_writer.add_string(gguf.Keys.Adapter.LORA_TASK_NAME, lora_name)
+            if lora_prompt_prefixes:
+                lora_writer.add_string(gguf.Keys.Adapter.LORA_PROMPT_PREFIX, lora_prompt_prefixes[lora_name])
+
+    def write(self):
+        super().write()
+        for lora_writer in self._lora_files.values():
+            lora_writer.write_header_to_file()
+            lora_writer.write_kv_data_to_file()
+            lora_writer.write_tensors_to_file(progress=True)
+            lora_writer.close()
+
 
 @ModelBase.register("GemmaForCausalLM")
 class GemmaModel(TextModel):
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index b9d1235d17..a581f9601f 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -231,8 +231,10 @@ class Keys:
         MIDDLE_ID            = "tokenizer.ggml.middle_token_id"
 
     class Adapter:
-        TYPE       = "adapter.type"
-        LORA_ALPHA = "adapter.lora.alpha"
+        TYPE               = "adapter.type"
+        LORA_ALPHA         = "adapter.lora.alpha"
+        LORA_TASK_NAME     = "adapter.lora.task_name"
+        LORA_PROMPT_PREFIX = "adapter.lora.prompt_prefix"
 
     class IMatrix:
         CHUNK_COUNT = "imatrix.chunk_count"
@@ -315,6 +317,7 @@ class MODEL_ARCH(IntEnum):
     NOMIC_BERT_MOE   = auto()
     NEO_BERT         = auto()
     JINA_BERT_V2     = auto()
+    JINA_BERT_V3     = auto()
     BLOOM            = auto()
     STABLELM         = auto()
     QWEN             = auto()
@@ -647,6 +650,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.NOMIC_BERT_MOE:   "nomic-bert-moe",
     MODEL_ARCH.NEO_BERT:         "neo-bert",
     MODEL_ARCH.JINA_BERT_V2:     "jina-bert-v2",
+    MODEL_ARCH.JINA_BERT_V3:     "jina-bert-v3",
     MODEL_ARCH.BLOOM:            "bloom",
     MODEL_ARCH.STABLELM:         "stablelm",
     MODEL_ARCH.QWEN:             "qwen",
@@ -1234,6 +1238,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.LAYER_OUT_NORM,
         MODEL_TENSOR.CLS,
     ],
+    MODEL_ARCH.JINA_BERT_V3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.TOKEN_TYPES,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+    ],
     MODEL_ARCH.MPT: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/include/llama.h b/include/llama.h
index c5622cc16b..7025353850 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -553,6 +553,24 @@ extern "C" {
             struct llama_model * model,
             const char * path_lora);
 
+    // Functions to access the adapter's GGUF metadata scalar values
+    // - The functions return the length of the string on success, or -1 on failure
+    // - The output string is always null-terminated and cleared on failure
+    // - When retrieving a string, an extra byte must be allocated to account for the null terminator
+    // - GGUF array values are not supported by these functions
+
+    // Get metadata value as a string by key name
+    LLAMA_API int32_t llama_adapter_meta_val_str(const struct llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size);
+
+    // Get the number of metadata key/value pairs
+    LLAMA_API int32_t llama_adapter_meta_count(const struct llama_adapter_lora * adapter);
+
+    // Get metadata key name by index
+    LLAMA_API int32_t llama_adapter_meta_key_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size);
+
+    // Get metadata value as a string by index
+    LLAMA_API int32_t llama_adapter_meta_val_str_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size);
+
     // Manually free a LoRA adapter
     // Note: loaded adapters will be free when the associated model is deleted
     LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
diff --git a/src/llama-adapter.cpp b/src/llama-adapter.cpp
index 8d94034aed..772ce1b448 100644
--- a/src/llama-adapter.cpp
+++ b/src/llama-adapter.cpp
@@ -163,13 +163,38 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
 
     // check metadata
     {
+        const gguf_context * gguf_ctx = ctx_gguf.get();
+
+        LLAMA_LOG_INFO("%s: Dumping metadata keys/values.\n", __func__);
+
+        // get metadata as string
+        for (int i = 0; i < gguf_get_n_kv(gguf_ctx); i++) {
+            gguf_type type = gguf_get_kv_type(gguf_ctx, i);
+            const std::string type_name =
+                type == GGUF_TYPE_ARRAY
+                ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(gguf_ctx, i)), gguf_get_arr_n(gguf_ctx, i))
+                : gguf_type_name(type);
+            const char * name = gguf_get_key(gguf_ctx, i);
+            const std::string value = gguf_kv_to_str(gguf_ctx, i);
+
+            if (type != GGUF_TYPE_ARRAY) {
+                adapter.gguf_kv.emplace(name, value);
+            }
+
+            const size_t MAX_VALUE_LEN = 40;
+            std::string print_value = value.size() > MAX_VALUE_LEN ? format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()) : value;
+            replace_all(print_value, "\n", "\\n");
+
+            LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), print_value.c_str());
+        }
+
         auto get_kv_str = [&](const std::string & key) -> std::string {
-            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
-            return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
+            int id = gguf_find_key(gguf_ctx, key.c_str());
+            return id < 0 ? "" : std::string(gguf_get_val_str(gguf_ctx, id));
         };
         auto get_kv_f32 = [&](const std::string & key) -> float {
-            int id = gguf_find_key(ctx_gguf.get(), key.c_str());
-            return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
+            int id = gguf_find_key(gguf_ctx, key.c_str());
+            return id < 0 ? 0.0f : gguf_get_val_f32(gguf_ctx, id);
         };
         LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
 
@@ -383,6 +408,45 @@ llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * p
     return nullptr;
 }
 
+int32_t llama_adapter_meta_val_str(const llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size) {
+    const auto & it = adapter->gguf_kv.find(key);
+    if (it == adapter->gguf_kv.end()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
+int32_t llama_adapter_meta_count(const llama_adapter_lora * adapter) {
+    return (int)adapter->gguf_kv.size();
+}
+
+int32_t llama_adapter_meta_key_by_index(const llama_adapter_lora * adapter, int i, char * buf, size_t buf_size) {
+    if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    auto it = adapter->gguf_kv.begin();
+    std::advance(it, i);
+    return snprintf(buf, buf_size, "%s", it->first.c_str());
+}
+
+int32_t llama_adapter_meta_val_str_by_index(const llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size) {
+    if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
+        if (buf_size > 0) {
+            buf[0] = '\0';
+        }
+        return -1;
+    }
+    auto it = adapter->gguf_kv.begin();
+    std::advance(it, i);
+    return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
 void llama_adapter_lora_free(llama_adapter_lora * adapter) {
     delete adapter;
 }
diff --git a/src/llama-adapter.h b/src/llama-adapter.h
index 65824e9727..9084e7cab0 100644
--- a/src/llama-adapter.h
+++ b/src/llama-adapter.h
@@ -67,6 +67,9 @@ struct llama_adapter_lora {
 
     float alpha;
 
+    // gguf metadata
+    std::unordered_map<std::string, std::string> gguf_kv;
+
     llama_adapter_lora() = default;
     ~llama_adapter_lora() = default;
 
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 0ca0a4c22f..a61dc177ac 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -22,6 +22,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_NOMIC_BERT_MOE,   "nomic-bert-moe"   },
     { LLM_ARCH_NEO_BERT,         "neo-bert"         },
     { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
+    { LLM_ARCH_JINA_BERT_V3,     "jina-bert-v3"     },
     { LLM_ARCH_BLOOM,            "bloom"            },
     { LLM_ARCH_STABLELM,         "stablelm"         },
     { LLM_ARCH_QWEN,             "qwen"             },
@@ -234,8 +235,10 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_FIM_REP_ID,           "tokenizer.ggml.fim_rep_token_id"         },
     { LLM_KV_TOKENIZER_FIM_SEP_ID,           "tokenizer.ggml.fim_sep_token_id"         },
 
-    { LLM_KV_ADAPTER_TYPE,       "adapter.type"       },
-    { LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
+    { LLM_KV_ADAPTER_TYPE,               "adapter.type"               },
+    { LLM_KV_ADAPTER_LORA_ALPHA,         "adapter.lora.alpha"         },
+    { LLM_KV_ADAPTER_LORA_TASK_NAME,     "adapter.lora.task_name"     },
+    { LLM_KV_ADAPTER_LORA_PROMPT_PREFIX, "adapter.lora.prompt_prefix" },
 
     // deprecated
     { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
@@ -575,6 +578,20 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_CLS,             "cls" },
         },
     },
+    {
+        LLM_ARCH_JINA_BERT_V3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+        },
+    },
     {
         LLM_ARCH_BLOOM,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 7008c2514c..94b0bef719 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -26,6 +26,7 @@ enum llm_arch {
     LLM_ARCH_NOMIC_BERT_MOE,
     LLM_ARCH_NEO_BERT,
     LLM_ARCH_JINA_BERT_V2,
+    LLM_ARCH_JINA_BERT_V3,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
     LLM_ARCH_QWEN,
@@ -230,6 +231,8 @@ enum llm_kv {
 
     LLM_KV_ADAPTER_TYPE,
     LLM_KV_ADAPTER_LORA_ALPHA,
+    LLM_KV_ADAPTER_LORA_TASK_NAME,
+    LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,
 
     LLM_KV_POSNET_EMBEDDING_LENGTH,
     LLM_KV_POSNET_BLOCK_COUNT,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 7d3429617b..30974a723f 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -47,6 +47,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_410M:          return "410M";
         case LLM_TYPE_450M:          return "450M";
         case LLM_TYPE_475M:          return "475M";
+        case LLM_TYPE_558M:          return "558M";
         case LLM_TYPE_700M:          return "700M";
         case LLM_TYPE_770M:          return "770M";
         case LLM_TYPE_780M:          return "780M";
@@ -772,6 +773,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_JINA_BERT_V3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+
+                switch (hparams.n_layer) {
+                    case 24:
+                        type = LLM_TYPE_558M; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
             {
@@ -2631,6 +2644,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_BERT:
             case LLM_ARCH_NOMIC_BERT:
             case LLM_ARCH_NOMIC_BERT_MOE:
+            case LLM_ARCH_JINA_BERT_V3:
                 {
                     tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
                     type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
@@ -2666,24 +2680,22 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         }
 
                         layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}, 0);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
 
                         layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
                         layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
 
                         if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
-                            layer.bo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
                             layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
                             layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
                             layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
                         } else {
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd}, 0);
+                            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
 
-                            if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE) {
-                                layer.bo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-                                layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, 0);
-                                layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
-                            } else {
+                            if (arch == LLM_ARCH_NOMIC_BERT) {
                                 layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
                             }
                         }
@@ -7461,7 +7473,7 @@ struct llm_build_bert : public llm_graph_context {
                 }
 
                 // RoPE
-                if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
+                if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
                     Qcur = ggml_rope_ext(
                             ctx0, Qcur, inp_pos, nullptr,
                             n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -7520,7 +7532,7 @@ struct llm_build_bert : public llm_graph_context {
                         0.0f,
                         LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
                 cb(cur, "ffn_moe_out", il);
-            } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
+            } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
                 cur = build_ffn(cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
@@ -18241,6 +18253,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
         // switch statement
         case LLM_ARCH_BERT:
         case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_JINA_BERT_V3:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
         case LLM_ARCH_NEO_BERT:
@@ -18395,6 +18408,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             } break;
         case LLM_ARCH_BERT:
         case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_JINA_BERT_V3:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
             {
@@ -18885,6 +18899,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_GROK:
         case LLM_ARCH_DBRX:
         case LLM_ARCH_BERT:
+        case LLM_ARCH_JINA_BERT_V3:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_NOMIC_BERT_MOE:
         case LLM_ARCH_STABLELM:
diff --git a/src/llama-model.h b/src/llama-model.h
index af4460cc01..fa44d800d5 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -40,6 +40,7 @@ enum llm_type {
     LLM_TYPE_450M,
     LLM_TYPE_475M,
     LLM_TYPE_537M,
+    LLM_TYPE_558M,
     LLM_TYPE_700M,
     LLM_TYPE_770M,
     LLM_TYPE_780M,
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index de5d1681df..ca02b63a58 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -2470,7 +2470,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         // set attributes by model/tokenizer/architecture name
         if (false
                 || _contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})
-                || _contains_any(general_arch, {"nomic-bert-moe"})
+                || _contains_any(general_arch, {"nomic-bert-moe", "jina-bert-v3"})
            ) {
             if (token_to_id.count("<mask>") == 0) {
                 LLAMA_LOG_WARN("%s: Mask token is missing in vocab, please reconvert model!\n", __func__);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 6eb5aeb582..6aa319d2f1 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -4898,6 +4898,8 @@ int main(int argc, char ** argv) {
                 {"id", i},
                 {"path", lora.path},
                 {"scale", lora.scale},
+                {"task_name", lora.task_name},
+                {"prompt_prefix", lora.prompt_prefix},
             });
         }
         res_ok(res, result);

From c8d0d14e77c3c45df5cbbddde9c2b4c377ec4e7a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 28 Aug 2025 17:09:05 +0300
Subject: [PATCH 213/287] kv-cache : fix find_slot to not search for continuous
 slot (#15638)

ggml-ci
---
 src/llama-kv-cache.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 4485f78d5f..f1c6918738 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -540,7 +540,7 @@ llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_
 
     for (const auto & ubatch : ubatches) {
         // only find a suitable slot for the ubatch. don't modify the cells yet
-        const auto sinfo_new = find_slot(ubatch, true);
+        const auto sinfo_new = find_slot(ubatch, false);
         if (sinfo_new.empty()) {
             success = false;
             break;

From 73804145ab6c06888e314046abf08f66a0133679 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Thu, 28 Aug 2025 10:11:36 -0400
Subject: [PATCH 214/287] ggml : fix SSM_SCAN for n_groups > 1 (#15625)

---
 ggml/src/ggml-cpu/ops.cpp            | 21 +++++++++++----------
 ggml/src/ggml-cuda/ssm-scan.cu       |  2 +-
 ggml/src/ggml-metal/ggml-metal.metal | 10 ++++++----
 3 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 93330b43a9..8c1f794885 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -9003,8 +9003,7 @@ static void ggml_compute_forward_ssm_scan_f32(
     GGML_ASSERT(src4->nb[0] == sizeof(float));
     GGML_ASSERT(src5->nb[0] == sizeof(float));
     GGML_ASSERT(src6->nb[0] == sizeof(int32_t));
-    // allows optimizing the modulo since n_group should be a power of 2
-    GGML_ASSERT((ng & -ng) == ng);
+    GGML_ASSERT(nh % ng == 0);
 
     // heads per thread
     const int dh = (nh + nth - 1)/nth;
@@ -9035,6 +9034,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                     // ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
                     const float dt_soft_plus = dt[h] <= 20.0f ? log1pf(expf(dt[h])) : dt[h];
                     const float dA = expf(dt_soft_plus * A[h]);
+                    const int g = h / (nh / ng); // repeat_interleave
 
                     // dim
                     for (int i1 = 0; i1 < nr; ++i1) {
@@ -9057,8 +9057,8 @@ static void ggml_compute_forward_ssm_scan_f32(
                             // TODO: maybe unroll more?
                             for (int j = 0; j < 1; j++) {
                                 GGML_F32_VEC t0 = GGML_F32_VEC_LOAD(s0 + i + j*ggml_f32_epr + ii*nc);
-                                GGML_F32_VEC t1 = GGML_F32_VEC_LOAD(B + i + j*ggml_f32_epr + (h & (ng - 1))*nc);
-                                GGML_F32_VEC t2 = GGML_F32_VEC_LOAD(C + i + j*ggml_f32_epr + (h & (ng - 1))*nc);
+                                GGML_F32_VEC t1 = GGML_F32_VEC_LOAD(B + i + j*ggml_f32_epr + g*nc);
+                                GGML_F32_VEC t2 = GGML_F32_VEC_LOAD(C + i + j*ggml_f32_epr + g*nc);
 
                                 t0 = GGML_F32_VEC_MUL(t0, adA);
                                 t1 = GGML_F32_VEC_MUL(t1, axdt);
@@ -9090,8 +9090,8 @@ static void ggml_compute_forward_ssm_scan_f32(
                         for (int i = 0; i < np; i += GGML_F32_STEP) {
                             for (int j = 0; j < GGML_F32_ARR; j++) {
                                 ax[j] = GGML_F32_VEC_LOAD(s0 + i + j*GGML_F32_EPR + ii*nc);
-                                ay[j] = GGML_F32_VEC_LOAD(B + i + j*GGML_F32_EPR + (h & (ng - 1))*nc);
-                                az[j] = GGML_F32_VEC_LOAD(C + i + j*GGML_F32_EPR + (h & (ng - 1))*nc);
+                                ay[j] = GGML_F32_VEC_LOAD(B + i + j*GGML_F32_EPR + g*nc);
+                                az[j] = GGML_F32_VEC_LOAD(C + i + j*GGML_F32_EPR + g*nc);
 
                                 ax[j] = GGML_F32_VEC_MUL(ax[j], adA);
                                 ay[j] = GGML_F32_VEC_MUL(ay[j], axdt);
@@ -9113,7 +9113,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                         // d_state
                         for (int i0 = np; i0 < nc; ++i0) {
                             const int i = i0 + ii*nc;
-                            const int ig = i0 + (h & (ng - 1))*nc;
+                            const int ig = i0 + g*nc;
                             // state = prev_state * dA + dB * x
                             const float state = (s0[i] * dA) + (B[ig] * x_dt);
                             // y = rowwise_dotprod(state, C)
@@ -9130,6 +9130,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                 for (int h = ih0; h < ih1; ++h) {
                     // ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
                     const float dt_soft_plus = dt[h] <= 20.0f ? log1pf(expf(dt[h])) : dt[h];
+                    const int g = h / (nh / ng); // repeat_interleave
 
                     // dim
                     for (int i1 = 0; i1 < nr; ++i1) {
@@ -9144,8 +9145,8 @@ static void ggml_compute_forward_ssm_scan_f32(
                         // TODO: what happens when (d_state % svcntw()) != 0?
                         for (int64_t k = 0; k < nc; k += svcntw()) {
                             svfloat32_t vA = GGML_F32_VEC_LOAD(&A[h*nc + k]);
-                            svfloat32_t vB = GGML_F32_VEC_LOAD(&B[k + (h & (ng - 1))*nc]);
-                            svfloat32_t vC = GGML_F32_VEC_LOAD(&C[k + (h & (ng - 1))*nc]);
+                            svfloat32_t vB = GGML_F32_VEC_LOAD(&B[k + g*nc]);
+                            svfloat32_t vC = GGML_F32_VEC_LOAD(&C[k + g*nc]);
                             svfloat32_t vs0 = GGML_F32_VEC_LOAD(&s0[ii*nc + k]);
 
                             svfloat32_t t1 = GGML_F32_VEC_MUL(vdt_soft_plus, vA);
@@ -9165,7 +9166,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                         // d_state
                         for (int i0 = 0; i0 < nc; ++i0) {
                             const int i = i0 + ii*nc;
-                            const int ig = i0 + (h & (ng - 1))*nc;
+                            const int ig = i0 + g*nc;
                             // state = prev_state * dA + dB * x
                             const float state = (s0[i] * expf(dt_soft_plus * A[i0 + h*nc])) + (B[ig] * x_dt);
                             // y = rowwise_dotprod(state, C)
diff --git a/ggml/src/ggml-cuda/ssm-scan.cu b/ggml/src/ggml-cuda/ssm-scan.cu
index dc9a7d58d0..6b424381df 100644
--- a/ggml/src/ggml-cuda/ssm-scan.cu
+++ b/ggml/src/ggml-cuda/ssm-scan.cu
@@ -129,7 +129,7 @@ __global__ void __launch_bounds__(d_state, 1)
     const int head_off = ((blockIdx.x * splitH) % d_head) * sizeof(float);
     const int seq_idx = blockIdx.y;
 
-    const int group_off = (head_idx & (n_group - 1)) * d_state * sizeof(float);
+    const int group_off = (head_idx / (n_head / n_group)) * d_state * sizeof(float);
 
     const float * s0_block = (const float *) ((const char *) src0 + src6[seq_idx] * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
     const float * x_block  = (const float *) ((const char *) src1 + (seq_idx * src1_nb3) + blockIdx.x * splitH * sizeof(float));
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index fa80d6e405..4fa16c4a55 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -1983,14 +1983,15 @@ kernel void kernel_ssm_scan_f32(
     device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
     device       float * s_buff  = (device       float *) ((device       char *) dst  + ir*args.nb02 +      i3*args.nb03 + s_off);
     const int64_t i = i0 + i1*nc;
+    const int64_t g = ir / (nh / ng); // repeat_interleave
     float s0 = s0_buff[i];
     float s  = s_buff[i];
 
         device const float * A        = (device const float *) ((device const char *) src3 + ir*args.nb31);
         device const float * x_block  = (device const float *) ((device const char *) src1 + i1*nb10 + ir*args.nb11 + i3*args.nb13);
         device const float * dt_block = (device const float *) ((device const char *) src2 + ir*nb20 + i3*args.nb22);
-        device const float * B_block  = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*args.nb41 + i3*args.nb43);
-        device const float * C_block  = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*args.nb51 + i3*args.nb53);
+        device const float * B_block  = (device const float *) ((device const char *) src4 + g*args.nb41 + i3*args.nb43);
+        device const float * C_block  = (device const float *) ((device const char *) src5 + g*args.nb51 + i3*args.nb53);
         device       float * y_block  = (device       float *) ((device       char *) dst  + (i1 + ir*(nr) + i3*(n_t*nh*nr))*nb00);
 
     for (int64_t i2 = 0; i2 < n_t; ++i2) {
@@ -2098,14 +2099,15 @@ kernel void kernel_ssm_scan_f32_group(
     device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
     device       float * s_buff  = (device       float *) ((device       char *) dst  + ir*args.nb02 +      i3*args.nb03 + s_off);
     const int64_t i = i0 + i1*nc;
+    const int64_t g = ir / (nh / ng); // repeat_interleave
     float s0 = s0_buff[i];
     float s  = s_buff[i];
 
     device const float * A        = (device const float *) ((device const char *) src3 + ir*args.nb31); // {1, nh}
     device const float * x_block  = (device const float *) ((device const char *) src1 + i1*nb10 + ir*args.nb11 + i3*args.nb13);
     device const float * dt_block = (device const float *) ((device const char *) src2 + ir*nb20 + i3*args.nb22);
-    device const float * B_block  = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*args.nb41 + i3*args.nb43);
-    device const float * C_block  = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*args.nb51 + i3*args.nb53);
+    device const float * B_block  = (device const float *) ((device const char *) src4 + g*args.nb41 + i3*args.nb43);
+    device const float * C_block  = (device const float *) ((device const char *) src5 + g*args.nb51 + i3*args.nb53);
     device       float * y_block  = (device       float *) ((device       char *) dst  + (i1 + ir*(nr) + i3*(n_t*nh*nr))*nb00);
 
     for (int64_t i2 = 0; i2 < n_t; ++i2) {

From 6c442f42ff25564a0cd6b1435d9abc1b0178eac5 Mon Sep 17 00:00:00 2001
From: Aaron Teo <aaron.teo1@ibm.com>
Date: Thu, 28 Aug 2025 22:39:27 +0800
Subject: [PATCH 215/287] ggml-cpu: fix invalid hsum build in debug s390x
 (#15634)

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
---
 ggml/src/ggml-cpu/ggml-cpu-impl.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h
index 1f6844e16c..e08c30a348 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-impl.h
+++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h
@@ -489,7 +489,7 @@ inline static int16x8_t vec_padd_s16(int16x8_t a, int16x8_t b) {
 /**
  * @see https://github.com/ggml-org/llama.cpp/pull/14037
  */
-inline float vec_hsum(float32x4_t v) {
+inline static float vec_hsum(float32x4_t v) {
     float32x4_t v_temp = v + vec_reve(v);
     return v_temp[0] + v_temp[1];
 }

From c97dc093912ad014f6d22743ede0d4d7fd82365a Mon Sep 17 00:00:00 2001
From: mnehete32 <33429707+mnehete32@users.noreply.github.com>
Date: Fri, 29 Aug 2025 00:03:03 +0530
Subject: [PATCH 216/287] CUDA: add conv2d (#15635)

* CUDA: add conv2d

* CUDA: conv2d - correct formatting and added const
---
 ggml/src/ggml-cuda/conv2d.cu    | 171 ++++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/conv2d.cuh   |   5 +
 ggml/src/ggml-cuda/ggml-cuda.cu |   5 +
 3 files changed, 181 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/conv2d.cu
 create mode 100644 ggml/src/ggml-cuda/conv2d.cuh

diff --git a/ggml/src/ggml-cuda/conv2d.cu b/ggml/src/ggml-cuda/conv2d.cu
new file mode 100644
index 0000000000..cf878d1fd1
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv2d.cu
@@ -0,0 +1,171 @@
+#include "conv2d.cuh"
+
+struct conv_params {
+    const int64_t IW, IH;
+    const int64_t OW, OH;
+    const int64_t KW, KH;
+    const int64_t ST_X, ST_Y;
+    const int64_t PD_X, PD_Y;
+    const int64_t DL_X, DL_Y;
+    const int64_t IC, OC;
+    const int64_t B;
+    const int64_t TOTAL;
+};
+
+struct kernel_bounds {
+    int64_t y_min, y_max;
+    int64_t x_min, x_max;
+};
+
+__device__ __forceinline__ int64_t max64(int64_t a, int64_t b) {
+    return (a > b) ? a : b;
+}
+
+__device__ __forceinline__ int64_t min64(int64_t a, int64_t b) {
+    return (a < b) ? a : b;
+}
+
+__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int64_t out_x, int64_t out_y, const conv_params & P) {
+    kernel_bounds bounds;
+    bounds.y_min = max64(0, (P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.y_max = min64(P.KH, (P.IH + P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.x_min = max64(0, (P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    bounds.x_max = min64(P.KW, (P.IW + P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    return bounds;
+}
+
+__device__ __forceinline__ int calculate_input_coord(int64_t out_coord,
+                                                     int64_t kern_coord,
+                                                     int64_t stride,
+                                                     int64_t dilation,
+                                                     int64_t padding) {
+    return out_coord * stride + kern_coord * dilation - padding;
+}
+
+struct whcn_layout {
+    __device__ static int64_t input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
+        return n * (P.IC * P.IW * P.IH) + c * P.IW * P.IH + y * P.IW + x;
+    }
+
+    __device__ static int64_t kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv_params & P) {
+        return c_out * (P.IC * P.KH * P.KW) + c_in * (P.KH * P.KW) + ky * P.KW + kx;
+    }
+
+    __device__ static int64_t output_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
+        return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x;
+    }
+
+    __device__ static void unpack_indices(int64_t             global_idx,
+                                          const conv_params & P,
+                                          int64_t &           n,
+                                          int64_t &           c,
+                                          int64_t &           out_y,
+                                          int64_t &           out_x) {
+        out_x = global_idx % P.OW;
+        out_y = (global_idx / P.OW) % P.OH;
+        c     = (global_idx / (P.OW * P.OH)) % P.OC;
+        n     = global_idx / (P.OW * P.OH * P.OC);
+    }
+};
+
+template <typename T, typename Layout>
+static __global__ void conv2d_kernel(const float * __restrict__ input,
+                                     const T * __restrict__ kernel,
+                                     float * __restrict__ output,
+                                     const conv_params P) {
+    const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (global_idx >= P.TOTAL) {
+        return;
+    }
+
+    int64_t n, c_out, out_y, out_x;
+    Layout::unpack_indices(global_idx, P, n, c_out, out_y, out_x);
+
+    T acc = 0;
+
+    for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
+        kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);
+
+        for (int64_t ky = bounds.y_min; ky < bounds.y_max; ++ky) {
+            const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y);
+
+            for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
+                const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
+
+                T input_val;
+                if (std::is_same<T, half>::value) {
+                    input_val = __float2half(input[Layout::input_index(n, c_in, in_y, in_x, P)]);
+                } else {
+                    input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
+                }
+
+                T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
+                acc += (input_val * kernel_val);
+            }
+        }
+    }
+
+    // [N, OC, OH, OW]
+    output[Layout::output_index(n, c_out, out_y, out_x, P)] = (float) acc;
+}
+
+template <typename T>
+static void conv2d_cuda(const float * X_D, const T * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    const int blocks = (P.TOTAL + CUDA_CONV2D_BLOCK_SIZE - 1) / CUDA_CONV2D_BLOCK_SIZE;
+    conv2d_kernel<T, whcn_layout><<<blocks, CUDA_CONV2D_BLOCK_SIZE, 0, st>>>(X_D, K_D, Y_D, P);
+}
+
+static void conv2d_cuda_f16(const float * X_D, const half * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    conv2d_cuda<half>(X_D, K_D, Y_D, P, st);
+}
+
+static void conv2d_cuda_f32(const float * X_D, const float * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    conv2d_cuda<float>(X_D, K_D, Y_D, P, st);
+}
+
+void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+    float *             K_D    = (float *) kernel->data;
+    const float *       X_D    = (const float *) input->data;
+    float *             Y_D    = (float *) dst->data;
+
+    GGML_ASSERT(ggml_is_contiguous(kernel));
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+
+    // same number of input channels
+    GGML_ASSERT(input->ne[2] == kernel->ne[2]);
+
+    cudaStream_t st = ctx.stream();
+
+    const int32_t * p    = (const int32_t *) dst->op_params;
+    const int       ST_X = p[0];  // stride_x
+    const int       ST_Y = p[1];  // stride_y
+    const int       PD_X = p[2];  // padding_x
+    const int       PD_Y = p[3];  // padding_y
+    const int       DL_X = p[4];  // dilation_x
+    const int       DL_Y = p[5];  // dilation_y
+
+    // No cwhn
+    GGML_ASSERT(p[6] == false);
+
+    const int IW = input->ne[0];   // input_w
+    const int IH = input->ne[1];   // input_h
+    const int OW = dst->ne[0];     // output_w
+    const int OH = dst->ne[1];     // output_h
+    const int KW = kernel->ne[0];  // kernel_w
+    const int KH = kernel->ne[1];  // kernel_h
+    const int IC = input->ne[2];   // input_channels
+    const int OC = kernel->ne[3];  // ouptut_chanles
+    const int B  = input->ne[3];   // n_batches
+
+    const int64_t total  = B * OC * OH * OW;
+    conv_params   params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total };
+
+    if (kernel->type == GGML_TYPE_F16) {
+        conv2d_cuda_f16(X_D, (half *) K_D, Y_D, params, st);
+    } else {
+        conv2d_cuda_f32(X_D, K_D, Y_D, params, st);
+    }
+}
diff --git a/ggml/src/ggml-cuda/conv2d.cuh b/ggml/src/ggml-cuda/conv2d.cuh
new file mode 100644
index 0000000000..ce4802c7ed
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv2d.cuh
@@ -0,0 +1,5 @@
+#pragma once
+#include "common.cuh"
+
+#define CUDA_CONV2D_BLOCK_SIZE 256
+void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 3a50527248..4c02b57227 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -12,6 +12,7 @@
 #include "ggml-cuda/clamp.cuh"
 #include "ggml-cuda/concat.cuh"
 #include "ggml-cuda/conv-transpose-1d.cuh"
+#include "ggml-cuda/conv2d.cuh"
 #include "ggml-cuda/conv2d-dw.cuh"
 #include "ggml-cuda/conv2d-transpose.cuh"
 #include "ggml-cuda/convert.cuh"
@@ -2451,6 +2452,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
+        case GGML_OP_CONV_2D:
+            ggml_cuda_op_conv2d(ctx, dst);
+            break;
         case GGML_OP_CONV_2D_DW:
             ggml_cuda_op_conv2d_dw(ctx, dst);
             break;
@@ -3501,6 +3505,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
         }
         case GGML_OP_IM2COL:
+        case GGML_OP_CONV_2D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_CONV_TRANSPOSE_2D:
         case GGML_OP_POOL_2D:

From a8bca68f727844e7dcf24a956003b3c2039ea563 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 28 Aug 2025 15:27:36 -0500
Subject: [PATCH 217/287] fix: Compute the full sum in llama-eval-callback, not
 just the sum of printed values (#15637)

This makes it much easier to compare between llama.cpp and transformers!

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---
 examples/eval-callback/eval-callback.cpp | 50 +++++++++++++++---------
 1 file changed, 32 insertions(+), 18 deletions(-)

diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 61eefc7248..d4ef751fbb 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -28,9 +28,40 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
     return str;
 }
 
+static float ggml_get_float_value(uint8_t * data, ggml_type type, const size_t * nb, size_t i0, size_t i1, size_t i2, size_t i3) {
+    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
+    float v;
+    if (type == GGML_TYPE_F16) {
+        v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
+    } else if (type == GGML_TYPE_F32) {
+        v = *(float *) &data[i];
+    } else if (type == GGML_TYPE_I64) {
+        v = (float) *(int64_t *) &data[i];
+    } else if (type == GGML_TYPE_I32) {
+        v = (float) *(int32_t *) &data[i];
+    } else if (type == GGML_TYPE_I16) {
+        v = (float) *(int16_t *) &data[i];
+    } else if (type == GGML_TYPE_I8) {
+        v = (float) *(int8_t *) &data[i];
+    } else {
+        GGML_ABORT("fatal error");
+    }
+    return v;
+}
+
 static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
     GGML_ASSERT(n > 0);
     float sum = 0;
+    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
+                    sum += v;
+                }
+            }
+        }
+    }
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
         LOG("                                     [\n");
         for (int64_t i2 = 0; i2 < ne[2]; i2++) {
@@ -50,25 +81,8 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
                         LOG("..., ");
                         i0 = ne[0] - n;
                     }
-                    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
-                    float v;
-                    if (type == GGML_TYPE_F16) {
-                        v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
-                    } else if (type == GGML_TYPE_F32) {
-                        v = *(float *) &data[i];
-                    } else if (type == GGML_TYPE_I64) {
-                        v = (float) *(int64_t *) &data[i];
-                    } else if (type == GGML_TYPE_I32) {
-                        v = (float) *(int32_t *) &data[i];
-                    } else if (type == GGML_TYPE_I16) {
-                        v = (float) *(int16_t *) &data[i];
-                    } else if (type == GGML_TYPE_I8) {
-                        v = (float) *(int8_t *) &data[i];
-                    } else {
-                        GGML_ABORT("fatal error");
-                    }
+                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
                     LOG("%12.4f", v);
-                    sum += v;
                     if (i0 < ne[0] - 1) LOG(", ");
                 }
                 LOG("],\n");

From e8d99dd0b67f2ecc1e45fca8074a3a18c3e036d2 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 28 Aug 2025 18:39:31 -0600
Subject: [PATCH 218/287] nvidia nemotron nano v2 (nemotronh) (#15507)

* feat: Add NEMOTRONH to python arch enum

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Add NEMOTRONH to c++ arch enum

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Add NEMOTRONH to llama-arch layer map

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: First pass at conversion for nemotronh

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Add a verbose log for each tensor loaded

This is really helpful for diagnosing mismatches between the expected and
received tensors

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: First (broken) pass at nemotronh model architecture

It generates tokens, just not valid ones!

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Explicitly enable add_bos_token during conversion

The `tokenizer.json`/`tokenizer_config.json` in the model are a bit
contradictory. In the config, add_bos_token is set to False, but the
tokenizer model itself has a post_processor that adds the BOS token via
type: TemplateProcessing

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Use relu2 (LLM_FFN_RELU_SQR) for activation in FFN layers

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Only allocate attention cache for attention layers (not non-recurrent)

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Move residual add to after every block

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Use the correct norm tensor for the MLP blocks

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* Nemotron-H: MLP gate cleanup (pass NULL for unused gate)

This model does not use a gate in MLP blocks; pass NULLs for gate tensors to make intent clear and avoid unused-pointer noise.

* SSM: respect ssm_dt_rank for dt_dim when provided

Use GGUF-provided time_step_rank (ssm_dt_rank) to set dt_dim when > 0; fallback to max(64, n_embd/16).

* fix: plamo2 - revert dt_dim to default (remove ssm_dt_rank usage)

* Rename nemotronh to nemotron_h for consistency

- Update architecture name from NEMOTRONH to NEMOTRON_H in constants.py
- Change architecture string from 'nemotronh' to 'nemotron_h' in all files
- Update enum LLM_ARCH_NEMOTRONH to LLM_ARCH_NEMOTRON_H
- Update class name llm_build_nemotronh to llm_build_nemotron_h
- Consistent naming with underscore convention (nemotron_h vs nemotronh)

* feat: Support conversion for older NemotronH models

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Maicon Domingues <dominguesm@outlook.com>
Co-authored-by: weatherman <fxdstudios@gmail.com>
---
 convert_hf_to_gguf.py          |  63 ++++++++-
 gguf-py/gguf/constants.py      |  21 +++
 gguf-py/gguf/tensor_mapping.py |   6 +
 src/llama-arch.cpp             |  27 ++++
 src/llama-arch.h               |   1 +
 src/llama-model-loader.cpp     |   1 +
 src/llama-model.cpp            | 251 ++++++++++++++++++++++++++++++++-
 7 files changed, 362 insertions(+), 8 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 6c8a034060..df37c4a6e4 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -7546,9 +7546,13 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
         ]
 
         # n_group and d_inner are used during reshape_tensors for mamba2
-        self.d_model = self.find_hparam(["hidden_size", "d_model"])
-        self.n_group = self.find_hparam(["n_groups"])
-        self.d_inner = self.find_hparam(["expand"]) * self.d_model
+        # NOTE: Explicitly include hparam prefix prefix for d_model to
+        #   disambiguate with top-level head_dim
+        # NOTE 2: If needed for future models, this can be isolated in a method
+        #   to separate the prefix setting and teh keys used
+        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
+        self.n_group = self.find_hparam(["n_groups", "num_groups"])
+        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model
 
     def get_attn_layers(self):
         # Explicit list of layer type names
@@ -7609,12 +7613,12 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
 
         ## Mamba mixer params ##
         self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
-        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state"]))
+        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
         self.gguf_writer.add_ssm_group_count(self.n_group)
         self.gguf_writer.add_ssm_inner_size(self.d_inner)
         # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
         #   in llama.cpp
-        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads"]))
+        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))
 
         ## Attention params ##
         head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
@@ -7641,6 +7645,55 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
         Mamba2Model.set_vocab(self)
 
 
+@ModelBase.register("NemotronHForCausalLM")
+class NemotronHModel(GraniteHybridModel):
+    """Hybrid mamba2/attention model from NVIDIA"""
+    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Save the top-level head_dim for later
+        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
+        assert self.head_dim is not None, "Could not find the attention head dim in config"
+
+        # Don't use expand to calculate d_inner
+        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
+
+        # Update the ssm / attn / mlp layers
+        # M: Mamba2, *: Attention, -: MLP
+        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
+        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "-"]
+
+    def get_attn_layers(self):
+        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!"
+        return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_key_length(self.head_dim)
+        self.gguf_writer.add_value_length(self.head_dim)
+
+        # Set feed_forward_length
+        # NOTE: This will trigger an override warning. This is preferrable to
+        #   duplicating all the parent logic
+        n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
+        self.gguf_writer.add_feed_forward_length([
+            n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
+        ])
+
+    def set_vocab(self):
+        super().set_vocab()
+
+        # The tokenizer _does_ add a BOS token (via post_processor type
+        # TemplateProcessing) but does not set add_bos_token to true in the
+        # config, so we need to explicitly override it here.
+        self.gguf_writer.add_add_bos_token(True)
+
+
 @ModelBase.register("BailingMoeForCausalLM")
 class BailingMoeModel(TextModel):
     model_arch = gguf.MODEL_ARCH.BAILINGMOE
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index a581f9601f..6156d35c2a 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -367,6 +367,7 @@ class MODEL_ARCH(IntEnum):
     T5ENCODER        = auto()
     JAIS             = auto()
     NEMOTRON         = auto()
+    NEMOTRON_H       = auto()
     EXAONE           = auto()
     EXAONE4          = auto()
     GRANITE          = auto()
@@ -700,6 +701,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.T5ENCODER:        "t5encoder",
     MODEL_ARCH.JAIS:             "jais",
     MODEL_ARCH.NEMOTRON:         "nemotron",
+    MODEL_ARCH.NEMOTRON_H:       "nemotron_h",
     MODEL_ARCH.EXAONE:           "exaone",
     MODEL_ARCH.EXAONE4:          "exaone4",
     MODEL_ARCH.GRANITE:          "granite",
@@ -2297,6 +2299,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.NEMOTRON_H: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_D,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_OUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.EXAONE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index abb21fa821..497f48809f 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -191,6 +191,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.q_proj",                       # llama4
             "model.transformer.blocks.{bid}.q_proj",                     # llada
             "layers.{bid}.self_attn.q_proj",                             # qwen3-embedding
+            "backbone.layers.{bid}.mixer.q_proj",                        # nemotron-h
         ),
 
         # Attention key
@@ -209,6 +210,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.k_proj",                     # llama4
             "model.transformer.blocks.{bid}.k_proj",                   # llada
             "layers.{bid}.self_attn.k_proj",                           # qwen3-embedding
+            "backbone.layers.{bid}.mixer.k_proj",                      # nemotron-h
         ),
 
         # Attention value
@@ -226,6 +228,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.v_proj",                       # llama4
             "model.transformer.blocks.{bid}.v_proj",                     # llada
             "layers.{bid}.self_attn.v_proj",                             # qwen3-embedding
+            "backbone.layers.{bid}.mixer.v_proj",                        # nemotron-h
         ),
 
         # Attention output
@@ -260,6 +263,7 @@ class TensorNameMap:
             "transformer_encoder.{bid}.wo",                                 # neobert
             "model.transformer.blocks.{bid}.attn_out",                      # llada
             "layers.{bid}.self_attn.o_proj",                                # qwen3-embedding
+            "backbone.layers.{bid}.mixer.o_proj",                           # nemotron-h
         ),
 
         # Attention output norm
@@ -387,6 +391,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.up",                 # smallthinker
             "model.transformer.blocks.{bid}.up_proj",                 # llada
             "layers.{bid}.mlp.up_proj",                               # qwen3-embedding
+            "backbone.layers.{bid}.mixer.up_proj",                    # nemotron-h
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -480,6 +485,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.down",               # smallthinker
             "model.transformer.blocks.{bid}.ff_out",                  # llada
             "layers.{bid}.mlp.down_proj",                             # qwen3-embedding
+            "backbone.layers.{bid}.mixer.down_proj",                  # nemotron-h
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index a61dc177ac..d5c8477f4a 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -69,6 +69,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_T5ENCODER,        "t5encoder"        },
     { LLM_ARCH_JAIS,             "jais"             },
     { LLM_ARCH_NEMOTRON,         "nemotron"         },
+    { LLM_ARCH_NEMOTRON_H,       "nemotron_h"       },
     { LLM_ARCH_EXAONE,           "exaone"           },
     { LLM_ARCH_EXAONE4,          "exaone4"          },
     { LLM_ARCH_RWKV6,            "rwkv6"            },
@@ -1550,6 +1551,31 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_NEMOTRON_H,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
+            { LLM_TENSOR_OUTPUT,         "output" },
+            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
+            // mamba(2) ssm layers
+            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
+            // attention layers
+            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
+            // dense FFN
+            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_EXAONE,
         {
@@ -2355,6 +2381,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_PLAMO2:
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_LFM2:
+        case LLM_ARCH_NEMOTRON_H:
             return true;
         default:
             return false;
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 94b0bef719..86c119692d 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -73,6 +73,7 @@ enum llm_arch {
     LLM_ARCH_T5ENCODER,
     LLM_ARCH_JAIS,
     LLM_ARCH_NEMOTRON,
+    LLM_ARCH_NEMOTRON_H,
     LLM_ARCH_EXAONE,
     LLM_ARCH_EXAONE4,
     LLM_ARCH_RWKV6,
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index f71c40f8e3..8182a9adf5 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -788,6 +788,7 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri
 }
 
 struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list<int64_t> & ne, int flags) {
+    LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, name.c_str());
     const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
 
     if (cur == NULL) {
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 30974a723f..f3e0e9ac64 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1570,6 +1570,27 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_NEMOTRON_H:
+            {
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+                // A layer is recurrent IFF the n_head_kv value is set to 0 and
+                // the n_ff value is set to 0
+                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                    hparams.recurrent_layer_arr[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
+                }
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 56: type = LLM_TYPE_9B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_EXAONE:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -4688,6 +4709,75 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_NEMOTRON_H:
+                {
+                    // mamba2 Mixer SSM params
+                    // NOTE: int64_t for tensor dimensions
+                    const int64_t d_conv     = hparams.ssm_d_conv;
+                    const int64_t d_inner    = hparams.ssm_d_inner;
+                    const int64_t d_state    = hparams.ssm_d_state;
+                    const int64_t n_ssm_head = hparams.ssm_dt_rank;
+                    const int64_t n_group    = hparams.ssm_n_group;
+                    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
+
+                    // embeddings
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    {
+                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+                        if (output == NULL) {
+                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        // all blocks use the attn norm
+                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (hparams.is_recurrent(i)) {
+                            // ssm layers
+                            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+
+                            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+                            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
+
+                            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
+
+                            // no "weight" suffix for these
+                            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
+                            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
+
+                            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+
+                            // out_proj
+                            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+                        } else if (hparams.n_ff(i) == 0) {
+                            // attention layers (with optional bias)
+                            const int64_t n_head_i = hparams.n_head(i);
+                            const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
+                            const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head_i}, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa_i}, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa_i}, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
+                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias",   i), {n_embd},         TENSOR_NOT_REQUIRED);
+                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias",   i), {n_embd_k_gqa_i}, TENSOR_NOT_REQUIRED);
+                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias",   i), {n_embd_v_gqa_i}, TENSOR_NOT_REQUIRED);
+                            layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd},         TENSOR_NOT_REQUIRED);
+                        } else {
+                            // mlp layers
+                            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
+                            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
+                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+                        }
+                    }
+                } break;
             case LLM_ARCH_EXAONE:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5862,7 +5952,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_JAMBA ||
         arch == LLM_ARCH_FALCON_H1 ||
         arch == LLM_ARCH_PLAMO2 ||
-        arch == LLM_ARCH_GRANITE_HYBRID) {
+        arch == LLM_ARCH_GRANITE_HYBRID ||
+        arch == LLM_ARCH_NEMOTRON_H) {
         LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
         LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
         LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
@@ -14129,6 +14220,138 @@ struct llm_build_nemotron : public llm_graph_context {
     }
 };
 
+struct llm_build_nemotron_h : public llm_graph_context_mamba {
+    llm_build_nemotron_h(
+            const llama_model      & model,
+            const llm_graph_params & params) :
+        llm_graph_context_mamba(params) {
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        auto * inp = build_inp_mem_hybrid();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            if (hparams.is_recurrent(il)) {
+                // ssm layer //
+                cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
+            } else if (hparams.n_ff(il) == 0) {
+                // attention layer //
+                cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);
+            } else {
+                cur = build_ffn_layer(cur, model, il);
+            }
+
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // add residual
+            cur = ggml_add(ctx0, cur, inpSA);
+            cb(cur, "block_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+
+    ggml_tensor * build_attention_layer(
+              ggml_tensor             * cur,
+              llm_graph_input_attn_kv * inp_attn,
+        const llama_model             & model,
+        const int64_t                   n_embd_head,
+        const int                       il) {
+
+        // compute Q and K and (optionally) RoPE them
+        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+        cb(Qcur, "Qcur", il);
+        if (model.layers[il].bq) {
+            Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+            cb(Qcur, "Qcur", il);
+        }
+
+        ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+        cb(Kcur, "Kcur", il);
+        if (model.layers[il].bk) {
+            Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+            cb(Kcur, "Kcur", il);
+        }
+
+        ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+        cb(Vcur, "Vcur", il);
+        if (model.layers[il].bv) {
+            Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+            cb(Vcur, "Vcur", il);
+        }
+
+        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, hparams.n_head(il),    n_tokens);
+        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+        cur = build_attn(inp_attn,
+                model.layers[il].wo, model.layers[il].bo,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+        return cur;
+    }
+
+    ggml_tensor * build_ffn_layer(
+              ggml_tensor * cur,
+        const llama_model & model,
+        const int           il) {
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                NULL,                      NULL,                        NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        return cur;
+    }
+};
+
 struct llm_build_exaone : public llm_graph_context {
     llm_build_exaone(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -18277,6 +18500,23 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             cparams.n_seq_max,
                             nullptr);
                 } else if (llm_arch_is_hybrid(arch)) {
+
+                    // The main difference between hybrid architectures is the
+                    // layer filters, so pick the right one here
+                    llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
+                    llama_memory_hybrid::layer_filter_cb filter_recr = nullptr;
+                    if (arch == LLM_ARCH_FALCON_H1) {
+                        filter_attn = [&](int32_t) { return true; };
+                        filter_recr = [&](int32_t) { return true; };
+                    } else if (arch == LLM_ARCH_NEMOTRON_H) {
+                        filter_attn = [&](int32_t il) {
+                            return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                        };
+                        filter_recr = [&](int32_t il) {
+                            return hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                        };
+                    }
+
                     const auto padding = llama_kv_cache::get_padding(cparams);
 
                     cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding);
@@ -18296,8 +18536,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         /* n_seq_max         */ cparams.n_seq_max,
                         /* offload           */ cparams.offload_kqv,
                         /* unified           */ cparams.kv_unified,
-                        /* filter_attn       */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr,
-                        /* filter_recr       */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr);
+                        /* filter_attn       */ std::move(filter_attn),
+                        /* filter_recr       */ std::move(filter_recr));
                 } else {
                     const auto padding = llama_kv_cache::get_padding(cparams);
 
@@ -18625,6 +18865,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_nemotron>(*this, params);
             } break;
+        case LLM_ARCH_NEMOTRON_H:
+            {
+                llm = std::make_unique<llm_build_nemotron_h>(*this, params);
+            } break;
         case LLM_ARCH_EXAONE:
             {
                 llm = std::make_unique<llm_build_exaone>(*this, params);
@@ -18860,6 +19104,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_RWKV7:
         case LLM_ARCH_ARWKV7:
         case LLM_ARCH_WAVTOKENIZER_DEC:
+        case LLM_ARCH_NEMOTRON_H:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values

From 009b709d6efd24820ac67765ed339a72dc797814 Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Fri, 29 Aug 2025 11:35:58 +0800
Subject: [PATCH 219/287] CUDA: fuse adds, fuse add with rms norm (#15631)

* CUDA: fused add with rms_norm_mul

* Non-broadcast fuse works

* Add fused adds

* format

* Remove n_fuse from template params

* Address review comments

* Move template inside binbcast
---
 ggml/src/ggml-cuda/binbcast.cu  | 415 +++++++++++++++++++-------------
 ggml/src/ggml-cuda/binbcast.cuh |   2 +
 ggml/src/ggml-cuda/ggml-cuda.cu |  58 ++++-
 ggml/src/ggml-cuda/norm.cu      | 211 ++++++++++++++--
 ggml/src/ggml-cuda/norm.cuh     |   5 +
 5 files changed, 501 insertions(+), 190 deletions(-)

diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index e1fbf0e136..99a98fcbfc 100644
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -1,5 +1,6 @@
 #include "binbcast.cuh"
 #include <cstdint>
+#include <utility>
 
 static __device__ __forceinline__ float op_repeat(const float a, const float b) {
     return b;
@@ -22,13 +23,16 @@ static __device__ __forceinline__ float op_div(const float a, const float b) {
     return a / b;
 }
 
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+
+
+template <float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t, typename... src1_ptrs>
 static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s00,*/ int s01, int s02, int s03,
-        /*int s10,*/ int s11, int s12, int s13) {
+        const int ne0, const int ne1, const int ne2, const int ne3,
+        const int ne10, const int ne11, const int ne12, const int ne13,
+        /*int s0, */ const int s1, const int s2, const int s3,
+        /*int s00,*/ const int s01, const int s02, const int s03,
+        /*int s10,*/ const int s11, const int s12, const int s13,
+        src1_ptrs... src1s) {
     const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
     const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
     const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
@@ -46,24 +50,27 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst
     const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
     const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
 
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
+    const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
     for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
         const int i10 = i0 % ne10;
-        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+
+        float result = src0_row ? (float) src0_row[i0] : 0.0f;
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+
+        dst_row[i0] = (dst_t) result;
     }
 }
 
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s00,*/ int s01, int s02, int s03,
-        /*int s10,*/ int s11, int s12, int s13) {
-
+template <float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t, typename... src1_ptrs>
+static __global__ void k_bin_bcast_unravel(const src0_t *   src0, const src1_t *   src1, dst_t *          dst,
+        const int ne0, const int ne1, const int ne2,const int ne3,
+        const int ne10, const int ne11, const int ne12, const int ne13,
+        /*int s0, */ const int s1, const int s2, const int s3,
+        /*int s00,*/ const int s01, const int s02, const int s03,
+        /*int s10,*/ const int s11, const int s12, const int s13,
+        src1_ptrs ... src1s) {
     const int i = blockDim.x*blockIdx.x + threadIdx.x;
 
     const int i3 = i/(ne2*ne1*ne0);
@@ -83,12 +90,166 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
     const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
     const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
 
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
+    const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
     const int i10 = i0 % ne10;
-    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+
+    float result = src0_row ? (float) src0_row[i0] : 0.0f;
+    result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+
+    dst_row[i0] = (dst_t) result;
+}
+
+template <float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t, size_t... I>
+static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+                                  const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
+                                  cudaStream_t stream, std::index_sequence<I...>) {
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    int nr0 = ne10 / ne0;
+    int nr1 = ne11 / ne1;
+    int nr2 = ne12 / ne2;
+    int nr3 = ne13 / ne3;
+
+    int nr[4] = { nr0, nr1, nr2, nr3 };
+
+    int64_t cne[]  = { ne0, ne1, ne2, ne3 };
+    int64_t cne0[] = { ne00, ne01, ne02, ne03 };
+    int64_t cne1[] = { ne10, ne11, ne12, ne13 };
+
+    size_t cnb[]  = { nb0, nb1, nb2, nb3 };
+    size_t cnb0[] = { nb00, nb01, nb02, nb03 };
+    size_t cnb1[] = { nb10, nb11, nb12, nb13 };
+
+    auto collapse = [](int64_t cne[]) {
+        cne[0] *= cne[1];
+        cne[1] = cne[2];
+        cne[2] = cne[3];
+        cne[3] = 1;
+    };
+
+    auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
+        cnb[1] *= cne[1];
+        cnb[2] *= cne[2];
+        cnb[3] *= cne[3];
+    };
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+        for (int i = 0; i < 4; i++) {
+            if (nr[i] != 1) {
+                break;
+            }
+            if (i > 0) {
+                collapse_nb(cnb, cne);
+                collapse_nb(cnb0, cne0);
+                collapse_nb(cnb1, cne1);
+                collapse(cne);
+                collapse(cne0);
+                collapse(cne1);
+            }
+        }
+    }
+
+    {
+        int64_t ne0 = cne[0];
+        int64_t ne1 = cne[1];
+        int64_t ne2 = cne[2];
+        int64_t ne3 = cne[3];
+
+        //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
+        //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
+        //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
+        //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);
+
+        int64_t ne10 = cne1[0];
+        int64_t ne11 = cne1[1];
+        int64_t ne12 = cne1[2];
+        int64_t ne13 = cne1[3];
+
+        size_t nb0 = cnb[0];
+        size_t nb1 = cnb[1];
+        size_t nb2 = cnb[2];
+        size_t nb3 = cnb[3];
+
+        size_t nb00 = cnb0[0];
+        size_t nb01 = cnb0[1];
+        size_t nb02 = cnb0[2];
+        size_t nb03 = cnb0[3];
+
+        size_t nb10 = cnb1[0];
+        size_t nb11 = cnb1[1];
+        size_t nb12 = cnb1[2];
+        size_t nb13 = cnb1[3];
+
+        size_t s0 = nb0 / sizeof(dst_t);
+        size_t s1 = nb1 / sizeof(dst_t);
+        size_t s2 = nb2 / sizeof(dst_t);
+        size_t s3 = nb3 / sizeof(dst_t);
+
+        size_t s10 = nb10 / sizeof(src1_t);
+        size_t s11 = nb11 / sizeof(src1_t);
+        size_t s12 = nb12 / sizeof(src1_t);
+        size_t s13 = nb13 / sizeof(src1_t);
+
+        size_t s00 = nb00 / sizeof(src0_t);
+        size_t s01 = nb01 / sizeof(src0_t);
+        size_t s02 = nb02 / sizeof(src0_t);
+        size_t s03 = nb03 / sizeof(src0_t);
+
+        GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
+        GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
+        GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
+        GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
+
+        GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
+        GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
+        GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
+        GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
+
+        GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
+        GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
+        GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
+        GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
+
+        GGML_ASSERT(s0 == 1);
+        GGML_ASSERT(s00 == 1);
+        GGML_ASSERT(s10 == 1);
+
+        const int block_size = 128;
+
+        int64_t hne0 = std::max(ne0 / 2LL, 1LL);
+
+        dim3 block_dims;
+        block_dims.x = std::min<unsigned int>(hne0, block_size);
+        block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
+        block_dims.z = std::min(std::min<unsigned int>(ne2 * ne3, block_size / block_dims.x / block_dims.y), 64U);
+
+        dim3 block_nums((hne0 + block_dims.x - 1) / block_dims.x,
+                        (ne1 + block_dims.y - 1) / block_dims.y,
+                        (ne2 * ne3 + block_dims.z - 1) / block_dims.z);
+
+        if (block_nums.z > 65535) {
+            int block_num = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
+            k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
+                <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00,*/ s01, s02, s03,
+                    /* s10,*/ s11, s12,s13,
+                    (const src1_t *) dst->src[I + 1]->data...);
+        } else {
+            k_bin_bcast<bin_op, src0_t, src1_t, dst_t>
+                <<<block_nums, block_dims, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00,*/ s01, s02, s03,
+                    /* s10,*/ s11, s12,s13,
+                    (const src1_t *) dst->src[I + 1]->data...);
+        }
+    }
 }
 
 template <typename T>
@@ -120,160 +281,14 @@ static __global__ void k_repeat_back(
     dst[tid3*ne2*ne1*ne0 + tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
 }
 
-template<float (*bin_op)(const float, const float)>
+template <float (*bin_op)(const float, const float), int n_fuse = 1>
 struct bin_bcast_cuda {
     template<typename src0_t, typename src1_t, typename dst_t>
     void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
             const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
             cudaStream_t stream) {
-
-        GGML_TENSOR_BINARY_OP_LOCALS
-
-        int nr0 = ne10/ne0;
-        int nr1 = ne11/ne1;
-        int nr2 = ne12/ne2;
-        int nr3 = ne13/ne3;
-
-        int nr[4] = { nr0, nr1, nr2, nr3 };
-
-        // collapse dimensions until first broadcast dimension
-        int64_t cne[] = {ne0, ne1, ne2, ne3};
-        int64_t cne0[] = {ne00, ne01, ne02, ne03};
-        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-
-        size_t cnb[] = {nb0, nb1, nb2, nb3};
-        size_t cnb0[] = {nb00, nb01, nb02, nb03};
-        size_t cnb1[] = {nb10, nb11, nb12, nb13};
-
-        auto collapse = [](int64_t cne[]) {
-            cne[0] *= cne[1];
-            cne[1] = cne[2];
-            cne[2] = cne[3];
-            cne[3] = 1;
-        };
-
-        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
-            cnb[1] *= cne[1];
-            cnb[2] *= cne[2];
-            cnb[3] *= cne[3];
-        };
-
-        if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
-            for (int i = 0; i < 4; i++) {
-                if (nr[i] != 1) {
-                    break;
-                }
-                if (i > 0) {
-                    collapse_nb(cnb, cne);
-                    collapse_nb(cnb0, cne0);
-                    collapse_nb(cnb1, cne1);
-                    collapse(cne);
-                    collapse(cne0);
-                    collapse(cne1);
-                }
-            }
-        }
-
-        {
-            int64_t ne0 = cne[0];
-            int64_t ne1 = cne[1];
-            int64_t ne2 = cne[2];
-            int64_t ne3 = cne[3];
-
-            //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
-            //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
-            //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
-            //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);
-
-            int64_t ne10 = cne1[0];
-            int64_t ne11 = cne1[1];
-            int64_t ne12 = cne1[2];
-            int64_t ne13 = cne1[3];
-
-            size_t nb0 = cnb[0];
-            size_t nb1 = cnb[1];
-            size_t nb2 = cnb[2];
-            size_t nb3 = cnb[3];
-
-            size_t nb00 = cnb0[0];
-            size_t nb01 = cnb0[1];
-            size_t nb02 = cnb0[2];
-            size_t nb03 = cnb0[3];
-
-            size_t nb10 = cnb1[0];
-            size_t nb11 = cnb1[1];
-            size_t nb12 = cnb1[2];
-            size_t nb13 = cnb1[3];
-
-            size_t s0 = nb0 / sizeof(dst_t);
-            size_t s1 = nb1 / sizeof(dst_t);
-            size_t s2 = nb2 / sizeof(dst_t);
-            size_t s3 = nb3 / sizeof(dst_t);
-
-            size_t s10 = nb10 / sizeof(src1_t);
-            size_t s11 = nb11 / sizeof(src1_t);
-            size_t s12 = nb12 / sizeof(src1_t);
-            size_t s13 = nb13 / sizeof(src1_t);
-
-            size_t s00 = nb00 / sizeof(src0_t);
-            size_t s01 = nb01 / sizeof(src0_t);
-            size_t s02 = nb02 / sizeof(src0_t);
-            size_t s03 = nb03 / sizeof(src0_t);
-
-            GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
-            GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
-            GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
-            GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
-
-            GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
-            GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
-            GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
-            GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
-
-            GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
-            GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
-            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
-            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
-
-            GGML_ASSERT(s0 == 1);
-            GGML_ASSERT(s00 == 1);
-            GGML_ASSERT(s10 == 1);
-
-            const int block_size = 128;
-
-            int64_t hne0 = std::max(ne0/2LL, 1LL);
-
-            dim3 block_dims;
-            block_dims.x = std::min<unsigned int>(hne0, block_size);
-            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
-            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
-
-            dim3 block_nums(
-                (hne0 + block_dims.x - 1) / block_dims.x,
-                (ne1 + block_dims.y - 1) / block_dims.y,
-                (ne2*ne3 + block_dims.z - 1) / block_dims.z
-            );
-
-            if (block_nums.z > 65535) {
-                // this is the maximum number of blocks in z dimension, fallback to 1D grid kernel
-                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
-                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00, */ s01, s02, s03,
-                    /* s10, */ s11, s12, s13);
-            } else {
-                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00, */ s01, s02, s03,
-                    /* s10, */ s11, s12, s13);
-            }
-        }
+        launch_bin_bcast_pack<bin_op, src0_t, src1_t, dst_t>(
+            src0, src1, dst, src0_dd, src1_dd, dst_dd, stream, std::make_index_sequence<n_fuse>{});
     }
 };
 
@@ -331,6 +346,68 @@ void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
 }
 
+template <float (*op)(const float, const float), int n_fuse>
+static void ggml_cuda_op_fused_binbcast_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    cudaStream_t stream = ctx.stream();
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        launch_bin_bcast_pack<op, float, float, float>(src0, src1, dst,
+            (const float *) src0->data, (const float *) src1->data, (float *) dst->data,
+            stream, std::make_index_sequence<n_fuse>{});
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        launch_bin_bcast_pack<op, half, half, half>(src0, src1, dst,
+            (const half *) src0->data, (const half *) src1->data, (half *) dst->data,
+            stream, std::make_index_sequence<n_fuse>{});
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
+        launch_bin_bcast_pack<op, half, float, half>(src0, src1, dst,
+            (const half *) src0->data, (const float *) src1->data, (half *) dst->data,
+            stream, std::make_index_sequence<n_fuse>{});
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        launch_bin_bcast_pack<op, half, float, float>(src0, src1, dst,
+            (const half *) src0->data, (const float *) src1->data, (float *) dst->data,
+            stream, std::make_index_sequence<n_fuse>{});
+    } else {
+        fprintf(stderr,
+                "%s: unsupported types for fusion: dst: %s, src0: %s, src1: %s\n",
+                __func__, ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ABORT("fatal error");
+    }
+}
+
+
+void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse) {
+    GGML_ASSERT(2 <= n_fuse && n_fuse <= 8);
+
+    switch (n_fuse) {
+        case 2:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 2>(ctx, dst);
+            break;
+        case 3:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 3>(ctx, dst);
+            break;
+        case 4:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 4>(ctx, dst);
+            break;
+        case 5:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 5>(ctx, dst);
+            break;
+        case 6:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 6>(ctx, dst);
+            break;
+        case 7:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 7>(ctx, dst);
+            break;
+        case 8:
+            ggml_cuda_op_fused_binbcast_impl<op_add, 8>(ctx, dst);
+            break;
+        default:
+            GGML_ASSERT(false && "Unsupported n_fuse value");
+    }
+}
+
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
 
diff --git a/ggml/src/ggml-cuda/binbcast.cuh b/ggml/src/ggml-cuda/binbcast.cuh
index 3ac1c9b03f..62bc950111 100644
--- a/ggml/src/ggml-cuda/binbcast.cuh
+++ b/ggml/src/ggml-cuda/binbcast.cuh
@@ -7,3 +7,5 @@ void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 4c02b57227..6a1b0fc936 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2821,9 +2821,14 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         return false;
     }
 
-    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) {
+    if ((ops.size() == 2 || ops.size() == 3) && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) {
         const ggml_tensor *rms_norm = cgraph->nodes[node_idx];
         const ggml_tensor *mul      = cgraph->nodes[node_idx+1];
+        const ggml_tensor *add      = nullptr;
+
+        if (ops.size() == 3 && ops.begin()[2] == GGML_OP_ADD) {
+            add = cgraph->nodes[node_idx+1];
+        }
 
         GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32);
         GGML_ASSERT(rms_norm->type == GGML_TYPE_F32);
@@ -2835,6 +2840,12 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
             return false;
         }
 
+        if (add && (add->src[0]->type != GGML_TYPE_F32 ||
+            add->src[1]->type != GGML_TYPE_F32 ||
+            add->type != GGML_TYPE_F32) ) {
+            return false;
+        }
+
         //if rms norm is the B operand, then we don't handle broadcast
         if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm->src[1])) {
             return false;
@@ -2845,6 +2856,10 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
             return false;
         }
 
+        if (add && (!ggml_is_contiguous(add->src[0]) || !ggml_is_contiguous_rows(add->src[1]))) {
+            return false;
+        }
+
         return true;
     }
 
@@ -2891,7 +2906,46 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
 
                 static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
                 if (!disable_fusion) {
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
+
+                    if (node->op == GGML_OP_ADD) {
+                        int n_fuse = 0;
+                        ggml_op ops[8];
+                        std::fill(ops, ops + 8, GGML_OP_ADD);
+
+                        for (; n_fuse <= 6; ++n_fuse){
+                            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
+                                break;
+                            }
+                            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
+                                break;
+                            }
+                            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
+                                break;
+                            }
+                        }
+
+                        n_fuse++;
+
+                        if (n_fuse > 1) {
+                            for (int j = 0; j < n_fuse - 1; ++j) {
+                                node->src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+                            }
+                            cgraph->nodes[i + n_fuse - 1]->data = node->data;
+                            ggml_cuda_op_fused_add(*cuda_ctx, node, n_fuse);
+                            i += n_fuse - 1;
+
+                            continue;
+                        }
+                    }
+
+
+                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD}, {})) {
+                        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
+                        i += 2;
+                        continue;
+                    }
+
+                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL}, {})) {
                         ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
                         i++;
                         continue;
diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
index bddcca51b7..293f6f68e5 100644
--- a/ggml/src/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -104,12 +104,29 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
     }
 }
 
-template <int block_size, bool do_multiply = false>
-static __global__ void rms_norm_f32(
-        const float * x, float * dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
-        const int64_t stride_sample, const float eps, const float * mul = nullptr, const int64_t mul_stride_row = 0,
-        const int64_t mul_stride_channel = 0, const int64_t mul_stride_sample = 0, const int mul_ncols = 0,
-        const int mul_nrows = 0, const int mul_nchannels = 0, const int mul_nsamples = 0) {
+template <int block_size, bool do_multiply = false, bool do_add = false>
+static __global__ void rms_norm_f32(const float * x, float *       dst,
+                                    const int     ncols,
+                                    const int64_t stride_row,
+                                    const int64_t stride_channel,
+                                    const int64_t stride_sample,
+                                    const float   eps,
+                                    const float * mul                = nullptr,
+                                    const int64_t mul_stride_row     = 0,
+                                    const int64_t mul_stride_channel = 0,
+                                    const int64_t mul_stride_sample  = 0,
+                                    const int     mul_ncols          = 0,
+                                    const int     mul_nrows          = 0,
+                                    const int     mul_nchannels      = 0,
+                                    const int     mul_nsamples       = 0,
+                                    const float * add                = nullptr,
+                                    const int64_t add_stride_row     = 0,
+                                    const int64_t add_stride_channel = 0,
+                                    const int64_t add_stride_sample  = 0,
+                                    const int     add_ncols          = 0,
+                                    const int     add_nrows          = 0,
+                                    const int     add_nchannels      = 0,
+                                    const int     add_nsamples       = 0) {
     const int nrows     = gridDim.x;
     const int nchannels = gridDim.y;
 
@@ -128,6 +145,13 @@ static __global__ void rms_norm_f32(
         mul += mul_sample*mul_stride_sample + mul_channel*mul_stride_channel + mul_row*mul_stride_row;
     }
 
+    if constexpr (do_add) {
+        const int add_row     = row % add_nrows;
+        const int add_channel = channel % add_nchannels;
+        const int add_sample  = sample % add_nsamples;
+        add += add_sample * add_stride_sample + add_channel * add_stride_channel + add_row * add_stride_row;
+    }
+
     float tmp = 0.0f; // partial sum for thread in warp
 
     for (int col = tid; col < ncols; col += block_size) {
@@ -154,9 +178,16 @@ static __global__ void rms_norm_f32(
     const float scale = rsqrtf(mean + eps);
 
     for (int col = tid; col < ncols; col += block_size) {
-        if constexpr (do_multiply) {
+        if constexpr (do_multiply && do_add) {
+            const int mul_col = col % mul_ncols;
+            const int add_col = col % add_ncols;
+            dst[col] = scale * x[col] * mul[mul_col] + add[add_col];
+        } else if constexpr (do_multiply) {
             const int mul_col = col % mul_ncols;
             dst[col] = scale * x[col] * mul[mul_col];
+        } else if constexpr (do_add) {
+            const int add_col = col % add_ncols;
+            dst[col] += add[add_col];
         } else {
             dst[col] = scale * x[col];
         }
@@ -331,23 +362,70 @@ static void rms_norm_f32_cuda(
     }
 }
 
-static void rms_norm_mul_f32_cuda(
-        const float * x, const float * mul, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
-        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample,
-        const int64_t mul_stride_row, const int64_t mul_stride_channel, const int64_t mul_stride_sample,
-        const int mul_ncols, const int mul_nrows, const int mul_nchannels, const int mul_nsamples,
-        const float eps, cudaStream_t stream) {
+static void rms_norm_mul_f32_cuda(const float * x,
+                                  const float * mul,
+                                  const float * add,
+                                  float *       dst,
+                                  const int     ncols,
+                                  const int     nrows,
+                                  const int     nchannels,
+                                  const int     nsamples,
+                                  const int64_t stride_row,
+                                  const int64_t stride_channel,
+                                  const int64_t stride_sample,
+                                  const int64_t mul_stride_row,
+                                  const int64_t mul_stride_channel,
+                                  const int64_t mul_stride_sample,
+                                  const int     mul_ncols,
+                                  const int     mul_nrows,
+                                  const int     mul_nchannels,
+                                  const int     mul_nsamples,
+                                  const int64_t add_stride_row,
+                                  const int64_t add_stride_channel,
+                                  const int64_t add_stride_sample,
+                                  const int     add_ncols,
+                                  const int     add_nrows,
+                                  const int     add_nchannels,
+                                  const int     add_nsamples,
+                                  const float   eps,
+                                  cudaStream_t  stream) {
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (mul == nullptr) {
         rms_norm_f32_cuda(x, dst, ncols, nrows, nchannels, nsamples, stride_row, stride_channel, stride_sample, eps, stream);
         return;
     }
-    if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        rms_norm_f32<WARP_SIZE, true><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+    if (add == nullptr) {
+        if (ncols < 1024) {
+            const dim3 block_dims(WARP_SIZE, 1, 1);
+            rms_norm_f32<WARP_SIZE, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
+                ncols, stride_row, stride_channel, stride_sample, eps,
+                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
+                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+        } else {
+            const dim3 block_dims(1024, 1, 1);
+            rms_norm_f32<1024, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
+                ncols, stride_row, stride_channel, stride_sample, eps,
+                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
+                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+        }
     } else {
-        const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024, true><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+        if (ncols < 1024) {
+            const dim3 block_dims(WARP_SIZE, 1, 1);
+            rms_norm_f32<WARP_SIZE, true, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
+                ncols, stride_row, stride_channel, stride_sample, eps,
+                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
+                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
+                add, add_stride_row, add_stride_channel, add_stride_sample,
+                add_ncols, add_nrows, add_nchannels, add_nsamples);
+        } else {
+            const dim3 block_dims(1024, 1, 1);
+            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
+                ncols, stride_row, stride_channel, stride_sample, eps,
+                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
+                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
+                add, add_stride_row, add_stride_channel, add_stride_sample,
+                add_ncols, add_nrows, add_nchannels, add_nsamples);
+        }
     }
 }
 
@@ -491,7 +569,102 @@ void ggml_cuda_op_rms_norm_fused(ggml_backend_cuda_context & ctx, ggml_tensor *
     const int mul_nchannels = mul_src->ne[2];
     const int mul_nsamples  = mul_src->ne[3];
 
-    rms_norm_mul_f32_cuda(src0_d, mul_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, mul_s01, mul_s02, mul_s03, mul_ncols, mul_nrows, mul_nchannels, mul_nsamples, eps, stream);
+    rms_norm_mul_f32_cuda(src0_d, mul_d, nullptr, dst_d,
+                          ne00, ne01, ne02, ne03,
+                          /*s00*/ s01, s02, s03,
+                          /*mul_s00*/ mul_s01, mul_s02, mul_s03,
+                          mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
+                          /*add_s00*/ 0, 0, 0,
+                          0, 0, 0, 0,
+                          eps, stream);
+}
+
+void ggml_cuda_op_rms_norm_fused_add(ggml_backend_cuda_context & ctx,
+                                     ggml_tensor *               dst,
+                                     ggml_tensor *               mul_tensor,
+                                     ggml_tensor *               add_tensor) {
+    const ggml_tensor * rms_norm_src = (ggml_tensor *) dst->src[0];
+    float               eps          = 0.0f;
+
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    const float *       src0_d  = (const float *) rms_norm_src->data;
+    const float *       mul_d   = nullptr;
+    const ggml_tensor * mul_src = nullptr;
+
+    if (mul_tensor->src[0] == dst) {
+        mul_d   = (float *) mul_tensor->src[1]->data;
+        mul_src = mul_tensor->src[1];
+    } else if (mul_tensor->src[1] == dst) {
+        mul_d   = (float *) mul_tensor->src[0]->data;
+        mul_src = mul_tensor->src[0];
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const float *       add_d   = nullptr;
+    const ggml_tensor * add_src = nullptr;
+
+    if (add_tensor->src[0] == mul_tensor) {
+        add_d   = (float *) add_tensor->src[1]->data;
+        add_src = add_tensor->src[1];
+    } else if (add_tensor->src[1] == mul_tensor) {
+        add_d   = (float *) add_tensor->src[0]->data;
+        add_src = add_tensor->src[0];
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    float *      dst_d  = (float *) add_tensor->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(rms_norm_src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(mul_tensor->type == GGML_TYPE_F32);
+    GGML_ASSERT(add_tensor->type == GGML_TYPE_F32);
+    GGML_ASSERT(eps >= 0.0f);
+
+    const int64_t ne00 = rms_norm_src->ne[0];
+    const int64_t ne01 = rms_norm_src->ne[1];
+    const int64_t ne02 = rms_norm_src->ne[2];
+    const int64_t ne03 = rms_norm_src->ne[3];
+
+    const size_t ts0 = ggml_type_size(rms_norm_src->type);
+    GGML_ASSERT(rms_norm_src->nb[0] == ts0);
+    const int64_t s01 = rms_norm_src->nb[1] / ts0;
+    const int64_t s02 = rms_norm_src->nb[2] / ts0;
+    const int64_t s03 = rms_norm_src->nb[3] / ts0;
+
+    const size_t ts_mul = ggml_type_size(mul_src->type);
+    GGML_ASSERT(mul_src->nb[0] == ts_mul);
+    const int64_t mul_s01 = mul_src->nb[1] / ts_mul;
+    const int64_t mul_s02 = mul_src->nb[2] / ts_mul;
+    const int64_t mul_s03 = mul_src->nb[3] / ts_mul;
+
+    const int mul_ncols     = mul_src->ne[0];
+    const int mul_nrows     = mul_src->ne[1];
+    const int mul_nchannels = mul_src->ne[2];
+    const int mul_nsamples  = mul_src->ne[3];
+
+    const size_t ts_add = ggml_type_size(add_src->type);
+    GGML_ASSERT(add_src->nb[0] == ts_add);
+    const int64_t add_s01 = add_src->nb[1] / ts_add;
+    const int64_t add_s02 = add_src->nb[2] / ts_add;
+    const int64_t add_s03 = add_src->nb[3] / ts_add;
+
+    const int add_ncols     = add_src->ne[0];
+    const int add_nrows     = add_src->ne[1];
+    const int add_nchannels = add_src->ne[2];
+    const int add_nsamples  = add_src->ne[3];
+
+    rms_norm_mul_f32_cuda(src0_d, mul_d,add_d,dst_d,
+                          ne00,ne01, ne02, ne03,
+                          /*s00*/ s01, s02, s03,
+                          /*mul_s00*/ mul_s01, mul_s02, mul_s03,
+                          mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
+                          /*add_s00*/ add_s01, add_s02, add_s03,
+                          add_ncols, add_nrows, add_nchannels, add_nsamples,
+                          eps, stream);
 }
 
 void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-cuda/norm.cuh b/ggml/src/ggml-cuda/norm.cuh
index 7ea7bd4df3..a74f637672 100644
--- a/ggml/src/ggml-cuda/norm.cuh
+++ b/ggml/src/ggml-cuda/norm.cuh
@@ -8,6 +8,11 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_rms_norm_fused(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * mul_tensor);
 
+void ggml_cuda_op_rms_norm_fused_add(ggml_backend_cuda_context & ctx,
+                                     ggml_tensor *               dst,
+                                     ggml_tensor *               mul_tensor,
+                                     ggml_tensor *               add_tensor);
+
 void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_l2_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

From 60e5eee31f1af9bb579ac45380e3857d610020b9 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Fri, 29 Aug 2025 14:53:41 +0200
Subject: [PATCH 220/287] chat : Seed OSS thinking + tool call support (#15552)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Reasoning and tool-calling support for Seed OSS

* Fix grammar and partial parsing

* Whitespace

* New chat template

* Update common/chat.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update common/chat.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Remove unused 'purge_healing_marker' helper

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 common/chat.cpp                           | 153 ++++++++++++++++++-
 common/chat.h                             |   1 +
 models/templates/ByteDance-Seed-OSS.jinja | 171 ++++++++++++++++++++++
 tests/test-chat.cpp                       | 134 +++++++++++++++++
 4 files changed, 458 insertions(+), 1 deletion(-)
 create mode 100644 models/templates/ByteDance-Seed-OSS.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index 111b4a21b3..955c42852a 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -622,6 +622,7 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
         case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
         case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
+        case COMMON_CHAT_FORMAT_SEED_OSS: return "Seed-OSS";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -2059,6 +2060,94 @@ static void common_chat_parse_granite(common_chat_msg_parser & builder) {
     }
 }
 
+static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
+    // Parse thinking tags first - this handles the main reasoning content
+    builder.try_parse_reasoning("<seed:think>", "</seed:think>");
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Parse tool calls - Seed-OSS uses <seed:tool_call> format
+    static const common_regex tool_call_begin_regex("<seed:tool_call>");
+    static const common_regex tool_call_end_regex("</seed:tool_call>");
+    static const common_regex function_regex("<function=([^>]+)>");
+    static const common_regex param_regex("<parameter=([^>]+)>");
+
+    while (auto tool_res = builder.try_find_regex(tool_call_begin_regex)) {
+        builder.consume_spaces();  // Consume whitespace after <seed:tool_call>
+
+        // Look for function call inside tool call, ignore any content before it
+        if (auto func_res = builder.try_find_regex(function_regex, std::string::npos, false)) {
+            auto function_name = builder.str(func_res->groups[1]);
+
+            // Parse Seed-OSS parameters <parameter=name>value</parameter>
+            json args = json::object();
+            // Parse all parameters
+            while (auto param_res = builder.try_find_regex(param_regex, std::string::npos, false)) {
+                // again, ignore noise around parameters
+                auto param_name = builder.str(param_res->groups[1]);
+                builder.move_to(param_res->groups[0].end);
+                builder.consume_spaces();  // Consume whitespace after parameter
+                auto savedPos = builder.pos();
+                if (auto param_parse = builder.try_find_literal("</parameter>")) {
+                    auto param = param_parse->prelude;
+                    builder.move_to(savedPos);
+                    try {
+                        if (auto param_res = builder.try_consume_json()) {
+                            args[param_name] = param_res->json;
+                        } else {
+                            args[param_name] = param;
+                        }
+                    } catch (json::exception &) {
+                        args[param_name] = param;
+                    }
+                } else {
+                    throw common_chat_msg_partial_exception("Incomplete tool parameter");
+                }
+            }
+            // Look for closing function tag
+            auto end_func = builder.try_find_literal("</function>");
+            if (end_func) {
+                builder.move_to(end_func->groups[0].end);
+                builder.consume_spaces();  // Consume whitespace after </function>
+
+                // Add the tool call with parsed arguments, but only if we REALLY got the literal
+                auto eaten_fragment = builder.input().substr(end_func->groups[0].begin, end_func->groups[0].end);
+                auto funlen = std::string("</function>").length();
+                if (eaten_fragment.length() >= funlen && eaten_fragment.substr(0, funlen) == std::string("</function>")) {
+                    if (!builder.add_tool_call(function_name, "", args.dump())) {
+                        throw common_chat_msg_partial_exception("Incomplete tool call");
+                    }
+                } else {
+                    throw common_chat_msg_partial_exception("Incomplete tool call");
+                }
+            } else {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+            // Look for closing tool call tag
+            if (auto end_tool = builder.try_find_regex(tool_call_end_regex, std::string::npos, false)) {
+                builder.move_to(end_tool->groups[0].end);
+                builder.consume_spaces();  // Consume trailing whitespace after tool call
+            } else {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+        } else {
+            // No function found - don't consume content here, let it be handled at the end
+            break;
+        }
+    }
+
+    // Consume any remaining whitespace after all tool call processing
+    builder.consume_spaces();
+    auto remaining = builder.consume_rest();
+    // If there's any non-whitespace content remaining, add it as content
+    if (!string_strip(remaining).empty()) {
+        builder.add_content(remaining);
+    }
+}
+
 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -2075,8 +2164,62 @@ static common_chat_params common_chat_params_init_without_tools(const common_cha
     return data;
 }
 
+static common_chat_params common_chat_params_init_seed_oss(
+    const common_chat_template         & tmpl,
+    templates_params                   & params,
+    const common_chat_templates_inputs & inputs)
+{
+    common_chat_params data;
+    data.prompt = apply(tmpl, params);
+    data.format = COMMON_CHAT_FORMAT_SEED_OSS;
+    if (string_ends_with(data.prompt, "<seed:think>")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</seed:think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    if (params.tools.is_array() && !params.tools.empty()) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(params.tools, [&](const json & tool) {
+                const auto & function   = tool.at("function");
+                std::string  name       = function.at("name");
+                auto         parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+
+                // Create rule for Seed-OSS function call format
+                std::string param_rules;
+                if (parameters.contains("properties")) {
+                    for (const auto & [key, value] : parameters.at("properties").items()) {
+                        param_rules += "\"<parameter=" + key + ">\"" + builder.add_schema(name + "-arg-" + key, value) +
+                                       "\"</parameter>\"";
+                    }
+                }
+
+                tool_rules.push_back(builder.add_rule(name + "-call",
+                                                      "\"<seed:tool_call>\" space \"<function=" + name + ">\" space " +
+                                                          param_rules +
+                                                          " \"</function>\" space \"</seed:tool_call>\""));
+            });
+
+            data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<seed:tool_call>" });
+
+            data.preserved_tokens = {
+                "<seed:think>", "</seed:think>", "<seed:tool_call>", "</seed:tool_call>",
+                "<function=",   "</function>",   "<parameter=",      "</parameter>",
+            };
+
+            builder.add_rule("root", string_join(tool_rules, " | "));
+        });
+    }
+    return data;
+}
+
 static common_chat_params common_chat_templates_apply_jinja(
-    const struct common_chat_templates * tmpls,
+    const struct common_chat_templates        * tmpls,
     const struct common_chat_templates_inputs & inputs)
 {
     templates_params params;
@@ -2145,6 +2288,11 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_gpt_oss(tmpl, params);
     }
 
+    // Seed-OSS
+    if (src.find("<seed:think>") != std::string::npos) {
+        return common_chat_params_init_seed_oss(tmpl, params, inputs);
+    }
+
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -2303,6 +2451,9 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_GPT_OSS:
             common_chat_parse_gpt_oss(builder);
             break;
+        case COMMON_CHAT_FORMAT_SEED_OSS:
+            common_chat_parse_seed_oss(builder);
+            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }
diff --git a/common/chat.h b/common/chat.h
index d1e480c918..b09ff3b126 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -111,6 +111,7 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_COMMAND_R7B,
     COMMON_CHAT_FORMAT_GRANITE,
     COMMON_CHAT_FORMAT_GPT_OSS,
+    COMMON_CHAT_FORMAT_SEED_OSS,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
diff --git a/models/templates/ByteDance-Seed-OSS.jinja b/models/templates/ByteDance-Seed-OSS.jinja
new file mode 100644
index 0000000000..903ebaaba7
--- /dev/null
+++ b/models/templates/ByteDance-Seed-OSS.jinja
@@ -0,0 +1,171 @@
+{# ----------‑‑‑ special token variables ‑‑‑---------- #}
+{%- set bos_token              = '<seed:bos>'               -%}
+{%- set eos_token              = '<seed:eos>'               -%}
+{%- set pad_token              = '<seed:pad>'               -%}
+{%- set toolcall_begin_token   = '<seed:tool_call>'         -%}
+{%- set toolcall_end_token     = '</seed:tool_call>'        -%}
+{%- set think_begin_token      = '<seed:think>'             -%}
+{%- set think_end_token        = '</seed:think>'            -%}
+{%- set budget_begin_token     = '<seed:cot_budget_reflect>'-%}
+{%- set budget_end_token       = '</seed:cot_budget_reflect>'-%}
+{# -------------- reflection-interval lookup -------------- #}
+{%- if not thinking_budget is defined %}
+{%- set thinking_budget = -1 -%}
+{%- endif -%}
+{%- set budget_reflections_v05 = {
+     0:      0,
+     512:    128,
+     1024:   256,
+     2048:   512,
+     4096:   512,
+     8192:   1024,
+     16384:  1024
+} -%}
+{# Find the first gear that is greater than or equal to the thinking_budget. #}
+{%- set ns = namespace(interval = None) -%}
+{%- for k, v in budget_reflections_v05 | dictsort -%}
+    {%- if ns.interval is none and thinking_budget <= k -%}
+        {%- set ns.interval = v -%}
+    {%- endif -%}
+{%- endfor -%}
+{# If it exceeds the maximum gear, use the value of the last gear #}
+{%- if ns.interval is none -%}
+    {%- set ns.interval = budget_reflections_v05[16384] -%}
+{%- endif -%}
+{# ---------- Preprocess the system message ---------- #}
+{%- if messages[0]["role"] == "system" %}
+{%- set system_message = messages[0]["content"] %}
+{%- set loop_messages = messages[1:] %}
+{%- else %}
+{%- set loop_messages = messages %}
+{%- endif %}
+{# ---------- Ensure tools exist ---------- #}
+{%- if not tools is defined or tools is none %}
+{%- set tools = [] %}
+{%- endif %}
+{# tools2doc.jinja #}
+{%- macro py_type(t) -%}
+    {%- if t == "string" -%}str
+    {%- elif t in ("number", "integer") -%}int
+    {%- elif t == "boolean" -%}bool
+    {%- elif t == "array" -%}list
+    {%- else -%}Any{%- endif -%}
+{%- endmacro -%}
+{# ---------- Output the system block ---------- #}
+{%- if system_message is defined %}
+{{ bos_token + "system\n" + system_message }}
+{%- else %}
+{%- if tools is iterable and tools | length > 0 %}
+{{ bos_token + "system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query." }}
+{%- endif %}
+{%- endif %}
+{%- if use_json_tooldef is defined and use_json_tooldef %}
+
+{{"Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing any task, you must decide how to call them based on the descriptions and parameters of these tools."}}
+{{ tools | tojson(ensure_ascii=False) }}
+{%- else %}
+{%- for item in tools if item.type == "function" %}
+
+
+Function:
+def {{ item.function.name }}(
+{%- for name, spec in item.function.parameters.properties.items() %}
+        {{- name }}: {{ py_type(spec.type) }}{% if not loop.last %},{% endif %}
+{%- endfor %}):
+    """
+    {{ item.function.description | trim }}
+
+    {# ---------- Args ---------- #}
+    {%- if item.function.parameters.properties %}
+    Args:
+    {%- for name, spec in item.function.parameters.properties.items() %}
+
+    - {{ name }} ({{ py_type(spec.type) }})
+      {%- if name in item.function.parameters.required %} [必填]{% else %} [选填]{% endif %}:
+      {{- " " ~ (spec.description or "") }}
+    {%- endfor %}
+    {%- endif %}
+
+    {# ---------- Returns ---------- #}
+    {%- if item.function.returns is defined
+          and item.function.returns.properties is defined
+          and item.function.returns.properties %}
+    Returns:
+    {%- for name, spec in item.function.returns.properties.items() %}
+
+    - {{ name }} ({{ py_type(spec.type) }}):
+      {{- " " ~ (spec.description or "") }}
+    {%- endfor %}
+    {%- endif %}
+
+    """
+{%- endfor %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+
+{{"工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple lines</parameter>\n</function>\n</seed:tool_call>\n"}}
+{%- endif %}
+{# End the system block line #}
+{%- if system_message is defined or tools is iterable and tools | length > 0 %}
+{{ eos_token }}
+{%- endif %}
+{# ---------- Thinking Budget ---------- #}
+{%- if thinking_budget is defined %}
+{%- if thinking_budget == 0 %}
+{{ bos_token+"system" }}
+{{ "You are an intelligent assistant that can answer questions in one step without the need for reasoning and thinking, that is, your thinking budget is 0. Next, please skip the thinking process and directly start answering the user's questions." }}
+{{ eos_token }}
+{%- elif not thinking_budget == -1 %}
+{{ bos_token+"system" }}
+{{ "You are an intelligent assistant with reflective ability. In the process of thinking and reasoning, you need to strictly follow the thinking budget, which is "}}{{thinking_budget}}{{". That is, you need to complete your thinking within "}}{{thinking_budget}}{{" tokens and start answering the user's questions. You will reflect on your thinking process every "}}{{ns.interval}}{{" tokens, stating how many tokens have been used and how many are left."}}
+{{ eos_token }}
+{%- endif %}
+{%- endif %}
+{# ---------- List the historical messages one by one ---------- #}
+{%- for message in loop_messages %}
+{%- if message.role == "assistant"
+  and message.tool_calls is defined
+  and message.tool_calls is iterable
+  and message.tool_calls | length > 0 %}
+{{ bos_token + message.role }}
+{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }}
+{%- endif %}
+{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
+{{ "\n" + message.content | trim + "\n" }}
+{%- endif %}
+{%- for tool_call in message.tool_calls %}
+{%- if tool_call.function is defined %}{% set tool_call = tool_call.function %}{% endif %}
+{{ "\n" + toolcall_begin_token + "\n<function=" + tool_call.name + ">\n" }}
+{%- if tool_call.arguments is defined %}
+{%- for arg_name, arg_value in tool_call.arguments | items %}
+{{ "<parameter=" + arg_name + ">" }}
+{%- set arg_value = arg_value if arg_value is string else arg_value | string %}
+{{ arg_value+"</parameter>\n" }}
+{%- endfor %}
+{%- endif %}
+{{ "</function>\n" + toolcall_end_token }}
+{%- endfor %}
+{{ eos_token }}
+{%- elif message.role in ["user", "system"] %}
+{{ bos_token + message.role + "\n" + message.content + eos_token }}
+{%- elif message.role == "assistant" %}
+{{ bos_token + message.role }}
+{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }}
+{%- endif %}
+{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
+{{ "\n" + message.content | trim + eos_token }}
+{%- endif %}
+{# Include the tool role #}
+{%- else %}
+{{ bos_token + message.role + "\n" + message.content + eos_token }}
+{%- endif %}
+{%- endfor %}
+{# ---------- Control the model to start continuation ---------- #}
+{%- if add_generation_prompt %}
+{{ bos_token+"assistant\n" }}
+{%- if thinking_budget == 0 %}
+{{ think_begin_token + "\n" + budget_begin_token + "The current thinking budget is 0, so I will directly start answering the question." + budget_end_token + "\n" + think_end_token }}
+{%- endif %}
+{%- endif %}
\ No newline at end of file
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index a6daa93a82..8120b45c4b 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -1621,6 +1621,140 @@ static void test_template_output_parsers() {
                     /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO,
                 }));
     }
+    {
+        // Seed-OSS format tests
+        auto tmpls = read_templates("models/templates/ByteDance-Seed-OSS.jinja");
+        std::vector<std::string> end_tokens{ "<seed:eos>" };
+
+        assert_equals(COMMON_CHAT_FORMAT_SEED_OSS, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_SEED_OSS, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
+
+        test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
+
+        // Test simple reasoning content
+        assert_msg_equals(
+            simple_assist_msg("Hello, world!", "I'm thinking about the answer"),
+            common_chat_parse(
+                "<seed:think>I'm thinking about the answer</seed:think>Hello, world!",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                }));
+
+        // Test budget reflection tags
+        common_chat_msg msg_budget_reflect;
+        msg_budget_reflect.role = "assistant";
+        msg_budget_reflect.content = "<seed:cot_budget_reflect>Token usage: 45/1000\nI should continue thinking to find the best solution.</seed:cot_budget_reflect>I need to calculate this step by step.";
+        msg_budget_reflect.reasoning_content = "Token usage: 45/1000\nI should continue thinking to find the best solution.";
+        assert_msg_equals(
+            msg_budget_reflect,
+            common_chat_parse(
+                "<seed:think>Token usage: 45/1000\nI should continue thinking to find the best solution.</seed:think>"
+                "<seed:cot_budget_reflect>Token usage: 45/1000\nI should continue thinking to find the best solution.</seed:cot_budget_reflect>"
+                "I need to calculate this step by step.",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                }));
+
+        // Test tool calls with Seed-OSS format
+        common_chat_msg msg_tool_call;
+        msg_tool_call.role = "assistant";
+        msg_tool_call.tool_calls.push_back({"calculate_sum", "{\"numbers\": [1, 2, 3]}", ""});
+        assert_msg_equals(
+            msg_tool_call,
+            common_chat_parse(
+                "<seed:tool_call>\n"
+                "<function=calculate_sum>\n"
+                "<parameter=numbers>[1, 2, 3]</parameter>\n"
+                "</function>\n"
+                "</seed:tool_call>",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_SEED_OSS}));
+
+        // Test reasoning + tool call combination
+        common_chat_msg msg_reasoning_tool;
+        msg_reasoning_tool.role = "assistant";
+        msg_reasoning_tool.content = "";
+        msg_reasoning_tool.reasoning_content = "I need to calculate the sum of these numbers";
+        msg_reasoning_tool.tool_calls.push_back({"calculate_sum", "{\"numbers\": [1, 2, 3]}", ""});
+        assert_msg_equals(
+            msg_reasoning_tool,
+            common_chat_parse(
+                "<seed:think>I need to calculate the sum of these numbers</seed:think>"
+                "<seed:tool_call>\n"
+                "<function=calculate_sum>\n"
+                "<parameter=numbers>[1, 2, 3]</parameter>\n"
+                "</function>\n"
+                "</seed:tool_call>",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                }));
+
+        // Test deltas: the number of tool calls in partial parses should never decrease
+        std::string tool_msg = "<seed:tool_call>\n"
+            "<function=fun>\n"
+            "<parameter=smth>[1, 2, 3]</parameter>\n"
+            "</function>";
+        std::size_t previousToolCalls = 0;
+        for (std::size_t i = std::string("<seed:tool_call>").length(); i < tool_msg.length() - 1; i++) {
+            auto partial = tool_msg.substr(0, i);
+            auto partial_res = common_chat_parse(partial, true, { COMMON_CHAT_FORMAT_SEED_OSS, COMMON_REASONING_FORMAT_DEEPSEEK });
+            if (partial_res.tool_calls.size() < previousToolCalls) {
+                throw std::runtime_error("Tool call size decreased on partial: " + partial + " from " + std::to_string(previousToolCalls) + " to " + std::to_string(partial_res.tool_calls.size()));
+            }
+            previousToolCalls = partial_res.tool_calls.size();
+        }
+
+        // Test multiple parameters in tool call
+        common_chat_msg msg_multi_param;
+        msg_multi_param.role = "assistant";
+        msg_multi_param.tool_calls.push_back({"process_data", "{\"input\": \"test\", \"format\": \"json\"}", ""});
+        assert_msg_equals(
+            msg_multi_param,
+            common_chat_parse(
+                "<seed:tool_call>\n"
+                "<function=process_data>\n"
+                "<parameter=input>test</parameter>\n"
+                "<parameter=format>json</parameter>\n"
+                "</function>\n"
+                "</seed:tool_call>",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_SEED_OSS}));
+
+        // Test partial parsing for incomplete tool call - don't actually add the call until parsing parameters is done
+        assert_msg_equals(
+            simple_assist_msg("", ""),
+            common_chat_parse(
+                "<seed:tool_call>\n"
+                "<function=calculate_sum>\n"
+                "<parameter=numbers>[1,\n",
+                /* is_partial= */ true,
+                {COMMON_CHAT_FORMAT_SEED_OSS}));
+
+        // Test incomplete reasoning tag
+        assert_msg_equals(
+            simple_assist_msg("", "I was thinking"),
+            common_chat_parse(
+                "<seed:think>I was thinking",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                }));
+
+        // Test content without reasoning
+        assert_msg_equals(
+            simple_assist_msg("This is a simple response without reasoning."),
+            common_chat_parse(
+                "This is a simple response without reasoning.",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_SEED_OSS}));
+    }
 }
 
 static void test_msg_diffs_compute() {

From 81017865ee444cf49ce0136f2be1e41a0270ff91 Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Fri, 29 Aug 2025 21:30:06 +0800
Subject: [PATCH 221/287] CUDA: fix bug in rms_norm fusion (#15660)

* CUDA: fix bug in rms_norm fusion

* Fix bug for OP_REPEAT

* Fix index for add
---
 ggml/src/ggml-cuda/binbcast.cu  | 66 +++++++++++++++++++++++----------
 ggml/src/ggml-cuda/ggml-cuda.cu |  2 +-
 ggml/src/ggml-cuda/norm.cu      |  6 +--
 3 files changed, 51 insertions(+), 23 deletions(-)

diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index 99a98fcbfc..1c76566344 100644
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -57,7 +57,11 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst
         const int i10 = i0 % ne10;
 
         float result = src0_row ? (float) src0_row[i0] : 0.0f;
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+        if constexpr (sizeof...(src1_ptrs) > 0) {
+            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+        } else {
+            result = bin_op(result, (float)src1[i_src1 + i10]);
+        }
 
         dst_row[i0] = (dst_t) result;
     }
@@ -96,7 +100,11 @@ static __global__ void k_bin_bcast_unravel(const src0_t *   src0, const src1_t *
     const int i10 = i0 % ne10;
 
     float result = src0_row ? (float) src0_row[i0] : 0.0f;
-    result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+    if constexpr (sizeof...(src1_ptrs) > 0) {
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+    } else {
+        result = bin_op(result, (float)src1[i_src1 + i10]);
+    }
 
     dst_row[i0] = (dst_t) result;
 }
@@ -231,23 +239,43 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
 
         if (block_nums.z > 65535) {
             int block_num = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
-            k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
-                <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12,s13,
-                    (const src1_t *) dst->src[I + 1]->data...);
+            if constexpr (sizeof...(I) > 0) {
+                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
+                    <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                        ne0, ne1, ne2, ne3,
+                        ne10, ne11, ne12, ne13,
+                        /* s0, */ s1, s2, s3,
+                        /* s00,*/ s01, s02, s03,
+                        /* s10,*/ s11, s12,s13,
+                        (const src1_t *) dst->src[I + 1]->data...);
+            } else {
+                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
+                    <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                        ne0, ne1, ne2, ne3,
+                        ne10, ne11, ne12, ne13,
+                        /* s0, */ s1, s2, s3,
+                        /* s00,*/ s01, s02, s03,
+                        /* s10,*/ s11, s12,s13);
+            }
         } else {
-            k_bin_bcast<bin_op, src0_t, src1_t, dst_t>
-                <<<block_nums, block_dims, 0, stream>>>(src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12,s13,
-                    (const src1_t *) dst->src[I + 1]->data...);
+            if constexpr (sizeof...(I) > 0) {
+                k_bin_bcast<bin_op, src0_t, src1_t, dst_t>
+                    <<<block_nums, block_dims, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                        ne0, ne1, ne2, ne3,
+                        ne10, ne11, ne12, ne13,
+                        /* s0, */ s1, s2, s3,
+                        /* s00,*/ s01, s02, s03,
+                        /* s10,*/ s11, s12,s13,
+                        (const src1_t *) dst->src[I + 1]->data...);
+            } else {
+                k_bin_bcast<bin_op, src0_t, src1_t, dst_t>
+                    <<<block_nums, block_dims, 0, stream>>>(src0_dd, src1_dd, dst_dd,
+                        ne0, ne1, ne2, ne3,
+                        ne10, ne11, ne12, ne13,
+                        /* s0, */ s1, s2, s3,
+                        /* s00,*/ s01, s02, s03,
+                        /* s10,*/ s11, s12,s13);
+            }
         }
     }
 }
@@ -327,7 +355,7 @@ static void ggml_cuda_op_bin_bcast(
 }
 
 void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat, 0>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
 }
 
 void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 6a1b0fc936..e06f95f081 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2827,7 +2827,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         const ggml_tensor *add      = nullptr;
 
         if (ops.size() == 3 && ops.begin()[2] == GGML_OP_ADD) {
-            add = cgraph->nodes[node_idx+1];
+            add = cgraph->nodes[node_idx+2];
         }
 
         GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32);
diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
index 293f6f68e5..d5157d958b 100644
--- a/ggml/src/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -127,6 +127,7 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
                                     const int     add_nrows          = 0,
                                     const int     add_nchannels      = 0,
                                     const int     add_nsamples       = 0) {
+
     const int nrows     = gridDim.x;
     const int nchannels = gridDim.y;
 
@@ -135,6 +136,8 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
     const int sample    = blockIdx.z;
     const int tid       = threadIdx.x;
 
+    static_assert(!do_add || do_multiply, "fusing add is not supported without multiplying");
+
     x   += sample*stride_sample + channel*stride_channel + row*stride_row;
     dst += ((sample*nchannels + channel)*nrows + row)*ncols;
 
@@ -185,9 +188,6 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
         } else if constexpr (do_multiply) {
             const int mul_col = col % mul_ncols;
             dst[col] = scale * x[col] * mul[mul_col];
-        } else if constexpr (do_add) {
-            const int add_col = col % add_ncols;
-            dst[col] += add[add_col];
         } else {
             dst[col] = scale * x[col];
         }

From 792b44f2ed9668cce7f267ff0ae4950ed9b4a5de Mon Sep 17 00:00:00 2001
From: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com>
Date: Fri, 29 Aug 2025 19:25:40 +0200
Subject: [PATCH 222/287] server : add documentation for `parallel_tool_calls`
 param (#15647)

Co-authored-by: Pierre F <no@p.e>
---
 docs/function-calling.md | 2 ++
 tools/server/README.md   | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/docs/function-calling.md b/docs/function-calling.md
index 37eacaf310..67cf785c7a 100644
--- a/docs/function-calling.md
+++ b/docs/function-calling.md
@@ -21,6 +21,8 @@ Function calling is supported for all models (see https://github.com/ggml-org/ll
   - Use `--chat-template-file` to override the template when appropriate (see examples below)
   - Generic support may consume more tokens and be less efficient than a model's native format.
 
+- Multiple/parallel tool calling is supported on some models but disabled by default, enable it by passing `"parallel_tool_calls": true` in the completion endpoint payload.
+
 <details>
 <summary>Show some common templates and which format handler they use</summary>
 
diff --git a/tools/server/README.md b/tools/server/README.md
index baf3730add..6962b0d3a2 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1143,6 +1143,8 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `parse_tool_calls`: Whether to parse the generated tool call.
 
+`parallel_tool_calls` : Whether to enable parallel/multiple tool calls (only supported on some models, verification is based on jinja template).
+
 *Examples:*
 
 You can use either Python `openai` library with appropriate checkpoints:

From 3d16b29c3bb1ec816ac0e782f20d169097063919 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 29 Aug 2025 22:04:08 +0200
Subject: [PATCH 223/287] scripts: strip "AMD Instinct" from GPU name (#15668)

---
 scripts/compare-llama-bench.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 2cfca66e09..c45c83fdb5 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -96,7 +96,7 @@ DEFAULT_HIDE_LLAMA_BENCH = ["model_filename"]  # Always hide these properties by
 DEFAULT_SHOW_TEST_BACKEND_OPS = ["backend_name", "op_name"]  # Always show these properties by default.
 DEFAULT_HIDE_TEST_BACKEND_OPS = ["error_message"]  # Always hide these properties by default.
 
-GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "]  # Strip prefixes for smaller tables.
+GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon ", "AMD Instinct "]  # Strip prefixes for smaller tables.
 MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"}
 
 DESCRIPTION = """Creates tables from llama-bench or test-backend-ops data written to multiple JSON/CSV files, a single JSONL file or SQLite database. Example usage (Linux):

From d82f6aa34a216f5df1945cdfe121ba5e6cd80be0 Mon Sep 17 00:00:00 2001
From: Sergey Alirzaev <l29ah@riseup.net>
Date: Sat, 30 Aug 2025 00:12:53 +0200
Subject: [PATCH 224/287] server : removed obsolete doc (#15670)

completing a4090d1174aed22dde5cacce2a4c27656b987a2f
---
 tools/server/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index 6962b0d3a2..b7285b2319 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -62,7 +62,6 @@ The project is under active development, and we are [looking for feedback and co
 | `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
 | `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
-| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
 | `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |

From ef476916bba4b44f44be0c98babc1cb025968e75 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Sat, 30 Aug 2025 10:18:35 +0800
Subject: [PATCH 225/287] CANN: FIx compiler warnings (#15661)

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/ggml-cann.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 558121dff7..7b3aca9db9 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1155,7 +1155,7 @@ namespace {
  * @note The workspace buffer used in this function is managed globally and reused
  *       across calls. This reduces overhead from repeated memory allocation and deallocation.
  */
-static void weight_format_to_nz(ggml_tensor *tensor, const void *data, size_t offset) {
+static void weight_format_to_nz(ggml_tensor *tensor, size_t offset) {
     aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
                                     tensor->nb, 2, ACL_FORMAT_ND, offset);
     uint64_t workspaceSize = 0;
@@ -1203,7 +1203,7 @@ static void ggml_backend_cann_buffer_set_tensor(
         if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
             GGML_ASSERT(tensor->ne[2] == 1);
             GGML_ASSERT(tensor->ne[3] == 1);
-            weight_format_to_nz(tensor, data, offset);
+            weight_format_to_nz(tensor, offset);
         }
     } else {
         void *transform_buffer = malloc(size);
@@ -2491,7 +2491,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             return true;
         case GGML_OP_SCALE:
             float bias;
-            memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
+            memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float));
             return bias == 0.0f; // TODO: support bias != 0.0f
         case GGML_OP_SOFT_MAX:
             // TODO: support attention sinks [TAG_ATTN_SINKS]
@@ -2534,7 +2534,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 return false;
             }
             float logitSoftcap = 0.0f;
-            memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
+            memcpy(&logitSoftcap, (const float *)(op->op_params) + 2, sizeof(float));
             if(logitSoftcap != 0.0f) {
                 return false;
             }

From 696fccf354e9dbdfbce135bc40b44c9dcc64dda9 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 30 Aug 2025 04:11:22 -0500
Subject: [PATCH 226/287] vulkan: Skip syncing for prealloc_y when it is reused
 (#15544)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 35 ++++++++++++----------------
 1 file changed, 15 insertions(+), 20 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 04ad664e61..40962de508 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5800,11 +5800,6 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
             ggml_vk_sync_buffers(ctx, subctx);
         }
     }
-    if (y_non_contig || quantize_y) {
-        if (ctx->prealloc_y_need_sync) {
-            ggml_vk_sync_buffers(ctx, subctx);
-        }
-    }
 
     if (x_non_contig) {
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
@@ -5816,6 +5811,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     if (y_non_contig) {
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
             ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
             ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
             ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
             ctx->prealloc_y_last_tensor_used = src1;
@@ -5824,6 +5822,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     if (quantize_y) {
         if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
             ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
             ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13);
             ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
             ctx->prealloc_y_last_tensor_used = src1;
@@ -6008,11 +6009,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
             ggml_vk_sync_buffers(ctx, subctx);
         }
     }
-    if (y_non_contig) {
-        if (ctx->prealloc_y_need_sync) {
-            ggml_vk_sync_buffers(ctx, subctx);
-        }
-    }
 
     if (x_non_contig) {
         GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
@@ -6022,6 +6018,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
             ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
             ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
             ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
             ctx->prealloc_y_last_tensor_used = src1;
@@ -6454,11 +6453,6 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
             ggml_vk_sync_buffers(ctx, subctx);
         }
     }
-    if (y_non_contig) {
-        if (ctx->prealloc_y_need_sync) {
-            ggml_vk_sync_buffers(ctx, subctx);
-        }
-    }
 
     if (x_non_contig) {
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
@@ -6471,6 +6465,9 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     if (y_non_contig) {
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
             ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
             ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
             ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
             ctx->prealloc_y_last_tensor_used = src1;
@@ -6668,11 +6665,6 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
             ggml_vk_sync_buffers(ctx, subctx);
         }
     }
-    if (y_non_contig) {
-        if (ctx->prealloc_y_need_sync) {
-            ggml_vk_sync_buffers(ctx, subctx);
-        }
-    }
 
     if (x_non_contig) {
         GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
@@ -6682,6 +6674,9 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
         if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
             ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
             ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
             ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
             ctx->prealloc_y_last_tensor_used = src1;

From 38ad381f9f5d4dd368a96d844fb19cf501ed9d22 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 30 Aug 2025 16:20:32 +0200
Subject: [PATCH 227/287] CUDA: use FP32 arithmetic for conv2d (#15683)

---
 ggml/src/ggml-cuda/conv2d.cu | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/ggml/src/ggml-cuda/conv2d.cu b/ggml/src/ggml-cuda/conv2d.cu
index cf878d1fd1..bcb70762ee 100644
--- a/ggml/src/ggml-cuda/conv2d.cu
+++ b/ggml/src/ggml-cuda/conv2d.cu
@@ -82,7 +82,7 @@ static __global__ void conv2d_kernel(const float * __restrict__ input,
     int64_t n, c_out, out_y, out_x;
     Layout::unpack_indices(global_idx, P, n, c_out, out_y, out_x);
 
-    T acc = 0;
+    float acc = 0.0f;
 
     for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
         kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);
@@ -93,21 +93,15 @@ static __global__ void conv2d_kernel(const float * __restrict__ input,
             for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
                 const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
 
-                T input_val;
-                if (std::is_same<T, half>::value) {
-                    input_val = __float2half(input[Layout::input_index(n, c_in, in_y, in_x, P)]);
-                } else {
-                    input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
-                }
-
-                T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
+                const float input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
+                const float kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
                 acc += (input_val * kernel_val);
             }
         }
     }
 
     // [N, OC, OH, OW]
-    output[Layout::output_index(n, c_out, out_y, out_x, P)] = (float) acc;
+    output[Layout::output_index(n, c_out, out_y, out_x, P)] = acc;
 }
 
 template <typename T>

From e81b8e4b7f5ab870836fad26d154a7507b341b36 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 30 Aug 2025 16:32:10 +0200
Subject: [PATCH 228/287] llama: use FA + max. GPU layers by default (#15434)

* llama: use max. GPU layers by default, auto -fa

* ggml-backend: abort instead of segfault
---
 common/arg.cpp                              | 30 ++++----
 common/common.cpp                           |  8 ++-
 common/common.h                             |  2 +-
 examples/diffusion/diffusion-cli.cpp        |  2 +-
 ggml/src/ggml-backend.cpp                   | 78 +++++++++++++++++++++
 include/llama.h                             | 10 ++-
 scripts/server-bench.py                     |  6 --
 scripts/tool_bench.py                       |  2 +-
 src/llama-context.cpp                       | 75 ++++++++++++++++++--
 src/llama-graph.cpp                         | 24 +++++--
 src/llama-graph.h                           |  3 +-
 src/llama-impl.h                            |  2 +
 src/llama-model.cpp                         |  7 +-
 src/llama.cpp                               | 12 ++++
 tools/batched-bench/batched-bench.cpp       |  4 +-
 tools/llama-bench/llama-bench.cpp           | 20 +++---
 tools/server/tests/unit/test_ctx_shift.py   | 15 ++--
 tools/server/tests/unit/test_speculative.py |  1 +
 tools/server/tests/utils.py                 |  6 +-
 19 files changed, 235 insertions(+), 72 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 93f0108b2b..72c69c39a0 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1545,10 +1545,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
-        {"-fa", "--flash-attn"},
-        string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.flash_attn = true;
+        {"-fa", "--flash-attn"}, "FA",
+        string_format("set Flash Attention use ('on', 'off', or 'auto', default: '%s')", llama_flash_attn_type_name(params.flash_attn_type)),
+        [](common_params & params, const std::string & value) {
+            if (value == "on" || value == "enabled") {
+                params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
+            } else if (value == "off" || value == "disabled") {
+                params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
+            } else if (value == "auto") {
+                params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
+            } else {
+                throw std::runtime_error(string_format("error: unkown value for --flash-attn: '%s'\n", value.c_str()));
+            }
         }
     ).set_env("LLAMA_ARG_FLASH_ATTN"));
     add_opt(common_arg(
@@ -3459,8 +3467,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf";
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3475,8 +3481,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-3b-q8_0.gguf";
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3491,8 +3495,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3508,10 +3510,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
             params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
             params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
-            params.speculative.n_gpu_layers = 99;
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3527,10 +3526,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf";
             params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
             params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
-            params.speculative.n_gpu_layers = 99;
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3545,8 +3541,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF";
             params.model.hf_file = "qwen3-coder-30b-a3b-instruct-q8_0.gguf";
             params.port = 8012;
-            params.n_gpu_layers = 99;
-            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
diff --git a/common/common.cpp b/common/common.cpp
index 054b43be77..0c92d4d57d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -901,7 +901,8 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
         return iparams;
     }
 
@@ -911,7 +912,8 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
         llama_model_free(model);
         return iparams;
     }
@@ -1157,10 +1159,10 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
     cparams.pooling_type      = params.pooling_type;
     cparams.attention_type    = params.attention_type;
+    cparams.flash_attn_type   = params.flash_attn_type;
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
-    cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
     cparams.op_offload        = !params.no_op_offload;
     cparams.swa_full          = params.swa_full;
diff --git a/common/common.h b/common/common.h
index 87ea060695..02ca093bdf 100644
--- a/common/common.h
+++ b/common/common.h
@@ -312,6 +312,7 @@ struct common_params {
     enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
     enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
+    enum llama_flash_attn_type   flash_attn_type   = LLAMA_FLASH_ATTN_TYPE_AUTO; // whether to use Flash Attention
 
     struct common_params_sampling    sampling;
     struct common_params_speculative speculative;
@@ -375,7 +376,6 @@ struct common_params {
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
-    bool flash_attn        = false; // flash attention
     bool no_perf           = false; // disable performance metrics
     bool ctx_shift         = false;  // context shift on infinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp
index 8431dcea8f..abf7fb3573 100644
--- a/examples/diffusion/diffusion-cli.cpp
+++ b/examples/diffusion/diffusion-cli.cpp
@@ -564,7 +564,7 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx                = params.n_ctx;
     ctx_params.n_batch              = params.n_batch;
     ctx_params.n_ubatch             = params.n_ubatch;
-    ctx_params.flash_attn           = params.flash_attn;
+    ctx_params.flash_attn_type      = params.flash_attn_type;
     ctx_params.no_perf              = params.no_perf;
     ctx_params.type_k               = params.cache_type_k;
     ctx_params.type_v               = params.cache_type_v;
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index e34feccc98..02375337c4 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -31,6 +31,7 @@
 // backend buffer type
 
 const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->iface.get_name(buft);
 }
 
@@ -40,14 +41,17 @@ ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t
         return ggml_backend_buffer_init(buft, {}, NULL, 0);
     }
 
+    GGML_ASSERT(buft);
     return buft->iface.alloc_buffer(buft, size);
 }
 
 size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->iface.get_alignment(buft);
 }
 
 size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     // get_max_size is optional, defaults to SIZE_MAX
     if (buft->iface.get_max_size) {
         return buft->iface.get_max_size(buft);
@@ -56,6 +60,7 @@ size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
 }
 
 size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) {
+    GGML_ASSERT(buft);
     // get_alloc_size is optional, defaults to ggml_nbytes
     if (buft->iface.get_alloc_size) {
         size_t size = buft->iface.get_alloc_size(buft, tensor);
@@ -66,6 +71,7 @@ size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const s
 }
 
 bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     if (buft->iface.is_host) {
         return buft->iface.is_host(buft);
     }
@@ -73,6 +79,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
 }
 
 ggml_backend_dev_t ggml_backend_buft_get_device(ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(buft);
     return buft->device;
 }
 
@@ -110,10 +117,12 @@ void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
 }
 
 size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->size;
 }
 
 void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     // get_base is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return NULL;
@@ -127,6 +136,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+    GGML_ASSERT(buffer);
     // init_tensor is optional
     if (buffer->iface.init_tensor) {
         return buffer->iface.init_tensor(buffer, tensor);
@@ -135,6 +145,7 @@ enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, s
 }
 
 void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     // clear is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return;
@@ -160,6 +171,7 @@ bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
 }
 
 void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    GGML_ASSERT(buffer);
     buffer->usage = usage;
 
     // FIXME: add a generic callback to the buffer interface
@@ -169,14 +181,17 @@ void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backe
 }
 
 enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->usage;
 }
 
 ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->buft;
 }
 
 void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     if (buffer->iface.reset) {
         buffer->iface.reset(buffer);
     }
@@ -215,6 +230,7 @@ void ggml_backend_free(ggml_backend_t backend) {
 }
 
 ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_buffer_type(backend->device);
 }
 
@@ -231,6 +247,8 @@ size_t ggml_backend_get_max_size(ggml_backend_t backend) {
 }
 
 void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
@@ -242,6 +260,8 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor *
 }
 
 void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
@@ -283,6 +303,7 @@ void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, siz
 }
 
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     if (size == 0) {
@@ -298,6 +319,7 @@ void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     if (backend->iface.synchronize == NULL) {
         return;
     }
@@ -306,18 +328,21 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_create != NULL);
 
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_free != NULL);
 
     backend->iface.graph_plan_free(backend, plan);
 }
 
 enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
 
     return backend->iface.graph_plan_compute(backend, plan);
@@ -330,22 +355,27 @@ enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_
 }
 
 enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend);
     return backend->iface.graph_compute(backend, cgraph);
 }
 
 bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_op(backend->device, op);
 }
 
 bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_buft(backend->device, buft);
 }
 
 bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_ASSERT(backend);
     return ggml_backend_dev_offload_op(backend->device, op);
 }
 
 ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     return backend->device;
 }
 
@@ -381,6 +411,7 @@ void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t b
         return;
     }
 
+    GGML_ASSERT(backend_dst);
     if (backend_dst->iface.cpy_tensor_async != NULL) {
         if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
             return;
@@ -412,18 +443,21 @@ void ggml_backend_event_free(ggml_backend_event_t event) {
 }
 
 void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_record != NULL);
 
     backend->iface.event_record(backend, event);
 }
 
 void ggml_backend_event_synchronize(ggml_backend_event_t event) {
+    GGML_ASSERT(event);
     GGML_ASSERT(event->device->iface.event_synchronize);
 
     event->device->iface.event_synchronize(event->device, event);
 }
 
 void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_wait != NULL);
 
     backend->iface.event_wait(backend, event);
@@ -432,18 +466,22 @@ void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event)
 // Backend device
 
 const char * ggml_backend_dev_name(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_name(device);
 }
 
 const char * ggml_backend_dev_description(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_description(device);
 }
 
 void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
+    GGML_ASSERT(device);
     device->iface.get_memory(device, free, total);
 }
 
 enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_type(device);
 }
 
@@ -453,18 +491,22 @@ void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_d
 }
 
 ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->reg;
 }
 
 ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params) {
+    GGML_ASSERT(device);
     return device->iface.init_backend(device, params);
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     return device->iface.get_buffer_type(device);
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device) {
+    GGML_ASSERT(device);
     if (device->iface.get_host_buffer_type == NULL) {
         return NULL;
     }
@@ -473,18 +515,22 @@ ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t
 }
 
 ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size) {
+    GGML_ASSERT(device);
     return device->iface.buffer_from_host_ptr(device, ptr, size, max_tensor_size);
 }
 
 bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
+    GGML_ASSERT(device);
     return device->iface.supports_op(device, op);
 }
 
 bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(device);
     return device->iface.supports_buft(device, buft);
 }
 
 bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
+    GGML_ASSERT(device);
     if (device->iface.offload_op != NULL) {
         return device->iface.offload_op(device, op);
     }
@@ -495,18 +541,22 @@ bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_te
 // Backend (reg)
 
 const char * ggml_backend_reg_name(ggml_backend_reg_t reg) {
+    GGML_ASSERT(reg);
     return reg->iface.get_name(reg);
 }
 
 size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg) {
+    GGML_ASSERT(reg);
     return reg->iface.get_device_count(reg);
 }
 
 ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(reg);
     return reg->iface.get_device(reg, index);
 }
 
 void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    GGML_ASSERT(reg);
     if (!reg->iface.get_proc_address) {
         return NULL;
     }
@@ -521,6 +571,7 @@ struct ggml_backend_multi_buffer_context {
 };
 
 static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_free(ctx->buffers[i]);
@@ -531,6 +582,7 @@ static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer)
 }
 
 static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_clear(ctx->buffers[i], value);
@@ -566,10 +618,12 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer
 }
 
 bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer;
 }
 
 void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    GGML_ASSERT(buffer);
     GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
@@ -1349,6 +1403,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
 }
 
 static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     struct ggml_backend_sched_split * splits = sched->splits;
 
     ggml_tensor * prev_ids_tensor = nullptr;
@@ -1617,6 +1672,7 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     // reset state for the next run
     if (!sched->is_reset) {
         ggml_hash_set_reset(&sched->hash_set);
@@ -1628,6 +1684,7 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
+    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
     ggml_backend_sched_synchronize(sched);
@@ -1644,6 +1701,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
 }
 
 bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
     GGML_ASSERT(!sched->is_alloc);
 
@@ -1668,6 +1726,7 @@ enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, st
 }
 
 enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT(sched);
     if (!sched->is_reset && !sched->is_alloc) {
         ggml_backend_sched_reset(sched);
     }
@@ -1682,6 +1741,7 @@ enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sch
 }
 
 void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     for (int i = 0; i < sched->n_backends; i++) {
         ggml_backend_synchronize(sched->backends[i]);
     }
@@ -1694,28 +1754,34 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
+    GGML_ASSERT(sched);
     sched->callback_eval = callback;
     sched->callback_eval_user_data = user_data;
 }
 
 int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_splits;
 }
 
 int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_copies;
 }
 
 int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
+    GGML_ASSERT(sched);
     return sched->n_backends;
 }
 
 ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
+    GGML_ASSERT(sched);
     GGML_ASSERT(i >= 0 && i < sched->n_backends);
     return sched->backends[i];
 }
 
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
+    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
 
@@ -1723,6 +1789,7 @@ size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backe
 }
 
 void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
+    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
@@ -1731,6 +1798,7 @@ void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct gg
 }
 
 ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
+    GGML_ASSERT(sched);
     int backend_index = tensor_backend_id(node);
     if (backend_index == -1) {
         return NULL;
@@ -1741,6 +1809,7 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched,
 // utils
 
 enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
@@ -1752,6 +1821,7 @@ enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
 }
 
 enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
+    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->data == NULL);
     GGML_ASSERT(tensor->view_src == NULL);
@@ -1825,6 +1895,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_
 }
 
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
+    GGML_ASSERT(graph);
     struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
     struct ggml_tensor ** node_copies = (ggml_tensor **) calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
     bool * node_init = (bool *) calloc(hash_set.size, sizeof(node_init[0]));
@@ -1969,6 +2040,7 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
 // CPU backend - buffer
 
 static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     uintptr_t data = (uintptr_t)buffer->context;
 
     // align the buffer
@@ -1980,28 +2052,33 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
     ggml_aligned_free(buffer->context, buffer->size);
 }
 
 static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memset((char *)tensor->data + offset, value, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memcpy((char *)tensor->data + offset, data, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_ASSERT(tensor);
     memcpy(data, (const char *)tensor->data + offset, size);
 
     GGML_UNUSED(buffer);
 }
 
 static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    GGML_ASSERT(src);
     if (ggml_backend_buffer_is_host(src->buffer)) {
         memcpy(dst->data, src->data, ggml_nbytes(src));
         return true;
@@ -2012,6 +2089,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
 }
 
 static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
     memset(buffer->context, value, buffer->size);
 }
 
diff --git a/include/llama.h b/include/llama.h
index 7025353850..346135c71e 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -179,6 +179,14 @@ extern "C" {
         LLAMA_ATTENTION_TYPE_NON_CAUSAL  = 1,
     };
 
+    enum llama_flash_attn_type {
+        LLAMA_FLASH_ATTN_TYPE_AUTO     = -1,
+        LLAMA_FLASH_ATTN_TYPE_DISABLED = 0,
+        LLAMA_FLASH_ATTN_TYPE_ENABLED  = 1,
+    };
+
+    LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type);
+
     enum llama_split_mode {
         LLAMA_SPLIT_MODE_NONE  = 0, // single GPU
         LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
@@ -303,6 +311,7 @@ extern "C" {
         enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
         enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
         enum llama_attention_type    attention_type;    // attention type to use for embeddings
+        enum llama_flash_attn_type   flash_attn_type;   // when to enable Flash Attention
 
         // ref: https://github.com/ggml-org/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -329,7 +338,6 @@ extern "C" {
         // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU
-        bool flash_attn;  // use flash attention [EXPERIMENTAL]
         bool no_perf;     // measure performance timings
         bool op_offload;  // offload host tensor operations to device
         bool swa_full;    // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
diff --git a/scripts/server-bench.py b/scripts/server-bench.py
index a716020173..dbbb0939ff 100755
--- a/scripts/server-bench.py
+++ b/scripts/server-bench.py
@@ -151,12 +151,6 @@ def benchmark(
     if os.environ.get("LLAMA_ARG_N_PARALLEL") is None:
         logger.info("LLAMA_ARG_N_PARALLEL not explicitly set, using 32")
         os.environ["LLAMA_ARG_N_PARALLEL"] = "32"
-    if not external_server and os.environ.get("LLAMA_ARG_N_GPU_LAYERS") is None:
-        logger.info("LLAMA_ARG_N_GPU_LAYERS not explicitly set, using 999")
-        os.environ["LLAMA_ARG_N_GPU_LAYERS"] = "999"
-    if not external_server and os.environ.get("LLAMA_ARG_FLASH_ATTN") is None:
-        logger.info("LLAMA_ARG_FLASH_ATTN not explicitly set, using 'true'")
-        os.environ["LLAMA_ARG_FLASH_ATTN"] = "true"
 
     parallel: int = int(os.environ.get("LLAMA_ARG_N_PARALLEL")) # type: ignore
     prompts: Union[None, list[str], list[list[int]]] = get_prompts_text(prompt_source, n_prompts)
diff --git a/scripts/tool_bench.py b/scripts/tool_bench.py
index d8018e2e23..05d6dfc30a 100755
--- a/scripts/tool_bench.py
+++ b/scripts/tool_bench.py
@@ -323,7 +323,7 @@ def run(
                     server.jinja = True
                     server.ctk = ctk
                     server.ctv = ctv
-                    server.fa = fa
+                    server.fa = "on" if fa else "off"
                     server.n_predict = n_predict
                     server.model_hf_repo = hf
                     server.model_hf_file = None
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 6b20161a38..ac8453ab74 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -41,7 +41,6 @@ llama_context::llama_context(
     cparams.yarn_beta_slow   = params.yarn_beta_slow;
     cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
-    cparams.flash_attn       = params.flash_attn;
     cparams.no_perf          = params.no_perf;
     cparams.pooling_type     = params.pooling_type;
     cparams.warmup           = false;
@@ -86,6 +85,8 @@ llama_context::llama_context(
         cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
     }
 
+    cparams.flash_attn = params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED;
+
     // with causal attention, the batch size is limited by the context size
     cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
 
@@ -119,7 +120,7 @@ llama_context::llama_context(
     LLAMA_LOG_INFO("%s: n_batch       = %u\n",   __func__, cparams.n_batch);
     LLAMA_LOG_INFO("%s: n_ubatch      = %u\n",   __func__, cparams.n_ubatch);
     LLAMA_LOG_INFO("%s: causal_attn   = %d\n",   __func__, cparams.causal_attn);
-    LLAMA_LOG_INFO("%s: flash_attn    = %d\n",   __func__, cparams.flash_attn);
+    LLAMA_LOG_INFO("%s: flash_attn    = %s\n",   __func__, llama_flash_attn_type_name(params.flash_attn_type));
     LLAMA_LOG_INFO("%s: kv_unified    = %s\n",   __func__, cparams.kv_unified ? "true" : "false");
     LLAMA_LOG_INFO("%s: freq_base     = %.1f\n", __func__, cparams.rope_freq_base);
     LLAMA_LOG_INFO("%s: freq_scale    = %g\n",   __func__, cparams.rope_freq_scale);
@@ -269,7 +270,7 @@ llama_context::llama_context(
         }
     }
 
-    // reserve worst-case graph
+    // resolve automatic Flash Attention use and reserve worst-case graph
     if (!hparams.vocab_only) {
         const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
@@ -300,6 +301,48 @@ llama_context::llama_context(
                 throw std::runtime_error("failed to allocate compute pp buffers");
             }
 
+            if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
+                ggml_backend_sched_alloc_graph(sched.get(), gf);
+
+                const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
+                bool fa_device_mismatch = false;
+                for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
+                    ggml_tensor * n = ggml_graph_node(gf, i);
+                    if (n->op != GGML_OP_FLASH_ATTN_EXT) {
+                        continue;
+                    }
+                    ggml_backend_dev_t device_fa = ggml_backend_get_device(
+                        ggml_backend_sched_get_tensor_backend(sched.get(), n));
+
+                    // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
+                    GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
+                    const int il = std::stoi(n->name + prefix_len);
+                    ggml_backend_dev_t device_kv = model.dev_layer(il);
+                    if (device_fa != device_kv) {
+                        LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
+                            "is assigned to device %s (usually due to missing support)\n",
+                            __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
+                        // FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
+                        fa_device_mismatch = true;
+                        break;
+                    }
+                }
+                if (fa_device_mismatch) {
+                    cparams.flash_attn = false;
+                    LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
+                    if (ggml_is_quantized(params.type_v)) {
+                        throw std::runtime_error("quantized V cache was requested, but this requires Flash Attention");
+                    }
+                    auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
+                    if (!gf) {
+                        throw std::runtime_error("failed to allocate compute pp buffers");
+                    }
+                } else {
+                    cparams.flash_attn = true;
+                    LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
+                }
+            }
+
             n_splits_pp = ggml_backend_sched_get_n_splits(sched.get());
             n_nodes_pp  = ggml_graph_n_nodes(gf);
         }
@@ -2208,6 +2251,7 @@ llama_context_params llama_context_default_params() {
         /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
         /*.attention_type              =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
+        /*.flash_attn_type             =*/ LLAMA_FLASH_ATTN_TYPE_AUTO,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -2224,7 +2268,6 @@ llama_context_params llama_context_default_params() {
         /*.abort_callback_data         =*/ nullptr,
         /*.embeddings                  =*/ false,
         /*.offload_kqv                 =*/ true,
-        /*.flash_attn                  =*/ false,
         /*.no_perf                     =*/ true,
         /*.op_offload                  =*/ true,
         /*.swa_full                    =*/ true,
@@ -2252,12 +2295,30 @@ llama_context * llama_init_from_model(
         return nullptr;
     }
 
-    if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
+    if (params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED && model->arch == LLM_ARCH_GROK) {
         LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
-        params.flash_attn = false;
+        params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
     }
 
-    if (ggml_is_quantized(params.type_v) && !params.flash_attn) {
+    if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_k)) {
+        const uint32_t blck_size = ggml_blck_size(params.type_k);
+        if (model->hparams.n_embd_head_k % blck_size != 0) {
+            LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n",
+                __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k);
+            return nullptr;
+        }
+    }
+
+    if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_v)) {
+        const uint32_t blck_size = ggml_blck_size(params.type_v);
+        if (model->hparams.n_embd_head_v % blck_size != 0) {
+            LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_k=%u\n",
+                __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v);
+            return nullptr;
+        }
+    }
+
+    if (ggml_is_quantized(params.type_v) && params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) {
         LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
         return nullptr;
     }
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 1f2fc3ab62..49ea5da7cb 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1221,7 +1221,8 @@ ggml_tensor * llm_graph_context::build_attn_mha(
          ggml_tensor * kq_mask,
          ggml_tensor * sinks,
          ggml_tensor * v_mla,
-             float     kq_scale) const {
+               float   kq_scale,
+                 int   il) const {
     const bool v_trans = v->nb[1] > v->nb[2];
 
     // split the batch into streams if needed
@@ -1256,6 +1257,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
 
         cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
                                   hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
+        cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
 
         ggml_flash_attn_ext_add_sinks(cur, sinks);
         ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
@@ -1271,6 +1273,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
             // The permutations are noops and only change how the tensor data is interpreted.
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
             cur = ggml_mul_mat(ctx0, v_mla, cur);
+            cb(cur, "fattn_mla", il);
             cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
             cur = ggml_cont(ctx0, cur); // Needed because ggml_reshape_2d expects contiguous inputs.
 #endif
@@ -1279,6 +1282,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
         cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
     } else {
         ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+        cb(kq, "kq", il);
 
         // note: this op tends to require high floating point range
         //       while for some models F16 is enough, for others it is not, so we default to F32 here
@@ -1292,32 +1296,42 @@ ggml_tensor * llm_graph_context::build_attn_mha(
             // before the softmax below
 
             kq = ggml_tanh(ctx0, ggml_scale(ctx0, kq, 0.08838834764831845f/30.0f));
+            cb(kq, "kq_tanh", il);
             kq = ggml_scale(ctx0, kq, 30);
+            cb(kq, "kq_scaled", il);
         }
 
         if (hparams.attn_soft_cap) {
             kq = ggml_scale(ctx0, kq, 1.0f / hparams.f_attn_logit_softcapping);
+            cb(kq, "kq_scaled_1", il);
             kq = ggml_tanh (ctx0, kq);
+            cb(kq, "kq_tanh", il);
             kq = ggml_scale(ctx0, kq, hparams.f_attn_logit_softcapping);
+            cb(kq, "kq_scaled_2", il);
         }
 
         if (kq_b) {
             kq = ggml_add(ctx0, kq, kq_b);
+            cb(kq, "kq_plus_kq_b", il);
         }
 
         kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
         ggml_soft_max_add_sinks(kq, sinks);
+        cb(kq, "kq_soft_max", il);
 
         if (!v_trans) {
             // note: avoid this branch
             v = ggml_cont(ctx0, ggml_transpose(ctx0, v));
+            cb(v, "v_cont", il);
         }
 
         ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+        cb(kqv, "kqv", il);
 
         // for MLA with the absorption optimization, we need to "decompress" from MQA back to MHA
         if (v_mla) {
             kqv = ggml_mul_mat(ctx0, v_mla, kqv);
+            cb(kqv, "kqv_mla", il);
         }
 
         cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
@@ -1378,7 +1392,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = k_cur;
     ggml_tensor * v = v_cur;
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1467,7 +1481,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1534,7 +1548,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
     cb(cur, "kqv_out", il);
 
     if (wo) {
@@ -1589,7 +1603,7 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_tensor * k = k_cur;
     ggml_tensor * v = v_cur;
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
     cb(cur, "kqv_out", il);
 
     if (wo) {
diff --git a/src/llama-graph.h b/src/llama-graph.h
index e11d91d529..3c85333fde 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -687,7 +687,8 @@ struct llm_graph_context {
             ggml_tensor * kq_mask,
             ggml_tensor * sinks,   // [n_head_q]
             ggml_tensor * v_mla,   // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                  float   kq_scale) const;
+                  float   kq_scale,
+                    int   il) const;
 
     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
 
diff --git a/src/llama-impl.h b/src/llama-impl.h
index 02b1d07f84..c5163e9225 100644
--- a/src/llama-impl.h
+++ b/src/llama-impl.h
@@ -59,3 +59,5 @@ std::string llama_format_tensor_shape(const std::vector<int64_t> & ne);
 std::string llama_format_tensor_shape(const struct ggml_tensor * t);
 
 std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i);
+
+#define LLAMA_TENSOR_NAME_FATTN "__fattn__"
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index f3e0e9ac64..58a0581e26 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -18994,7 +18994,7 @@ llama_model_params llama_model_default_params() {
     llama_model_params result = {
         /*.devices                     =*/ nullptr,
         /*.tensor_buft_overrides       =*/ nullptr,
-        /*.n_gpu_layers                =*/ 0,
+        /*.n_gpu_layers                =*/ 999,
         /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
@@ -19008,11 +19008,6 @@ llama_model_params llama_model_default_params() {
         /*.use_extra_bufts             =*/ true,
     };
 
-#ifdef GGML_USE_METAL
-    // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
-    result.n_gpu_layers = 999;
-#endif
-
     return result;
 }
 
diff --git a/src/llama.cpp b/src/llama.cpp
index 34906cdb62..f0d4f5f891 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -25,6 +25,18 @@
 // interface implementation
 //
 
+const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type) {
+    switch (flash_attn_type) {
+        case LLAMA_FLASH_ATTN_TYPE_AUTO:
+            return "auto";
+        case LLAMA_FLASH_ATTN_TYPE_DISABLED:
+            return "disabled";
+        case LLAMA_FLASH_ATTN_TYPE_ENABLED:
+            return "enabled";
+    }
+    GGML_ABORT("fatal error");
+}
+
 struct llama_sampler_chain_params llama_sampler_chain_default_params() {
     struct llama_sampler_chain_params result = {
         /*.no_perf                     =*/ true,
diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp
index 23d03039dc..46dd12caae 100644
--- a/tools/batched-bench/batched-bench.cpp
+++ b/tools/batched-bench/batched-bench.cpp
@@ -111,7 +111,7 @@ int main(int argc, char ** argv) {
 
     if (!params.batched_bench_output_jsonl) {
         LOG("\n");
-        LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+        LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
         LOG("\n");
         LOG("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
         LOG("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
@@ -197,7 +197,7 @@ int main(int argc, char ** argv) {
                     LOG(
                         "{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"is_pp_shared\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
                         "\"pp\": %d, \"tg\": %d, \"pl\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f, \"t\": %f, \"speed\": %f}\n",
-                        n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
+                        n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
                         pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed
                     );
                 } else {
diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index 9378706a12..9b9803deda 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -987,16 +987,16 @@ struct cmd_params_instance {
     llama_context_params to_llama_cparams() const {
         llama_context_params cparams = llama_context_default_params();
 
-        cparams.n_ctx        = n_prompt + n_gen + n_depth;
-        cparams.n_batch      = n_batch;
-        cparams.n_ubatch     = n_ubatch;
-        cparams.type_k       = type_k;
-        cparams.type_v       = type_v;
-        cparams.offload_kqv  = !no_kv_offload;
-        cparams.flash_attn   = flash_attn;
-        cparams.embeddings   = embeddings;
-        cparams.op_offload   = !no_op_offload;
-        cparams.swa_full     = false;
+        cparams.n_ctx           = n_prompt + n_gen + n_depth;
+        cparams.n_batch         = n_batch;
+        cparams.n_ubatch        = n_ubatch;
+        cparams.type_k          = type_k;
+        cparams.type_v          = type_v;
+        cparams.offload_kqv     = !no_kv_offload;
+        cparams.flash_attn_type = flash_attn ? LLAMA_FLASH_ATTN_TYPE_ENABLED : LLAMA_FLASH_ATTN_TYPE_DISABLED;
+        cparams.embeddings      = embeddings;
+        cparams.op_offload      = !no_op_offload;
+        cparams.swa_full        = false;
 
         return cparams;
     }
diff --git a/tools/server/tests/unit/test_ctx_shift.py b/tools/server/tests/unit/test_ctx_shift.py
index 8f51bc301a..92e49f2bb0 100644
--- a/tools/server/tests/unit/test_ctx_shift.py
+++ b/tools/server/tests/unit/test_ctx_shift.py
@@ -15,25 +15,26 @@ Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deseru
 def create_server():
     global server
     server = ServerPreset.tinyllama2()
-    server.n_ctx = 256
+    server.n_ctx = 512
     server.n_slots = 2
+    server.n_predict = 128
 
 
 def test_ctx_shift_enabled():
     # the prompt is 301 tokens
-    # the slot context is 256/2 = 128 tokens
-    # the prompt is truncated to keep the last 109 tokens
-    # 64 tokens are generated thanks to shifting the context when it gets full
+    # the slot context is 512/2 = 256 tokens
+    # the prompt is truncated to keep the last (301 - 256/2) = 173 tokens
+    # 96 tokens are generated thanks to shifting the context when it gets full
     global server
     server.enable_ctx_shift = True
     server.start()
     res = server.make_request("POST", "/completion", data={
-        "n_predict": 64,
+        "n_predict": 96,
         "prompt": LONG_TEXT,
     })
     assert res.status_code == 200
-    assert res.body["timings"]["prompt_n"] == 109
-    assert res.body["timings"]["predicted_n"] == 64
+    assert res.body["timings"]["prompt_n"] == 173
+    assert res.body["timings"]["predicted_n"] == 96
     assert res.body["truncated"] is True
 
 
diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py
index 38ca4325ba..65952de8b8 100644
--- a/tools/server/tests/unit/test_speculative.py
+++ b/tools/server/tests/unit/test_speculative.py
@@ -14,6 +14,7 @@ def create_server():
     server.model_draft = download_file(MODEL_DRAFT_FILE_URL)
     server.draft_min = 4
     server.draft_max = 8
+    server.fa = "off"
 
 
 @pytest.fixture(autouse=True)
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index f55a539471..82f7215d53 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -66,7 +66,7 @@ class ServerProcess:
     n_slots: int | None = None
     ctk: str | None = None
     ctv: str | None = None
-    fa: bool | None = None
+    fa: str | None = None
     server_continuous_batching: bool | None = False
     server_embeddings: bool | None = False
     server_reranking: bool | None = False
@@ -161,7 +161,7 @@ class ServerProcess:
         if self.ctv:
             server_args.extend(["-ctv", self.ctv])
         if self.fa is not None:
-            server_args.append("-fa")
+            server_args.extend(["-fa", self.fa])
         if self.n_predict:
             server_args.extend(["--n-predict", self.n_predict])
         if self.slot_save_path:
@@ -427,7 +427,7 @@ class ServerPreset:
         server.n_batch = 300
         server.n_ubatch = 300
         server.n_slots = 2
-        server.fa = True
+        server.fa = "on"
         server.seed = 42
         server.server_embeddings = True
         return server

From dd892555b0681b7f56d38780f6fdfe00a195160f Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Sat, 30 Aug 2025 08:51:28 -0700
Subject: [PATCH 229/287] Update build.md to remove MSVC arm64 notes (#15684)

Removed information about MSVC compiler limitations for arm64 builds.
---
 docs/build.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/build.md b/docs/build.md
index b35a898ba9..dcbcce7549 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -59,8 +59,6 @@ cmake --build build --config Release
     cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
     cmake --build build-arm64-windows-llvm-release
     ```
-    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels.
-
     For building with ninja generator and clang compiler as default:
       -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
       ```bash

From 4d74393bcc956ccd7df68a6a06d1a0575cfa712c Mon Sep 17 00:00:00 2001
From: Charles Xu <charles.xu@arm.com>
Date: Sat, 30 Aug 2025 18:03:42 +0200
Subject: [PATCH 230/287] ggml: update kleidiai to v1.13.0 (#15663)

---
 ggml/CMakeLists.txt                     |  2 +-
 ggml/src/ggml-cpu/CMakeLists.txt        |  8 ++--
 ggml/src/ggml-cpu/kleidiai/kernels.cpp  | 49 ++++++++++++++++++++++---
 ggml/src/ggml-cpu/kleidiai/kernels.h    |  5 ++-
 ggml/src/ggml-cpu/kleidiai/kleidiai.cpp | 23 +++++++-----
 5 files changed, 67 insertions(+), 20 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 2ead001e2c..96be001f8c 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
-project("ggml" C CXX)
+project("ggml" C CXX ASM)
 include(CheckIncludeFileCXX)
 
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index b70302ec8c..040b7ded90 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -497,9 +497,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.11.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.13.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "3fe9e5ab964c375c53839296eb71eaa2")
+        set(KLEIDIAI_ARCHIVE_MD5  "d82a8de939d9814621a5ba23907bdac1")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)
@@ -555,6 +555,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         list(APPEND GGML_KLEIDIAI_SOURCES
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32.c
+            ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c)
@@ -576,7 +577,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c
-                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c)
+                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c
+                ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S)
             set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2")
         endif()
 
diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp
index ddd29d002d..7ba659124c 100644
--- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp
+++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp
@@ -14,6 +14,7 @@
 
 #include "kai_lhs_pack_bf16p2vlx2_f32_sme.h"
 #include "kai_lhs_quant_pack_qsi8d32p_f32.h"
+#include "kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.h"
 #include "kai_lhs_quant_pack_qsi8d32p_f32_neon.h"
 
 #include "kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.h"
@@ -127,6 +128,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon,
+            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon,
+        },
         /* SME GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
@@ -141,7 +148,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon,
@@ -173,6 +180,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme,
+            /* .pack_func             = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme,
+        },
         /* SME GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
@@ -187,7 +200,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme,
@@ -222,6 +235,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
+            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
+        },
         /* DOTPROD GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
@@ -236,7 +255,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -270,6 +289,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+        },
         /* i8mm GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
@@ -284,7 +309,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -319,6 +344,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
+        },
         /* i8mm GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
@@ -333,7 +364,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -367,6 +398,12 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
         },
+        /* .gemm_lhs_info = */ {
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
+            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
+            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
+        },
         /* DOTPROD GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
@@ -381,7 +418,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
         },
-        /* .lhs_info = */ {
+        /* .gemv_lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h
index bc8f33405d..2ad6ad6fd0 100644
--- a/ggml/src/ggml-cpu/kleidiai/kernels.h
+++ b/ggml/src/ggml-cpu/kleidiai/kernels.h
@@ -84,8 +84,11 @@ struct rhs_packing_info {
 
 struct ggml_kleidiai_kernels {
     kernel_info gemm;
+    lhs_packing_info gemm_lhs_info;
+
     kernel_info gemv;
-    lhs_packing_info lhs_info;
+    lhs_packing_info gemv_lhs_info;
+
     rhs_packing_info rhs_info;
 
     cpu_feature required_cpu;
diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
index dff8fa244a..7a830448eb 100644
--- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
+++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
@@ -123,7 +123,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         }
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op);
         GGML_ASSERT(kernels);
-        kernel_info * kernel = op->src[1]->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
+        bool is_gemv = op->src[1]->ne[1] == 1;
+        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
+        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
 
         size_t k = op->src[0]->ne[0];
         size_t n = op->src[0]->ne[1];
@@ -134,9 +136,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         size_t sr = kernel->get_sr();
 
         if (kernels->rhs_type == GGML_TYPE_Q4_0) {
-            size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, QK4_0, mr, kr, sr);
+            size = variant_call<size_t>(lhs_info->packed_size, m, k, QK4_0, mr, kr, sr);
         } else if (kernels->rhs_type == GGML_TYPE_F16) {
-            size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, mr, kr, sr) +
+            size = variant_call<size_t>(lhs_info->packed_size, m, k, mr, kr, sr) +
                    variant_call<size_t>(kernels->rhs_info.packed_size, n, k) +
                    k * n * sizeof(float) + n * sizeof(float);
         } else {
@@ -173,7 +175,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst);
         GGML_ASSERT(kernels);
 
-        kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
+        bool is_gemv = src1->ne[1] == 1;
+        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
+        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
         GGML_ASSERT(kernel);
 
         const int nth = params->nth;
@@ -198,7 +202,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         const int64_t kr = static_cast<int64_t>(kernel->get_kr());
         const int64_t sr = static_cast<int64_t>(kernel->get_sr());
 
-        const size_t lhs_packed_size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, mr, kr, sr);
+        const size_t lhs_packed_size = variant_call<size_t>(lhs_info->packed_size, m, k, mr, kr, sr);
         const size_t rhs_packed_size = variant_call<size_t>(kernels->rhs_info.packed_size, n, k);
         const size_t kxn_size        = k * n * sizeof(float);
         const size_t bias_size       = n * sizeof(float);
@@ -229,12 +233,12 @@ class tensor_traits : public ggml::cpu::tensor_traits {
                     const int64_t num_m_per_thread = (ith == num_threads - 1) ? num_m_per_threadN_1 : num_m_per_thread0;
 
                     const size_t lhs_offset        = variant_call<size_t>(kernels->gemm.get_lhs_offset, m_start, lhs_stride);
-                    const size_t lhs_packed_offset = variant_call<size_t>(kernels->lhs_info.get_packed_offset, m_start, k, mr, kr, sr);
+                    const size_t lhs_packed_offset = variant_call<size_t>(lhs_info->get_packed_offset, m_start, k, mr, kr, sr);
 
                     const void * src_ptr = static_cast<const uint8_t *>(lhs_batch) + lhs_offset;
                     void * dst_ptr       = static_cast<uint8_t *>(lhs_packed) + lhs_packed_offset;
 
-                    variant_call<void>(kernels->lhs_info.pack_func, num_m_per_thread, k, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr);
+                    variant_call<void>(lhs_info->pack_func, num_m_per_thread, k, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr);
                 }
             }
 
@@ -306,8 +310,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst);
         GGML_ASSERT(kernels);
 
-        kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
-        lhs_packing_info * lhs_info = &kernels->lhs_info;
+        bool is_gemv = src1->ne[1] == 1;
+        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
+        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
 
         GGML_ASSERT(kernel);
 

From 94e82c7eadeb8fff0db4bfd1ab6d8cf65fa6f2e0 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 31 Aug 2025 01:27:57 -0500
Subject: [PATCH 231/287] vulkan: clamp matmul and FA results to the max finite
 value (#15652)

* vulkan: clamp matmul and FA results to the max finite value

* only clamp for fp16
---
 .../vulkan-shaders/flash_attn.comp            |  3 +++
 .../vulkan-shaders/flash_attn_cm1.comp        |  3 +++
 .../vulkan-shaders/flash_attn_cm2.comp        |  4 ++++
 .../flash_attn_split_k_reduce.comp            |  4 ++++
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    | 14 +++++++++++
 .../vulkan-shaders/mul_mm_cm2.comp            | 15 ++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     | 23 ++++++++++++-------
 7 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index d40848e15f..482445c6fe 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -334,6 +334,9 @@ void main() {
     [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
         [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
             Of[r][d] *= Lfrcp[r];
+#if defined(ACC_TYPE_MAX)
+            Of[r][d] = clamp(Of[r][d], -vec4(ACC_TYPE_MAX), vec4(ACC_TYPE_MAX));
+#endif
         }
     }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 97c2a54129..63b32171b0 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -373,6 +373,9 @@ void main() {
     [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) {
         [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
             Of[r][d] *= ACC_TYPE(Lfrcp[r]);
+#if defined(ACC_TYPE_MAX)
+            Of[r][d] = clamp(Of[r][d], -ACC_TYPE_MAX, ACC_TYPE_MAX);
+#endif
         }
     }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index 77ae5ff01d..ab647e9bc8 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -283,6 +283,10 @@ void main() {
 
     O = Ldiag*O;
 
+#if defined(ACC_TYPE_MAX)
+    [[unroll]] for (uint i = 0; i < O.length(); ++i) { O[i] = clamp(O[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
+
     uint32_t o_offset = iq3*p.ne2*p.ne1*HSV;
 
     coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(O);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
index 76ef4b6dfb..06e83822fe 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp
@@ -111,6 +111,10 @@ void main() {
             }
         }
         O *= L;
+
+        const float FLT_MAX = uintBitsToFloat(0x7F7FFFFF);
+        O = clamp(O, -FLT_MAX, FLT_MAX);
+
         data_d[iq3 * D * N + D * n + d] = O;
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index 5ecf68a643..7e10e99e9e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -891,6 +891,20 @@ void main() {
         barrier();
     }
 
+#if defined(ACC_TYPE_MAX)
+#ifdef COOPMAT
+    [[unroll]] for (uint j = 0; j < cms_per_row * cms_per_col; j++) {
+        [[unroll]] for (uint i = 0; i < sums[j].length(); ++i) {
+            sums[j][i] = clamp(sums[j][i], -ACC_TYPE_MAX, ACC_TYPE_MAX);
+        }
+    }
+#else
+    [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) {
+        sums[i] = clamp(sums[i], -ACC_TYPE_MAX, ACC_TYPE_MAX);
+    }
+#endif
+#endif
+
     const uint dr = ir * BM + warp_r * WM;
     const uint dc = ic * BN + warp_c * WN;
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index f5aebf6e93..dd1b176049 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -349,6 +349,10 @@ void main() {
                 sum = coopMatMulAdd(mat_a, mat_b, sum);
                 block_k += BK;
             }
+#if defined(ACC_TYPE_MAX)
+            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
+
             coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator>(sum);
 
             coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover4, ir * BM, BM), tensorViewTranspose);
@@ -388,6 +392,10 @@ void main() {
                 sum = coopMatMulAdd(mat_a, mat_b, sum);
                 block_k += BK;
             }
+#if defined(ACC_TYPE_MAX)
+            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
+
             coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator>(sum);
 
             coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover2, ir * BM, BM), tensorViewTranspose);
@@ -428,6 +436,10 @@ void main() {
                 sum = coopMatMulAdd(mat_a, mat_b, sum);
                 block_k += BK;
             }
+#if defined(ACC_TYPE_MAX)
+            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
+
             coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(sum);
 
             coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose);
@@ -485,6 +497,9 @@ void main() {
                 sum = coopMatMulAdd(mat_a, mat_b, sum);
             }
         }
+#if defined(ACC_TYPE_MAX)
+        [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
 
         // Convert from ACC_TYPE to D_TYPE
         coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> mat_d;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index a973625857..d81bb47e7b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -323,6 +323,9 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
     }
 
     base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float";
+    if (f16acc) {
+        base_dict["ACC_TYPE_MAX"] = "\"float16_t(65504.0)\"";
+    }
 
     if (coopmat) {
         base_dict["COOPMAT"] = "1";
@@ -437,8 +440,12 @@ void process_shaders() {
 
     // flash attention
     for (const auto& f16acc : {false, true}) {
-        std::string acctype = f16acc ? "float16_t" : "float";
-        std::string acctypev4 = f16acc ? "f16vec4" : "vec4";
+        std::map<std::string, std::string> fa_base_dict = base_dict;
+        fa_base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float";
+        fa_base_dict["ACC_TYPEV4"] = f16acc ? "f16vec4" : "vec4";
+        if (f16acc) {
+            fa_base_dict["ACC_TYPE_MAX"] = "\"float16_t(65504.0)\"";
+        }
 
         for (const auto& tname : type_names) {
             if (tname == "f32") {
@@ -449,30 +456,30 @@ void process_shaders() {
 #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
             if (tname == "f16") {
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
-                    merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, false, true, f16acc);
+                    merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}}), true, false, true, f16acc);
             } else {
                 std::string data_a_key = "DATA_A_" + to_uppercase(tname);
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
-                    merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, true, f16acc);
+                    merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, true, f16acc);
             }
 #endif
 #if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
             if (tname == "f16") {
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
-                    merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"ACC_TYPEV4", acctypev4}, {"COOPMAT", "1"}}), true, true, false, f16acc);
+                    merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"COOPMAT", "1"}}), true, true, false, f16acc);
             } else if (tname == "q4_0" || tname == "q8_0") {
                 std::string data_a_key = "DATA_A_" + to_uppercase(tname);
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
-                    merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"ACC_TYPEV4", acctypev4}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), true, true, false, f16acc);
+                    merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), true, true, false, f16acc);
             }
 #endif
             if (tname == "f16") {
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",
-                    merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, false, false, f16acc);
+                    merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}}), true, false, false, f16acc);
             } else if (tname == "q4_0" || tname == "q8_0") {
                 std::string data_a_key = "DATA_A_" + to_uppercase(tname);
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",
-                    merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, false, f16acc);
+                    merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, false, f16acc);
             }
         }
     }

From b97c9edc59d4a1b4069991aa670411190f4f3a3e Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 31 Aug 2025 01:30:54 -0500
Subject: [PATCH 232/287] vulkan: Allow fallback to sysmem memory when vidmem
 is full (#15649)

* vulkan: Allow fallback to sysmem memory when vidmem is full

* vulkan: Add env var GGML_VK_ALLOW_SYSMEM_FALLBACK
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 103 ++++++++++++++-------------
 1 file changed, 53 insertions(+), 50 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 40962de508..7658f56f2d 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -566,6 +566,7 @@ struct vk_device_struct {
 
     bool disable_fusion;
     bool disable_host_visible_vidmem;
+    bool allow_sysmem_fallback;
 
 #ifdef GGML_VULKAN_MEMORY_DEBUG
     std::unique_ptr<vk_memory_logger> memory_logger;
@@ -1808,8 +1809,8 @@ static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_pr
     return UINT32_MAX;
 }
 
-static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
-    VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")");
+static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std::initializer_list<vk::MemoryPropertyFlags> & req_flags_list) {
+    VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags_list.begin()[0]) << ", " << to_string(req_flags_list.begin()[req_flags_list.size()-1]) << ")");
     if (size > device->max_memory_allocation_size) {
         throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device memory allocation limit");
     }
@@ -1836,42 +1837,27 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor
 
     vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();
 
-    uint32_t memory_type_index = UINT32_MAX;
+    for (auto &req_flags : req_flags_list) {
+        uint32_t memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
 
-    memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
-    buf->memory_property_flags = req_flags;
+        if (memory_type_index == UINT32_MAX) {
+            continue;
+        }
+        buf->memory_property_flags = req_flags;
 
-    if (memory_type_index == UINT32_MAX && fallback_flags) {
-        memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
-        buf->memory_property_flags = fallback_flags;
+        try {
+            buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
+            break;
+        } catch (const vk::SystemError& e) {
+            // loop and retry
+        }
     }
 
-    if (memory_type_index == UINT32_MAX) {
+    if (buf->device_memory == VK_NULL_HANDLE) {
         device->device.destroyBuffer(buf->buffer);
         throw vk::OutOfDeviceMemoryError("No suitable memory type found");
     }
 
-    try {
-        buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
-    } catch (const vk::SystemError& e) {
-        if (buf->memory_property_flags != fallback_flags) {
-            // Try again with fallback flags
-            memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
-            buf->memory_property_flags = fallback_flags;
-
-            try {
-                buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
-            }
-            catch (const vk::SystemError& e) {
-                device->device.destroyBuffer(buf->buffer);
-                throw e;
-            }
-        } else {
-            // Out of Host/Device memory, clean up buffer
-            device->device.destroyBuffer(buf->buffer);
-            throw e;
-        }
-    }
     buf->ptr = nullptr;
 
     if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
@@ -1892,7 +1878,7 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor
 
 static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
     try {
-        return ggml_vk_create_buffer(device, size, req_flags, fallback_flags);
+        return ggml_vk_create_buffer(device, size, {req_flags, fallback_flags});
     } catch (const vk::SystemError& e) {
         std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
         std::cerr << "ggml_vulkan: " << e.what() << std::endl;
@@ -1904,15 +1890,29 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
     vk_buffer buf;
     try {
         if (device->prefer_host_memory) {
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
+                                                       vk::MemoryPropertyFlagBits::eDeviceLocal});
         } else if (device->uma) {
             // Fall back to host memory type
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+            buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal,
+                                                       vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
         } else if (device->disable_host_visible_vidmem) {
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            if (device->allow_sysmem_fallback) {
+                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal,
+                                                           vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
+            } else {
+                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+            }
         } else {
             // use rebar if available, otherwise fallback to device only visible memory
-            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            if (device->allow_sysmem_fallback) {
+                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
+                                                           vk::MemoryPropertyFlagBits::eDeviceLocal,
+                                                           vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
+            } else {
+                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
+                                                           vk::MemoryPropertyFlagBits::eDeviceLocal});
+            }
         }
     } catch (const vk::SystemError& e) {
         std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
@@ -3437,6 +3437,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
         const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM");
         device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr;
 
+        const char* GGML_VK_ALLOW_SYSMEM_FALLBACK = getenv("GGML_VK_ALLOW_SYSMEM_FALLBACK");
+        device->allow_sysmem_fallback = GGML_VK_ALLOW_SYSMEM_FALLBACK != nullptr;
+
         bool fp16_storage = false;
         bool fp16_compute = false;
         bool maintenance4_support = false;
@@ -4774,8 +4777,8 @@ static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_
 static void * ggml_vk_host_malloc(vk_device& device, size_t size) {
     VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")");
     vk_buffer buf = ggml_vk_create_buffer(device, size,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+        {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+         vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
 
     if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
         fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
@@ -9182,7 +9185,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
             if (ctx->prealloc_split_k != nullptr) {
                 ggml_vk_destroy_buffer(ctx->prealloc_split_k);
             }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal});
         }
     }
 
@@ -9192,9 +9195,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     ggml_pipeline_allocate_descriptor_sets(ctx);
 
-    vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});
 
     X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
     Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
@@ -9420,8 +9423,8 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
     float * x = (float *) malloc(x_sz);
     void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, {vk::MemoryPropertyFlagBits::eDeviceLocal});
     float * x_ref = (float *) malloc(x_sz);
     ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
 
@@ -9526,8 +9529,8 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
 //     float * x = (float *) malloc(x_sz);
 //     block_q8_1 * qx     = (block_q8_1 *)malloc(qx_sz);
 //     block_q8_1 * qx_res = (block_q8_1 *)malloc(qx_sz);
-//     vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-//     vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+//     vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+//     vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
 //
 //     for (size_t i = 0; i < ne; i++) {
 //         x[i] = rand() / (float)RAND_MAX;
@@ -9674,10 +9677,10 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
     float * x = (float *) malloc(x_sz);
     float * y = (float *) malloc(y_sz);
     void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer qy_buf = ggml_vk_create_buffer_check(ctx->device, qy_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer qy_buf = ggml_vk_create_buffer_check(ctx->device, qy_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
+    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
     float * d = (float *) malloc(d_sz);
     float * d_chk = (float *) malloc(d_sz);
 
@@ -9704,7 +9707,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
             if (ctx->prealloc_split_k != nullptr) {
                 ggml_vk_destroy_buffer(ctx->prealloc_split_k);
             }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal});
         }
     }
     if (mmq) {

From 5c16b9c87d840e4d5d55fa83c732c6b693346f40 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sun, 31 Aug 2025 08:46:42 +0200
Subject: [PATCH 233/287] vulkan : remove unused portability_enumeration_ext
 variable (#15679)

This commit removes the portability_enumeration_ext variable from the
ggml_vk_instance_portability_enumeration_ext_available function as it
is initialized to false but never modified, making it redundant.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 7658f56f2d..a44a2770b8 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -12015,16 +12015,13 @@ static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::Exte
 }
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
 #ifdef __APPLE__
-    bool portability_enumeration_ext = false;
     // Check for portability enumeration extension for MoltenVK support
     for (const auto& properties : instance_extensions) {
         if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
             return true;
         }
     }
-    if (!portability_enumeration_ext) {
-        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
-    }
+    std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
 #endif
     return false;
 

From c37052ab4d6d1ae73c0e90bc6e560cc6409e1311 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 31 Aug 2025 02:06:43 -0500
Subject: [PATCH 234/287] vulkan: mul_mat_id coopmat2 optimizations (#15546)

* vulkan: mul_mat_id coopmat2 optimizations

Add a path for when the tile fits in BN/2, similar to what we have for mul_mat.

Only call fetch_scales/store_scales once per QUANT_K block, and once at the
beginning in case start_k is not aligned.

* Also add a path for BN/4 - worth a couple more percent
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  2 +-
 .../vulkan-shaders/mul_mm_cm2.comp            | 97 ++++++++++++++++++-
 2 files changed, 93 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a44a2770b8..5728514a9f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2225,7 +2225,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         s_mmq_wg_denoms_k = { 32,  64,  1 };
 
         // spec constants and tile sizes for quant matmul_id
-        l_warptile_mmqid = { 256, 128, 128, 16, 0, device->subgroup_size };
+        l_warptile_mmqid = { 256, 128, 128, 16, 1, device->subgroup_size };
         m_warptile_mmqid = { 256, 128, 64, 16, 0, device->subgroup_size };
         s_warptile_mmqid = { 256, 128, 64, 16, 0, device->subgroup_size };
         l_mmqid_wg_denoms = { 128, 128, 1 };
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index dd1b176049..654105a491 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -456,18 +456,105 @@ void main() {
 
         tensorLayoutBClamp = setTensorLayoutStrideNV(tensorLayoutBClamp, stride_b, 1);
 
-        coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> sum;
-        sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(0.0);
-
         uint k_iters = (end_k - start_k + BK - 1) / BK;
 
         fetch_scales(ir * BM, pos_a, stride_a, start_k, tid, false);
+        store_scales(tid);
+
+#ifdef MUL_MAT_ID
+        if (enable_smaller_matrices && ic * BN + BNover4 >= _ne1) {
+            coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> sum;
+            sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator>(0.0);
+
+            [[dont_unroll]]
+            for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {
+
+                if ((block_k % QUANT_K) == 0) {
+                    store_scales(tid);
+                }
+                if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) {
+                    fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false);
+                }
+
+                if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) {
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover4, gl_MatrixUseB> mat_b;
+
+                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
+                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose, decodeFuncB);
+
+                    sum = coopMatMulAdd(mat_a, mat_b, sum);
+                } else {
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover4, gl_MatrixUseB> mat_b;
+
+                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
+                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose, decodeFuncB);
+
+                    sum = coopMatMulAdd(mat_a, mat_b, sum);
+                }
+            }
+
+            // Convert from ACC_TYPE to D_TYPE
+            coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> mat_d;
+            mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator>(sum);
+
+            // Call callback to store each element, remapping row through shared memory
+            coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic);
+            return;
+        }
+        if (enable_smaller_matrices && ic * BN + BNover2 >= _ne1) {
+            coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> sum;
+            sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator>(0.0);
+
+            [[dont_unroll]]
+            for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {
+
+                if ((block_k % QUANT_K) == 0) {
+                    store_scales(tid);
+                }
+                if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) {
+                    fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false);
+                }
+
+                if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) {
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover2, gl_MatrixUseB> mat_b;
+
+                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
+                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose, decodeFuncB);
+
+                    sum = coopMatMulAdd(mat_a, mat_b, sum);
+                } else {
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
+                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover2, gl_MatrixUseB> mat_b;
+
+                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
+                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose, decodeFuncB);
+
+                    sum = coopMatMulAdd(mat_a, mat_b, sum);
+                }
+            }
+
+            // Convert from ACC_TYPE to D_TYPE
+            coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> mat_d;
+            mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator>(sum);
+
+            // Call callback to store each element, remapping row through shared memory
+            coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic);
+            return;
+        }
+#endif
+        coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> sum;
+        sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(0.0);
 
         [[dont_unroll]]
         for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {
 
-            store_scales(tid);
-            if (block_k + BK < end_k) {
+            if ((block_k % QUANT_K) == 0) {
+                store_scales(tid);
+            }
+            if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) {
                 fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false);
             }
 

From bbbf5ecccb35286521f735239d499eec4279a840 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 31 Aug 2025 03:13:27 -0500
Subject: [PATCH 235/287] vulkan: handle large sizes for get_rows (#15686)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  2 +
 .../ggml-vulkan/vulkan-shaders/get_rows.comp  | 29 +++++++++-----
 .../vulkan-shaders/get_rows_quant.comp        | 40 ++++++++++++-------
 3 files changed, 46 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 5728514a9f..f67ca966d1 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -7849,6 +7849,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         break;
     case GGML_OP_GET_ROWS:
         elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+        elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
+        elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
         break;
     case GGML_OP_ARGSORT:
         elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 };
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
index ee6b86a18d..7ef75cd7a4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
@@ -7,27 +7,36 @@ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
 
 void main() {
     const uint i00 = gl_GlobalInvocationID.x;
-    const uint i10 = gl_GlobalInvocationID.y;
-    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
-    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
 
     if (i00 >= p.ne00) {
         return;
     }
 
-    const uint i01 = data_b[get_boffset() + i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+    uint gid_z = gl_GlobalInvocationID.z;
+    while (gid_z < p.ne11 * p.ne12) {
+        uint gid_y = gl_GlobalInvocationID.y;
+        while (gid_y < p.ne10) {
+            const uint i10 = gid_y;
+            const uint i11 = gid_z / p.ne12;
+            const uint i12 = gid_z % p.ne12;
 
-    const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
-    const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+            const uint i01 = data_b[get_boffset() + i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+            const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+            const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
 
 #if defined(DATA_A_BF16)
-    FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
+            FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
 #else
-    FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]);
+            FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]);
 #endif
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-    data_d[d_offset + i00] = D_TYPE(v);
+            data_d[d_offset + i00] = D_TYPE(v);
 #else
-    data_d[d_offset + i00] = D_TYPE(v);
+            data_d[d_offset + i00] = D_TYPE(v);
 #endif
+            gid_y += gl_WorkGroupSize.y * gl_NumWorkGroups.y;
+        }
+        gid_z += gl_WorkGroupSize.z * gl_NumWorkGroups.z;
+    }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
index cfd645a38a..339f905fc7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
@@ -10,9 +10,6 @@ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
 
 void main() {
     const uint i00 = (gl_GlobalInvocationID.x)*2;
-    const uint i10 = gl_GlobalInvocationID.y;
-    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
-    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
 
 #ifdef NEEDS_INIT_IQ_SHMEM
     init_iq_shmem(gl_WorkGroupSize);
@@ -22,20 +19,33 @@ void main() {
         return;
     }
 
-    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+    uint gid_z = gl_GlobalInvocationID.z;
+    while (gid_z < p.ne11 * p.ne12) {
+        uint gid_y = gl_GlobalInvocationID.y;
+        while (gid_y < p.ne10) {
+            const uint i10 = gid_y;
+            const uint i11 = gid_z / p.ne12;
+            const uint i12 = gid_z % p.ne12;
 
-    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
-    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+            const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
 
-    const uint ib = a_offset + i00/QUANT_K; // block index
-    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
-    const uint iybs = i00 - i00%QUANT_K; // dst block start index
-    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+            const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+            const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
 
-    vec2 v = dequantize(ib, iqs, 0);
-    const vec2 dm = get_dm(ib, 0);
-    v = v * dm.x + dm.y;
+            const uint ib = a_offset + i00/QUANT_K; // block index
+            const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
+            const uint iybs = i00 - i00%QUANT_K; // dst block start index
+            const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
 
-    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
-    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
+            vec2 v = dequantize(ib, iqs, 0);
+            const vec2 dm = get_dm(ib, 0);
+            v = v * dm.x + dm.y;
+
+            data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
+            data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
+
+            gid_y += gl_WorkGroupSize.y * gl_NumWorkGroups.y;
+        }
+        gid_z += gl_WorkGroupSize.z * gl_NumWorkGroups.z;
+    }
 }

From 7d3c9f2b217acf0ce5db81ae83d3f375f49ab2c7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 31 Aug 2025 15:30:20 +0200
Subject: [PATCH 236/287] ci : explicitly set fa off or on (#15692)

---
 ci/run.sh | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/ci/run.sh b/ci/run.sh
index d51ba44385..a250393ee9 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -386,10 +386,10 @@ function gg_run_open_llama_7b_v2 {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -520,8 +520,8 @@ function gg_run_pythia_1_4b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -651,10 +651,10 @@ function gg_run_pythia_2_8b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"

From 9777032dccd67bdc7785aeab7497014a8be8dacc Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Sun, 31 Aug 2025 06:49:03 -0700
Subject: [PATCH 237/287] llama : separate compute buffer reserve from fattn
 check (#15696)

Exposes ggml_backend_sched_split_graph() to allow splitting the graph without allocating compute buffers and uses it to split the graph for the automatic Flash Attention check.
---
 ggml/include/ggml-backend.h |   3 +
 ggml/src/ggml-backend.cpp   |   4 +-
 src/llama-context.cpp       | 113 ++++++++++++++++++------------------
 src/llama-context.h         |   2 +-
 4 files changed, 64 insertions(+), 58 deletions(-)

diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index a2977ea2e5..4f246f6ccd 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -307,6 +307,9 @@ extern "C" {
     GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
     GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
+    // Split graph without allocating it
+    GGML_API void                 ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+
     // Allocate and compute graph on the backend scheduler
     GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
     GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 02375337c4..0cdbf18017 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -902,7 +902,7 @@ static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, stru
 }
 
 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
-static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     // reset splits
     sched->n_splits = 0;
     sched->n_graph_inputs = 0;
@@ -1687,6 +1687,8 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
     GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
+    ggml_backend_sched_reset(sched);
+
     ggml_backend_sched_synchronize(sched);
 
     ggml_backend_sched_split_graph(sched, measure_graph);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index ac8453ab74..7e20ee9f8b 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -270,8 +270,60 @@ llama_context::llama_context(
         }
     }
 
-    // resolve automatic Flash Attention use and reserve worst-case graph
     if (!hparams.vocab_only) {
+        llama_memory_context_ptr mctx;
+        if (memory) {
+            LLAMA_LOG_DEBUG("%s: reserving full memory module\n", __func__);
+            mctx = memory->init_full();
+            if (!mctx) {
+                throw std::runtime_error("failed to initialize memory module");
+            }
+        }
+
+        cross.v_embd.clear();
+        // resolve automatic Flash Attention use
+        if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
+            auto * gf = graph_reserve(1, 1, 0, mctx.get(), true);
+            if (!gf) {
+                throw std::runtime_error("failed to split graph for Flash Attention check");
+            }
+
+            const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
+            bool fa_device_mismatch = false;
+            for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
+                ggml_tensor * n = ggml_graph_node(gf, i);
+                if (n->op != GGML_OP_FLASH_ATTN_EXT) {
+                    continue;
+                }
+                ggml_backend_dev_t device_fa = ggml_backend_get_device(
+                    ggml_backend_sched_get_tensor_backend(sched.get(), n));
+
+                // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
+                GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
+                const int il = std::stoi(n->name + prefix_len);
+                ggml_backend_dev_t device_kv = model.dev_layer(il);
+                if (device_fa != device_kv) {
+                    LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
+                        "is assigned to device %s (usually due to missing support)\n",
+                        __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
+                    // FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
+                    fa_device_mismatch = true;
+                    break;
+                }
+            }
+            if (fa_device_mismatch) {
+                cparams.flash_attn = false;
+                LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
+                if (ggml_is_quantized(params.type_v)) {
+                    throw std::runtime_error("quantized V cache was requested, but this requires Flash Attention");
+                }
+            } else {
+                cparams.flash_attn = true;
+                LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
+            }
+        }
+
+        // reserve worst-case graph
         const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
 
@@ -283,17 +335,6 @@ llama_context::llama_context(
         int n_splits_tg = -1;
         int n_nodes_tg  = -1;
 
-        llama_memory_context_ptr mctx;
-        if (memory) {
-            LLAMA_LOG_DEBUG("%s: reserving full memory module\n", __func__);
-            mctx = memory->init_full();
-            if (!mctx) {
-                throw std::runtime_error("failed to initialize memory module");
-            }
-        }
-
-        cross.v_embd.clear();
-
         // reserve pp (prompt processing) graph first so that buffers are only allocated once
         {
             auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
@@ -301,48 +342,6 @@ llama_context::llama_context(
                 throw std::runtime_error("failed to allocate compute pp buffers");
             }
 
-            if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
-                ggml_backend_sched_alloc_graph(sched.get(), gf);
-
-                const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
-                bool fa_device_mismatch = false;
-                for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
-                    ggml_tensor * n = ggml_graph_node(gf, i);
-                    if (n->op != GGML_OP_FLASH_ATTN_EXT) {
-                        continue;
-                    }
-                    ggml_backend_dev_t device_fa = ggml_backend_get_device(
-                        ggml_backend_sched_get_tensor_backend(sched.get(), n));
-
-                    // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
-                    GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
-                    const int il = std::stoi(n->name + prefix_len);
-                    ggml_backend_dev_t device_kv = model.dev_layer(il);
-                    if (device_fa != device_kv) {
-                        LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
-                            "is assigned to device %s (usually due to missing support)\n",
-                            __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
-                        // FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
-                        fa_device_mismatch = true;
-                        break;
-                    }
-                }
-                if (fa_device_mismatch) {
-                    cparams.flash_attn = false;
-                    LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
-                    if (ggml_is_quantized(params.type_v)) {
-                        throw std::runtime_error("quantized V cache was requested, but this requires Flash Attention");
-                    }
-                    auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
-                    if (!gf) {
-                        throw std::runtime_error("failed to allocate compute pp buffers");
-                    }
-                } else {
-                    cparams.flash_attn = true;
-                    LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
-                }
-            }
-
             n_splits_pp = ggml_backend_sched_get_n_splits(sched.get());
             n_nodes_pp  = ggml_graph_n_nodes(gf);
         }
@@ -1366,7 +1365,7 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
     return static_cast<llm_graph_result *>(gf_res_reserve.get());
 }
 
-ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx) {
+ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
     LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
 
     if (n_tokens % n_seqs != 0) {
@@ -1401,7 +1400,9 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
     this->n_outputs = save_n_outputs;
 
     // initialize scheduler with the specified graph
-    if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+    if (split_only) {
+        ggml_backend_sched_split_graph(sched.get(), gf);
+    } else if (!ggml_backend_sched_reserve(sched.get(), gf)) {
         LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
         return nullptr;
     }
diff --git a/src/llama-context.h b/src/llama-context.h
index a372bcfbe4..f23aa8ee13 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -196,7 +196,7 @@ public:
     ggml_status graph_compute(ggml_cgraph * gf, bool batched);
 
     // reserve a graph with a dummy ubatch of the specified size
-    ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx);
+    ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
 
 private:
     llm_graph_params graph_params(

From 274966226f87f301ac132da898280ca3142b60e5 Mon Sep 17 00:00:00 2001
From: Diego Devesa <slarengh@gmail.com>
Date: Sun, 31 Aug 2025 08:47:05 -0700
Subject: [PATCH 238/287] llama : fix fattn reserve call n_seqs parameter
 (#15699)

ggml-ci
---
 src/llama-context.cpp | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 7e20ee9f8b..2de6fcf0cb 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -281,9 +281,15 @@ llama_context::llama_context(
         }
 
         cross.v_embd.clear();
+
+        const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
+        const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
+
+        LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
+
         // resolve automatic Flash Attention use
         if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
-            auto * gf = graph_reserve(1, 1, 0, mctx.get(), true);
+            auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
             if (!gf) {
                 throw std::runtime_error("failed to split graph for Flash Attention check");
             }
@@ -324,11 +330,6 @@ llama_context::llama_context(
         }
 
         // reserve worst-case graph
-        const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
-        const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-
-        LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
-
         int n_splits_pp = -1;
         int n_nodes_pp  = -1;
 

From 4efd5a83163ff383285b3a4c2106feabf5c69557 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 31 Aug 2025 19:43:30 +0300
Subject: [PATCH 239/287] metal : fix checks for available FA kernels (#15700)

* metal : fix checks for available FA kernels

ggml-ci

* cont : fix comment [no ci]
---
 ggml/src/ggml-metal/ggml-metal.m     | 50 ++++++----------------------
 ggml/src/ggml-metal/ggml-metal.metal | 13 ++------
 2 files changed, 14 insertions(+), 49 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 1f93633d91..3d16a1dcd4 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -523,13 +523,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,
@@ -1562,13 +1555,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128, flash_attn_ext_q8_0_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,        flash_attn_ext_q8_0_h256,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK576_HV512, flash_attn_ext_q8_0_hk576_hv512, has_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40,      flash_attn_ext_vec_f16_h40,      has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40,     flash_attn_ext_vec_bf16_h40,     has_simdgroup_reduction && use_bfloat);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40,     flash_attn_ext_vec_q4_0_h40,     has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40,     flash_attn_ext_vec_q4_1_h40,     has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40,     flash_attn_ext_vec_q5_0_h40,     has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40,     flash_attn_ext_vec_q5_1_h40,     has_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40,     flash_attn_ext_vec_q8_0_h40,     has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H64,      flash_attn_ext_vec_f16_h64,      has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H64,     flash_attn_ext_vec_bf16_h64,     has_simdgroup_reduction && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H64,     flash_attn_ext_vec_q4_0_h64,     has_simdgroup_reduction);
@@ -1909,9 +1895,15 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_ARANGE:
             return true;
         case GGML_OP_FLASH_ATTN_EXT:
-            if (op->src[0]->ne[0] == 32) {
-                // head size == 32 (e.g. bert-bge-small)
-                // TODO: not sure if it is worth adding kernels for this size
+            // for new head sizes, add checks here
+            if (op->src[0]->ne[0] != 40 &&
+                op->src[0]->ne[0] != 64 &&
+                op->src[0]->ne[0] != 80 &&
+                op->src[0]->ne[0] != 96 &&
+                op->src[0]->ne[0] != 112 &&
+                op->src[0]->ne[0] != 128 &&
+                op->src[0]->ne[0] != 192 &&
+                op->src[0]->ne[0] != 256) {
                 return false;
             }
             if (op->src[0]->ne[0] == 576) {
@@ -5138,10 +5130,8 @@ static int ggml_metal_encode_node(
 
                 bool use_vec_kernel = false;
 
-                // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0)
-                //       for now avoiding mainly to keep the number of templates/kernels a bit lower
-                //       these are now trivial to add after: https://github.com/ggml-org/llama.cpp/pull/12612
-                if (ne01 >= 20 || (ne00%128 != 0 && ne00 != 64 && ne00 != 96 && ne00 != 192 && ne00 != 576)) {
+                // use non-vec kernel if the batch size is large or if the vec-kernel is not supported for this head size
+                if (ne01 >= 20 || (ne00 == 40 || ne00 == 80 || ne00 == 112)) {
                     switch (src1->type) {
                         case GGML_TYPE_F16:
                             {
@@ -5329,24 +5319,6 @@ static int ggml_metal_encode_node(
                     use_vec_kernel = true;
 
                     switch (ne00) {
-                        case 40:
-                            {
-                                switch (src1->type) {
-                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H40].pipeline; break;
-                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H40].pipeline; break;
-                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H40].pipeline; break;
-                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H40].pipeline; break;
-                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H40].pipeline; break;
-                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H40].pipeline; break;
-                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H40].pipeline; break;
-                                    default:
-                                        {
-                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
-                                            GGML_LOG_ERROR("add template specialization for this type\n");
-                                            GGML_ABORT("add template specialization for this type");
-                                        }
-                                }
-                            } break;
                         case 64:
                             {
                                 switch (src1->type) {
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 4fa16c4a55..9c5933d24a 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -4803,6 +4803,9 @@ kernel void kernel_flash_attn_ext_vec(
         ushort3   ntg[[threads_per_threadgroup]],
         ushort  tiisg[[thread_index_in_simdgroup]],
         ushort  sgitg[[simdgroup_index_in_threadgroup]]) {
+    static_assert(DK % 32 == 0, "DK must be divisible by 32");
+    static_assert(DV % 32 == 0, "DV must be divisible by 32");
+
     const short nsg = ntg.y; // number of simdgroups
     const short iwg = tgpig[2]%nwg;
 
@@ -5160,16 +5163,6 @@ kernel void kernel_flash_attn_ext_vec(
 
 typedef decltype(kernel_flash_attn_ext_vec<FA_TYPES, half4, 1, dequantize_f16_t4, half4, 1, dequantize_f16_t4, 128, 128, 4>) flash_attn_ext_vec_t;
 
-template [[host_name("kernel_flash_attn_ext_vec_f16_h40")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  40, 40, 8>;
-#if defined(GGML_METAL_USE_BF16)
-template [[host_name("kernel_flash_attn_ext_vec_bf16_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 40, 40, 8>;
-#endif
-template [[host_name("kernel_flash_attn_ext_vec_q4_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_0,        8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 40, 40, 8>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_1_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_1,        8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 40, 40, 8>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_0,        8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 40, 40, 8>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_1_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_1,        8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 40, 40, 8>;
-template [[host_name("kernel_flash_attn_ext_vec_q8_0_h40")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q8_0,        8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 40, 40, 8>;
-
 template [[host_name("kernel_flash_attn_ext_vec_f16_h64")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  64, 64, 8>;
 #if defined(GGML_METAL_USE_BF16)
 template [[host_name("kernel_flash_attn_ext_vec_bf16_h64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 64, 64, 8>;

From 0d161f021aa33ec0e90cce96f5d1a88925557327 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 31 Aug 2025 20:11:58 +0300
Subject: [PATCH 240/287] server : enable /slots by default and make it secure
 (#15630)

* server : enable /slots by default and make it secure

ggml-ci

* server : fix tests to pass `--no-slots` when necessary

* server : extend /props with info about enabled endpoints
---
 common/arg.cpp              |  14 ++--
 common/common.h             |   2 +-
 tools/server/README.md      | 148 ++++++++++++++++++++++++++++--------
 tools/server/server.cpp     |  81 ++++++++++++++++++--
 tools/server/tests/utils.py |   2 +
 5 files changed, 200 insertions(+), 47 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 72c69c39a0..4fa214d3d2 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2962,13 +2962,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.endpoint_metrics = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
-    add_opt(common_arg(
-        {"--slots"},
-        string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.endpoint_slots = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
     add_opt(common_arg(
         {"--props"},
         string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
@@ -2976,6 +2969,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.endpoint_props = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS"));
+    add_opt(common_arg(
+        {"--slots"},
+        string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.endpoint_slots = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
     add_opt(common_arg(
         {"--no-slots"},
         "disables slots monitoring endpoint",
diff --git a/common/common.h b/common/common.h
index 02ca093bdf..85b3b879d4 100644
--- a/common/common.h
+++ b/common/common.h
@@ -444,7 +444,7 @@ struct common_params {
 
     // "advanced" endpoints are disabled by default for better security
     bool webui            = true;
-    bool endpoint_slots   = false;
+    bool endpoint_slots   = true;
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
 
diff --git a/tools/server/README.md b/tools/server/README.md
index b7285b2319..b0527f3cbe 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -37,7 +37,7 @@ The project is under active development, and we are [looking for feedback and co
 | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
 | `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask |
 | `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)<br/> |
-| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
+| `--prio N` | set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0)<br/> |
 | `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
 | `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
 | `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch |
@@ -49,6 +49,8 @@ The project is under active development, and we are [looking for feedback and co
 | `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
 | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
+| `--swa-full` | use full-size SWA cache (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)<br/>(env: LLAMA_ARG_SWA_FULL) |
+| `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)<br/>(env: LLAMA_ARG_KV_SPLIT) |
 | `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
 | `--no-perf` | disable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_NO_PERF) |
 | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
@@ -63,6 +65,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
+| `-nr, --no-repack` | disable weight repacking<br/>(env: LLAMA_ARG_NO_REPACK) |
 | `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
@@ -73,12 +76,15 @@ The project is under active development, and we are [looking for feedback and co
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
 | `--override-tensor, -ot <tensor name pattern>=<buffer type>,...` | override tensor buffer type |
+| `--cpu-moe, -cmoe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
+| `--n-cpu-moe, -ncmoe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
 | `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `--check-tensors` | check model tensor data for invalid values (default: false) |
 | `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false |
+| `--no-op-offload` | disable offloading host tensor operations to device (default: false) |
 | `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) |
 | `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) |
 | `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
@@ -96,9 +102,12 @@ The project is under active development, and we are [looking for feedback and co
 | `--log-file FNAME` | Log to file |
 | `--log-colors` | Enable colored logging<br/>(env: LLAMA_LOG_COLORS) |
 | `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
+| `--offline` | Offline mode: forces use of cache, prevents network access<br/>(env: LLAMA_OFFLINE) |
 | `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.<br/>(env: LLAMA_LOG_VERBOSITY) |
 | `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
 | `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
+| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
+| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
 
 
 **Sampling params**
@@ -113,6 +122,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
 | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
 | `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) |
+| `--top-nsigma N` | top-n-sigma sampling (default: -1.0, -1.0 = disabled) |
 | `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) |
 | `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) |
@@ -141,7 +151,10 @@ The project is under active development, and we are [looking for feedback and co
 
 | Argument | Explanation |
 | -------- | ----------- |
-| `--no-context-shift` | disables context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
+| `--swa-checkpoints N` | max number of SWA checkpoints per slot to create (default: 3)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_SWA_CHECKPOINTS) |
+| `--no-context-shift` | disables context shift on infinite text generation (default: enabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
+| `--context-shift` | enables context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
+| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode<br/> |
 | `-sp, --special` | special tokens output enabled (default: false) |
 | `--no-warmup` | skip warming up the model with an empty run |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
@@ -152,10 +165,14 @@ The project is under active development, and we are [looking for feedback and co
 | `--mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
 | `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf<br/>(env: LLAMA_ARG_NO_MMPROJ) |
 | `--no-mmproj-offload` | do not offload multimodal projector to GPU<br/>(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) |
+| `--override-tensor-draft, -otd <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
+| `--cpu-moe-draft, -cmoed` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_CPU_MOE_DRAFT) |
+| `--n-cpu-moe-draft, -ncmoed N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
 | `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_ALIAS) |
 | `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
+| `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
 | `--no-webui` | Disable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_NO_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
@@ -163,23 +180,25 @@ The project is under active development, and we are [looking for feedback and co
 | `--api-key-file FNAME` | path to file containing API keys (default: none) |
 | `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key<br/>(env: LLAMA_ARG_SSL_KEY_FILE) |
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
-| `--chat-template-kwargs STRING` | JSON object containing additional params for the json template parser. Example: `--chat_template_kwargs "{\"enable_thinking\":false}`"<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
+| `--chat-template-kwargs STRING` | sets additional params for the json template parser<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
 | `-to, --timeout N` | server read/write timeout in seconds (default: 600)<br/>(env: LLAMA_ARG_TIMEOUT) |
 | `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
 | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)<br/>[(card)](https://ggml.ai/f0.png)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
 | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
-| `--slots` | enable slots monitoring endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_SLOTS) |
 | `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
+| `--slots` | enable slots monitoring endpoint (default: enabled)<br/>(env: LLAMA_ARG_ENDPOINT_SLOTS) |
 | `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
 | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
 | `--jinja` | use jinja template for chat (default: disabled)<br/>(env: LLAMA_ARG_JINJA) |
-| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)<br/>(default: deepseek)<br/>(env: LLAMA_ARG_THINK) |
+| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
-| `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/>(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
+| `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
+| `-tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
 | `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
 | `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
 | `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
@@ -187,8 +206,7 @@ The project is under active development, and we are [looking for feedback and co
 | `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
 | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
-| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for speculative decoding model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
-| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for speculative decoding model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
+| `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
 | `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) |
 | `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall |
 | `--embd-bge-small-en-default` | use default bge-small-en-v1.5 model (note: can download weights from the internet) |
@@ -199,6 +217,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--fim-qwen-7b-default` | use default Qwen 2.5 Coder 7B (note: can download weights from the internet) |
 | `--fim-qwen-7b-spec` | use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can download weights from the internet) |
 | `--fim-qwen-14b-spec` | use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note: can download weights from the internet) |
+| `--fim-qwen-30b-default` | use default Qwen 3 Coder 30B A3B Instruct (note: can download weights from the internet) |
 
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
@@ -865,25 +884,23 @@ Same as the `/v1/embeddings` endpoint.
 
 ### GET `/slots`: Returns the current slots processing state
 
-> [!WARNING]
-> This endpoint is intended for debugging and may be modified in future versions. For security reasons, we strongly advise against enabling it in production environments.
-
-This endpoint is disabled by default and can be enabled with `--slots`
+This endpoint is enabled by default and can be disabled with `--no-slots`. It can be used to query various per-slot metrics, such as speed, processed tokens, sampling parameters, etc.
 
 If query param `?fail_on_no_slot=1` is set, this endpoint will respond with status code 503 if there is no available slots.
 
 **Response format**
 
-Example:
+<details>
+<summary>Example with 2 slots</summary>
 
 ```json
 [
   {
     "id": 0,
-    "id_task": -1,
-    "n_ctx": 1024,
+    "id_task": 135,
+    "n_ctx": 65536,
     "speculative": false,
-    "is_processing": false,
+    "is_processing": true,
     "params": {
       "n_predict": -1,
       "seed": 4294967295,
@@ -893,6 +910,7 @@ Example:
       "top_k": 40,
       "top_p": 0.949999988079071,
       "min_p": 0.05000000074505806,
+      "top_n_sigma": -1.0,
       "xtc_probability": 0.0,
       "xtc_threshold": 0.10000000149011612,
       "typical_p": 1.0,
@@ -903,17 +921,10 @@ Example:
       "dry_multiplier": 0.0,
       "dry_base": 1.75,
       "dry_allowed_length": 2,
-      "dry_penalty_last_n": -1,
-      "dry_sequence_breakers": [
-        "\n",
-        ":",
-        "\"",
-        "*"
-      ],
+      "dry_penalty_last_n": 131072,
       "mirostat": 0,
       "mirostat_tau": 5.0,
       "mirostat_eta": 0.10000000149011612,
-      "stop": [],
       "max_tokens": -1,
       "n_keep": 0,
       "n_discard": 0,
@@ -921,8 +932,12 @@ Example:
       "stream": true,
       "n_probs": 0,
       "min_keep": 0,
-      "grammar": "",
+      "chat_format": "GPT-OSS",
+      "reasoning_format": "none",
+      "reasoning_in_content": false,
+      "thinking_forced_open": false,
       "samplers": [
+        "penalties",
         "dry",
         "top_k",
         "typ_p",
@@ -932,22 +947,89 @@ Example:
         "temperature"
       ],
       "speculative.n_max": 16,
-      "speculative.n_min": 5,
-      "speculative.p_min": 0.8999999761581421,
-      "timings_per_token": false
+      "speculative.n_min": 0,
+      "speculative.p_min": 0.75,
+      "timings_per_token": false,
+      "post_sampling_probs": false,
+      "lora": []
     },
-    "prompt": "",
     "next_token": {
       "has_next_token": true,
       "has_new_line": false,
       "n_remain": -1,
-      "n_decoded": 0,
-      "stopping_word": ""
+      "n_decoded": 0
+    }
+  },
+  {
+    "id": 1,
+    "id_task": 0,
+    "n_ctx": 65536,
+    "speculative": false,
+    "is_processing": true,
+    "params": {
+      "n_predict": -1,
+      "seed": 4294967295,
+      "temperature": 0.800000011920929,
+      "dynatemp_range": 0.0,
+      "dynatemp_exponent": 1.0,
+      "top_k": 40,
+      "top_p": 0.949999988079071,
+      "min_p": 0.05000000074505806,
+      "top_n_sigma": -1.0,
+      "xtc_probability": 0.0,
+      "xtc_threshold": 0.10000000149011612,
+      "typical_p": 1.0,
+      "repeat_last_n": 64,
+      "repeat_penalty": 1.0,
+      "presence_penalty": 0.0,
+      "frequency_penalty": 0.0,
+      "dry_multiplier": 0.0,
+      "dry_base": 1.75,
+      "dry_allowed_length": 2,
+      "dry_penalty_last_n": 131072,
+      "mirostat": 0,
+      "mirostat_tau": 5.0,
+      "mirostat_eta": 0.10000000149011612,
+      "max_tokens": -1,
+      "n_keep": 0,
+      "n_discard": 0,
+      "ignore_eos": false,
+      "stream": true,
+      "n_probs": 0,
+      "min_keep": 0,
+      "chat_format": "GPT-OSS",
+      "reasoning_format": "none",
+      "reasoning_in_content": false,
+      "thinking_forced_open": false,
+      "samplers": [
+        "penalties",
+        "dry",
+        "top_k",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature"
+      ],
+      "speculative.n_max": 16,
+      "speculative.n_min": 0,
+      "speculative.p_min": 0.75,
+      "timings_per_token": false,
+      "post_sampling_probs": false,
+      "lora": []
+    },
+    "next_token": {
+      "has_next_token": true,
+      "has_new_line": true,
+      "n_remain": -1,
+      "n_decoded": 136
     }
   }
 ]
 ```
 
+</details>
+
 ### GET `/metrics`: Prometheus compatible metrics exporter
 
 This endpoint is only accessible if `--metrics` is set.
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 6aa319d2f1..aebd886ea2 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -141,7 +141,7 @@ struct slot_params {
     // Embeddings
     int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)
 
-    json to_json() const {
+    json to_json(bool only_metrics = false) const {
         std::vector<std::string> samplers;
         samplers.reserve(sampling.samplers.size());
         for (const auto & sampler : sampling.samplers) {
@@ -153,9 +153,55 @@ struct slot_params {
             lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
         }
 
+        if (only_metrics) {
+            return json {
+                {"n_predict",                 n_predict},     // Server configured n_predict
+                {"seed",                      sampling.seed},
+                {"temperature",               sampling.temp},
+                {"dynatemp_range",            sampling.dynatemp_range},
+                {"dynatemp_exponent",         sampling.dynatemp_exponent},
+                {"top_k",                     sampling.top_k},
+                {"top_p",                     sampling.top_p},
+                {"min_p",                     sampling.min_p},
+                {"top_n_sigma",               sampling.top_n_sigma},
+                {"xtc_probability",           sampling.xtc_probability},
+                {"xtc_threshold",             sampling.xtc_threshold},
+                {"typical_p",                 sampling.typ_p},
+                {"repeat_last_n",             sampling.penalty_last_n},
+                {"repeat_penalty",            sampling.penalty_repeat},
+                {"presence_penalty",          sampling.penalty_present},
+                {"frequency_penalty",         sampling.penalty_freq},
+                {"dry_multiplier",            sampling.dry_multiplier},
+                {"dry_base",                  sampling.dry_base},
+                {"dry_allowed_length",        sampling.dry_allowed_length},
+                {"dry_penalty_last_n",        sampling.dry_penalty_last_n},
+                {"mirostat",                  sampling.mirostat},
+                {"mirostat_tau",              sampling.mirostat_tau},
+                {"mirostat_eta",              sampling.mirostat_eta},
+                {"max_tokens",                n_predict}, // User configured n_predict
+                {"n_keep",                    n_keep},
+                {"n_discard",                 n_discard},
+                {"ignore_eos",                sampling.ignore_eos},
+                {"stream",                    stream},
+                {"n_probs",                   sampling.n_probs},
+                {"min_keep",                  sampling.min_keep},
+                {"chat_format",               common_chat_format_name(oaicompat_chat_syntax.format)},
+                {"reasoning_format",          common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)},
+                {"reasoning_in_content",      oaicompat_chat_syntax.reasoning_in_content},
+                {"thinking_forced_open",      oaicompat_chat_syntax.thinking_forced_open},
+                {"samplers",                  samplers},
+                {"speculative.n_max",         speculative.n_max},
+                {"speculative.n_min",         speculative.n_min},
+                {"speculative.p_min",         speculative.p_min},
+                {"timings_per_token",         timings_per_token},
+                {"post_sampling_probs",       post_sampling_probs},
+                {"lora",                      lora},
+            };
+        }
+
         auto grammar_triggers = json::array();
         for (const auto & trigger : sampling.grammar_triggers) {
-            server_grammar_trigger ct(std::move(trigger));
+            server_grammar_trigger ct(trigger);
             grammar_triggers.push_back(ct.to_json());
         }
 
@@ -1572,7 +1618,26 @@ struct server_slot {
         }
     }
 
-    json to_json() const {
+    json to_json(bool only_metrics = false) const {
+        if (only_metrics) {
+            return json {
+                {"id",            id},
+                {"id_task",       id_task},
+                {"n_ctx",         n_ctx},
+                {"speculative",   can_speculate()},
+                {"is_processing", is_processing()},
+                {"params",        params.to_json(true)},
+                {"next_token",
+                    {
+                        {"has_next_token", has_next_token},
+                        {"has_new_line",   has_new_line},
+                        {"n_remain",       n_remaining},
+                        {"n_decoded",      n_decoded},
+                    }
+                },
+            };
+        }
+
         return json {
             {"id",            id},
             {"id_task",       id_task},
@@ -2874,7 +2939,7 @@ struct server_context {
                     int n_processing_slots = 0;
 
                     for (server_slot & slot : slots) {
-                        json slot_data = slot.to_json();
+                        json slot_data = slot.to_json(true);
 
                         if (slot.is_processing()) {
                             n_processing_slots++;
@@ -4271,16 +4336,20 @@ int main(int argc, char ** argv) {
         }
     };
 
-    const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+    const auto handle_props = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
         // this endpoint is publicly available, please only return what is safe to be exposed
         json data = {
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model.path },
-            { "modalities",                  json{
+            { "modalities",                  json {
                 {"vision", ctx_server.oai_parser_opt.allow_image},
                 {"audio",  ctx_server.oai_parser_opt.allow_audio},
             } },
+            { "endpoint_slots",              params.endpoint_slots },
+            { "endpoint_props",              params.endpoint_props },
+            { "endpoint_metrics",            params.endpoint_metrics },
+            { "webui",                       params.webui },
             { "chat_template",               common_chat_templates_source(ctx_server.chat_templates.get()) },
             { "bos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_bos(ctx_server.vocab), /* special= */ true)},
             { "eos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_eos(ctx_server.vocab), /* special= */ true)},
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index 82f7215d53..cda7434d7c 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -148,6 +148,8 @@ class ServerProcess:
             server_args.append("--metrics")
         if self.server_slots:
             server_args.append("--slots")
+        else:
+            server_args.append("--no-slots")
         if self.pooling:
             server_args.extend(["--pooling", self.pooling])
         if self.model_alias:

From e92d53b29e393fc4c0f9f1f7c3fe651be8d36faa Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 31 Aug 2025 20:41:02 +0300
Subject: [PATCH 241/287] sampling : optimize samplers by reusing bucket sort
 (#15665)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* sampling : optimize sorting using bucket sort in more places

ggml-ci

* sampling : do not sort in dist sampler

ggml-ci

* sampling : avoid heap allocations for sort buffers

ggml-ci

* common : add option to sort sampling candidates by probability

ggml-ci

* sampling : revert the change for preserving sort buffers

* sampling : use std::copy instead of memcpy

* sampling : clarify purpose of partial sort helpers

ggml-ci

* cont : remove wrong comment [no ci]

* common : update comment

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 common/sampling.cpp                  |  25 ++-
 common/sampling.h                    |   4 +-
 common/speculative.cpp               |   2 +-
 examples/speculative/speculative.cpp |   4 +-
 include/llama.h                      |   7 +-
 src/llama-sampling.cpp               | 289 +++++++++++++++------------
 tests/test-sampling.cpp              |  60 +++---
 tools/server/server.cpp              |   5 +-
 tools/tts/tts.cpp                    |   2 +-
 9 files changed, 227 insertions(+), 171 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 9c04d35fd0..c710ee173c 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -426,8 +426,29 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
 
 // helpers
 
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
-    return &gsmpl->cur_p;
+llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort) {
+    auto * res = &gsmpl->cur_p;
+
+    if (do_sort && !res->sorted) {
+        // remember the selected token before sorting
+        const llama_token id = res->data[res->selected].id;
+
+        std::sort(res->data, res->data + res->size, [](const llama_token_data & a, const llama_token_data & b) {
+            return a.p > b.p;
+        });
+
+        // restore the selected token after sorting
+        for (size_t i = 0; i < res->size; ++i) {
+            if (res->data[i].id == id) {
+                res->selected = i;
+                break;
+            }
+        }
+
+        res->sorted = true;
+    }
+
+    return res;
 }
 
 llama_token common_sampler_last(const struct common_sampler * gsmpl) {
diff --git a/common/sampling.h b/common/sampling.h
index 2064421db4..e198eecda3 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -86,7 +86,9 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 // helpers
 
 // access the internal list of current candidate tokens
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl);
+// if do_sort == true, the candidates are guaranteed to be sorted afterwards (in descending order of probability)
+// the .sorted flag of the result indicates whether the returned candidates are sorted
+llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort);
 
 // get the last accepted token
 llama_token common_sampler_last(const struct common_sampler * gsmpl);
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 262b2c23e7..3e83b0964c 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -317,7 +317,7 @@ llama_tokens common_speculative_gen_draft(
 
         common_sampler_sample(smpl, ctx_dft, 0, true);
 
-        const auto * cur_p = common_sampler_get_candidates(smpl);
+        const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
         for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
             LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 8449406a6d..5f5ac5eb64 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -244,7 +244,7 @@ int main(int argc, char ** argv) {
                     // stochastic verification
                     common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
 
-                    auto & dist_tgt = *common_sampler_get_candidates(smpl);
+                    auto & dist_tgt = *common_sampler_get_candidates(smpl, true);
 
                     float p_tgt = 0.0f;
                     float p_dft = 0.0f;
@@ -493,7 +493,7 @@ int main(int argc, char ** argv) {
 
                 common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
 
-                const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl);
+                const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true);
 
                 for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
                     LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
diff --git a/include/llama.h b/include/llama.h
index 346135c71e..11f8a363a5 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -206,7 +206,7 @@ extern "C" {
         llama_token_data * data;
         size_t size;
         int64_t selected; // this is the index in the data array (i.e. not the token id)
-        bool sorted;
+        bool sorted;      // note: do not assume the data is sorted - always check this flag
     } llama_token_data_array;
 
     typedef bool (*llama_progress_callback)(float progress, void * user_data);
@@ -1156,11 +1156,6 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
     LLAMA_API struct llama_sampler * llama_sampler_init_dist  (uint32_t seed);
 
-    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-    /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
-    DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax    (void),
-        "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
-
     /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
     /// Setting k <= 0 makes this a noop
     LLAMA_API struct llama_sampler * llama_sampler_init_top_k      (int32_t k);
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index bfbf5fa230..e8c0fc3418 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -128,6 +128,89 @@ struct ring_buffer {
     std::vector<T> data;
 };
 
+// writes result in res, does not mutate cur
+static void llama_token_data_array_partial_sort(const llama_token_data_array & cur, int npartial, std::vector<llama_token_data> & res) {
+    static const auto comp = [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit > b.logit;
+    };
+
+    constexpr int   nbuckets     = 128;
+    constexpr float bucket_low   = -10.0f;
+    constexpr float bucket_high  =  10.0f;
+    constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
+    constexpr float bucket_inter = -bucket_low * bucket_scale;
+
+    std::vector<int> bucket_idx;
+    std::vector<int> histo(nbuckets, 0);
+
+    std::vector<llama_token_data*> bucket_ptrs;
+
+    bucket_idx.reserve(cur.size);
+
+    for (int i = 0; i < (int)cur.size; ++i) {
+        const float val = cur.data[i].logit;
+        int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
+        ib = std::max(0, std::min(nbuckets - 1, ib));
+        bucket_idx.push_back(ib);
+        ++histo[ib];
+    }
+    int nhave = 0;
+    int ib = nbuckets - 1;
+    for ( ; ib >= 0; --ib) {
+        nhave += histo[ib];
+        if (nhave >= npartial) {
+            break;
+        }
+    }
+    res.resize(nhave);
+    auto * ptr = res.data();
+    bucket_ptrs.reserve(nbuckets - ib);
+    for (int j = nbuckets - 1; j >= ib; --j) {
+        bucket_ptrs.push_back(ptr);
+        ptr += histo[j];
+    }
+    for (int i = 0; i < (int)cur.size; ++i) {
+        int j = bucket_idx[i];
+        if (j >= ib) {
+            *bucket_ptrs[nbuckets - 1 - j]++ = cur.data[i];
+        }
+    }
+
+    ptr = res.data();
+    int ndone = 0;
+    for (int j = nbuckets - 1; j > ib; --j) {
+        std::sort(ptr, ptr + histo[j], comp);
+        ptr += histo[j];
+        ndone += histo[j];
+    }
+    std::partial_sort(ptr, ptr + npartial - ndone, ptr + histo[ib], comp);
+}
+
+// reduces the size of cur_p to npartial, keeping only the top npartial elements
+static void llama_token_data_array_partial_sort_inplace(llama_token_data_array * cur_p, int npartial) {
+    static const auto comp = [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit > b.logit;
+    };
+
+    if (npartial <= 128) {
+        std::partial_sort(cur_p->data, cur_p->data + npartial, cur_p->data + cur_p->size, comp);
+
+        cur_p->size = npartial;
+        cur_p->sorted = true;
+
+        return;
+    }
+
+    std::vector<llama_token_data> tmp;
+
+    llama_token_data_array_partial_sort(*cur_p, npartial, tmp);
+
+    std::copy(tmp.data(), tmp.data() + npartial, cur_p->data);
+
+    cur_p->size = npartial;
+    cur_p->sorted = true;
+}
+
 static int llama_sample_dist(llama_token_data_array * cur_p, std::mt19937 & rng) {
     // iterator for the probabilities
 #ifdef __GNUC__
@@ -200,18 +283,21 @@ static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp)
     }
 }
 
-static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) {
+static void llama_sampler_softmax_impl(llama_token_data_array * cur_p, bool do_sort) {
     GGML_ASSERT(cur_p->size > 0);
 
-    // Sort the logits in descending order
-    if (!cur_p->sorted) {
-        std::sort(cur_p->data, cur_p->data + cur_p->size, [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        });
-        cur_p->sorted = true;
+    // Sort the logits in descending order if requested
+    if (do_sort && !cur_p->sorted) {
+        llama_token_data_array_partial_sort_inplace(cur_p, cur_p->size);
     }
 
     float max_l = cur_p->data[0].logit;
+    if (!cur_p->sorted) {
+        for (size_t i = 1; i < cur_p->size; ++i) {
+            max_l = std::max(max_l, cur_p->data[i].logit);
+        }
+    }
+
     float cum_sum = 0.0f;
 
     for (size_t i = 0; i < cur_p->size; ++i) {
@@ -226,7 +312,6 @@ static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) {
 }
 
 static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) {
-    // TODO: move bucket sort to separate function so that top_p/typical/softmax first is equally fast
     // if (k >= (int32_t)cur_p->size) {
     //     return;
     // }
@@ -239,64 +324,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
 
     // Sort scores in descending order
     if (!cur_p->sorted) {
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        };
-        if (k <= 128) {
-            std::partial_sort(cur_p->data, cur_p->data + k, cur_p->data + cur_p->size, comp);
-        } else {
-            constexpr int   nbuckets     = 128;
-            constexpr float bucket_low   = -10.0f;
-            constexpr float bucket_high  =  10.0f;
-            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
-            constexpr float bucket_inter = -bucket_low * bucket_scale;
-
-            std::vector<int> bucket_idx(cur_p->size);
-            std::vector<int> histo(nbuckets, 0);
-
-            for (int i = 0; i < (int)cur_p->size; ++i) {
-                const float val = cur_p->data[i].logit;
-                int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
-                ib = std::max(0, std::min(nbuckets - 1, ib));
-                bucket_idx[i] = ib;
-                ++histo[ib];
-            }
-            int nhave = 0;
-            int ib = nbuckets - 1;
-            for ( ; ib >= 0; --ib) {
-                nhave += histo[ib];
-                if (nhave >= k) {
-                    break;
-                }
-            }
-            std::vector<llama_token_data> tmp_tokens(nhave);
-            auto * ptr = tmp_tokens.data();
-            std::vector<llama_token_data*> bucket_ptrs;
-            bucket_ptrs.reserve(nbuckets - ib);
-            for (int j = nbuckets - 1; j >= ib; --j) {
-                bucket_ptrs.push_back(ptr);
-                ptr += histo[j];
-            }
-            for (int i = 0; i < (int)cur_p->size; ++i) {
-                int j = bucket_idx[i];
-                if (j >= ib) {
-                    *bucket_ptrs[nbuckets - 1 - j]++ = cur_p->data[i];
-                }
-            }
-
-            ptr = tmp_tokens.data();
-            int ndone = 0;
-            for (int j = nbuckets - 1; j > ib; --j) {
-                std::sort(ptr, ptr + histo[j], comp);
-                ptr += histo[j];
-                ndone += histo[j];
-            }
-            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
-
-            std::memcpy(cur_p->data, tmp_tokens.data(), k*sizeof(llama_token_data));
-
-        }
-        cur_p->sorted = true;
+        llama_token_data_array_partial_sort_inplace(cur_p, k);
     }
 
     cur_p->size = k;
@@ -576,7 +604,8 @@ static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl*
 static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_dist *) smpl->ctx;
 
-    llama_sampler_softmax_impl(cur_p);
+    // sorting is not necessary here
+    llama_sampler_softmax_impl(cur_p, false);
 
     cur_p->selected = llama_sample_dist(cur_p, ctx->rng);
 }
@@ -626,32 +655,6 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
     );
 }
 
-// softmax
-
-static const char * llama_sampler_softmax_name(const struct llama_sampler * /*smpl*/) {
-    return "softmax";
-}
-
-static void llama_sampler_softmax_apply(struct llama_sampler * /*smpl*/, llama_token_data_array * cur_p) {
-    llama_sampler_softmax_impl(cur_p);
-}
-
-static struct llama_sampler_i llama_sampler_softmax_i = {
-    /* .name   = */ llama_sampler_softmax_name,
-    /* .accept = */ nullptr,
-    /* .apply  = */ llama_sampler_softmax_apply,
-    /* .reset  = */ nullptr,
-    /* .clone  = */ nullptr,
-    /* .free   = */ nullptr,
-};
-
-struct llama_sampler * llama_sampler_init_softmax() {
-    return llama_sampler_init(
-        /* .iface = */ &llama_sampler_softmax_i,
-        /* .ctx   = */ nullptr
-    );
-}
-
 // top-k
 
 struct llama_sampler_top_k {
@@ -663,7 +666,7 @@ static const char * llama_sampler_top_k_name(const struct llama_sampler * /*smpl
 }
 
 static void llama_sampler_top_k_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_top_k *) smpl->ctx;
+    auto * ctx = (llama_sampler_top_k *) smpl->ctx;
     llama_sampler_top_k_impl(cur_p, ctx->k);
 }
 
@@ -699,6 +702,8 @@ struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
 struct llama_sampler_top_p {
     const float  p;
     const size_t min_keep;
+
+    std::vector<llama_token_data> buf_sort;
 };
 
 static const char * llama_sampler_top_p_name(const struct llama_sampler * /*smpl*/) {
@@ -706,20 +711,35 @@ static const char * llama_sampler_top_p_name(const struct llama_sampler * /*smpl
 }
 
 static void llama_sampler_top_p_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_top_p *) smpl->ctx;
+    auto * ctx = (llama_sampler_top_p *) smpl->ctx;
 
     if (ctx->p >= 1.0f) {
         return;
     }
 
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, false);
+
+    size_t k = cur_p->size;
+    auto * pdata = cur_p->data;
+
+    auto & buf_sort = ctx->buf_sort;
+
+    // if not sorted, try adaptive top-k sorting
+    if (!cur_p->sorted && cur_p->size > 1024) {
+        k = std::min<size_t>(256, cur_p->size);
+        llama_token_data_array_partial_sort(*cur_p, k, buf_sort);
+        pdata = buf_sort.data();
+    } else if (!cur_p->sorted) {
+        // small candidates -> sort inplace
+        llama_token_data_array_partial_sort_inplace(cur_p, k);
+    }
 
     // Compute the cumulative probabilities
     float cum_sum = 0.0f;
     size_t last_idx = cur_p->size;
 
     for (size_t i = 0; i < cur_p->size; ++i) {
-        cum_sum += cur_p->data[i].p;
+        cum_sum += pdata[i].p;
 
         // Check if the running sum is at least p or if we have kept at least min_keep tokens
         // we set the last index to i+1 to indicate that the current iterate should be included in the set
@@ -727,9 +747,21 @@ static void llama_sampler_top_p_apply(struct llama_sampler * smpl, llama_token_d
             last_idx = i + 1;
             break;
         }
+
+        // we exceeded the current top-k heuristic -> increase k and continue
+        if (!cur_p->sorted && i == k - 1) {
+            k = cur_p->size;
+            llama_token_data_array_partial_sort(*cur_p, k, buf_sort);
+            pdata = buf_sort.data();
+        }
     }
 
     // Resize the output vector to keep only the top-p tokens
+    if (!cur_p->sorted) {
+        std::copy(buf_sort.data(), buf_sort.data() + last_idx, cur_p->data);
+        cur_p->sorted = true;
+    }
+
     cur_p->size = last_idx;
 }
 
@@ -757,6 +789,7 @@ struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
         /* .ctx   = */ new llama_sampler_top_p {
             /* .p        = */ p,
             /* .min_keep = */ min_keep,
+            /* .buf_sort = */ {},
         }
     );
 }
@@ -773,7 +806,7 @@ static const char * llama_sampler_min_p_name(const struct llama_sampler * /*smpl
 }
 
 static void llama_sampler_min_p_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_min_p *) smpl->ctx;
+    auto * ctx = (llama_sampler_min_p *) smpl->ctx;
 
     if (ctx->p <= 0.0f || !cur_p->size) {
         return;
@@ -799,7 +832,7 @@ static void llama_sampler_min_p_apply(struct llama_sampler * smpl, llama_token_d
 
         // if we have enough values the operation was a success
         if (!filtered_tokens.empty() && filtered_tokens.size() >= ctx->min_keep) {
-            memcpy(cur_p->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
+            std::copy(filtered_tokens.begin(), filtered_tokens.end(), cur_p->data);
             cur_p->size = filtered_tokens.size();
             min_p_applied = true;
         }
@@ -809,10 +842,7 @@ static void llama_sampler_min_p_apply(struct llama_sampler * smpl, llama_token_d
     if (!min_p_applied) {
         // Sort the logits in descending order
         if (!cur_p->sorted) {
-            std::sort(cur_p->data, cur_p->data + cur_p->size, [](const llama_token_data & a, const llama_token_data & b) {
-                return a.logit > b.logit;
-            });
-            cur_p->sorted = true;
+            llama_token_data_array_partial_sort_inplace(cur_p, cur_p->size);
         }
 
         const float min_logit = cur_p->data[0].logit + logf(ctx->p); // min logit for p_i >= p * p_max
@@ -869,7 +899,7 @@ static const char * llama_sampler_typical_name(const struct llama_sampler * /*sm
 }
 
 static void llama_sampler_typical_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_typical *) smpl->ctx;
+    auto * ctx = (llama_sampler_typical *) smpl->ctx;
 
     // Reference implementation:
     // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
@@ -878,7 +908,7 @@ static void llama_sampler_typical_apply(struct llama_sampler * smpl, llama_token
     }
 
     // Compute the softmax of logits and calculate entropy
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
     float entropy = 0.0f;
     for (size_t i = 0; i < cur_p->size; ++i) {
@@ -1012,7 +1042,7 @@ static const char * llama_sampler_temp_ext_name(const struct llama_sampler * /*s
 }
 
 static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_temp_ext *) smpl->ctx;
+    auto * ctx = (llama_sampler_temp_ext *) smpl->ctx;
     if (ctx->delta > 0) {
         const float min_temp = std::max(0.0f, ctx->temp - ctx->delta);
         const float max_temp = ctx->temp + ctx->delta;
@@ -1027,7 +1057,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke
         // Calculate maximum possible entropy
         float max_entropy = -logf(1.0f / cur_p->size);
 
-        llama_sampler_softmax_impl(cur_p);
+        llama_sampler_softmax_impl(cur_p, true);
 
         // Calculate entropy of the softmax probabilities
         float entropy = 0.0f;
@@ -1121,7 +1151,7 @@ struct llama_sampler_xtc {
     const uint32_t seed;
     uint32_t       seed_cur;
 
-    std::mt19937   rng;
+    std::mt19937    rng;
 };
 
 static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) {
@@ -1139,17 +1169,20 @@ static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data
 
     std::uniform_real_distribution<float> distribution(0.0f, 1.0f);
     float chance = distribution(ctx->rng);
-    if (chance > ctx->probability) return;
+    if (chance > ctx->probability) {
+        return;
+    }
 
-    // in case it's not sorted/recalculated yet
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
     int pos_last = 0;
 
     for (size_t i = 0; i < cur_p->size; ++i) {
         if (cur_p->data[i].p >= ctx->threshold) {
             pos_last = i;
-        } else break;
+        } else {
+            break;
+        }
     }
 
     if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) {
@@ -1221,7 +1254,7 @@ struct llama_sampler_mirostat {
 
     float mu;
 
-    std::mt19937 rng;
+    std::mt19937    rng;
 };
 
 static const char * llama_sampler_mirostat_name(const struct llama_sampler * /*smpl*/) {
@@ -1231,7 +1264,7 @@ static const char * llama_sampler_mirostat_name(const struct llama_sampler * /*s
 static void llama_sampler_mirostat_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_mirostat *) smpl->ctx;
 
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
     // Estimate s_hat using the most probable m tokens
     float s_hat = 0.0;
@@ -1250,7 +1283,8 @@ static void llama_sampler_mirostat_apply(struct llama_sampler * smpl, llama_toke
     float k = powf((epsilon_hat * powf(2, ctx->mu)) / (1 - powf(ctx->n_vocab, -epsilon_hat)), 1 / s_hat);
 
     llama_sampler_top_k_impl(cur_p, std::max(int(k), 1));
-    llama_sampler_softmax_impl(cur_p);
+
+    llama_sampler_softmax_impl(cur_p, true);
 
     const int idx = llama_sample_dist(cur_p, ctx->rng);
 
@@ -1336,7 +1370,7 @@ static const char * llama_sampler_mirostat_v2_name(const struct llama_sampler *
 static void llama_sampler_mirostat_v2_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_mirostat_v2 *) smpl->ctx;
 
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
     // Truncate the words with surprise values greater than mu
     cur_p->size = std::distance(cur_p->data, std::find_if(cur_p->data, cur_p->data + cur_p->size, [&](const llama_token_data & candidate) {
@@ -1348,7 +1382,7 @@ static void llama_sampler_mirostat_v2_apply(struct llama_sampler * smpl, llama_t
     }
 
     // Normalize the probabilities of the remaining words
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
     const int idx = llama_sample_dist(cur_p, ctx->rng);
 
@@ -1540,7 +1574,7 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
                 trigger_pattern += std::regex_replace(trigger_words[i], special_chars, "\\$0");
             }
             trigger_pattern += ")[\\s\\S]*";
-            auto trigger_pattern_c = trigger_pattern.c_str();
+            const auto * trigger_pattern_c = trigger_pattern.c_str();
             trigger_patterns = &trigger_pattern_c;
             num_trigger_patterns = 1;
         }
@@ -1748,7 +1782,7 @@ static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler *
 }
 
 static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx;
+    auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx;
 
     if (ctx->n <= 0.0f || cur_p->size <= 1) {
         return;
@@ -1780,13 +1814,14 @@ static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_t
     }
     float std = valid_count > 0 ? sqrt(acc/valid_count) : 0;
 
-    //apply mask
+    // apply mask
     for (size_t i = 0; i < cur_p->size; ++i) {
         if (cur_p->data[i].logit < max - (ctx->n * std)) {
             cur_p->data[i].logit = -INFINITY;
         }
     }
-    llama_sampler_softmax_impl(cur_p);
+
+    llama_sampler_softmax_impl(cur_p, true);
 }
 
 static struct llama_sampler * llama_sampler_top_n_sigma_clone(const struct llama_sampler * smpl) {
@@ -1991,7 +2026,9 @@ static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_dat
 
     {
         const int last = last_n_repeat - 1;
-        int rt = 0, lt = 0;
+
+        int rt = 0;
+        int lt = 0;
 
         for (int k = 1; k < last_n_repeat; ++k) {
             if (k > rt) {
@@ -2135,8 +2172,8 @@ static struct llama_sampler_i llama_sampler_dry_i = {
     /* .free   = */ llama_sampler_dry_free,
 };
 
-struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
-    int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? context_size : std::max(dry_penalty_last_n, 0);
+struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t n_ctx_train, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
+    int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? n_ctx_train : std::max(dry_penalty_last_n, 0);
     std::unordered_multimap<llama_token, std::vector<llama_token>> processed_breakers;
     const int MAX_CHAR_LEN = 40;
     const int MAX_SEQ_LEN = 20;
@@ -2169,7 +2206,7 @@ struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab,
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_dry_i,
         /* .ctx   = */ new llama_sampler_dry {
-            /* .total_context_size     = */ context_size,
+            /* .total_context_size     = */ n_ctx_train,
             /* .dry_multiplier         = */ dry_multiplier,
             /* .dry_base               = */ dry_base,
             /* .dry_allowed_length     = */ dry_allowed_length,
@@ -2308,7 +2345,7 @@ static const char * llama_sampler_infill_name(const struct llama_sampler * /*smp
 static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_infill *) smpl->ctx;
 
-    llama_sampler_softmax_impl(cur_p);
+    llama_sampler_softmax_impl(cur_p, true);
 
 #if defined(GGML_DEBUG_SAMPLER_INFILL)
 #define LOG_DBG_CUR LLAMA_LOG_DEBUG
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 6300f25cae..7cd96c5cd3 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -197,10 +197,10 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
     sampler_tester tester(n_vocab);
 
           llama_token min_token_id = 0;
-    const llama_token max_token_id = n_vocab-1;
+    const llama_token max_token_id = n_vocab - 1;
 
     for (auto s : samplers_sequence) {
-        switch (s){
+        switch (s) {
             case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break;
             case 'y': GGML_ABORT("typical test not implemented");
             case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
@@ -243,10 +243,10 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
             }
 
             GGML_ASSERT(size == expected_size);
-            GGML_ASSERT(cur_p.data[0].id == max_token_id);
-            GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
+            GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
+            GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
         } else if (s == 'm') {
-            int expected_size = ceilf((1.0f-min_p) * n_vocab);
+            int expected_size = ceilf((1.0f - min_p) * n_vocab);
             expected_size = std::max(expected_size, 1);
             expected_size = std::min(expected_size, size);
 
@@ -256,14 +256,14 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
             min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
 
             GGML_ASSERT(size == expected_size);
-            GGML_ASSERT(cur_p.data[0].id == max_token_id);
-            GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
+            GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
+            GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
         } else {
             GGML_ABORT("fatal error");
         }
     }
 
-    printf("Sampler queue %3s OK with n_vocab=%05zu top_k=%05d top_p=%f min_p=%f\n",
+    printf("Sampler queue %3s OK with n_vocab=%05zu top_k=%5d top_p=%f min_p=%f\n",
            samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
 }
 
@@ -308,28 +308,28 @@ static void test_perf() {
 int main(void) {
     ggml_time_init();
 
-    test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f);
-    test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f);
+    test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
+    test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f);
 
-    test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f, 0.0f, 1.0f);
-    test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f, 0.0f, 1.0f);
+    test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f, 0.0f, 1.0f);
+    test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f, 0.0f, 1.0f);
 
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1);
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3);
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
-    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
+    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0);
 
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 0);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f}, 0.7f);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 0.8f);
-    test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f);
+    test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
 
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f);
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f);
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.26f);
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.49f);
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.51f);
-    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.74f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.00f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.24f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f},            0.26f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f},            0.49f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f},                       0.51f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f},                       0.74f);
     test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  0.76f);
     test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.00f);
     test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.05f);
@@ -345,23 +345,23 @@ int main(void) {
     test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f},            0.5f);
     test_typical({0.4f, 0.2f, 0.2f, 0.2f},     {0.2f, 0.2f, 0.2f}, 0.5f);
 
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0},   50.0f, 0.0f, 0.0f);
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0},       50.0f, 0.0f, 0.0f);
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0, 0.25f, 0.25f, 0.25f, 0.25f},   50.0f, 0.0f, 0.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0, 0, 0, 0.5f, 0.5f},       50.0f, 0.0f, 0.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0, 0, 0, 0.5f, 0.5f}, 50.0f, 0.0f, 0.0f);
 
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0},             {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 1.0f, 5.0f, 5.0f);
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
-    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0},             {0.000011f, 0.249997f, 0.249997f, 0.249997f, 0.249997f}, 1.0f, 5.0f, 5.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.000023f, 0.000023f, 0.000023f, 0.499966f, 0.499966f}, 1.0f, 5.0f, 5.0f);
+    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.000000f, 0.000023f, 0.000023f, 0.499977f, 0.499977f}, 1.0f, 5.0f, 5.0f);
 
 
     test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {});
-    test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.296923f, 0.109232f}, 1.0f, 1.1f, 2, 5, {});
+    test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.109232f, 0.296923f}, 1.0f, 1.1f, 2, 5, {});
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}});
-    test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.241818f, 0.241818f, 0.032727f}, 2.0f, 1.1f, 2, 5, {});
+    test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.032727f, 0.241818f, 0.241818f}, 2.0f, 1.1f, 2, 5, {});
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
 
     test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f, 0.0f, 0.0f}, 1.00f);
-    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345
     test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 3.00f);
 
     test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
@@ -372,7 +372,7 @@ int main(void) {
     test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12);
 
     test_sampler_queue(10000, "k",   100, 1.0000f, 1.0f);
-    test_sampler_queue(10000, "p", 10000, 0.0002f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.0003f, 1.0f);
     test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f);
     test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f);
     test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index aebd886ea2..e0302e2f2f 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -2550,11 +2550,12 @@ struct server_context {
         return slot.has_next_token; // continue
     }
 
-    void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) {
+    void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const {
         size_t n_probs = slot.params.sampling.n_probs;
         size_t n_vocab = llama_vocab_n_tokens(vocab);
+
         if (post_sampling) {
-            const auto * cur_p = common_sampler_get_candidates(slot.smpl);
+            const auto * cur_p = common_sampler_get_candidates(slot.smpl, true);
             const size_t max_probs = cur_p->size;
 
             // set probability for sampled token
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index 18f01a9946..eaf56591d9 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -895,7 +895,7 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
 
                 codes.push_back(new_token_id);
 
-                const auto * cands = common_sampler_get_candidates(smpl[i]);
+                const auto * cands = common_sampler_get_candidates(smpl[i], false);
 
                 // is it an end of generation? -> mark the stream as finished
                 if (llama_vocab_is_eog(vocab, new_token_id) || n_decode == n_predict) {

From 3dc7397a2799bdc07bccf637ab7ae5a1e786d1a4 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Mon, 1 Sep 2025 08:57:00 +0800
Subject: [PATCH 242/287] CANN: fix RoPE cache issue on multi-device (#15629)

* CANN: fix RoPE cache issue on multi-device

RoPE cache only needs to be computed once per token.
However, in multi-device scenarios, not every device starts
computation from layer 0, which may lead to unallocated memory
issues and precision errors.

This commit records the first layer of each device to avoid
the above issues.

* CANN: Optimize first-layer detection method

* CANN: Remove trailing whitespace

* CANN: Only cache the data that can be determined as unchanged through the parameters.

* CANN: Update function comment
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 149 ++++++++++++++++---------------
 ggml/src/ggml-cann/common.h      |  51 ++++++-----
 ggml/src/ggml-cann/ggml-cann.cpp |   1 +
 3 files changed, 105 insertions(+), 96 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index c42871c575..1f1d489ffa 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -964,8 +964,8 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     }
     aclTensor* acl_gamma = get_f32_cache_acl_tensor(
         ctx,
-        &ctx.f32_one_cache,
-        ctx.f32_one_cache_element,
+        &ctx.rms_norm_one_tensor_cache.cache,
+        ctx.rms_norm_one_tensor_cache.size,
         src->ne,
         acl_gamma_nb,
         1,        // dims
@@ -980,8 +980,8 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     }
     aclTensor* acl_rstd = get_f32_cache_acl_tensor(
         ctx,
-        &ctx.f32_zero_cache,
-        ctx.f32_zero_cache_element,
+        &ctx.rms_norm_zero_tensor_cache.cache,
+        ctx.rms_norm_zero_tensor_cache.size,
         src->ne,
         acl_rstd_nb,
         GGML_MAX_DIMS,
@@ -2248,43 +2248,31 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
  *   5. Compute sin(θ), cos(θ) and optionally scale by attn_factor.
  *   6. Expand sin/cos values by repeat or repeat_interleave depending
  *      on whether @param is_neox is enabled.
- *   7. Store the computed values into persistent buffers
- *      (ctx.rope_sin_ptr / ctx.rope_cos_ptr).
  *
- * @param ctx         The CANN backend context, holding memory pool,
- *                    stream, and persistent buffers for rope init/cache.
- * @param dst         The destination ggml_tensor whose computation
- *                    depends on the cached RoPE values (usually Qcur/Kcur).
- * @param theta_scale Scalar exponent base for computing theta scale values.
- * @param freq_scale  Frequency scaling factor, applied to theta scale.
- * @param attn_factor Attention scaling factor, applied to sin/cos.
- * @param is_neox     Whether to use Neox-style repeat strategy
- *                    (dim expansion vs repeat_interleave).
+ * @param ctx                The CANN backend context, holding memory pool,
+ *                           stream, and persistent buffers for rope init/cache.
+ * @param dst                The destination ggml_tensor whose computation
+ *                           depends on the RoPE values (usually Qcur/Kcur).
+ * @param sin_tensor_buffer  Pre-allocated buffer for storing repeated sin values.
+ * @param cos_tensor_buffer  Pre-allocated buffer for storing repeated cos values.
+ * @param theta_scale        Scalar exponent base for computing theta scale values.
+ * @param freq_scale         Frequency scaling factor, applied to theta scale.
+ * @param attn_factor        Attention scaling factor, applied to sin/cos.
+ * @param is_neox            Whether to use Neox-style repeat strategy
+ *                           (dim expansion vs repeat_interleave).
  */
 static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
+                             void* sin_tensor_buffer, void* cos_tensor_buffer,
                              float theta_scale, float freq_scale,
                              float attn_factor, bool is_neox) {
     // int sin/cos cache, cache has different repeat method depond on
     // @param.is_neox
-    bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
-    bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);
-
-    // used for accuracy testing
-    bool is_attention = is_q || is_k;
-
-    // just compute in first layer in attention
-    bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
-    if(is_attention && !is_fisrt_layer) {
-        return;
-    }
 
     ggml_tensor* src0 = dst->src[0];  // input
     ggml_tensor* src1 = dst->src[1];  // position
     ggml_tensor* src2 = dst->src[2];  // freq_factors
 
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    int64_t theta_scale_length = ne00 / 2;
+    int64_t theta_scale_length = src0->ne[0] / 2;
     int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1};
     size_t theta_scale_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
                           theta_scale_length * sizeof(float_t)};
@@ -2302,21 +2290,32 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
     }
 
-    // init theta scale, just one time
-    if(ctx.rope_init_ptr == nullptr || !is_attention) {
-        // theta_scale arange, [0,1,...,ne00/2 - 1]
-        if(ctx.rope_init_ptr != nullptr){
-            ACL_CHECK(aclrtFree(ctx.rope_init_ptr));
-        }
-        ACL_CHECK(aclrtMalloc(&ctx.rope_init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+    // theta_scale arange, [0,1,...,ne00/2 - 1]
+    aclTensor* acl_theta_scale_tensor = nullptr;
+    // cache theta scale
+    if (ctx.rope_cache.theta_scale_length != theta_scale_length ||
+        // theta_scale and freq_scale should not change during the current token inference process,
+        // so we can directly use == here instead of comparing the absolute difference.
+        ctx.rope_cache.theta_scale != theta_scale ||
+        ctx.rope_cache.freq_scale != freq_scale) {
 
-        aclTensor* acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
+        ctx.rope_cache.theta_scale_length = theta_scale_length;
+        ctx.rope_cache.theta_scale = theta_scale;
+        ctx.rope_cache.freq_scale = freq_scale;
+
+        if (ctx.rope_cache.theta_scale_cache != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+
+        acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+
         float start = 0;
         float step = 1;
-        float stop = ne00 / 2;
-        float n_elements = ne00 / 2;
+        float stop = theta_scale_length;
+        float n_elements = theta_scale_length;
         aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
 
         // power
@@ -2328,34 +2327,29 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         if (freq_scale != 1) {
             aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true);
         }
-
-        // freq_factors
-        if (src2) {
-            aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
-                src2->data, ggml_cann_type_mapping(src2->type),
-                ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-            aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor);
-            ggml_cann_release_resources(ctx, acl_freq_factors_tensor);
-        }
-        // release
-        ggml_cann_release_resources(ctx, acl_theta_scale_tensor,acl_theta_scale);
-    }
-
-    // init sin_repeat && cos_repeat, one token just init in 0 layer
-    if(position_length > ctx.max_prompt_length) {
-        ctx.max_prompt_length = position_length;
-        int64_t repeat_theta_length = theta_scale_length * ctx.max_prompt_length * 2;
-        if(ctx.rope_sin_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(ctx.rope_sin_ptr));
-            ACL_CHECK(aclrtFree(ctx.rope_cos_ptr));
-        }
-        ACL_CHECK(aclrtMalloc(&ctx.rope_sin_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cos_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
-    }
-
-    aclTensor* acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_release_resources(ctx, acl_theta_scale);
+    } else {
+        // use cache
+        acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+    }
+
+    ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool());
+    // freq_factors
+    if (src2) {
+        freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float_t));
+        void* freq_fac_res_ptr = freq_fac_res_allocator.get();
+        aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
+            src2->data, ggml_cann_type_mapping(src2->type),
+            ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+        aclTensor* acl_freq_fac_res_tensor = ggml_cann_create_tensor(
+            freq_fac_res_ptr, ACL_FLOAT, sizeof(float_t),
+            theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+        aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, acl_freq_fac_res_tensor);
+        std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor);
+        ggml_cann_release_resources(ctx, acl_freq_factors_tensor, acl_freq_fac_res_tensor);
+    }
 
     // position
     aclTensor* acl_position_tensor = ggml_cann_create_tensor(
@@ -2397,17 +2391,17 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true);
     }
 
-    int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
+    int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
     sin_reshape_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_repeat_tensor =
-        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_repeat_tensor =
-        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     // repeat
@@ -2449,6 +2443,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // TODO: use ascendc
     // Only test with LLAMA model.
     ggml_tensor* src0 = dst->src[0];  // input
+    ggml_tensor* src1 = dst->src[1];
 
     // param
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
@@ -2481,8 +2476,16 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
 
+    // sin/cos tensor length.
+    int64_t repeat_theta_length = src0->ne[0] * src1->ne[0];
+    ggml_cann_pool_alloc sin_tensor_allocator(ctx.pool(), repeat_theta_length * sizeof(float));
+    ggml_cann_pool_alloc cos_tensor_allocator(ctx.pool(), repeat_theta_length * sizeof(float));
+    void *sin_tensor_buffer = sin_tensor_allocator.get();
+    void *cos_tensor_buffer = cos_tensor_allocator.get();
+
     // init ctx.rope_cos/rope_sin cache
-    aclnn_cache_init(ctx, dst, theta_scale, freq_scale, attn_factor, is_neox);
+    aclnn_cache_init(ctx, dst, sin_tensor_buffer, cos_tensor_buffer,
+                    theta_scale, freq_scale, attn_factor, is_neox);
 
     int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
@@ -2491,10 +2494,10 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_reshape_tensor =
-        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_reshape_tensor =
-        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     aclTensor* acl_src = ggml_cann_create_tensor(src0);
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index 88cc3f481e..f71aa9d1de 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -360,6 +360,30 @@ struct ggml_cann_graph {
 };
 #endif  // USE_ACL_GRAPH
 
+struct ggml_cann_rope_cache {
+    ~ggml_cann_rope_cache() {
+        if(theta_scale_cache != nullptr) {
+            ACL_CHECK(aclrtFree(theta_scale_cache));
+        }
+    }
+
+    void* theta_scale_cache = nullptr;
+    int64_t theta_scale_length = 0;
+    float theta_scale = 0.0f;
+    float freq_scale = 0.0f;
+};
+
+struct ggml_cann_tensor_cache {
+    ~ggml_cann_tensor_cache() {
+        if(cache != nullptr) {
+            ACL_CHECK(aclrtFree(cache));
+        }
+    }
+
+    void* cache = nullptr;
+    int64_t size = 0;
+};
+
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -375,15 +399,11 @@ struct ggml_backend_cann_context {
     cann_task_queue task_queue;
     bool async_mode;
     // Rope Cache
-    void* rope_init_ptr = nullptr;
-    void* rope_sin_ptr = nullptr;
-    void* rope_cos_ptr = nullptr;
-    int64_t max_prompt_length = 0;
+    ggml_cann_rope_cache rope_cache;
     // Constant Pool
-    void* f32_zero_cache = nullptr;
-    void* f32_one_cache = nullptr;
-    int64_t f32_zero_cache_element = 0;
-    int64_t f32_one_cache_element = 0;
+    ggml_cann_tensor_cache rms_norm_one_tensor_cache;
+    ggml_cann_tensor_cache rms_norm_zero_tensor_cache;
+
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -415,21 +435,6 @@ struct ggml_backend_cann_context {
                 ACL_CHECK(aclrtDestroyStream(streams[i]));
             }
         }
-        if(rope_init_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(rope_init_ptr));
-        }
-        if(rope_sin_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(rope_sin_ptr));
-        }
-        if(rope_cos_ptr != nullptr) {
-            ACL_CHECK(aclrtFree(rope_cos_ptr));
-        }
-        if(f32_zero_cache != nullptr) {
-            ACL_CHECK(aclrtFree(f32_zero_cache));
-        }
-        if(f32_one_cache != nullptr) {
-            ACL_CHECK(aclrtFree(f32_one_cache));
-        }
     }
 
     /**
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 7b3aca9db9..15ea85e273 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2247,6 +2247,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(
         (ggml_backend_cann_context*)backend->context;
     ggml_cann_set_device(cann_ctx->device);
     release_nz_workspace();
+
 #ifdef USE_ACL_GRAPH
     bool use_cann_graph = true;
     bool cann_graph_update_required = false;

From b9382c3877c6067feccf182efe9449a2d1cb24c7 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Mon, 1 Sep 2025 08:57:23 +0800
Subject: [PATCH 243/287] CANN: Optimize MUL_MAT_ID (#15658)

---
 ggml/src/ggml-cann/aclnn_ops.cpp | 193 ++++++-------------------------
 1 file changed, 34 insertions(+), 159 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 1f1d489ffa..84e705af9a 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -2867,174 +2867,49 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){
  */
 static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     //dst   [M, K, N, 1]
-    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
-    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
+    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]  -> [D, M, K, 1]
+    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1 -> [D, 1, K, 1]
     ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
 
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
 
-    // copy index from npu to cpu
-    int64_t n_as = ne02; // A
-    int64_t n_ids = ids->ne[0]; // K
+    int64_t batch = src1->ne[2];
+    GGML_ASSERT(batch == ids->ne[1]);
 
-    std::vector<char> ids_host(ggml_nbytes(ids));
-    ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids),
-        ACL_MEMCPY_DEVICE_TO_HOST);
-    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+    ggml_cann_pool_alloc export_allocator(ctx.pool(), src0->ne[0] * src0->ne[1] * ids->ne[0] * ggml_element_size(src0));
+    void* export_ptr = export_allocator.get();
+    for (int64_t i = 0; i < batch; i++) {
+        aclTensor *select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]);
+        aclTensor *export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3);
 
-    char * src0_original = (char *) src0->data;
-    char * src1_original = (char *) src1->data;
-    char * dst_original  = (char *)  dst->data;
-    size_t ori_src0_nb[4] = {nb00, nb01, nb02, nb03};
-
-    // src0 is F16, src1 is F32, dst is F32
-    ggml_cann_pool_alloc src0_cast_allocator;
-    if (src0->type == GGML_TYPE_F16) {
-        src0_cast_allocator.alloc(ctx.pool(), sizeof(float) * ggml_nelements(src0));
-        void* src0_cast_buf = src0_cast_allocator.get();
-
-        size_t cast_nb[GGML_MAX_DIMS];
-        cast_nb[0] = sizeof(float_t);
-        for (int i = 1; i < GGML_MAX_DIMS; i++) {
-            cast_nb[i] = cast_nb[i - 1] * src0->ne[i - 1];
+        int64_t select_export_ne[] = {src0->ne[0], src0->ne[1], ids->ne[0]};
+        size_t select_export_nb[3];
+        select_export_nb[0] = src0->nb[0];
+        for (int k = 1;k < 3; k++) {
+            select_export_nb[k] = select_export_nb[k-1] * select_export_ne[k-1];
         }
 
-        aclTensor* acl_src0_f16 = ggml_cann_create_tensor(src0);
-        aclTensor* acl_cast = ggml_cann_create_tensor(src0_cast_buf,
-            ACL_FLOAT, sizeof(float), src0->ne, cast_nb, 4);
-        GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src0_f16, ACL_FLOAT, acl_cast);
-        ggml_cann_release_resources(ctx, acl_cast, acl_src0_f16);
+        aclTensor *select_export = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_export_ne, select_export_nb, 3);
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, export_weight, 0, select_index, select_export);
 
-        src0_original = (char *) src0_cast_buf;
-        memcpy(ori_src0_nb, cast_nb, sizeof(ori_src0_nb));
+        int64_t select_transpose_ne[] = {select_export_ne[1], select_export_ne[0], select_export_ne[2]};
+        size_t select_transpose_nb[] = {select_export_nb[1], select_export_nb[0], select_export_nb[2]};
+        aclTensor *select_export_transpose = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_transpose_ne, select_transpose_nb, 3);
+
+        int64_t active_tensor_ne[] = {src1->ne[0], 1, src1->ne[1]};
+        size_t active_tensor_nb[] = {src1->nb[0], src1->nb[1], src1->nb[1]};
+        aclTensor *active_tensor = ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]);
+
+        int64_t dst_ne[] = {dst->ne[0], 1, dst->ne[1]};
+        size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[1]};
+        aclTensor *acl_dst = ggml_cann_create_tensor(dst, dst_ne,dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, active_tensor, select_export_transpose, acl_dst, 2);
+
+        ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, select_export_transpose);
     }
-
-#ifdef ASCEND_310P
-    ggml_tensor src0_row = *src0;
-    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row = *dst;
-
-    if (src0->type == GGML_TYPE_F16) {
-        src0_row.type = GGML_TYPE_F32;
-    }
-
-    // src0_row [D, M, 1, 1] weight without permute
-    src0_row.ne[2] = 1;
-    src0_row.ne[3] = 1;
-    src0_row.nb[0] = ori_src0_nb[0];
-    src0_row.nb[1] = ori_src0_nb[1];
-    src0_row.nb[2] = ori_src0_nb[1];
-    src0_row.nb[3] = ori_src0_nb[1];
-
-    // src1_row [D, 1, 1, 1] -> input
-    src1_row.ne[1] = 1;
-    src1_row.ne[2] = 1;
-    src1_row.ne[3] = 1;
-    src1_row.nb[2] = nb11;
-    src1_row.nb[3] = nb11;
-
-    // dst_row [M, 1, 1, 1] -> out
-    dst_row.ne[1] = 1;
-    dst_row.ne[2] = 1;
-    dst_row.ne[3] = 1;
-    dst_row.nb[2] = nb1;
-    dst_row.nb[3] = nb1;
-
-    //create weight for one row
-    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
-        for (int64_t id = 0; id < n_ids; id++) {
-            // expert index
-            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
-            GGML_ASSERT(i02 >= 0 && i02 < n_as);
-
-            // If B = 1 (broadcast), always use 0; otherwise, use id.
-            int64_t i11 = (ne11 == 1 ? 0 : id);
-            int64_t i12 = iid1;
-
-            int64_t i1 = id;
-            int64_t i2 = i12;
-
-            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
-            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
-            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
-
-            src0_row.data = src0_tmp_ptr;
-            src1_row.data = src1_tmp_ptr;
-            dst_row.data = dst_tmp_ptr;
-            dst_row.src[0] = &src0_row;
-            dst_row.src[1] = &src1_row;
-
-            ggml_cann_mul_mat(ctx, &dst_row);
-        }
-    }
-    return;
-#endif
-
-    std::vector<aclTensor*> src0_tensor_vec;
-    std::vector<aclTensor*> src1_tensor_vec;
-    std::vector<aclTensor*> dst_tensor_vec;
-    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
-        for (int64_t id = 0; id < n_ids; id++) {
-            // src0_row [M, D] -> weight && permute
-            int64_t src0_ne[2] = {ne01, ne00};
-            size_t src0_nb[2] = {ori_src0_nb[1], ori_src0_nb[0]};
-            // src1_row [D, 1] -> input
-            int64_t src1_ne[2] = {ne10, 1};
-            size_t src1_nb[2] = {nb10, nb11};
-            // dst_row [M, 1] -> out
-            int64_t dst_ne[2] = {ne0, 1};
-            size_t dst_nb[2] = {nb0, nb1};
-
-            // expert index
-            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
-            GGML_ASSERT(i02 >= 0 && i02 < n_as);
-
-            // If B = 1 (broadcast), always use 0; otherwise, use id.
-            int64_t i11 = (ne11 == 1 ? 0 : id);
-            int64_t i12 = iid1;
-
-            int64_t i1 = id;
-            int64_t i2 = i12;
-
-            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
-            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
-            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
-
-            aclTensor* acl_src0 = ggml_cann_create_tensor(src0_tmp_ptr,
-                ACL_FLOAT, sizeof(float),
-                src0_ne, src0_nb, 2);
-            aclTensor* acl_src1 = ggml_cann_create_tensor(src1_tmp_ptr,
-                ACL_FLOAT, sizeof(float),
-                src1_ne, src1_nb, 2);
-            aclTensor* acl_dst = ggml_cann_create_tensor(dst_tmp_ptr,
-                ACL_FLOAT, sizeof(float),
-                dst_ne, dst_nb, 2);
-
-            src0_tensor_vec.push_back(acl_src0);
-            src1_tensor_vec.push_back(acl_src1);
-            dst_tensor_vec.push_back(acl_dst);
-        }
-    }
-
-    size_t GROUP_SIZE = 128;
-    // GroupedMatmulV3 required tensor_list.size < 128
-    for (size_t i = 0; i < src0_tensor_vec.size(); i += GROUP_SIZE) {
-        // split and call GroupedMatmulV3
-        size_t end = std::min(i + GROUP_SIZE, src0_tensor_vec.size());
-        std::vector<aclTensor*> src0_tensor_vec_split(src0_tensor_vec.begin() + i, src0_tensor_vec.begin() + end);
-        std::vector<aclTensor*> src1_tensor_vec_split(src1_tensor_vec.begin() + i, src1_tensor_vec.begin() + end);
-        std::vector<aclTensor*> dst_tensor_vec_split(dst_tensor_vec.begin() + i, dst_tensor_vec.begin() + end);
-
-        aclTensorList* src0_tensor_list = aclCreateTensorList(src0_tensor_vec_split.data(), src0_tensor_vec_split.size());
-        aclTensorList* src1_tensor_list = aclCreateTensorList(src1_tensor_vec_split.data(), src1_tensor_vec_split.size());
-        aclTensorList* dst_tensor_list = aclCreateTensorList(dst_tensor_vec_split.data(), dst_tensor_vec_split.size());
-
-        GGML_CANN_CALL_ACLNN_OP(ctx, GroupedMatmulV3, src1_tensor_list, src0_tensor_list,
-            nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, -1, dst_tensor_list);
-
-        ggml_cann_release_resources(ctx, src0_tensor_list, src1_tensor_list, dst_tensor_list);
-    }
-    return;
 }
 
 /**

From b66df9d9c942254d03209186ef24ed7c994a576e Mon Sep 17 00:00:00 2001
From: Akarshan Biswas <akarshan@menlo.ai>
Date: Mon, 1 Sep 2025 06:55:06 +0530
Subject: [PATCH 244/287] CUDA: fix build error from ambiguous __half
 conversions in conv2d (#15690)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CUDA: fix build error from ambiguous __half conversions in conv2d

Building conv2d with half precision failed because `__half` defines
multiple implicit conversion operators (to float, int, short, etc.),
causing ambiguous overload resolution when multiplying with float.

Introduce a templated `to_float` helper that explicitly converts
`__half` via `__half2float`, while passing through float unchanged.
Use this helper in conv2d accumulation to ensure unambiguous and
correct promotion to float.

Fixes some build errors with half-precision kernels on CUDA.

ggml-ci

* CUDA: Replace custom to_float helper with unified ggml_cuda_cast and add half‑>float conversion

* CUDA: Add missing convert.cuh header

* CUDA: remove unnecessary extension in ggml_cuda_cast

* CUDA: Address review comment, remove second type template argument
---
 ggml/src/ggml-cuda/conv2d.cu | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/conv2d.cu b/ggml/src/ggml-cuda/conv2d.cu
index bcb70762ee..142dd66903 100644
--- a/ggml/src/ggml-cuda/conv2d.cu
+++ b/ggml/src/ggml-cuda/conv2d.cu
@@ -1,4 +1,5 @@
 #include "conv2d.cuh"
+#include "convert.cuh"
 
 struct conv_params {
     const int64_t IW, IH;
@@ -94,8 +95,8 @@ static __global__ void conv2d_kernel(const float * __restrict__ input,
                 const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
 
                 const float input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
-                const float kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
-                acc += (input_val * kernel_val);
+                const T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
+                acc += (input_val * ggml_cuda_cast<float>(kernel_val));
             }
         }
     }

From 4795c91c32fec7165a1364763d4d4f0c93abf933 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <jiefu@tencent.com>
Date: Mon, 1 Sep 2025 15:34:59 +0800
Subject: [PATCH 245/287] docs : add Hunyuan to models section (#15707)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index a01ef6d503..17f59e988e 100644
--- a/README.md
+++ b/README.md
@@ -137,6 +137,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
 - [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
+- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
 
 #### Multimodal
 

From 77dee9de97be75b7143a213bc48893e0c0b29af7 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 1 Sep 2025 14:28:49 +0200
Subject: [PATCH 246/287] ggml : WebGPU add TRANSPOSE and RESHAPE to supported
 ops (#15695)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml : WebGPU add TRANSPOSE and RESHAPE to supported ops

This commit adds support for the TRANSPOSE and RESHAPE operations in the
ggml webgpu backend.

Co-authored-by: Diego Devesa <slarengh@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 ggml/src/ggml-webgpu/ggml-webgpu.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index 32f1e304e1..e5df883c13 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -611,6 +611,8 @@ static bool ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
         case GGML_OP_NONE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_RESHAPE:
             return false;
         case GGML_OP_CPY:
             {
@@ -1062,6 +1064,8 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_NONE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_RESHAPE:
             return true;
         case GGML_OP_CPY:
         case GGML_OP_SET_ROWS:

From 02c1813517412f3e00aa6ca7c0273fea64edb492 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Mon, 1 Sep 2025 16:19:07 +0200
Subject: [PATCH 247/287] Vulkan: Add Integer Dot Product mul_mat_vec shader
 for legacy quants (#14903)

* vulkan: Add Integer Dot Product mul_mat_vec shader for legacy quants

* vulkan: use subgroup operations for quantize_q8_1 shader

* vulkan: add q8_1_x4 type with 128-bit alignment, use in mul_mat_vecq shader

* vulkan: use q8_1_x4 blocks in mul_mmq shader

* vulkan: do 8 calculations per invocation instead of 32 in mul_mat_vecq, similar to mul_mat_vec

* vulkan: tune mul_mat_vecq performance for Intel

* vulkan: fix quantizing issue when tensor is not divisible by 128

* vulkan: adapt integer dot mmv to mmv small m optimization (#15355)

* vulkan: allow all subgroup modes for mmv and mmvq

* vulkan: use prealloc intermediate reuse for mmvq path

* vulkan: tune mmvq for Intel, AMD GCN and Nvidia RTX 3090

* vulkan: adapt mmv quantize_y path to conditional sync logic

* vulkan: disable q8_0 mmvq on Nvidia

* vulkan: enable q8_0 on Nvidia pre-turing

* fix prealloc sync condition

* fix llvmpipe subgroup 8 issue
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 353 +++++++++++++-----
 .../vulkan-shaders/mul_mat_vec_base.comp      |  30 +-
 .../vulkan-shaders/mul_mat_vecq.comp          | 140 +++++++
 .../ggml-vulkan/vulkan-shaders/mul_mmq.comp   |  17 +-
 .../vulkan-shaders/mul_mmq_funcs.comp         |  18 +-
 .../vulkan-shaders/quantize_q8_1.comp         |  56 ++-
 .../src/ggml-vulkan/vulkan-shaders/types.comp |  12 +
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  48 ++-
 8 files changed, 559 insertions(+), 115 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index f67ca966d1..4057ce8556 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -360,6 +360,13 @@ struct vk_fa_pipeline_state {
     }
 };
 
+enum shader_reduction_mode {
+    SHADER_REDUCTION_MODE_SHMEM,
+    SHADER_REDUCTION_MODE_HYBRID,
+    SHADER_REDUCTION_MODE_SUBGROUP,
+    SHADER_REDUCTION_MODE_COUNT,
+};
+
 static constexpr uint32_t num_argsort_pipelines = 11;
 static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
 
@@ -386,15 +393,18 @@ struct vk_device_struct {
     bool uma;
     bool prefer_host_memory;
     bool float_controls_rte_fp16;
-    bool subgroup_add;
+    bool subgroup_arithmetic;
     bool subgroup_shuffle;
     bool subgroup_ballot;
+    bool subgroup_clustered;
     bool multi_add;
 
     bool add_rms_fusion;
     uint32_t partials_binding_alignment;
 
     bool integer_dot_product;
+    // 0: default, 1: force mmvq, -1: disable mmvq
+    int32_t mmvq_mode;
 
     bool subgroup_size_control;
     uint32_t subgroup_min_size;
@@ -452,12 +462,15 @@ struct vk_device_struct {
 
     vk_pipeline pipeline_matmul_split_k_reduce;
     vk_pipeline pipeline_quantize_q8_1;
+    vk_pipeline pipeline_quantize_q8_1_x4;
 
     vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
     vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
     vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
     vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
 
+    vk_pipeline pipeline_dequant_mul_mat_vec_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
+
     vk_pipeline pipeline_mul_mat_vec_p021_f16_f32[p021_max_gqa_ratio];
     vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
     vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT];
@@ -2911,60 +2924,91 @@ static void ggml_vk_load_shaders(vk_device& device) {
         rm_stdq = 2;
     uint32_t rm_iq = 2 * rm_kq;
 
-    for (uint32_t w = 0; w < DMMV_WG_SIZE_COUNT; ++w) {
-        uint32_t wg_size_subgroup16 = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_16 : (subgroup_size_16 * 4);
-        uint32_t wg_size_subgroup   = (w == DMMV_WG_SIZE_SUBGROUP) ? device->subgroup_size : (device->subgroup_size * 4);
+    const bool use_subgroups = device->subgroup_arithmetic && device->architecture != vk_device_architecture::AMD_GCN;
+    // Ensure a subgroup size >= 16 is available
+    const bool use_subgroups16 = use_subgroups &&
+                                    (!device->subgroup_size_control && device->subgroup_size >= 16 ||
+                                    device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16);
 
-        const bool s = device->subgroup_add && device->architecture != vk_device_architecture::AMD_GCN;
+    const uint32_t subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16) ? 16 : device->subgroup_size;
+    const uint32_t subgroup_size16 = std::max(subgroup_size, 16u);
+
+    const uint32_t force_subgroup_size = use_subgroups ? subgroup_size : 0;
+    const uint32_t force_subgroup_size16 = use_subgroups16 ? subgroup_size16 : 0;
+
+    for (uint32_t w = 0; w < DMMV_WG_SIZE_COUNT; ++w) {
+        const uint32_t wg_size_subgroup   = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size : (subgroup_size * 4);
+        const uint32_t wg_size_subgroup16 = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size16 : (subgroup_size16 * 4);
+
+        const shader_reduction_mode reduc = (use_subgroups && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP :
+                                            (use_subgroups && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID :
+                                            SHADER_REDUCTION_MODE_SHMEM;
+
+        const shader_reduction_mode reduc16 = (use_subgroups16 && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP :
+                                              (use_subgroups16 && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID :
+                                              SHADER_REDUCTION_MODE_SHMEM;
 
         for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[s],  arr_dmmv_f32_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[s],  arr_dmmv_f16_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[s], arr_dmmv_bf16_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[s], arr_dmmv_q4_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[s], arr_dmmv_q4_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[s], arr_dmmv_q5_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[s], arr_dmmv_q5_1_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[s], arr_dmmv_q8_0_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[s], arr_dmmv_q2_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[s], arr_dmmv_q3_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[s], arr_dmmv_q4_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[s], arr_dmmv_q5_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[s], arr_dmmv_q6_k_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[s],   arr_dmmv_iq1_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[s],   arr_dmmv_iq1_m_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[s], arr_dmmv_iq2_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[s],  arr_dmmv_iq2_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[s],   arr_dmmv_iq2_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[s], arr_dmmv_iq3_xxs_f32_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[s],   arr_dmmv_iq3_s_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[s],  arr_dmmv_iq4_xs_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[s],  arr_dmmv_iq4_nl_f32_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[s],   arr_dmmv_mxfp4_f32_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[reduc],  arr_dmmv_f32_f32_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[reduc],  arr_dmmv_f16_f32_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[reduc16], arr_dmmv_q6_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[reduc16],   arr_dmmv_iq1_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[reduc16],   arr_dmmv_iq1_m_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_iq2_xxs_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_iq2_xs_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[reduc16],   arr_dmmv_iq2_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_iq3_xxs_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[reduc16],   arr_dmmv_iq3_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_iq4_xs_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_iq4_nl_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[reduc16],   arr_dmmv_mxfp4_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
 
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[s],  arr_dmmv_f32_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[s],  arr_dmmv_f16_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[s], arr_dmmv_bf16_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[s], arr_dmmv_q4_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[s], arr_dmmv_q4_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[s], arr_dmmv_q5_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[s], arr_dmmv_q5_1_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[s], arr_dmmv_q8_0_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[s], arr_dmmv_q2_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[s], arr_dmmv_q3_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[s], arr_dmmv_q4_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[s], arr_dmmv_q5_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[s], arr_dmmv_q6_k_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[s],   arr_dmmv_iq1_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[s],   arr_dmmv_iq1_m_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[s], arr_dmmv_iq2_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[s],  arr_dmmv_iq2_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[s],   arr_dmmv_iq2_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[s], arr_dmmv_iq3_xxs_f16_f32_data[s], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[s],   arr_dmmv_iq3_s_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[s],  arr_dmmv_iq4_xs_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[s],  arr_dmmv_iq4_nl_f16_f32_data[s],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[s],   arr_dmmv_mxfp4_f16_f32_data[s],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[reduc],  arr_dmmv_f32_f16_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[reduc],  arr_dmmv_f16_f16_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[reduc16], arr_dmmv_q6_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[reduc16],   arr_dmmv_iq1_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[reduc16],   arr_dmmv_iq1_m_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[reduc16], arr_dmmv_iq2_xxs_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[reduc16],  arr_dmmv_iq2_xs_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[reduc16],   arr_dmmv_iq2_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[reduc16], arr_dmmv_iq3_xxs_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[reduc16],   arr_dmmv_iq3_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[reduc16],  arr_dmmv_iq4_xs_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[reduc16],  arr_dmmv_iq4_nl_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[reduc16],   arr_dmmv_mxfp4_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+
+#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+            if (device->integer_dot_product) {
+                const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
+                const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);
+
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+            }
+#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
         }
     }
 
@@ -3056,10 +3100,17 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, 5 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_len, quantize_q8_1_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
+
+    if (device->subgroup_clustered && device->subgroup_require_full_support) {
+        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_subgroup_len, quantize_q8_1_subgroup_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true);
+        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_subgroup_len, quantize_q8_1_x4_subgroup_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true);
+    } else {
+        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_len, quantize_q8_1_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_len, quantize_q8_1_x4_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
+    }
 
     for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
-        if (device->subgroup_add && device->subgroup_require_full_support) {
+        if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
             ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
         } else {
             ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
@@ -3578,11 +3629,12 @@ static vk_device ggml_vk_get_device(size_t idx) {
         }
         device->float_controls_rte_fp16 = vk12_props.shaderRoundingModeRTEFloat16;
 
-        device->subgroup_add = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
-                               (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eArithmetic);
-
+        device->subgroup_arithmetic = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
+                                      (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eArithmetic);
         device->subgroup_shuffle = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                    (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eShuffle);
+        device->subgroup_clustered = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
+                                     (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eClustered);
 
         device->subgroup_ballot = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                   (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBallot);
@@ -4038,11 +4090,18 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device->disable_fusion = getenv("GGML_VK_DISABLE_FUSION") != nullptr;
 
         device->add_rms_fusion = !device->disable_fusion &&
-                                 device->subgroup_add &&
+                                 device->subgroup_arithmetic &&
                                  device->vendor_id != VK_VENDOR_ID_INTEL;
         device->partials_binding_alignment =
             std::max(4u, (uint32_t)device->properties.limits.minStorageBufferOffsetAlignment);
 
+        device->mmvq_mode = 0;
+        if (getenv("GGML_VK_DISABLE_MMVQ")) {
+            device->mmvq_mode = -1;
+        } else if (getenv("GGML_VK_FORCE_MMVQ")) {
+            device->mmvq_mode = 1;
+        }
+
         return device;
     }
 
@@ -4556,9 +4615,22 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
 
 static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols, uint32_t m, uint32_t k) {
     VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
-    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
+    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16 || b_type == GGML_TYPE_Q8_1);
     GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols);
 
+    if (b_type == GGML_TYPE_Q8_1) {
+        switch (a_type) {
+            case GGML_TYPE_Q4_0:
+            case GGML_TYPE_Q4_1:
+            case GGML_TYPE_Q5_0:
+            case GGML_TYPE_Q5_1:
+            case GGML_TYPE_Q8_0:
+                break;
+            default:
+                return nullptr;
+        }
+    }
+
     switch (a_type) {
         case GGML_TYPE_F32:
         case GGML_TYPE_F16:
@@ -4605,6 +4677,13 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         }
     }
 
+    if (b_type == GGML_TYPE_Q8_1) {
+        if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
+            dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
+        }
+        return ctx->device->pipeline_dequant_mul_mat_vec_q8_1_f32[dmmv_wg][a_type][num_cols-1];
+    }
+
     return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[dmmv_wg][a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[dmmv_wg][a_type][num_cols-1];
 }
 
@@ -4674,7 +4753,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
 }
 
 static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
-    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
+    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec_id()");
     GGML_ASSERT(b_type == GGML_TYPE_F32);
 
     switch (a_type) {
@@ -5587,20 +5666,20 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context&
     ggml_vk_sync_buffers(ctx, subctx);
 }
 
-static vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type) {
+static vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type, bool use_x4_blocks) {
     switch(type) {
         case GGML_TYPE_Q8_1:
-            return ctx->device->pipeline_quantize_q8_1;
+            return use_x4_blocks ? ctx->device->pipeline_quantize_q8_1_x4 : ctx->device->pipeline_quantize_q8_1;
         default:
             std::cerr << "Missing quantize pipeline for type: " << ggml_type_name(type) << std::endl;
             GGML_ABORT("fatal error");
     }
 }
 
-static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& subctx, vk_subbuffer&& in, vk_subbuffer&& out, uint32_t ne) {
+static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& subctx, vk_subbuffer&& in, vk_subbuffer&& out, uint32_t ne, bool use_x4_blocks = false) {
     VK_LOG_DEBUG("ggml_vk_quantize_q8_1(" << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ", " << ne << ")");
 
-    vk_pipeline pipeline = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
+    vk_pipeline pipeline = use_x4_blocks ? ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1, true) : ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1, false);
 
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, std::array<uint32_t, 1>{ne}, { ne, 1, 1 });
     ggml_vk_sync_buffers(ctx, subctx);
@@ -5720,12 +5799,15 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
 
     if (quantize_y) {
-        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
+        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1, true);
     }
 
     if (dryrun) {
         const uint64_t x_sz_upd = x_sz * ne02 * ne03;
-        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        if (quantize_y) {
+            y_sz_upd = CEIL_DIV(y_sz_upd, 144) * 144;
+        }
         const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * split_k : 0;
         if (
                 (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
@@ -5791,7 +5873,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         GGML_ASSERT(d_Y->size >= y_sz * ne12 * ne13);
     } else if (quantize_y) {
         d_Y = ctx->prealloc_y;
-        GGML_ASSERT(d_Y->size >= y_ne * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1));
+        GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz * ne12 * ne13, 144) * 144);
     } else {
         d_Y = d_Qy;
         y_buf_offset = qy_buf_offset;
@@ -5828,7 +5910,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
             if (ctx->prealloc_y_need_sync) {
                 ggml_vk_sync_buffers(ctx, subctx);
             }
-            ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13);
+            ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13, true);
             ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
             ctx->prealloc_y_last_tensor_used = src1;
         }
@@ -5845,10 +5927,15 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
         stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
     }
 
+    uint32_t y_sz_total = y_sz * ne12 * ne13;
+    if (quantize_y) {
+        y_sz_total = CEIL_DIV(y_sz_total, 144) * 144;
+    }
+
     // compute
     ggml_vk_matmul(
         ctx, subctx, pipeline,
-        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz_total },
         { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
         ne01, ne11, ne10,
         ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21,
@@ -5863,6 +5950,51 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     }
 }
 
+// Device tuning
+static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_t n, uint32_t k, ggml_type src0_type) {
+    if (device->mmvq_mode == 1) {
+        return true;
+    } else if (device->mmvq_mode == -1) {
+        return false;
+    }
+
+    // MMVQ is generally good for batches
+    if (n > 1) {
+        return true;
+    }
+
+    switch (device->vendor_id) {
+    case VK_VENDOR_ID_NVIDIA:
+        switch (src0_type) {
+        case GGML_TYPE_Q8_0:
+            return device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
+        default:
+            return true;
+        }
+    case VK_VENDOR_ID_AMD:
+        switch (src0_type) {
+        case GGML_TYPE_Q8_0:
+            return device->architecture == vk_device_architecture::AMD_GCN;
+        default:
+            return true;
+        }
+    case VK_VENDOR_ID_INTEL:
+        switch (src0_type) {
+        // From tests on A770 Linux, may need more tuning
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q5_1:
+            return false;
+        default:
+            return true;
+        }
+    default:
+        return true;
+    }
+
+    GGML_UNUSED(m);
+    GGML_UNUSED(k);
+}
+
 static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
@@ -5917,22 +6049,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
 
     const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
-
-    const bool qx_needs_dequant = x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const uint64_t x_ne = ne01 * ne00;
-    const uint64_t y_ne = ne11 * ne10;
-    const uint64_t d_ne = ne11 * ne01;
-
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
-    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
-    const uint64_t d_sz = sizeof(float) * d_ne;
+    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne11, ne10, src0->type);
 
     vk_pipeline to_fp16_vk_0 = nullptr;
     vk_pipeline to_fp16_vk_1 = nullptr;
@@ -5944,14 +6061,47 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     } else {
         to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
     }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11, ne20, ne00);
+
+    // Check for mmq first
+    vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, GGML_TYPE_Q8_1, ne11, ne20, ne00) : nullptr;
+    vk_pipeline to_q8_1 = nullptr;
+
+    if (dmmv == nullptr) {
+        // Fall back to f16 dequant mul mat
+        dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11, ne20, ne00);
+        quantize_y = false;
+    }
+
+    if (quantize_y) {
+        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1, true);
+    }
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
     GGML_ASSERT(dmmv != nullptr);
 
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne11 * ne01;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = quantize_y ? (y_ne * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
     if (dryrun) {
         const uint64_t x_sz_upd = x_sz * ne02 * ne03;
-        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        if (quantize_y) {
+            y_sz_upd = CEIL_DIV(y_sz_upd, 144) * 144;
+        }
         if (
                 (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
                 (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) {
@@ -5960,7 +6110,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) {
             ctx->prealloc_size_x = x_sz_upd;
         }
-        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) {
+        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz_upd) {
             ctx->prealloc_size_y = y_sz_upd;
         }
 
@@ -5971,6 +6121,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         if (qy_needs_dequant) {
             ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
+        if (quantize_y) {
+            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
+        }
         ggml_pipeline_request_descriptor_sets(ctx, dmmv, 1);
         return;
     }
@@ -6001,6 +6154,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     }
     if (qy_needs_dequant) {
         d_Y = ctx->prealloc_y;
+    } else if (quantize_y) {
+        d_Y = ctx->prealloc_y;
+        GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz * ne12 * ne13, 144) * 144);
     } else {
         d_Y = d_Qy;
         y_buf_offset = qy_buf_offset;
@@ -6011,9 +6167,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         if (ctx->prealloc_x_need_sync) {
             ggml_vk_sync_buffers(ctx, subctx);
         }
-    }
 
-    if (x_non_contig) {
         GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
         ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
     }
@@ -6029,6 +6183,17 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
             ctx->prealloc_y_last_tensor_used = src1;
         }
     }
+    if (quantize_y) {
+        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
+            ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13, true);
+            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
+    }
 
     // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride
     uint32_t stride_batch_x = batch_n ? 0 : ne00*ne01;
@@ -6053,6 +6218,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         groups_x = CEIL_DIV(groups_x, groups_z);
     }
 
+    // TODO: Clean up this whole sz * ne_2 * ne_3 thing, it hasn't been necessary for a long time
+    uint32_t y_sz_total = y_sz * ne12 * ne13;
+    if (quantize_y) {
+        y_sz_total = CEIL_DIV(y_sz_total, 144) * 144;
+    }
+
     // compute
     const vk_mat_vec_push_constants pc = {
         (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
@@ -6060,13 +6231,13 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
     };
     ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
-                              { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} },
+                              { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz_total }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} },
                               pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z });
 
     if (x_non_contig) {
         ctx->prealloc_x_need_sync = true;
     }
-    if (y_non_contig) {
+    if (y_non_contig || quantize_y) {
         ctx->prealloc_y_need_sync = true;
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
index b93e9948f7..f761391eae 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
@@ -1,7 +1,8 @@
 #extension GL_EXT_control_flow_attributes : enable
 #extension GL_EXT_shader_16bit_storage : require
 #extension GL_EXT_shader_8bit_storage : require
-#if USE_SUBGROUP_ADD
+
+#if USE_SUBGROUP_ADD || USE_SUBGROUP_ADD_NO_SHMEM
 #extension GL_KHR_shader_subgroup_basic : require
 #extension GL_KHR_shader_subgroup_arithmetic : require
 #endif
@@ -12,10 +13,19 @@
 
 #include "types.comp"
 
+#ifndef MMQ
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+#else
+layout (binding = 0) readonly buffer A {A_TYPE_PACKED16 data_a[];};
+#endif
+
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+#ifdef B_TYPE_VEC2
 layout (binding = 1) readonly buffer BV2 {B_TYPE_VEC2 data_b_v2[];};
+#endif
+#ifdef B_TYPE_VEC4
 layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
+#endif
 
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 #ifdef MUL_MAT_ID
@@ -92,6 +102,23 @@ layout (constant_id = 0) const uint BLOCK_SIZE = 32;
 layout (constant_id = 1) const uint NUM_ROWS = 1;
 layout (constant_id = 2) const uint NUM_COLS = 1;
 
+#ifdef USE_SUBGROUP_ADD_NO_SHMEM
+void reduce_result(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) {
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            temp[j][n] = subgroupAdd(temp[j][n]);
+        }
+    }
+
+    if (tid == 0) {
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
+            }
+        }
+    }
+}
+#else
 shared FLOAT_TYPE tmpsh[NUM_COLS][NUM_ROWS][BLOCK_SIZE];
 
 void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) {
@@ -152,3 +179,4 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
     }
 #endif
 }
+#endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp
new file mode 100644
index 0000000000..8fb314fa0a
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp
@@ -0,0 +1,140 @@
+#version 450
+
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+#extension GL_EXT_integer_dot_product : require
+
+#define MMQ
+#define B_TYPE block_q8_1_x4
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+#define K_PER_ITER 8
+
+#include "mul_mmq_funcs.comp"
+
+uint a_offset, b_offset, d_offset;
+
+int32_t cache_b_qs[2];
+vec2 cache_b_ds;
+
+void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i) {
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        const uint col = i*BLOCK_SIZE + tid*K_PER_ITER;
+
+        // Preload data_b block
+        const uint b_block_idx = (j*p.batch_stride_b + col) / QUANT_K_Q8_1 + b_offset;
+        const uint b_qs_idx = tid % 4;
+        const uint b_block_idx_outer = b_block_idx / 4;
+        const uint b_block_idx_inner = b_block_idx % 4;
+        cache_b_ds = vec2(data_b[b_block_idx_outer].ds[b_block_idx_inner]);
+
+#if QUANT_R == 2
+        cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx];
+        cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx + 4];
+#else
+        cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2];
+        cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2 + 1];
+#endif
+
+        uint ibi = first_row*p.ncols;
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            const uint a_block_idx = (ibi + col)/QUANT_K + a_offset;
+            ibi += p.ncols;
+
+            int32_t q_sum = 0;
+#if QUANT_R == 2
+            const i32vec2 data_a_qs = repack(a_block_idx, b_qs_idx);
+            q_sum += dotPacked4x8EXT(data_a_qs.x,
+                                     cache_b_qs[0]);
+            q_sum += dotPacked4x8EXT(data_a_qs.y,
+                                     cache_b_qs[1]);
+#else
+            int32_t data_a_qs = repack(a_block_idx, b_qs_idx * 2);
+            q_sum += dotPacked4x8EXT(data_a_qs,
+                                     cache_b_qs[0]);
+            data_a_qs = repack(a_block_idx, b_qs_idx * 2 + 1);
+            q_sum += dotPacked4x8EXT(data_a_qs,
+                                     cache_b_qs[1]);
+#endif
+
+#if QUANT_AUXF == 1
+            temp[j][n] += mul_q8_1(q_sum,  get_d(a_block_idx), cache_b_ds, 4);
+#else
+            temp[j][n] += mul_q8_1(q_sum, get_dm(a_block_idx), cache_b_ds, 4);
+#endif
+        }
+    }
+}
+
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
+    const uint tid = gl_LocalInvocationID.x;
+
+    get_offsets(a_offset, b_offset, d_offset);
+    a_offset /= QUANT_K;
+    b_offset /= QUANT_K_Q8_1;
+
+    FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+            temp[j][n] = FLOAT_TYPE(0.0f);
+        }
+    }
+
+    uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE);
+    if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) {
+        num_iters++;
+    }
+    int unroll_count = 4;
+    uint unrolled_iters = num_iters & ~(unroll_count - 1);
+
+    uint i = 0;
+    while (i < unrolled_iters) {
+        // Manually partially unroll the loop
+        [[unroll]] for (uint k = 0; k < unroll_count; ++k) {
+            iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
+            i++;
+        }
+    }
+
+    unroll_count = 2;
+    unrolled_iters = num_iters & ~(unroll_count - 1);
+
+#if K_PER_ITER == 2
+    if ((p.ncols & 1) != 0 &&
+        unrolled_iters == num_iters &&
+        unrolled_iters > 0) {
+        unrolled_iters -= unroll_count;
+    }
+#endif
+
+    while (i < unrolled_iters) {
+        // Manually partially unroll the loop
+        [[unroll]] for (uint k = 0; k < unroll_count; ++k) {
+            iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
+            i++;
+        }
+    }
+    while (i < num_iters) {
+        iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
+        i++;
+    }
+
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
index 83de90eb7e..f36add62a9 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
@@ -28,7 +28,7 @@ layout (binding = 0) readonly buffer A {A_TYPE_PACKED16 data_a[];};
 #if defined(A_TYPE_PACKED32)
 layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
 #endif
-layout (binding = 1) readonly buffer B {block_q8_1_packed32 data_b[];};
+layout (binding = 1) readonly buffer B {block_q8_1_x4_packed128 data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 
 #ifdef MUL_MAT_ID
@@ -98,7 +98,7 @@ shared FLOAT_TYPE_VEC2 buf_b_ds[BN];
 #endif
 
 #define LOAD_VEC_A (4 * QUANT_R)
-#define LOAD_VEC_B 4
+#define LOAD_VEC_B 16
 
 #ifdef MUL_MAT_ID
 shared u16vec2 row_ids[4096];
@@ -270,15 +270,22 @@ void main() {
             const uint iqs = idx & 0x7;
 #else
             const uint ib = pos_b_ib + (loadc_b + l) * p.stride_b / BK;
+            const uint ib_outer = ib / 4;
+            const uint ib_inner = ib % 4;
+
             const uint iqs = loadr_b;
 #endif
 
             const uint buf_ib = loadc_b + l;
 
             if (iqs == 0) {
-                buf_b_ds[buf_ib] = FLOAT_TYPE_VEC2(data_b[ib].ds);
+                buf_b_ds[buf_ib] = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
             }
-            buf_b_qs[buf_ib * SHMEM_STRIDE + iqs] = data_b[ib].qs[iqs];
+            const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
+            buf_b_qs[buf_ib * SHMEM_STRIDE + iqs * 4    ] = values.x;
+            buf_b_qs[buf_ib * SHMEM_STRIDE + iqs * 4 + 1] = values.y;
+            buf_b_qs[buf_ib * SHMEM_STRIDE + iqs * 4 + 2] = values.z;
+            buf_b_qs[buf_ib * SHMEM_STRIDE + iqs * 4 + 3] = values.w;
         }
 
         barrier();
@@ -349,7 +356,7 @@ void main() {
                                                      cache_b_qs[cc * (BK / 4) + idx_k]);
                         }
 
-                        sums[sums_idx] += mul_q8_1(q_sum, cache_a_dm[cache_a_idx], cache_b_ds[cc]);
+                        sums[sums_idx] += mul_q8_1(q_sum, cache_a_dm[cache_a_idx], cache_b_ds[cc], 1);
                     }
                 }
             }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.comp
index 34e8db9770..cdfb230f4e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.comp
@@ -16,8 +16,8 @@ i32vec2 repack(uint ib, uint iqs) {
                    (vui >> 4) & 0x0F0F0F0F);
 }
 
-ACC_TYPE mul_q8_1(int32_t q_sum, float da, vec2 dsb) {
-    return ACC_TYPE(da * (float(q_sum) * dsb.x - 8.0f * dsb.y));
+ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return ACC_TYPE(da * (float(q_sum) * dsb.x - (8 / sum_divisor) * dsb.y));
 }
 #endif
 
@@ -29,8 +29,8 @@ i32vec2 repack(uint ib, uint iqs) {
                    (vui >> 4) & 0x0F0F0F0F);
 }
 
-ACC_TYPE mul_q8_1(int32_t q_sum, vec2 dma, vec2 dsb) {
-    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y);
+ACC_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
+    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
 }
 #endif
 
@@ -50,8 +50,8 @@ i32vec2 repack(uint ib, uint iqs) {
     return i32vec2(v0, v1);
 }
 
-ACC_TYPE mul_q8_1(int32_t q_sum, float da, vec2 dsb) {
-    return ACC_TYPE(da * (float(q_sum) * dsb.x - 16.0f * dsb.y));
+ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return ACC_TYPE(da * (float(q_sum) * dsb.x - (16 / sum_divisor) * dsb.y));
 }
 #endif
 
@@ -69,8 +69,8 @@ i32vec2 repack(uint ib, uint iqs) {
     return i32vec2(v0, v1);
 }
 
-ACC_TYPE mul_q8_1(int32_t q_sum, vec2 dma, vec2 dsb) {
-    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y);
+ACC_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
+    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
 }
 #endif
 
@@ -81,7 +81,7 @@ int32_t repack(uint ib, uint iqs) {
                           data_a[ib].qs[iqs * 2 + 1]));
 }
 
-ACC_TYPE mul_q8_1(int32_t q_sum, float da, vec2 dsb) {
+ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
     return ACC_TYPE(float(q_sum) * da * dsb.x);
 }
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp
index e2e020fec2..145c9fbdc9 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp
@@ -3,6 +3,15 @@
 #extension GL_EXT_control_flow_attributes : require
 #extension GL_EXT_shader_16bit_storage : require
 
+#ifdef USE_SUBGROUPS
+#extension GL_KHR_shader_subgroup_basic : require
+#extension GL_KHR_shader_subgroup_clustered : require
+
+#define INVOCATION_ID gl_SubgroupInvocationID.x
+#else
+#define INVOCATION_ID gl_LocalInvocationID.x
+#endif
+
 layout (push_constant) uniform parameter
 {
     uint ne;
@@ -14,13 +23,19 @@ layout(constant_id = 0) const uint GROUP_SIZE = 32;
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {vec4 data_a[];};
+#ifndef QBLOCK_X4
 layout (binding = 1) writeonly buffer D {block_q8_1_packed32 data_b[];};
+#else
+layout (binding = 1) writeonly buffer D {block_q8_1_x4 data_b[];};
+#endif
 
+#ifndef USE_SUBGROUPS
 shared float shmem[GROUP_SIZE];
+#endif
 
 void quantize() {
     const uint wgid = gl_WorkGroupID.x;
-    const uint tid = gl_LocalInvocationID.x;
+    const uint tid = INVOCATION_ID;
 
     // Each thread handles a vec4, so 8 threads handle a block
     const uint blocks_per_group = GROUP_SIZE / 8;
@@ -30,9 +45,19 @@ void quantize() {
     const uint ib = wgid * blocks_per_group + block_in_wg;
     const uint iqs = tid % 8;
 
+#ifndef QBLOCK_X4
     if (ib >= gl_NumWorkGroups.x * blocks_per_group) {
         return;
     }
+#else
+    const uint ibx4_outer = ib / 4;
+    const uint ibx4_inner = ib % 4;
+
+    const uint required_x4_blocks = (p.ne + 127) / 128;
+    if (ibx4_outer >= required_x4_blocks) {
+        return;
+    }
+#endif
 
     const uint a_idx = ib * 8 + iqs;
 
@@ -40,7 +65,9 @@ void quantize() {
     const vec4 abs_vals = abs(vals);
 
     // Find absolute max for each block
-    shmem[tid] = max(max(abs_vals.x, abs_vals.y), max(abs_vals.z, abs_vals.w));
+    const float thread_max = max(max(abs_vals.x, abs_vals.y), max(abs_vals.z, abs_vals.w));
+#ifndef USE_SUBGROUPS
+    shmem[tid] = thread_max;
     barrier();
     [[unroll]] for (uint s = 4; s > 0; s >>= 1) {
         if (iqs < s) {
@@ -50,14 +77,28 @@ void quantize() {
     }
 
     const float amax = shmem[block_in_wg * 8];
+#else
+    const float amax = subgroupClusteredMax(thread_max, 8);
+#endif
+
     const float d = amax / 127.0;
     const float d_inv = d != 0.0 ? 1.0 / d : 0.0;
     vals = round(vals * d_inv);
+
+#ifndef QBLOCK_X4
     data_b[ib].qs[iqs] = pack32(i8vec4(round(vals)));
+#else
+    data_b[ibx4_outer].qs[ibx4_inner * 8 + iqs] = pack32(i8vec4(round(vals)));
+#endif
+
+#ifndef USE_SUBGROUPS
     barrier();
+#endif
 
     // Calculate the sum for each block
-    shmem[tid] = vals.x + vals.y + vals.z + vals.w;
+    const float thread_sum = vals.x + vals.y + vals.z + vals.w;
+#ifndef USE_SUBGROUPS
+    shmem[tid] = thread_sum;
     barrier();
     [[unroll]] for (uint s = 4; s > 0; s >>= 1) {
         if (iqs < s) {
@@ -65,10 +106,19 @@ void quantize() {
         }
         barrier();
     }
+#else
+    const float sum = subgroupClusteredAdd(thread_sum, 8);
+#endif
     if (iqs == 0) {
+#ifndef USE_SUBGROUPS
         const float sum = shmem[tid];
+#endif
 
+#ifndef QBLOCK_X4
         data_b[ib].ds = f16vec2(vec2(d, sum * d));
+#else
+        data_b[ibx4_outer].ds[ibx4_inner] = f16vec2(vec2(d, sum * d));
+#endif
     }
 }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
index a36c33e267..408722c878 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp
@@ -207,6 +207,18 @@ struct block_q8_1_packed32
     int32_t qs[8];
 };
 
+// 4 blocks in one to allow 16-byte/128-bit alignment and loads
+struct block_q8_1_x4
+{
+    f16vec2 ds[4];
+    int32_t qs[32];
+};
+struct block_q8_1_x4_packed128
+{
+    f16vec2 ds[4];
+    ivec4 qs[8];
+};
+
 // K-quants
 #define QUANT_K_Q2_K 256
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index d81bb47e7b..6c64e1b513 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -206,6 +206,22 @@ bool string_ends_with(const std::string& str, const std::string& suffix) {
     return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
 }
 
+bool is_quantized_type(const std::string& type_name) {
+    return type_name != "f32" && type_name != "f16" && type_name != "bf16";
+}
+
+bool is_legacy_quant(const std::string& type_name) {
+    return type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0";
+}
+
+bool is_k_quant(const std::string& type_name) {
+    return string_ends_with(type_name, "_k");
+}
+
+bool is_iq_quant(const std::string& type_name) {
+    return string_starts_with(type_name, "iq");
+}
+
 static const char path_separator = '/';
 
 std::string join_paths(const std::string& path1, const std::string& path2) {
@@ -402,7 +418,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
         }
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-        if (!coopmat && !coopmat2 && matmul_id_type == MatMulIdType::NONE && (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "q8_0")) {
+        if (!coopmat && !coopmat2 && matmul_id_type == MatMulIdType::NONE && is_legacy_quant(tname)) {
             string_to_spv(shader_name + "_" + tname + "_q8_1", "mul_mmq.comp", merge_maps(base_dict, {{"FLOAT_TYPE", FLOAT_TYPE(tname)}, {data_a_key, "1"}, {"D_TYPE", "float"},}), fp16, coopmat, coopmat2, f16acc);
         }
 #endif
@@ -495,8 +511,20 @@ void process_shaders() {
         string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
         string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
 
+        string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
+        string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
+
         string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
 
+        // mul mat vec with integer dot product
+#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+        if (is_legacy_quant(tname)) {
+            string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}}));
+            string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
+            string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
+        }
+#endif
+
         // Dequant shaders
         if (tname != "f16" && tname != "bf16") {
             string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}}));
@@ -579,7 +607,12 @@ void process_shaders() {
 
     string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
     string_to_spv("fa_split_k_reduce", "flash_attn_split_k_reduce.comp", {});
+
     string_to_spv("quantize_q8_1", "quantize_q8_1.comp", {});
+    string_to_spv("quantize_q8_1_subgroup", "quantize_q8_1.comp", {{"USE_SUBGROUPS", "1"}});
+
+    string_to_spv("quantize_q8_1_x4", "quantize_q8_1.comp", {{"QBLOCK_X4", "1"}});
+    string_to_spv("quantize_q8_1_x4_subgroup", "quantize_q8_1.comp", {{"QBLOCK_X4", "1"}, {"USE_SUBGROUPS", "1"}});
 
     string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
 
@@ -821,12 +854,15 @@ void write_output_files() {
         fputs(len.c_str(), src);
     }
 
-    for (const std::string& btype : {"f16", "f32"}) {
+    for (const std::string& btype : {"f16", "f32", "q8_1"}) {
     for (const auto& tname : type_names) {
-        fprintf(hdr, "extern unsigned char *arr_dmmv_%s_%s_f32_data[2];\n", tname.c_str(), btype.c_str());
-        fprintf(hdr, "extern uint64_t arr_dmmv_%s_%s_f32_len[2];\n", tname.c_str(), btype.c_str());
-        std::string data = "unsigned char *arr_dmmv_" + tname + "_" + btype + "_f32_data[2] = {mul_mat_vec_" + tname + "_" + btype + "_f32_data, mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_data};\n";
-        std::string len =  "uint64_t arr_dmmv_"       + tname + "_" + btype + "_f32_len[2] =  {mul_mat_vec_" + tname + "_" + btype + "_f32_len,  mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_len};\n";
+        if (btype == "q8_1" && !is_legacy_quant(tname)) {
+            continue;
+        }
+        fprintf(hdr, "extern unsigned char *arr_dmmv_%s_%s_f32_data[3];\n", tname.c_str(), btype.c_str());
+        fprintf(hdr, "extern uint64_t arr_dmmv_%s_%s_f32_len[3];\n", tname.c_str(), btype.c_str());
+        std::string data = "unsigned char *arr_dmmv_" + tname + "_" + btype + "_f32_data[3] = {mul_mat_vec_" + tname + "_" + btype + "_f32_data, mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_data, mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_no_shmem_data};\n";
+        std::string len =  "uint64_t arr_dmmv_"       + tname + "_" + btype + "_f32_len[3] =  {mul_mat_vec_" + tname + "_" + btype + "_f32_len,  mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_len, mul_mat_vec_" + tname + "_" + btype + "_f32_subgroup_no_shmem_len};\n";
         fputs(data.c_str(), src);
         fputs(len.c_str(), src);
     }

From 4b20d8b7e31718d3fe8b4f12d220a9d19e4f1997 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jie=20Fu=20=28=E5=82=85=E6=9D=B0=29?= <jiefu@tencent.com>
Date: Mon, 1 Sep 2025 23:53:31 +0800
Subject: [PATCH 248/287] convert : remove redundant code (#15708)

Signed-off-by: Jie Fu <jiefu@tencent.com>
---
 convert_hf_to_gguf.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index df37c4a6e4..908435d7e5 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -302,10 +302,6 @@ class ModelBase:
                 # data = data_torch.squeeze().numpy()
                 data = data_torch.numpy()
 
-                # if data ends up empty, it means data_torch was a scalar tensor -> restore
-                if len(data.shape) == 0:
-                    data = data_torch.numpy()
-
                 n_dims = len(data.shape)
                 data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
 

From a0c2b207c596d1092a08615de61ab56a7d63515f Mon Sep 17 00:00:00 2001
From: Prashant Vithule <119530321+Vithulep@users.noreply.github.com>
Date: Mon, 1 Sep 2025 23:43:16 +0530
Subject: [PATCH 249/287] ggml: aarch64: Implement SVE F16 kernels for vector
 functions  (#15115)

* Added sve implementation for vec_dot_fp16 Kernel

* removed white spaces

* Added comment

* removed white spaces

* changed GGML_F16x_VEC_FMA for code consistency

* Update vec.h

---------

Co-authored-by: vithulep <p.m.vithule1517@gmail.com>
---
 ggml/src/ggml-cpu/simd-mappings.h |  41 ++++
 ggml/src/ggml-cpu/vec.cpp         | 100 +++++++--
 ggml/src/ggml-cpu/vec.h           | 355 +++++++++++++++++++++++-------
 3 files changed, 404 insertions(+), 92 deletions(-)

diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h
index f71ce58079..8bd56bdac1 100644
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -215,6 +215,47 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 #define GGML_F32_VEC_MUL    GGML_F32xt_MUL
 #define GGML_F32_VEC_REDUCE GGML_F32xt_REDUCE
 
+// F16 SVE
+#define DEFAULT_PG32    svptrue_b32()
+#define DEFAULT_PG16    svptrue_b16()
+
+#define GGML_F32Cxt                         svfloat16_t
+#define GGML_F32Cxt_ZERO                    svdup_n_f16(0.0f)
+#define GGML_F32Cxt_SET1(x)                 svdup_n_f16(x)
+#define GGML_F32Cxt_LOAD(p)                 svld1_f16(DEFAULT_PG16, (const __fp16 *)(p))
+#define GGML_F32Cxt_STORE(dst_ptr, src_vec) svst1_f16(DEFAULT_PG16, (__fp16 *)(dst_ptr), (src_vec))
+
+#define GGML_F32Cxt_FMA_IMPL(pg, a, b, c)   svmad_f16_x(pg, b, c, a)
+#define GGML_F32Cxt_FMA(...)                GGML_F32Cxt_FMA_IMPL(DEFAULT_PG16, __VA_ARGS__)
+#define GGML_F32Cxt_ADD_IMPL(pg, a, b)      svadd_f16_x(pg, a, b)
+#define GGML_F32Cxt_ADD(...)                GGML_F32Cxt_ADD_IMPL(DEFAULT_PG16, __VA_ARGS__)
+#define GGML_F32Cxt_MUL_IMPL(pg, a, b)      svmul_f16_x(pg, a, b)
+#define GGML_F32Cxt_MUL(...)                GGML_F32Cxt_MUL_IMPL(DEFAULT_PG16, __VA_ARGS__)
+#define GGML_F32Cxt_REDUCE                  GGML_F16xt_REDUCE_MIXED
+
+#define GGML_F16x_VEC                GGML_F32Cxt
+#define GGML_F16x_VEC_ZERO           GGML_F32Cxt_ZERO
+#define GGML_F16x_VEC_SET1           GGML_F32Cxt_SET1
+#define GGML_F16x_VEC_LOAD(p, i)     GGML_F32Cxt_LOAD(p)
+#define GGML_F16x_VEC_STORE(p, r, i) GGML_F32Cxt_STORE((__fp16 *)(p), r)
+#define GGML_F16x_VEC_FMA            GGML_F32Cxt_FMA
+#define GGML_F16x_VEC_ADD            GGML_F32Cxt_ADD
+#define GGML_F16x_VEC_MUL            GGML_F32Cxt_MUL
+#define GGML_F16x_VEC_REDUCE         GGML_F32Cxt_REDUCE
+
+#define GGML_F16xt_REDUCE_ONE_IMPL(pg, a) svaddv_f16(pg, a)
+#define GGML_F16xt_REDUCE_ONE(...)        GGML_F16xt_REDUCE_ONE_IMPL(DEFAULT_PG16, __VA_ARGS__)
+
+#define GGML_F16xt_REDUCE_MIXED_IMPL(pg16, res, sum1, sum2, sum3, sum4)  \
+{                                                      \
+    sum1 = svadd_f16_x(pg16, sum1, sum2);              \
+    sum3 = svadd_f16_x(pg16, sum3, sum4);              \
+    sum1 = svadd_f16_x(pg16, sum1, sum3);              \
+    __fp16 sum_f16 = svaddv_f16(pg16, sum1);           \
+    (res) = (ggml_float) sum_f16;                      \
+}
+#define GGML_F16xt_REDUCE_MIXED(...) GGML_F16xt_REDUCE_MIXED_IMPL(DEFAULT_PG16, __VA_ARGS__)
+
 // F16 NEON
 
 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp
index d8ec3b81d2..f2412dcc08 100644
--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -207,33 +207,97 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
 
     ggml_float sumf = 0.0;
 
+
 #if defined(GGML_SIMD) && !defined(__riscv_v_intrinsic)
-    const int np = (n & ~(GGML_F16_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = svcntb() * 8; //get vector length
+        const int ggml_f16_epr = sve_register_length / 16; // running when 16
+        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
 
-    GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
+        const int np= (n & ~(ggml_f16_step - 1));
+        svfloat16_t sum1 = svdup_n_f16(0.0f);
+        svfloat16_t sum2 = svdup_n_f16(0.0f);
+        svfloat16_t sum3 = svdup_n_f16(0.0f);
+        svfloat16_t sum4 = svdup_n_f16(0.0f);
 
-    GGML_F16_VEC ax[GGML_F16_ARR];
-    GGML_F16_VEC ay[GGML_F16_ARR];
+        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
+        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+        for (int i = 0; i < np; i += ggml_f16_step) {
+            ax1 = GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0);
+            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0);
+            sum1 = GGML_F16x_VEC_FMA(sum1, ax1, ay1);
 
-    for (int i = 0; i < np; i += GGML_F16_STEP) {
-        for (int j = 0; j < GGML_F16_ARR; j++) {
-            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
-            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ax2 = GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1);
+            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1);
+            sum2 = GGML_F16x_VEC_FMA(sum2, ax2, ay2);
 
-            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
+            ax3 = GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2);
+            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
+            sum3 = GGML_F16x_VEC_FMA(sum3, ax3, ay3);
+
+            ax4 = GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3);
+            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
+            sum4 = GGML_F16x_VEC_FMA(sum4, ax4, ay4);
+
+            ax5 = GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4);
+            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
+            sum1 = GGML_F16x_VEC_FMA(sum1, ax5, ay5);
+
+            ax6 = GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5);
+            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
+            sum2 = GGML_F16x_VEC_FMA(sum2, ax6, ay6);
+
+            ax7 = GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6);
+            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
+            sum3 = GGML_F16x_VEC_FMA(sum3, ax7, ay7);
+
+            ax8 = GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7);
+            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
+            sum4 = GGML_F16x_VEC_FMA(sum4, ax8, ay8);
         }
-    }
 
-    // reduce sum0..sum3 to sum0
-    GGML_F16_VEC_REDUCE(sumf, sum);
+        const int np2 = (n & ~(ggml_f16_epr - 1)); // round down to multiple of 8
+        for (int k = np; k < np2; k += ggml_f16_epr) {
+            svfloat16_t rx = GGML_F16x_VEC_LOAD(x + k, 0);
+            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
+            sum1 = GGML_F16x_VEC_FMA(sum1, rx, ry);
+        }
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
-    }
+        if (np2 < n) {
+            svbool_t pg = svwhilelt_b16(np2, n);
+            svfloat16_t hx = svld1_f16(pg, (const __fp16 *)(x + np2));
+            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
 
-    // if you hit this, you are likely running outside the FP range
-    assert(!isnan(sumf) && !isinf(sumf));
+            sum1 = svmad_f16_x(pg, hx, hy, sum1);
+        }
+        GGML_F16x_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4);
+    #else
+        const int np = (n & ~(GGML_F16_STEP - 1));
+
+        GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
+
+        GGML_F16_VEC ax[GGML_F16_ARR];
+        GGML_F16_VEC ay[GGML_F16_ARR];
+
+        for (int i = 0; i < np; i += GGML_F16_STEP) {
+            for (int j = 0; j < GGML_F16_ARR; j++) {
+                ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+
+                sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
+            }
+        }
+
+        // reduce sum0..sum3 to sum0
+        GGML_F16_VEC_REDUCE(sumf, sum);
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
+        }
+        // if you hit this, you are likely running outside the FP range
+        assert(!isnan(sumf) && !isinf(sumf));
+    #endif
 #else
     for (int i = 0; i < n; ++i) {
         sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h
index 8ccf340d47..1f7c5996be 100644
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -119,45 +119,149 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
     }
 
 #if defined(GGML_SIMD)
-#if defined(__riscv_v_intrinsic)
-    // todo: RVV impl
-    for (int i = 0; i < n; ++i) {
-        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
-            sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
+    #if defined(__ARM_FEATURE_SVE)
+
+        const int sve_register_length = svcntb() * 8;
+        const int ggml_f16_epr = sve_register_length / 16; // running when 16
+        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
+
+        const int np = (n & ~(ggml_f16_step - 1));
+
+        svfloat16_t sum_00 = svdup_n_f16(0.0f);
+        svfloat16_t sum_01 = svdup_n_f16(0.0f);
+        svfloat16_t sum_02 = svdup_n_f16(0.0f);
+        svfloat16_t sum_03 = svdup_n_f16(0.0f);
+
+        svfloat16_t sum_10 = svdup_n_f16(0.0f);
+        svfloat16_t sum_11 = svdup_n_f16(0.0f);
+        svfloat16_t sum_12 = svdup_n_f16(0.0f);
+        svfloat16_t sum_13 = svdup_n_f16(0.0f);
+
+        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
+        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+
+        for (int i = 0; i < np; i += ggml_f16_step) {
+            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements
+
+            ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elemnst
+            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1);     // sum_00 = sum_00+ax1*ay1
+            ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements
+            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1);
+
+            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements
+
+            ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 ekements
+            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2);
+            ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1);
+            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2);
+
+            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
+
+            ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2);
+            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3);
+            ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2);
+            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3);
+
+            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
+
+            ax4 = GGML_F16x_VEC_LOAD(x[0] + i + 3*ggml_f16_epr, 3);
+            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax4, ay4);
+            ax4 = GGML_F16x_VEC_LOAD(x[1] + i + 3*ggml_f16_epr, 3);
+            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax4, ay4);
+
+            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
+
+            ax5 = GGML_F16x_VEC_LOAD(x[0] + i + 4*ggml_f16_epr, 4);
+
+            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax5, ay5);
+            ax5 = GGML_F16x_VEC_LOAD(x[1] + i + 4*ggml_f16_epr, 4);
+            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax5, ay5);
+
+            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
+
+            ax6 = GGML_F16x_VEC_LOAD(x[0] + i + 5*ggml_f16_epr, 5);
+
+            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax6, ay6);
+            ax6 = GGML_F16x_VEC_LOAD(x[1] + i + 5*ggml_f16_epr, 5);
+            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax6, ay6);
+
+            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
+
+            ax7 = GGML_F16x_VEC_LOAD(x[0] + i + 6*ggml_f16_epr, 6);
+
+            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax7, ay7);
+            ax7 = GGML_F16x_VEC_LOAD(x[1] + i + 6*ggml_f16_epr, 6);
+            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax7, ay7);
+
+            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
+
+            ax8 = GGML_F16x_VEC_LOAD(x[0] + i + 7*ggml_f16_epr, 7);
+
+            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax8, ay8);
+            ax8 = GGML_F16x_VEC_LOAD(x[1] + i + 7*ggml_f16_epr, 7);
+            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax8, ay8);
         }
-    }
-#else
-    const int np = (n & ~(GGML_F16_STEP - 1));
 
-    GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
+        const int np2 = (n & ~(ggml_f16_epr - 1));
+        for (int k = np; k < np2; k += ggml_f16_epr) {
+            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
 
-    GGML_F16_VEC ax[GGML_F16_ARR];
-    GGML_F16_VEC ay[GGML_F16_ARR];
+            svfloat16_t rx = GGML_F16x_VEC_LOAD(x[0] + k, 0);
+            sum_00 = GGML_F16x_VEC_FMA(sum_00, rx, ry);
+            rx = GGML_F16x_VEC_LOAD(x[1] + k, 0);
+            sum_10 = GGML_F16x_VEC_FMA(sum_10, rx, ry);
+        }
 
-    for (int i = 0; i < np; i += GGML_F16_STEP) {
-        for (int j = 0; j < GGML_F16_ARR; j++) {
-            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+        if (np2 < n) {
+            svbool_t pg = svwhilelt_b16(np2, n);
+            svfloat16_t hx_0 = svld1_f16(pg, (const __fp16 *)(x[0] + np2));
+            svfloat16_t hx_1 = svld1_f16(pg, (const __fp16 *)(x[1] + np2));
+            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
 
-            for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
-                ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
+            sum_00 = svmad_f16_x(pg, hx_0, hy, sum_00);
+            sum_10 = svmad_f16_x(pg, hx_1, hy, sum_10);
+        }
+        GGML_F16x_VEC_REDUCE(sumf[0], sum_00, sum_01, sum_02, sum_03);
+        GGML_F16x_VEC_REDUCE(sumf[1], sum_10, sum_11, sum_12, sum_13);
+    #elif defined(__riscv_v_intrinsic)
+      // todo: RVV impl
+      for (int i = 0; i < n; ++i) {
+          for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+              sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
+          }
+      }
+    #else
+        const int np = (n & ~(GGML_F16_STEP - 1));
 
-                sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
+        GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
+
+        GGML_F16_VEC ax[GGML_F16_ARR];
+        GGML_F16_VEC ay[GGML_F16_ARR];
+
+        for (int i = 0; i < np; i += GGML_F16_STEP) {
+            for (int j = 0; j < GGML_F16_ARR; j++) {
+                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+
+                for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+                    ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
+
+                    sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
+                }
             }
         }
-    }
 
-    // reduce sum0..sum3 to sum0
-    for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
-        GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
-    }
-
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
-            sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
+        // reduce sum0..sum3 to sum0
+        for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+            GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
         }
-    }
-#endif
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+                sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
+            }
+        }
+    #endif
 #else
     for (int i = 0; i < n; ++i) {
         for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
@@ -293,35 +397,112 @@ inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const
 
 inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y, const ggml_fp16_t * GGML_RESTRICT x, const float v) {
 #if defined(GGML_SIMD)
-#if defined(__riscv_v_intrinsic)
-    // todo: RVV impl
-    // scalar
-    for (int i = 0; i < n; ++i) {
-        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
-    }
-#else
-    const int np = (n & ~(GGML_F16_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = svcntb() * 8;
+        const int ggml_f16_epr = sve_register_length / 16;
+        const int ggml_f16_step = 8 * ggml_f16_epr;
 
-    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+        GGML_F16x_VEC vx = GGML_F16x_VEC_SET1(v);
 
-    GGML_F16_VEC ax[GGML_F16_ARR];
-    GGML_F16_VEC ay[GGML_F16_ARR];
+        const int np= (n & ~(ggml_f16_step - 1));
 
-    for (int i = 0; i < np; i += GGML_F16_STEP) {
-        for (int j = 0; j < GGML_F16_ARR; j++) {
-            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
-            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-            ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
+        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
+        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+        for (int i = 0; i < np; i += ggml_f16_step) {
+            ax1 = GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0);
+            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0);
+            ay1 = GGML_F16x_VEC_FMA(ay1, ax1, vx);
 
-            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+            GGML_F16x_VEC_STORE(y + i + 0 * ggml_f16_epr, ay1, 0);
+
+            ax2 = GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1);
+            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1);
+            ay2 = GGML_F16x_VEC_FMA(ay2, ax2, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 1 * ggml_f16_epr, ay2, 1);
+
+            ax3 = GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2);
+            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
+            ay3 = GGML_F16x_VEC_FMA(ay3, ax3, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 2 * ggml_f16_epr, ay3, 2);
+
+            ax4 = GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3);
+            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
+            ay4 = GGML_F16x_VEC_FMA(ay4, ax4, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 3 * ggml_f16_epr, ay4, 3);
+
+            ax5 = GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4);
+            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
+            ay5 = GGML_F16x_VEC_FMA(ay5, ax5, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 4 * ggml_f16_epr, ay5, 4);
+
+            ax6 = GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5);
+            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
+            ay6 = GGML_F16x_VEC_FMA(ay6, ax6, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 5 * ggml_f16_epr, ay6, 5);
+
+            ax7 = GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6);
+            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
+            ay7 = GGML_F16x_VEC_FMA(ay7, ax7, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 6 * ggml_f16_epr, ay7, 6);
+
+            ax8 = GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7);
+            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
+            ay8 = GGML_F16x_VEC_FMA(ay8, ax8, vx);
+
+            GGML_F16x_VEC_STORE(y + i + 7 * ggml_f16_epr, ay8, 7);
         }
-    }
+        const int np2 = (n & ~(ggml_f16_epr - 1));
+        for (int k = np; k < np2; k += ggml_f16_epr) {
+            svfloat16_t rx = GGML_F16x_VEC_LOAD(x + k, 0);
+            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
+            ry = GGML_F16x_VEC_FMA(ry, rx, vx);
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
-    }
-#endif
+            GGML_F16x_VEC_STORE(y + k, ry, 0);
+        }
+
+        if (np2 < n) {
+            svbool_t pg = svwhilelt_b16(np2, n);
+            svfloat16_t hx = svld1_f16(pg, (const __fp16 *)(x + np2));
+            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
+            hy = svmad_f16_x(pg, hx, vx, hy);
+            svst1_f16(pg, (__fp16 *)(y + np2), hy);
+        }
+
+    #elif defined(__riscv_v_intrinsic)
+        // todo: RVV impl
+        // scalar
+        for (int i = 0; i < n; ++i) {
+            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
+        }
+    #else
+        const int np = (n & ~(GGML_F16_STEP - 1));
+
+        GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+        GGML_F16_VEC ax[GGML_F16_ARR];
+        GGML_F16_VEC ay[GGML_F16_ARR];
+
+        for (int i = 0; i < np; i += GGML_F16_STEP) {
+            for (int j = 0; j < GGML_F16_ARR; j++) {
+                ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+                ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
+
+                GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
+        }
+    #endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -517,33 +698,59 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 
 inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
 #if defined(GGML_SIMD)
-#if defined(__riscv_v_intrinsic)
-    // todo: RVV impl
-    // scalar
-    for (int i = 0; i < n; ++i) {
-        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
-    }
-#else
-    const int np = (n & ~(GGML_F16_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = svcntb() * 8;
+        const int ggml_f16_epr = sve_register_length / 16;
+        const int ggml_f16_step = 2 * ggml_f16_epr;
 
-    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+        GGML_F16x_VEC vx =  GGML_F16x_VEC_SET1(v);
+        const int np = (n & ~(ggml_f16_step - 1));
+        svfloat16_t ay1, ay2;
 
-    GGML_F16_VEC ay[GGML_F16_ARR];
+        for (int i = 0; i < np; i += ggml_f16_step) {
+            ay1 = GGML_F16x_VEC_LOAD(y + i + 0*ggml_f16_epr, 0);
+            ay1 = GGML_F16x_VEC_MUL(ay1, vx);
+            GGML_F16x_VEC_STORE(y + i + 0*ggml_f16_epr, ay1, 0);
 
-    for (int i = 0; i < np; i += GGML_F16_STEP) {
-        for (int j = 0; j < GGML_F16_ARR; j++) {
-            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-            ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
-
-            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+            ay2 = GGML_F16x_VEC_LOAD(y + i + 1*ggml_f16_epr, 1);
+            ay2 = GGML_F16x_VEC_MUL(ay2, vx);
+            GGML_F16x_VEC_STORE(y + i + 1*ggml_f16_epr, ay2, 1);
         }
-    }
+        // leftovers
+        // maximum number of leftover elements will be less that ggmlF_16x_epr. Apply predicated svmad on available elements only
+        if (np < n) {
+            svbool_t pg = svwhilelt_b16(np, n);
+            svfloat16_t hy = svld1_f16(pg, (__fp16 *)(y + np));
+            svfloat16_t out = svmul_f16_m(pg, hy, vx);
+            svst1_f16(pg, (__fp16 *)(y + np), out);
+        }
+    #elif defined(__riscv_v_intrinsic)
+        // todo: RVV impl
+        // scalar
+        for (int i = 0; i < n; ++i) {
+            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
+        }
+    #else
+        const int np = (n & ~(GGML_F16_STEP - 1));
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
-    }
-#endif
+        GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+        GGML_F16_VEC ay[GGML_F16_ARR];
+
+        for (int i = 0; i < np; i += GGML_F16_STEP) {
+            for (int j = 0; j < GGML_F16_ARR; j++) {
+                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+                ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
+
+                GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
+        }
+    #endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {

From 078ce23ea77988f2fe1a42afb18d58bc084d55fa Mon Sep 17 00:00:00 2001
From: s-goto-11 <206795233+s-goto-11@users.noreply.github.com>
Date: Tue, 2 Sep 2025 03:13:49 +0900
Subject: [PATCH 250/287] ggml: SVE support for exponential functions (#15145)

* SVE support for exponential functions

Add const notation to variable pg

* Update ggml/src/ggml-cpu/vec.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Add const

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-cpu/vec.cpp | 21 +++++++++++++++++++++
 ggml/src/ggml-cpu/vec.h   | 34 +++++++++++++++++++++++++++++++++-
 2 files changed, 54 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp
index f2412dcc08..0652155cf7 100644
--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -321,6 +321,12 @@ void ggml_vec_silu_f32(const int n, float * y, const float * x) {
     for (; i + 3 < n; i += 4) {
         _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i)));
     }
+#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+    const int vlen = svcntw();
+    for (; i < n; i += vlen) {
+        const svbool_t pg = svwhilelt_b32_s32(i, n);
+        svst1_f32(pg, y + i, ggml_v_silu(pg, svld1_f32(pg, x + i)));
+    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i)));
@@ -345,6 +351,12 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
     for (; i + 3 < n; i += 4) {
         _mm_storeu_ps(y + i, _mm_mul_ps(ggml_v_silu(_mm_loadu_ps(x + i)), _mm_loadu_ps(g + i)));
     }
+#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+    const int vlen = svcntw();
+    for (; i < n; i += vlen) {
+        const svbool_t pg = svwhilelt_b32_s32(i, n);
+        svst1_f32(pg, y + i, svmul_f32_x(pg, ggml_v_silu(pg, svld1_f32(pg, x + i)), svld1_f32(pg, g + i)));
+    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, vmulq_f32(ggml_v_silu(vld1q_f32(x + i)), vld1q_f32(g + i)));
@@ -392,6 +404,15 @@ ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float
 #endif
         sum += (ggml_float)_mm_cvtss_f32(val);
     }
+#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+    const int vlen = svcntw();
+    for (; i < n; i += vlen) {
+        const svbool_t pg = svwhilelt_b32_s32(i, n);
+        svfloat32_t val = ggml_v_expf(pg, svsub_f32_x(pg, svld1_f32(pg, x + i),
+                                                svdup_n_f32_x(pg, max)));
+        svst1_f32(pg, y + i, val);
+        sum += (ggml_float)svaddv_f32(pg, val);
+    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i),
diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h
index 1f7c5996be..1346e7d7e0 100644
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -1002,7 +1002,39 @@ https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/sr
     }
 #endif
 
-#if defined(__ARM_NEON) && defined(__aarch64__)
+#if defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+
+inline static svfloat32_t ggml_v_expf(svbool_t pg, svfloat32_t x) {
+    const svfloat32_t r = svdup_n_f32_x(pg, 0x1.8p23f);
+    const svfloat32_t z = svmla_n_f32_x(pg, r, x, 0x1.715476p+0f);
+    const svfloat32_t n = svsub_f32_x(pg, z, r);
+    const svfloat32_t b = svmls_n_f32_x(pg, svmls_n_f32_x(pg, x, n, 0x1.62e4p-1f), n, 0x1.7f7d1cp-20f);
+    const svuint32_t e = svlsl_n_u32_x(pg, svreinterpret_u32_f32(z), 23);
+    const svfloat32_t k = svreinterpret_f32_u32(svadd_u32_x(pg, e, svreinterpret_u32_f32(svdup_n_f32_x(pg, 1))));
+    const svbool_t c = svacgt_n_f32(pg, n, 126);
+    const svfloat32_t u = svmul_f32_x(pg, b, b);
+    const svfloat32_t j = svmla_f32_x(pg,
+        svmul_n_f32_x(pg, b, 0x1.ffffecp-1f),
+        svmla_f32_x(pg, svmla_f32_x(pg, svdup_n_f32_x(pg, 0x1.fffdb6p-2f), svdup_n_f32_x(pg, 0x1.555e66p-3f), b),
+                        svmla_f32_x(pg, svdup_n_f32_x(pg, 0x1.573e2ep-5f), svdup_n_f32_x(pg, 0x1.0e4020p-7f), b), u), u);
+    const svuint32_t d = svdup_n_u32_z(svcmple_n_f32(pg, n, 0.0), 0x82000000);
+    const svfloat32_t s1 = svreinterpret_f32_u32(svadd_n_u32_x(pg, d, 0x7f000000));
+    const svfloat32_t s2 = svreinterpret_f32_u32(svsub_u32_x(pg, e, d));
+    return svsel_f32(svacgt_f32(pg, n, svdup_n_f32_x(pg, 192)), svmul_f32_x(pg, s1, s1),
+                     svsel_f32(c, svmul_f32_x(pg, svmla_f32_x(pg, s2, s2, j), s1), svmla_f32_x(pg, k, k, j)));
+}
+
+// computes silu x/(1+exp(-x)) in single precision vector
+inline static svfloat32_t ggml_v_silu(svbool_t pg, svfloat32_t x) {
+    const svfloat32_t one = svdup_n_f32_x(pg, 1.0f);
+    const svfloat32_t zero = svdup_n_f32_x(pg, 0.0f);
+    const svfloat32_t neg_x = svsub_f32_x(pg, zero, x);
+    const svfloat32_t exp_neg_x = ggml_v_expf(pg, neg_x);
+    const svfloat32_t one_plus_exp_neg_x = svadd_f32_x(pg, one, exp_neg_x);
+    return svdiv_f32_x(pg, x, one_plus_exp_neg_x);
+}
+
+#elif defined(__ARM_NEON) && defined(__aarch64__)
 
 // adapted from arm limited optimized routine
 // the maximum error is 1.45358 plus 0.5 ulps

From fec7911f8febb22c27ce1acb12441800da24cc06 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Mon, 1 Sep 2025 20:58:35 +0200
Subject: [PATCH 251/287] vulkan: disable large mmv subgroups on older Nvidia
 GPUs (#15717)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4057ce8556..3ac459bc4f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -4662,7 +4662,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
 
     // heuristic to choose workgroup size
     uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
-    if (ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
+    if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
         // Prefer larger workgroups when M is small, to spread the work out more
         // and keep more SMs busy.
         // q6_k seems to prefer small workgroup size even for "medium" values of M.

From 35a42edac84690990a75726898d975cd08d220c0 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Mon, 1 Sep 2025 14:01:10 -0500
Subject: [PATCH 252/287] vulkan: add missing clamps in new mul_mat_id paths
 (#15702)

This is a missing interaction between #15546 and #15652
---
 ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 654105a491..69ac38fd41 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -494,6 +494,9 @@ void main() {
                     sum = coopMatMulAdd(mat_a, mat_b, sum);
                 }
             }
+#if defined(ACC_TYPE_MAX)
+            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
 
             // Convert from ACC_TYPE to D_TYPE
             coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> mat_d;
@@ -535,6 +538,9 @@ void main() {
                     sum = coopMatMulAdd(mat_a, mat_b, sum);
                 }
             }
+#if defined(ACC_TYPE_MAX)
+            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
+#endif
 
             // Convert from ACC_TYPE to D_TYPE
             coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> mat_d;

From d4d8dbe383e8b9600cbe8b42016e3a4529b51219 Mon Sep 17 00:00:00 2001
From: "Gilad S." <7817232+giladgd@users.noreply.github.com>
Date: Mon, 1 Sep 2025 22:17:42 +0300
Subject: [PATCH 253/287] vulkan: use memory budget extension to read memory
 usage (#15545)

* vulkan: use memory budget extension to read memory usage

* fix: formatting and names

* formatting

* fix: detect and cache memory budget extension availability on init

* fix: read `budgetprops.heapBudget` instead of `heap.size` when memory budget extension is available

* style: lints
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 41 +++++++++++++++++++++++-----
 1 file changed, 34 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 3ac459bc4f..f7812ab37c 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1370,6 +1370,7 @@ struct vk_instance_t {
     PFN_vkCmdInsertDebugUtilsLabelEXT  pfn_vkCmdInsertDebugUtilsLabelEXT  = {};
 
     std::vector<size_t> device_indices;
+    std::vector<bool>   device_supports_membudget;
     vk_device devices[GGML_VK_MAX_DEVICES];
 };
 
@@ -4340,15 +4341,16 @@ static void ggml_vk_instance_init() {
         vk_instance.pfn_vkCmdBeginDebugUtilsLabelEXT = (PFN_vkCmdBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdBeginDebugUtilsLabelEXT");
         vk_instance.pfn_vkCmdEndDebugUtilsLabelEXT =   (PFN_vkCmdEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdEndDebugUtilsLabelEXT");
         vk_instance.pfn_vkCmdInsertDebugUtilsLabelEXT = (PFN_vkCmdInsertDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdInsertDebugUtilsLabelEXT");
-
     }
 
     vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
 
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
     // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
     char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
     if (devices_env != nullptr) {
-        size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
+        size_t num_available_devices = devices.size();
 
         std::string devices(devices_env);
         std::replace(devices.begin(), devices.end(), ',', ' ');
@@ -4363,8 +4365,6 @@ static void ggml_vk_instance_init() {
             vk_instance.device_indices.push_back(tmp);
         }
     } else {
-        std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
-
         // If no vulkan devices are found, return early
         if (devices.empty()) {
             GGML_LOG_INFO("ggml_vulkan: No devices found.\n");
@@ -4469,6 +4469,19 @@ static void ggml_vk_instance_init() {
     GGML_LOG_DEBUG("ggml_vulkan: Found %zu Vulkan devices:\n", vk_instance.device_indices.size());
 
     for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        vk::PhysicalDevice vkdev = devices[vk_instance.device_indices[i]];
+        std::vector<vk::ExtensionProperties> extensionprops = vkdev.enumerateDeviceExtensionProperties();
+
+        bool membudget_supported = false;
+        for (const auto & ext : extensionprops) {
+            if (strcmp(VK_EXT_MEMORY_BUDGET_EXTENSION_NAME, ext.extensionName) == 0) {
+                membudget_supported = true;
+                break;
+            }
+        }
+
+        vk_instance.device_supports_membudget.push_back(membudget_supported);
+
         ggml_vk_print_gpu_info(i);
     }
 }
@@ -11654,15 +11667,29 @@ void ggml_backend_vk_get_device_description(int device, char * description, size
 
 void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
     GGML_ASSERT(device < (int) vk_instance.device_indices.size());
+    GGML_ASSERT(device < (int) vk_instance.device_supports_membudget.size());
 
     vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
+    vk::PhysicalDeviceMemoryBudgetPropertiesEXT budgetprops;
+    vk::PhysicalDeviceMemoryProperties2 memprops = {};
+    bool membudget_supported = vk_instance.device_supports_membudget[device];
 
-    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
+    if (membudget_supported) {
+        memprops.pNext = &budgetprops;
+    }
+    vkdev.getMemoryProperties2(&memprops);
+
+    for (uint32_t i = 0; i < memprops.memoryProperties.memoryHeapCount; ++i) {
+        const vk::MemoryHeap & heap = memprops.memoryProperties.memoryHeaps[i];
 
-    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
         if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
             *total = heap.size;
-            *free = heap.size;
+
+            if (membudget_supported && i < budgetprops.heapUsage.size()) {
+                *free = budgetprops.heapBudget[i] - budgetprops.heapUsage[i];
+            } else {
+                *free = heap.size;
+            }
             break;
         }
     }

From 5d804a4938d896f9089687a8b99911cdb43b0b94 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 2 Sep 2025 01:14:55 +0200
Subject: [PATCH 254/287] ggml-backend: raise GGML_MAX_SPLIT_INPUTS (#15722)

---
 ggml/src/ggml-backend.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 0cdbf18017..f615ab4bee 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -651,7 +651,7 @@ static bool ggml_is_view_op(enum ggml_op op) {
 #endif
 
 #ifndef GGML_SCHED_MAX_SPLIT_INPUTS
-#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
+#define GGML_SCHED_MAX_SPLIT_INPUTS 30
 #endif
 
 #ifndef GGML_SCHED_MAX_COPIES

From ef2af57ddf04ab367f6ba6e8ed100fc56785e4b7 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Tue, 2 Sep 2025 14:05:23 +0800
Subject: [PATCH 255/287] CANN: Support ext_factor in rope (#15710)

---
 ggml/src/ggml-cann/aclnn_ops.cpp | 61 ++++++++++++++++++++++++++++----
 ggml/src/ggml-cann/ggml-cann.cpp | 16 ---------
 2 files changed, 55 insertions(+), 22 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 84e705af9a..11fbd1bc67 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -70,6 +70,8 @@
 #include <aclnnop/aclnn_zero.h>
 #include <aclnnop/aclnn_index_copy.h>
 #include <aclnnop/aclnn_index_select.h>
+#include <aclnnop/aclnn_clamp.h>
+#include <aclnnop/aclnn_threshold.h>
 #include <float.h>
 
 #include <cmath>
@@ -2263,6 +2265,7 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
  */
 static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
                              void* sin_tensor_buffer, void* cos_tensor_buffer,
+                             float* corr_dims, float ext_factor,
                              float theta_scale, float freq_scale,
                              float attn_factor, bool is_neox) {
     // int sin/cos cache, cache has different repeat method depond on
@@ -2318,16 +2321,60 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         float n_elements = theta_scale_length;
         aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
 
+        ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool());
+        aclTensor* acl_yarn_ramp_tensor = nullptr;
+        if (ext_factor != 0) {
+            // -rope_yarn_ramp
+            // const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
+            // return MIN(1, MAX(0, y)) - 1;
+            yarn_ramp_allocator.alloc(theta_scale_length * sizeof(float));
+            void* yarn_ramp_buffer = yarn_ramp_allocator.get();
+            acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float_t),
+                                           theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+            float zero_value = 0, one_value = 1;
+            float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]);
+            aclScalar* low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT);
+            aclScalar* zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT);
+            aclScalar* one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT);
+            aclScalar* denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT);
+            aclScalar* ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT);
+
+            GGML_CANN_CALL_ACLNN_OP(ctx, Subs, acl_theta_scale_tensor, low, one, acl_yarn_ramp_tensor);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDivs, acl_yarn_ramp_tensor, denom_safe);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceThreshold, acl_yarn_ramp_tensor, zero, zero);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceClampMax, acl_yarn_ramp_tensor, one);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSubs, acl_yarn_ramp_tensor, one, one);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, ext_factor_sc);
+
+            // theta_interp = freq_scale * theta_extrap;
+            // theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+            // theta = freq_scale * theta_extrap * (1 - ramp_mix) + theta_extrap * ramp_mix;
+            // theta = freq_scale * theta_extrap - freq_scale * theta_extrap * ramp_mix + theta_extrap * ramp_mix;
+            // theta = theta_extrap * (freq_scale - freq_scale * ramp_mix + ramp_mix);
+            //
+            // we cache (freq_scale - freq_scale * ramp_mix + ramp_mix), Considering that the rope_yarn_ramp here is the inverse
+            // cache freq_scale + (freq_scale - 1) * ramp_mix
+            float freq_scale_1 = freq_scale - 1;
+            aclScalar* freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT);
+            aclScalar* freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, freq_scale_1_sc);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor, freq_scale_sc, one);
+
+            ggml_cann_release_resources(ctx, low, zero, one, denom_safe, ext_factor_sc, freq_scale_sc, freq_scale_1_sc);
+        }
+
         // power
         aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
         GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor,
                                 acl_theta_scale_tensor);
 
-        // freq_scale
-        if (freq_scale != 1) {
+        if (ext_factor != 0) {
+            aclnn_mul(ctx, acl_theta_scale_tensor, acl_yarn_ramp_tensor);
+        } else if (freq_scale != 1) {
             aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true);
         }
-        ggml_cann_release_resources(ctx, acl_theta_scale);
+
+        ggml_cann_release_resources(ctx, acl_yarn_ramp_tensor, acl_theta_scale);
     } else {
         // use cache
         acl_theta_scale_tensor =
@@ -2385,6 +2432,10 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
 
+    if (ext_factor != 0) {
+        attn_factor *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+
     // attn_factor
     if (attn_factor != 1) {
         aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true);
@@ -2465,8 +2516,6 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // TODO: n_dims <= ne0
     GGML_ASSERT(n_dims == ne0);
     GGML_ASSERT(n_dims % 2 == 0);
-    // TODO: ext_factor != 0
-    GGML_ASSERT(ext_factor == 0);
 
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
@@ -2484,7 +2533,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     void *cos_tensor_buffer = cos_tensor_allocator.get();
 
     // init ctx.rope_cos/rope_sin cache
-    aclnn_cache_init(ctx, dst, sin_tensor_buffer, cos_tensor_buffer,
+    aclnn_cache_init(ctx, dst, sin_tensor_buffer, cos_tensor_buffer, corr_dims, ext_factor,
                     theta_scale, freq_scale, attn_factor, is_neox);
 
     int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 15ea85e273..da6d74d48a 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2401,16 +2401,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
         }
         case GGML_OP_ROPE: {
             // TODO: with ops-test v == 1
-            float ext_factor = 0.0f;
-            memcpy(&ext_factor, (const float *) op->op_params + 7, sizeof(float));
             // TODO: n_dims <= ne0
             if (op->src[0]->ne[0] != op->op_params[1]) {
                 return false;
             }
-            // TODO: ext_factor != 0
-            if (ext_factor != 0) {
-                return false;
-            }
 
             const int mode = ((const int32_t *) op->op_params)[2];
             if (mode & GGML_ROPE_TYPE_MROPE) {
@@ -2420,9 +2414,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 return false;
             }
 
-            if(!ggml_is_contiguous(op->src[0])){
-                return false;
-            }
             return true;
         }
         case GGML_OP_UPSCALE: {
@@ -2523,13 +2514,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // different head sizes of K and V are not supported yet
                 return false;
             }
-            if (op->src[0]->ne[0] == 192) {
-                return false;
-            }
-            if (op->src[0]->ne[0] == 576) {
-                // DeepSeek MLA
-                return false;
-            }
             if (op->src[0]->ne[0] % 16 != 0) {
                 // TODO: padding to support
                 return false;

From 2f853687b3bce15e143a22f678d1715060fd606c Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Tue, 2 Sep 2025 14:07:48 +0800
Subject: [PATCH 256/287] CANN: Support eager execution mode under ACL graph
 compilation (#15712)

* [CANN] Support eager execution mode under ACL graph compilation

Add support for running operators in eager mode while ACL graph
compilation is enabled. This allows bypassing graph execution
and directly submitting ops, which is useful for debugging and
reducing graph build overhead in certain scenarios.

Signed-off-by: noemotiovon <757486878@qq.com>

* fix typo

Signed-off-by: noemotiovon <757486878@qq.com>

* rename to acl_graph_mode

Signed-off-by: noemotiovon <757486878@qq.com>

---------

Signed-off-by: noemotiovon <757486878@qq.com>
---
 docs/backend/CANN.md             | 4 ++++
 ggml/src/ggml-cann/common.h      | 9 ++++++++-
 ggml/src/ggml-cann/ggml-cann.cpp | 4 ++++
 3 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md
index 325e09bd38..2d8866e3bd 100755
--- a/docs/backend/CANN.md
+++ b/docs/backend/CANN.md
@@ -314,3 +314,7 @@ Controls automatic cleanup of the memory pool. This option is only effective whe
 
 Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
 
+### GGML_CANN_DISABLE_ACL_GRAPH
+
+When this variable is set, ACL graph execution is disabled and operators are executed in an op-by-op (eager) mode.
+This mode is mainly intended for debugging or for cases where the overhead of graph construction and execution is not desirable.
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index f71aa9d1de..a041a157c3 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -395,6 +395,7 @@ struct ggml_backend_cann_context {
 #ifdef USE_ACL_GRAPH
     /// Cached CANN ACL graph used for executing the current ggml computation graph.
     std::unique_ptr<ggml_cann_graph> cann_graph;
+    bool acl_graph_mode = true;
 #endif
     cann_task_queue task_queue;
     bool async_mode;
@@ -404,7 +405,6 @@ struct ggml_backend_cann_context {
     ggml_cann_tensor_cache rms_norm_one_tensor_cache;
     ggml_cann_tensor_cache rms_norm_zero_tensor_cache;
 
-
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
     /**
@@ -419,6 +419,13 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
+#ifdef USE_ACL_GRAPH
+        acl_graph_mode = !(parse_bool(get_env("GGML_CANN_DISABLE_ACL_GRAPH").value_or("")));
+        GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n",
+              __func__, device,
+              acl_graph_mode ? "GRAPH" : "EAGER",
+              acl_graph_mode ? "acl graph enabled" : "acl graph disabled");
+#endif
     }
 
     /**
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index da6d74d48a..0d9eb8fa1b 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2252,6 +2252,10 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     bool use_cann_graph = true;
     bool cann_graph_update_required = false;
 
+    if (!cann_ctx->acl_graph_mode) {
+        use_cann_graph = false;
+    }
+
     if (use_cann_graph) {
         if (cann_ctx->cann_graph == nullptr) {
             cann_ctx->cann_graph.reset(new ggml_cann_graph());

From 97669e40735d08db65ef094a8faae8e8411a97db Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Tue, 2 Sep 2025 08:26:53 +0200
Subject: [PATCH 257/287] opencl: add attn sinks support for FA kernels
 (#15706)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 13 ++++---
 .../src/ggml-opencl/kernels/flash_attn_f16.cl | 35 ++++++++++++++++---
 .../src/ggml-opencl/kernels/flash_attn_f32.cl | 35 ++++++++++++++++---
 .../ggml-opencl/kernels/flash_attn_f32_f16.cl | 35 ++++++++++++++++---
 4 files changed, 102 insertions(+), 16 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index c25c2daaf6..a9a91ca585 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2776,10 +2776,6 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
         case GGML_OP_FLASH_ATTN_EXT:
             {
-                if (op->src[4]) {
-                    return false;
-                }
-
                 const ggml_tensor * q = op->src[0];
                 const ggml_tensor * k = op->src[1];
                 const ggml_tensor * v = op->src[2];
@@ -5765,6 +5761,7 @@ static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor
 static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, const ggml_tensor * k, ggml_tensor * dst) {
     const ggml_tensor * v = dst->src[2];
     const ggml_tensor * mask = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
     GGML_ASSERT(q->extra);
     GGML_ASSERT(k->extra);
     GGML_ASSERT(v->extra);
@@ -5772,6 +5769,9 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     if (mask) {
         GGML_ASSERT(mask->extra);
     }
+    if (sinks) {
+        GGML_ASSERT(sinks->extra);
+    }
 
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
@@ -5813,6 +5813,7 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     ggml_tensor_extra_cl * extra_v = (ggml_tensor_extra_cl *)v->extra;
     ggml_tensor_extra_cl * extra_o = (ggml_tensor_extra_cl *)dst->extra;
     ggml_tensor_extra_cl * extra_mask = mask ? (ggml_tensor_extra_cl *)mask->extra : NULL;
+    ggml_tensor_extra_cl * extra_sinks = sinks ? (ggml_tensor_extra_cl *)sinks->extra : NULL;
 
     cl_ulong offset_q = extra_q->offset + q->view_offs;
     cl_ulong offset_k = extra_k->offset + k->view_offs;
@@ -5820,6 +5821,8 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     cl_ulong offset_o = extra_o->offset + dst->view_offs;
     cl_mem   mask_buffer = extra_mask ? extra_mask->data_device : NULL;
     cl_ulong offset_mask = extra_mask ? extra_mask->offset + mask->view_offs : 0;
+    cl_mem   sinks_buffer = extra_sinks ? extra_sinks->data_device : NULL;
+    cl_ulong offset_sinks = extra_sinks ? extra_sinks->offset + sinks->view_offs : 0;
 
     const cl_ulong q_nb1 = q->nb[1], q_nb2 = q->nb[2], q_nb3 = q->nb[3];
     const cl_ulong k_nb1 = k->nb[1], k_nb2 = k->nb[2], k_nb3 = k->nb[3];
@@ -5874,6 +5877,8 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     CL_CHECK(clSetKernelArg(kernel, 35, sizeof(cl_ulong), &mask_nb3));
     CL_CHECK(clSetKernelArg(kernel, 36, sizeof(int),      &mask_ne2));
     CL_CHECK(clSetKernelArg(kernel, 37, sizeof(int),      &mask_ne3));
+    CL_CHECK(clSetKernelArg(kernel, 38, sizeof(cl_mem),   &sinks_buffer));
+    CL_CHECK(clSetKernelArg(kernel, 39, sizeof(cl_ulong), &offset_sinks));
 
     if (n_q == 1) {
         const size_t wg_size = 64;
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
index fea06867e1..8f43c4f27d 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
@@ -49,7 +49,9 @@ __kernel void flash_attn_f16(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int block_q_idx = get_group_id(0);
@@ -171,6 +173,20 @@ __kernel void flash_attn_f16(
     }
 
     if (my_query_row < n_q) {
+        if (sinks_void != NULL) {
+            const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+            const ACC_TYPE m_sink = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+
+            const ACC_TYPE scale_o = exp(m_i - m_final);
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] *= scale_o;
+            }
+
+            l_i = l_i * exp(m_i - m_final) + exp(m_sink - m_final);
+        }
+
         const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
         global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
@@ -214,7 +230,9 @@ __kernel void flash_attn_f16_q1(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int head_batch_idx = get_global_id(1);
@@ -247,7 +265,12 @@ __kernel void flash_attn_f16_q1(
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
-    ACC_TYPE m_i = -INFINITY;
+    const global ACC_TYPE* sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+    }
+
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
@@ -320,7 +343,11 @@ __kernel void flash_attn_f16_q1(
 
     const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
     global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
-    const ACC_TYPE l_final = local_l[0];
+    ACC_TYPE l_final = local_l[0];
+
+    if (sinks_ptr != NULL) {
+        l_final += exp(sinks_ptr[head_idx] - m_final);
+    }
 
     if (l_final > 0.0f) {
         const ACC_TYPE l_inv = 1.0f / l_final;
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
index 2d657327d6..9c0bab135a 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
@@ -49,7 +49,9 @@ __kernel void flash_attn_f32(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int block_q_idx = get_group_id(0);
@@ -171,6 +173,20 @@ __kernel void flash_attn_f32(
     }
 
     if (my_query_row < n_q) {
+        if (sinks_void != NULL) {
+            const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+            const ACC_TYPE m_sink = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+
+            const ACC_TYPE scale_o = exp(m_i - m_final);
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] *= scale_o;
+            }
+
+            l_i = l_i * exp(m_i - m_final) + exp(m_sink - m_final);
+        }
+
         const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
         global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
@@ -214,7 +230,9 @@ __kernel void flash_attn_f32_q1(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int head_batch_idx = get_global_id(1);
@@ -247,7 +265,12 @@ __kernel void flash_attn_f32_q1(
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
-    ACC_TYPE m_i = -INFINITY;
+    const global ACC_TYPE* sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+    }
+
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
@@ -320,7 +343,11 @@ __kernel void flash_attn_f32_q1(
 
     const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
     global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
-    const ACC_TYPE l_final = local_l[0];
+    ACC_TYPE l_final = local_l[0];
+
+    if (sinks_ptr != NULL) {
+        l_final += exp(sinks_ptr[head_idx] - m_final);
+    }
 
     if (l_final > 0.0f) {
         const ACC_TYPE l_inv = 1.0f / l_final;
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
index 7067bd2591..ec7361b9e3 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
@@ -52,7 +52,9 @@ __kernel void flash_attn_f32_f16(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int block_q_idx = get_group_id(0);
@@ -174,6 +176,20 @@ __kernel void flash_attn_f32_f16(
     }
 
     if (my_query_row < n_q) {
+        if (sinks_void != NULL) {
+            const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+            const ACC_TYPE m_sink = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+
+            const ACC_TYPE scale_o = exp(m_i - m_final);
+            #pragma unroll
+            for (int i = 0; i < DV_VEC; ++i) {
+                o_acc[i] *= scale_o;
+            }
+
+            l_i = l_i * exp(m_i - m_final) + exp(m_sink - m_final);
+        }
+
         const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
         global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
@@ -217,7 +233,9 @@ __kernel void flash_attn_f32_f16_q1(
     const ulong mask_nb2,
     const ulong mask_nb3,
     const int mask_ne2,
-    const int mask_ne3
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
 ) {
     const int tid = get_local_id(0);
     const int head_batch_idx = get_global_id(1);
@@ -250,7 +268,12 @@ __kernel void flash_attn_f32_f16_q1(
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
-    ACC_TYPE m_i = -INFINITY;
+    const global ACC_TYPE* sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+    }
+
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
@@ -323,7 +346,11 @@ __kernel void flash_attn_f32_f16_q1(
 
     const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
     global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
-    const ACC_TYPE l_final = local_l[0];
+    ACC_TYPE l_final = local_l[0];
+
+    if (sinks_ptr != NULL) {
+        l_final += exp(sinks_ptr[head_idx] - m_final);
+    }
 
     if (l_final > 0.0f) {
         const ACC_TYPE l_inv = 1.0f / l_final;

From 25f1045f07cf0daf667d63e35618842e3174a8c7 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 2 Sep 2025 01:37:01 -0500
Subject: [PATCH 258/287] vulkan: Fix macro parameter order for f32 matmul
 shaders (#15716)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index f7812ab37c..7189fc1cfa 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2778,11 +2778,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, REQSUBGROUPSIZE > 0, false, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \

From 9961d244f2df6baf40af2f1ddc0927f8d91578c8 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Tue, 2 Sep 2025 17:12:37 +0800
Subject: [PATCH 259/287] CANN: Resolve soft_max precision issue (#15730)

Previously, the slope tensor was set to fp16 to improve efficiency.
While this worked correctly in FA, it caused precision issues in soft_max.
This change applies different data types for different operators
to balance both accuracy and performance.
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 11fbd1bc67..9c312faab7 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1425,21 +1425,25 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
  * @param start         Starting exponent offset.
  * @param stop          Stopping exponent offset (exclusive).
  * @param step          Step size for the exponent increment.
+ * @param dtype         Data type for slope tensor.
  */
 static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer,
-    float m, int64_t size, float start, float stop, float step){
-    int64_t ne[] = {size};
-    size_t nb[] = {sizeof(uint16_t)};
+    float m, int64_t size, float start, float stop, float step, ggml_type dtype){
+    aclDataType acl_type = ggml_cann_type_mapping(dtype);
+    size_t type_size = ggml_type_size(dtype);
 
-    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * sizeof(uint16_t));
+    int64_t ne[] = {size};
+    size_t nb[] = {type_size};
+
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * type_size);
     void* arange_buffer = arange_allocator.get();
 
     aclTensor* arange_tensor = ggml_cann_create_tensor(
-        arange_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
+        arange_buffer, acl_type, type_size, ne, nb, 1);
     aclnn_arange(ctx, arange_tensor, start, stop, step, size);
 
     aclTensor* slope_tensor = ggml_cann_create_tensor(
-        slope_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
+        slope_buffer, acl_type, type_size, ne, nb, 1);
 
     aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT);
 
@@ -1470,10 +1474,11 @@ static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_bu
  * @param n_head        Total number of attention heads.
  * @param slope_buffer  Pointer to the output buffer (float array) for storing slopes.
  * @param max_bias      Maximum bias value for slope computation.
+ * @param dtype         Data type for slope tensor.
  *
 */
 static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
-    void* slope_buffer, float max_bias) {
+    void* slope_buffer, float max_bias, ggml_type dtype) {
     const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
 
     float m0 = powf(2.0f, -(max_bias) / n_head_log2);
@@ -1490,7 +1495,7 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
     float step  = 1;
     float count = n_head_log2;
     // end needs to be +1 because aclnn uses a left-closed, right-open interval.
-    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step);
+    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step, dtype);
     if (n_head_log2 < n_head) {
         // arange2
         start = 2 * (n_head_log2 - n_head_log2) + 1;
@@ -1499,7 +1504,7 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
         count = n_head - n_head_log2;
         aclnn_get_slope_inner(
             ctx, (char *) slope_buffer + n_head_log2 * sizeof(float),
-            m1, count, start, end + 1, step);
+            m1, count, start, end + 1, step, dtype);
     }
 }
 
@@ -1536,7 +1541,7 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask,
         ggml_cann_pool_alloc bias_allocator(
                     ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst));
         bias_buffer = bias_allocator.get();
-        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias);
+        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias, GGML_TYPE_F32);
     }
 
     // broadcast for mask, slop and dst;
@@ -3269,7 +3274,7 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
                 const int64_t n_heads = src0->ne[2];
                 ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t));
                 void* slope_buffer = slope_allocator.get();
-                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias);
+                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias, GGML_TYPE_F16);
 
                 int64_t slope_ne[] = {1, 1, n_heads, 1};
                 size_t slope_nb[GGML_MAX_DIMS];

From 0a2a3841e8ebc570da343e8cf58a21c1010b41e7 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Tue, 2 Sep 2025 16:02:26 +0200
Subject: [PATCH 260/287] vulkan: fix shaders gen when no integer dot is
 available (#15740)

---
 .../src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 6c64e1b513..1263a70e4f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -854,7 +854,13 @@ void write_output_files() {
         fputs(len.c_str(), src);
     }
 
-    for (const std::string& btype : {"f16", "f32", "q8_1"}) {
+    std::vector<std::string> btypes = {"f16", "f32"};
+
+#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+    btypes.push_back("q8_1");
+#endif
+
+    for (const std::string& btype : btypes) {
     for (const auto& tname : type_names) {
         if (btype == "q8_1" && !is_legacy_quant(tname)) {
             continue;

From c466abe1587bde458c806e2134e37750f2edf8fb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 2 Sep 2025 18:17:26 +0200
Subject: [PATCH 261/287] llama: -fa 1/0/-1 aliases for -fa on/off/auto
 (#15746)

---
 common/arg.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 4fa214d3d2..fcee0c4470 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1548,11 +1548,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-fa", "--flash-attn"}, "FA",
         string_format("set Flash Attention use ('on', 'off', or 'auto', default: '%s')", llama_flash_attn_type_name(params.flash_attn_type)),
         [](common_params & params, const std::string & value) {
-            if (value == "on" || value == "enabled") {
+            if (value == "on" || value == "enabled" || value == "1") {
                 params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
-            } else if (value == "off" || value == "disabled") {
+            } else if (value == "off" || value == "disabled" || value == "0") {
                 params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
-            } else if (value == "auto") {
+            } else if (value == "auto" || value == "-1") {
                 params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
             } else {
                 throw std::runtime_error(string_format("error: unkown value for --flash-attn: '%s'\n", value.c_str()));

From 69db8a52e6dd0db81ec05c581150cc1e43b8ac46 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Tue, 2 Sep 2025 19:40:37 +0200
Subject: [PATCH 262/287] chore: Update `.clang-format` to use
 `BinPackArguments=true` (#15744)

This seems to correspond with what we want to do, see
[here](https://github.com/ggml-org/llama.cpp/pull/15715#discussion_r2315613796)
and [clang-format docs](https://clang.llvm.org/docs/ClangFormatStyleOptions.html#binpackarguments)
---
 .clang-format | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.clang-format b/.clang-format
index 47d96b6b40..117e6986f6 100644
--- a/.clang-format
+++ b/.clang-format
@@ -22,7 +22,7 @@ AllowShortIfStatementsOnASingleLine: Never
 AllowShortLambdasOnASingleLine: Inline
 AllowShortLoopsOnASingleLine: false
 AlwaysBreakBeforeMultilineStrings: true
-BinPackArguments: false
+BinPackArguments: true
 BinPackParameters: false # OnePerLine
 BitFieldColonSpacing: Both
 BreakBeforeBraces: Custom # Attach

From 3de008208b9b8a33f49f979097a99b4d59e6e521 Mon Sep 17 00:00:00 2001
From: SnA1lGo <44647694+skrandy@users.noreply.github.com>
Date: Wed, 3 Sep 2025 03:27:30 +0800
Subject: [PATCH 263/287] fix: resolve unsigned int initialization warning for
 n_dims/size in gguf.cpp (#15754)

---
 ggml/src/gguf.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp
index 53504399c5..af57a88c60 100644
--- a/ggml/src/gguf.cpp
+++ b/ggml/src/gguf.cpp
@@ -273,7 +273,7 @@ struct gguf_reader {
     }
 
     bool read(std::string & dst) const {
-        uint64_t size = -1;
+        uint64_t size = 0;
         if (!read(size)) {
             return false;
         }
@@ -523,7 +523,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
         // tensor shape
         {
-            uint32_t n_dims = -1;
+            uint32_t n_dims = 0;
             ok = ok && gr.read(n_dims);
             if (n_dims > GGML_MAX_DIMS) {
                 GGML_LOG_ERROR("%s: tensor '%s' has invalid number of dimensions: %" PRIu32 " > %" PRIu32 "\n",

From 8a2234ea0c89f212190c176d741b7742f0082582 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Wed, 3 Sep 2025 10:43:53 +0800
Subject: [PATCH 264/287] CANN: Fix type float_t to float (#15736)

Signed-off-by: noemotiovon <757486878@qq.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 84 ++++++++++++++++----------------
 1 file changed, 42 insertions(+), 42 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 9c312faab7..3ec5bbf45b 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1767,10 +1767,10 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         case GGML_TYPE_F16: {
             aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
             ggml_cann_pool_alloc src_buffer_allocator(
-                ctx.pool(), ggml_nelements(src0) * sizeof(float_t));
+                ctx.pool(), ggml_nelements(src0) * sizeof(float));
             void* src_trans_buffer = src_buffer_allocator.get();
             size_t src_trans_nb[GGML_MAX_DIMS];
-            src_trans_nb[0] = sizeof(float_t);
+            src_trans_nb[0] = sizeof(float);
             for (int i = 1; i < GGML_MAX_DIMS; i++) {
                 src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
             }
@@ -1814,14 +1814,14 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
             // [3,4,5,64] -> [3,4,5,2,32]
             dequant_ne = weight_ne;
-            dequant_nb[0] = sizeof(float_t);
+            dequant_nb[0] = sizeof(float);
             for (int i = 1; i < GGML_MAX_DIMS + 1; i++) {
                 dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1];
             }
 
             scale_offset = ggml_nelements(src0) * sizeof(int8_t);
             ggml_cann_pool_alloc dequant_buffer_allocator(
-                ctx.pool(), ggml_nelements(src0) * sizeof(float_t));
+                ctx.pool(), ggml_nelements(src0) * sizeof(float));
 
             aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
                 src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb,
@@ -1830,11 +1830,11 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
                 GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
             aclTensor* dequant_tensor = ggml_cann_create_tensor(
-                dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float_t),
+                dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float),
                 dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
 
             aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor);
-            dequant_nb[0] = sizeof(float_t);
+            dequant_nb[0] = sizeof(float);
             dequant_ne = src0->ne;
             for (int i = 1; i < GGML_MAX_DIMS; i++) {
                 dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
@@ -2282,8 +2282,8 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     int64_t theta_scale_length = src0->ne[0] / 2;
     int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1};
-    size_t theta_scale_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
-                          theta_scale_length * sizeof(float_t)};
+    size_t theta_scale_nb[] = {sizeof(float), sizeof(float), sizeof(float),
+                          theta_scale_length * sizeof(float)};
 
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
     int64_t position_length = src1->ne[0];
@@ -2293,7 +2293,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1};
     size_t theta_nb[GGML_MAX_DIMS];
-    theta_nb[0] = sizeof(float_t);
+    theta_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
     }
@@ -2314,10 +2314,10 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
         if (ctx.rope_cache.theta_scale_cache != nullptr) {
             ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache));
         }
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
 
         acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float_t),
+            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
 
         float start = 0;
@@ -2383,20 +2383,20 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     } else {
         // use cache
         acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float_t),
+            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
     }
 
     ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool());
     // freq_factors
     if (src2) {
-        freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float_t));
+        freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float));
         void* freq_fac_res_ptr = freq_fac_res_allocator.get();
         aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
             src2->data, ggml_cann_type_mapping(src2->type),
             ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
         aclTensor* acl_freq_fac_res_tensor = ggml_cann_create_tensor(
-            freq_fac_res_ptr, ACL_FLOAT, sizeof(float_t),
+            freq_fac_res_ptr, ACL_FLOAT, sizeof(float),
             theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
         aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, acl_freq_fac_res_tensor);
         std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor);
@@ -2411,29 +2411,29 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     // power * position
     int64_t theta_length = theta_scale_length * position_length;
     ggml_cann_pool_alloc theta_allocator(ctx.pool(),
-                                        theta_length * sizeof(float_t));
+                                        theta_length * sizeof(float));
     void* theta_buffer = theta_allocator.get();
 
     aclTensor* acl_theta_tensor =
-        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float),
                                 theta_ne, theta_nb, GGML_MAX_DIMS);
     aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
             acl_theta_tensor);
 
     // sin/cos
     ggml_cann_pool_alloc sin_allocator(ctx.pool(),
-                                    theta_length * sizeof(float_t));
+                                    theta_length * sizeof(float));
     void* sin_buffer = sin_allocator.get();
     aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-        sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+        sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
 
     ggml_cann_pool_alloc cos_allocator(ctx.pool(),
-                                    theta_length * sizeof(float_t));
+                                    theta_length * sizeof(float));
     void* cos_buffer = cos_allocator.get();
     aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-        cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
+        cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
 
@@ -2449,15 +2449,15 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
-    sin_reshape_nb[0] = sizeof(float_t);
+    sin_reshape_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_repeat_tensor =
-        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_repeat_tensor =
-        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     // repeat
@@ -2543,15 +2543,15 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
-    sin_reshape_nb[0] = sizeof(float_t);
+    sin_reshape_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_reshape_tensor =
-        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(sin_tensor_buffer, ACL_FLOAT, sizeof(float),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_reshape_tensor =
-        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float_t),
+        ggml_cann_create_tensor(cos_tensor_buffer, ACL_FLOAT, sizeof(float),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     aclTensor* acl_src = ggml_cann_create_tensor(src0);
@@ -2566,7 +2566,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     void* minus_one_scale_buffer = nullptr;
     ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0));
     ggml_cann_pool_alloc minus_one_scale_allocator(
-        ctx.pool(), sizeof(float_t) * src0->ne[0]);
+        ctx.pool(), sizeof(float) * src0->ne[0]);
     if (!is_neox) {
         // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...]
         input_roll_buffer = roll_allocator.get();
@@ -2596,13 +2596,13 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
         int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
         size_t minus_one_nb[GGML_MAX_DIMS];
-        minus_one_nb[0] = sizeof(float_t);
+        minus_one_nb[0] = sizeof(float);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
         }
         acl_minus_one_tensor = aclnn_values(
-            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
-            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+            ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
         int64_t dim = 3;
         int64_t* index = new int64_t[src0->ne[0]];
         for (int i = 0; i < src0->ne[0]; i++) {
@@ -2630,22 +2630,22 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         minus_one_scale_buffer = minus_one_scale_allocator.get();
         int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
         size_t minus_one_nb[GGML_MAX_DIMS];
-        minus_one_nb[0] = sizeof(float_t);
+        minus_one_nb[0] = sizeof(float);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
         }
         acl_minus_one_tensor = aclnn_values(
-            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
-            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+            ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
         // -1 * first half
         int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1};
         size_t first_half_nb[GGML_MAX_DIMS];
-        first_half_nb[0] = sizeof(float_t);
+        first_half_nb[0] = sizeof(float);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1];
         }
         aclTensor* acl_first_half_tensor = ggml_cann_create_tensor(
-            minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne,
+            minus_one_scale_buffer, ACL_FLOAT, sizeof(float), first_half_ne,
             first_half_nb, GGML_MAX_DIMS);
         bool inplace = true;
         float scale = -1;
@@ -2685,28 +2685,28 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         // TODO: ne0 != n_dims in mode2
     } else if (src0->type == GGML_TYPE_F16) {
         size_t input_fp32_nb[GGML_MAX_DIMS];
-        input_fp32_nb[0] = sizeof(float_t);
+        input_fp32_nb[0] = sizeof(float);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1];
         }
         ggml_cann_pool_alloc fp32_allocator1(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float));
         void* input_fp32_buffer1 = fp32_allocator1.get();
         aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor(
-            input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
         ggml_cann_pool_alloc fp32_allocator2(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float));
         void* input_fp32_buffer2 = fp32_allocator2.get();
         aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor(
-            input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
 
         ggml_cann_pool_alloc fp32_allocator(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float));
         output_fp32_buffer = fp32_allocator.get();
         aclTensor* output_fp32_tensor = ggml_cann_create_tensor(
-            output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne,
+            output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
         aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1);
         aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor,

From f6da8cb86a28f0319b40d9d2a957a26a7d875f8c Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Wed, 3 Sep 2025 14:08:22 +0800
Subject: [PATCH 265/287] CANN: Mask unsupported TRANSPOSE_1D operator (#15733)

CANN currently does not support kernels larger than 255.
This change disables such cases.
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 4 +---
 ggml/src/ggml-cann/ggml-cann.cpp | 4 +++-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 3ec5bbf45b..80b9a932db 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -2803,8 +2803,6 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
     aclIntArray *padding = aclCreateIntArray(paddingVal, 1);
     int64_t dilationVal[] = {1};
     aclIntArray *dilation = aclCreateIntArray(dilationVal, 1);
-    bool transposed = true;
-    int64_t groups = 1;
     int8_t cubeMathType = 0;
 
 #ifdef ASCEND_310P
@@ -2812,7 +2810,7 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
 #endif
 
     GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride,
-        padding, dilation, transposed, padding, groups, acl_dst, cubeMathType);
+        padding, dilation, true, padding, 1, acl_dst, cubeMathType);
 
     ggml_cann_release_resources(ctx, acl_weight, acl_dst, stride, padding, dilation);
 }
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 0d9eb8fa1b..bd2fcd3761 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2479,12 +2479,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
         case GGML_OP_ARGMAX:
         case GGML_OP_COS:
         case GGML_OP_SIN:
-        case GGML_OP_CONV_TRANSPOSE_1D:
         case GGML_OP_LOG:
         case GGML_OP_MEAN:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_COUNT_EQUAL:
             return true;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            // TODO: ((weightL - 1) * dilationW - padLeft)=1336 should not be larger than 255.
+            return (op->src[0]->ne[0] - 1) <= 255;
         case GGML_OP_SCALE:
             float bias;
             memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float));

From 8c3fdf44ecf08335942f2ba558955f55e88c7991 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Sep 2025 09:48:35 +0200
Subject: [PATCH 266/287] model-conversion : add missing curl script [no ci]
 (#15761)

This commit adds a curl script to the model-conversion examples
which is currently missing. This script is required for the running the
embedding server targets to test llama-server embeddings functionality.
---
 .../model-conversion/scripts/utils/curl-embedding-server.sh | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100755 examples/model-conversion/scripts/utils/curl-embedding-server.sh

diff --git a/examples/model-conversion/scripts/utils/curl-embedding-server.sh b/examples/model-conversion/scripts/utils/curl-embedding-server.sh
new file mode 100755
index 0000000000..b6898665fa
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/curl-embedding-server.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+curl --request POST \
+    --url http://localhost:8080/embedding \
+    --header "Content-Type: application/json" \
+    --data '{"input": "Hello world today"}' \
+    --silent

From 05c0380f2ae5c7193445e03ebc7b6de9ce49f1a6 Mon Sep 17 00:00:00 2001
From: xctan <xc-tan@outlook.com>
Date: Wed, 3 Sep 2025 16:16:21 +0800
Subject: [PATCH 267/287] ggml-cpu : optimize RVV kernels (#15720)

* ggml-cpu : optimize rvv ggml_vec_dot_f32

* ggml-cpu : optimize 128-bit rvv ggml_vec_dot_q4_K_q8_K

* ggml-cpu : fix riscv arch flags

* ggml-cpu : add more rvv ops

* ggml-cpu : optimize rvv ggml_vec_dot_q4_K_q8_K

* ggml-cpu : optimize rvv ggml_vec_dot_q6_K_q8_K

* ggml-cpu : minor rvv adjustments

* ggml-cpu : fix riscv include
---
 ggml/CMakeLists.txt                   |   4 +-
 ggml/src/ggml-cpu/CMakeLists.txt      |  21 +-
 ggml/src/ggml-cpu/arch/riscv/quants.c | 306 ++++++++++++++++++--------
 ggml/src/ggml-cpu/ggml-cpu.c          |   7 +
 ggml/src/ggml-cpu/vec.cpp             |  57 ++++-
 ggml/src/ggml-cpu/vec.h               |   8 +
 6 files changed, 289 insertions(+), 114 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 96be001f8c..9ef88c6fd0 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -129,7 +129,9 @@ endif()
 option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
-option(GGML_RV_ZFH           "ggml: enable riscv zfh"        OFF)
+option(GGML_RV_ZFH           "ggml: enable riscv zfh"        ON)
+option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"       ON)
+option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"     ON)
 option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ON)
 option(GGML_NNPA             "ggml: enable nnpa"             OFF)  # temp disabled by default, see: https://github.com/ggml-org/llama.cpp/issues/14877
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 040b7ded90..dd8c1cf678 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -433,15 +433,22 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             ggml-cpu/arch/riscv/quants.c
             ggml-cpu/arch/riscv/repack.cpp
             )
-        if (GGML_RVV)
-            if (GGML_XTHEADVECTOR)
-                list(APPEND ARCH_FLAGS -march=rv64gc_zfhmin_xtheadvector -mabi=lp64d)
-            elseif (GGML_RV_ZFH)
-                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
-            else()
-                list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
+        set(MARCH_STR "rv64gc")
+        if (GGML_RV_ZFH)
+            string(APPEND MARCH_STR "_zfh")
+        endif()
+        if (GGML_XTHEADVECTOR)
+            string(APPEND MARCH_STR "_xtheadvector")
+        elseif (GGML_RVV)
+            string(APPEND MARCH_STR "_v")
+            if (GGML_RV_ZVFH)
+                string(APPEND MARCH_STR "_zvfh")
             endif()
         endif()
+        if (GGML_RV_ZICBOP)
+            string(APPEND MARCH_STR "_zicbop")
+        endif()
+        list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
     elseif (GGML_SYSTEM_ARCH STREQUAL "s390x")
         message(STATUS "s390x detected")
         list(APPEND GGML_CPU_SOURCES ggml-cpu/arch/s390/quants.c)
diff --git a/ggml/src/ggml-cpu/arch/riscv/quants.c b/ggml/src/ggml-cpu/arch/riscv/quants.c
index 6c74417c90..ee41a3502e 100644
--- a/ggml/src/ggml-cpu/arch/riscv/quants.c
+++ b/ggml/src/ggml-cpu/arch/riscv/quants.c
@@ -1270,29 +1270,40 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
             const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
 
-            int tmp, tmp2, sumi;
+            float ftmp, ft2;
+            const uint8_t * restrict q40;
+            const uint8_t * restrict q41;
+            const uint8_t * restrict q42;
+            const uint8_t * restrict q43;
+            const int8_t  * restrict q80;
+            const int8_t  * restrict q81;
+            const int8_t  * restrict q82;
+            const int8_t  * restrict q83;
+            int s0, s1, s2, s3;
+
             __asm__ __volatile__(
-                "vsetivli zero, 12, e8, m1\n\t"
-                "vle8.v v1, (%[s6b])\n\t" // {aux[0], aux[1], aux[2]}
-                "vsetivli zero, 4, e32, m1\n\t"
+                "li %[s1], 8\n\t"
+                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
+                "vle32.v v1, (%[s6b])\n\t"
+                "vslide1down.vx v1, v1, zero\n\t"
+                "vmv.v.x v16, zero\n\t"
                 "vslidedown.vi v2, v1, 2\n\t"
                 "vmv1r.v v3, v2\n\t"
                 "vslideup.vi v2, v3, 1\n\t" // {aux[2], aux[2]}
-                "vsetivli zero, 2, e32, m1\n\t"
+                "vsetivli zero, 2, e32, m1, ta, ma\n\t"
                 "vmv.v.i v4, 4\n\t"
                 "vand.vx v8, v1, %[kmask1]\n\t"
                 "vslide1up.vx v5, v4, zero\n\t" // {0, 4}
                 "vsrl.vi v6, v1, 6\n\t"
                 "vsrl.vv v7, v2, v5\n\t"
+                "vsse32.v v8, (%[utmp]), %[s1]\n\t"
                 "vand.vx v0, v6, %[kmask3]\n\t"
                 "vand.vx v2, v7, %[kmask2]\n\t"
                 "vsll.vi v6, v0, 4\n\t"
-                "li %[t2], 8\n\t"
-                "addi %[t1], %[utmp], 4\n\t"
+                "addi %[s0], %[utmp], 4\n\t"
                 "vor.vv v1, v6, v2\n\t"
-                "vsse32.v v8, (%[utmp]), %[t2]\n\t"
-                "vsse32.v v1, (%[t1]), %[t2]\n\t"
-                "vsetivli zero, 8, e16, m1\n\t"
+                "vsse32.v v1, (%[s0]), %[s1]\n\t"
+                "vsetivli zero, 8, e16, m1, ta, ma\n\t"
                 "vle32.v v2, (%[bsums])\n\t"
                 "vnsrl.wi v0, v2, 0\n\t"
                 "vnsrl.wi v1, v2, 16\n\t"
@@ -1300,13 +1311,131 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                 "vle8.v v3, (%[mins])\n\t"
                 "vzext.vf2 v4, v3\n\t"
                 "vwmul.vv v6, v4, v2\n\t"
+                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
+                "vredsum.vs v0, v6, v16\n\t"
+                "vredsum.vs v0, v7, v0\n\t"
+                "vfcvt.f.x.v v0, v0\n\t"
+                "vfmv.f.s %[ftmp], v0\n\t"
+                "vsetivli zero, 16, e8, m1, ta, ma\n\t"
+                "vle8.v v0, (%[xs])\n\t"
+                "fnmsub.s %[sumf], %[dmin], %[ftmp], %[sumf]\n\t"
+                "addi %[q40], %[xs], 64\n\t"
+                "addi %[q41], %[xs], 16\n\t"
+                "addi %[q42], %[xs], 32\n\t"
+                "addi %[q43], %[xs], 48\n\t"
+                "addi %[q80], %[ys], 64\n\t"
+                "vle8.v v1, (%[q41])\n\t"
+                "vle8.v v2, (%[q42])\n\t"
+                "addi %[q81], %[ys], 16\n\t"
+                "addi %[q41], %[q41], 64\n\t"
+                "addi %[q82], %[ys], 32\n\t"
+                "vle8.v v3, (%[q43])\n\t"
+                "vle8.v v8, (%[ys])\n\t"
+                "addi %[q42], %[q42], 64\n\t"
+                "addi %[q83], %[ys], 48\n\t"
+                "addi %[q43], %[q43], 64\n\t"
+                "vsrl.vi v4, v0, 4\n\t"
+                "vle8.v v9, (%[q81])\n\t"
+                "vle8.v v10, (%[q82])\n\t"
+                "vand.vi v0, v0, 0xF\n\t"
+                "addi %[q81], %[q81], 64\n\t"
+                "vsrl.vi v5, v1, 4\n\t"
+                "addi %[q82], %[q82], 64\n\t"
+                "vle8.v v11, (%[q83])\n\t"
+                "vle8.v v12, (%[q80])\n\t"
+                "vand.vi v1, v1, 0xF\n\t"
+                "addi %[q83], %[q83], 64\n\t"
+                "vsrl.vi v6, v2, 4\n\t"
+                "addi %[q80], %[q80], 64\n\t"
+                "vle8.v v13, (%[q81])\n\t"
+                "vle8.v v14, (%[q82])\n\t"
+                "vand.vi v2, v2, 0xF\n\t"
+                "addi %[q81], %[q81], 64\n\t"
+                "vsrl.vi v7, v3, 4\n\t"
+                "addi %[q82], %[q82], 64\n\t"
+                "vwmul.vv v16, v0, v8\n\t"
+                "vle8.v v15, (%[q83])\n\t"
+                "vle8.v v0, (%[q40])\n\t"
+                "vand.vi v3, v3, 0xF\n\t"
+                "addi %[q83], %[q83], 64\n\t"
+                "vwmul.vv v24, v2, v12\n\t"
+                "vwmul.vv v20, v4, v10\n\t"
+                "vwmul.vv v28, v6, v14\n\t"
+                "vwmacc.vv v16, v1, v9\n\t"
+                "vle8.v v1, (%[q41])\n\t"
+                "vle8.v v2, (%[q42])\n\t"
+                "vwmacc.vv v24, v3, v13\n\t"
+                "vwmacc.vv v20, v5, v11\n\t"
+                "vwmacc.vv v28, v7, v15\n\t"
+                "addi %[q40], %[q80], 64\n\t"
+                "addi %[q41], %[q81], 64\n\t"
+                "vle8.v v3, (%[q43])\n\t"
+                "vle8.v v8, (%[q80])\n\t"
+                "addi %[q42], %[q82], 64\n\t"
+                "addi %[q43], %[q83], 64\n\t"
+                "vsrl.vi v4, v0, 4\n\t"
+                "vle8.v v9, (%[q81])\n\t"
+                "vle8.v v10, (%[q82])\n\t"
+                "vand.vi v0, v0, 0xF\n\t"
+                "vsrl.vi v5, v1, 4\n\t"
+                "vsrl.vi v7, v3, 4\n\t"
+                "vand.vi v3, v3, 0xF\n\t"
+                "vle8.v v11, (%[q83])\n\t"
+                "vle8.v v12, (%[q40])\n\t"
+                "vand.vi v1, v1, 0xF\n\t"
+                "vsrl.vi v6, v2, 4\n\t"
+                "vand.vi v2, v2, 0xF\n\t"
+                "vwmul.vv v18, v0, v8\n\t"
+                "vle8.v v13, (%[q41])\n\t"
+                "vle8.v v14, (%[q42])\n\t"
+                "vwmul.vv v26, v2, v12\n\t"
+                "vwmul.vv v22, v4, v10\n\t"
+                "vwmul.vv v30, v6, v14\n\t"
+                "vwmacc.vv v18, v1, v9\n\t"
+                "vle8.v v15, (%[q43])\n\t"
+                "vwmacc.vv v26, v3, v13\n\t"
+                "vwmacc.vv v22, v5, v11\n\t"
+                "vwmacc.vv v30, v7, v15\n\t"
                 "vmv.v.x v0, zero\n\t"
-                "vsetivli zero, 8, e32, m2\n\t"
-                "vredsum.vs v0, v6, v0\n\t"
-                "vmv.x.s %[sumi], v0"
-                : [t1] "=&r" (tmp), [t2] "=&r" (tmp2), [sumi] "=&r" (sumi)
-                : [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
-                , [s6b] "r" (x[i].scales), [kmask1] "r" (kmask1)
+                "vsetivli zero, 16, e16, m2, ta, ma\n\t"
+                "vwredsum.vs v4, v16, v0\n\t"
+                "lbu %[s0], 0(%[scale])\n\t"
+                "vwredsum.vs v5, v20, v0\n\t"
+                "lbu %[s1], 1(%[scale])\n\t"
+                "vwredsum.vs v6, v24, v0\n\t"
+                "lbu %[s2], 2(%[scale])\n\t"
+                "vwredsum.vs v7, v28, v0\n\t"
+                "lbu %[s3], 3(%[scale])\n\t"
+                "vwredsum.vs v8, v18, v0\n\t"
+                "lbu %[q40], 4(%[scale])\n\t"
+                "vwredsum.vs v9, v22, v0\n\t"
+                "lbu %[q41], 5(%[scale])\n\t"
+                "vwredsum.vs v10, v26, v0\n\t"
+                "lbu %[q42], 6(%[scale])\n\t"
+                "vwredsum.vs v11, v30, v0\n\t"
+                "lbu %[q43], 7(%[scale])\n\t"
+                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
+                "vmul.vx v0, v4, %[s0]\n\t"
+                "vmul.vx v1, v8, %[q40]\n\t"
+                "vmacc.vx v0, %[s1], v5\n\t"
+                "vmacc.vx v1, %[q41], v9\n\t"
+                "vmacc.vx v0, %[s2], v6\n\t"
+                "vmacc.vx v1, %[q42], v10\n\t"
+                "vmacc.vx v0, %[s3], v7\n\t"
+                "vmacc.vx v1, %[q43], v11\n\t"
+                "vfcvt.f.x.v v0, v0\n\t"
+                "vfcvt.f.x.v v1, v1\n\t"
+                "vfmv.f.s %[ft2], v0\n\t"
+                "vfmv.f.s %[ftmp], v1\n\t"
+                "fadd.s %[ft2], %[ft2], %[ftmp]\n\t"
+                "fmadd.s %[sumf], %[d], %[ft2], %[sumf]"
+                : [ftmp] "=&f" (ftmp), [sumf] "+&f" (sumf), [ft2] "=&f" (ft2)
+                , [s0] "=&r" (s0), [s1] "=&r" (s1), [s2] "=&r" (s2), [s3] "=&r" (s3)
+                , [q40] "=&r" (q40), [q41] "=&r" (q41), [q42] "=&r" (q42), [q43] "=&r" (q43)
+                , [q80] "=&r" (q80), [q81] "=&r" (q81), [q82] "=&r" (q82), [q83] "=&r" (q83)
+                : [d] "f" (d), [ys] "r" (y[i].qs), [xs] "r" (x[i].qs), [scale] "r" (scales)
+                , [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
+                , [s6b] "r" (&x[i]), [kmask1] "r" (kmask1), [dmin] "f" (dmin)
                 , [kmask2] "r" (kmask2), [kmask3] "r" (kmask3)
                 : "memory"
                 , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
@@ -1314,59 +1443,6 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                 , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
                 , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
             );
-            sumf -= dmin * sumi;
-
-            const uint8_t * restrict q4 = x[i].qs;
-            const int8_t  * restrict q8 = y[i].qs;
-
-            sumi = 0;
-            const uint8_t * scale = scales;
-
-            for (int j = 0; j < QK_K/128; ++j) {
-                int vl128 = 128, vl64 = 64, vl32 = 32;
-                __asm__ __volatile__(
-                    "vsetvli zero, %[vl128], e8, m8\n\t"
-                    "vle8.v v8, (%[q8])\n\t"
-                    "vsetvli zero, %[vl64], e8, m4\n\t"
-                    "vle8.v v0, (%[q4])\n\t"
-                    "vsrl.vi v4, v0, 4\n\t"
-                    "vand.vi v0, v0, 0xF\n\t"
-                    "vsetvli zero, %[vl32], e8, m2\n\t"
-                    "vwmul.vv v28, v6, v14\n\t"
-                    "vwmul.vv v20, v4, v10\n\t"
-                    "vwmul.vv v24, v2, v12\n\t"
-                    "vwmul.vv v16, v0, v8\n\t"
-                    "vsetivli zero, 4, e32, m1\n\t"
-                    "vle8.v v2, (%[scale])\n\t"
-                    "vmv.v.x v0, zero\n\t"
-                    "vzext.vf4 v1, v2\n\t"
-                    "vsetvli zero, %[vl32], e16, m4\n\t"
-                    "vwredsum.vs v6, v24, v0\n\t"
-                    "vwredsum.vs v7, v28, v0\n\t"
-                    "vwredsum.vs v4, v16, v0\n\t"
-                    "vwredsum.vs v5, v20, v0\n\t"
-                    "vsetivli zero, 4, e32, m1\n\t"
-                    "vslideup.vi v6, v7, 1\n\t"
-                    "vslideup.vi v4, v5, 1\n\t"
-                    "vslideup.vi v4, v6, 2\n\t"
-                    "vmul.vv v8, v4, v1\n\t"
-                    "vredsum.vs v0, v8, v0\n\t"
-                    "vmv.x.s %[tmp], v0\n\t"
-                    "add %[sumi], %[sumi], %[tmp]"
-                    : [tmp] "=&r" (tmp), [sumi] "+&r" (sumi)
-                    : [vl128] "r" (vl128), [vl64] "r" (vl64), [vl32] "r" (vl32)
-                    , [q4] "r" (q4), [q8] "r" (q8), [scale] "r" (scale)
-                    : "memory"
-                    , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
-                    , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
-                    , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
-                    , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
-                );
-
-                q4 += 64;    q8 += 128;    scale += 4;
-            }
-
-            sumf += d * sumi;
         }
         break;
     default:
@@ -1693,6 +1769,8 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     case 128:
         for (int i = 0; i < nb; ++i) {
 
+            __builtin_prefetch(&x[i + 1].d, 0, 1);
+
             const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
 
             const uint8_t * restrict q6 = x[i].ql;
@@ -1701,23 +1779,59 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
             const int8_t * restrict scale = x[i].scales;
 
-            int sum_t = 0;
-            int t0;
+            int q6h;
+            float ftmp;
 
             for (int j = 0; j < QK_K/128; ++j) {
                 __asm__ __volatile__(
+                    "addi %[q6h], %[q6], 32\n\t"
+                    "ld t0, 0(%[scale])\n\t"
+                    "addi %[scale], %[scale], 8\n\t"
+                    "slli t6, t0, 1 * 8\n\t"
+                    "lb zero, 0(%[q6])\n\t"
+                    "slli t5, t0, 2 * 8\n\t"
+                    "slli t4, t0, 3 * 8\n\t"
+                    "lb zero, 0(%[q6h])\n\t"
+                    "slli t3, t0, 4 * 8\n\t"
+                    "slli t2, t0, 5 * 8\n\t"
+                    "lb zero, 0(%[qh])\n\t"
+                    "lb zero, 31(%[q6h])\n\t"
+                    "slli t1, t0, 6 * 8\n\t"
+                    "srai a7, t0, 56\n\t"
                     "vsetvli zero, %[vl32], e8, m2\n\t"
+                    "vle8.v v8, (%[q6])\n\t"
+                    "srai t6, t6, 56\n\t"
+                    "srai t5, t5, 56\n\t"
+                    "srai t4, t4, 56\n\t"
+                    "srai t3, t3, 56\n\t"
+                    "vle8.v v10, (%[q6h])\n\t"
+                    "addi %[q6], %[q6], 64\n\t"
+                    "slli t0, t0, 7 * 8\n\t"
+                    "srai t2, t2, 56\n\t"
+                    "srai t1, t1, 56\n\t"
+                    "srai t0, t0, 56\n\t"
                     "vle8.v v4, (%[qh])\n\t"
+                    "vsrl.vi v12, v8, 4\n\t"
+                    "vsrl.vi v14, v10, 4\n\t"
+                    "lb zero, 0(%[q8])\n\t"
+                    "vand.vi v8, v8, 0xF\n\t"
+                    "vand.vi v10, v10, 0xF\n\t"
+                    "lb zero, 32(%[q8])\n\t"
                     "vsll.vi v0, v4, 4\n\t"
                     "vsll.vi v2, v4, 2\n\t"
+                    "lb zero, 64(%[q8])\n\t"
                     "vsrl.vi v6, v4, 2\n\t"
-                    "vsetvli zero, %[vl64], e8, m4\n\t"
-                    "vle8.v v8, (%[q6])\n\t"
-                    "vsrl.vi v12, v8, 4\n\t"
-                    "vand.vi v8, v8, 0xF\n\t"
-                    "vsetvli zero, %[vl128], e8, m8\n\t"
                     "vand.vx v0, v0, %[mask]\n\t"
+                    "lb zero, 96(%[q8])\n\t"
+                    "vand.vx v2, v2, %[mask]\n\t"
+                    "vand.vx v4, v4, %[mask]\n\t"
+                    "vand.vx v6, v6, %[mask]\n\t"
                     "vor.vv v8, v8, v0\n\t"
+                    "lb zero, 127(%[q8])\n\t"
+                    "vor.vv v10, v10, v2\n\t"
+                    "vor.vv v12, v12, v4\n\t"
+                    "vor.vv v14, v14, v6\n\t"
+                    "vsetvli zero, %[vl128], e8, m8\n\t"
                     "vle8.v v0, (%[q8])\n\t"
                     "vsub.vx v8, v8, %[vl32]\n\t"
                     "vsetvli zero, %[vl64], e8, m4\n\t"
@@ -1734,34 +1848,34 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                     "vwredsum.vs v13, v28, v0\n\t"
                     "vwredsum.vs v14, v30, v0\n\t"
                     "vsetivli zero, 4, e32, m1\n\t"
-                    "vslideup.vi v10, v9, 1\n\t"
-                    "vslideup.vi v8, v7, 1\n\t"
-                    "vslideup.vi v11, v12, 1\n\t"
-                    "vslideup.vi v13, v14, 1\n\t"
-                    "vslideup.vi v10, v8, 2\n\t"
-                    "vslideup.vi v11, v13, 2\n\t"
-                    "vsetivli zero, 8, e32, m2\n\t"
-                    "vle8.v v2, (%[scale])\n\t"
-                    "vsext.vf4 v4, v2\n\t"
-                    "vmul.vv v2, v4, v10\n\t"
-                    "vredsum.vs v0, v2, v0\n\t"
-                    "vmv.x.s %[t0], v0\n\t"
-                    "add %[sumi], %[sumi], %[t0]"
-                    : [sumi] "+&r" (sum_t), [t0] "=&r" (t0)
-                    : [qh] "r" (qh), [q6] "r" (q6), [q8] "r" (q8), [scale] "r" (scale)
+                    "vmul.vx v0, v10, t0\n\t"
+                    "vmul.vx v1, v9, t1\n\t"
+                    "vmacc.vx v0, t2, v8\n\t"
+                    "vmacc.vx v1, t3, v7\n\t"
+                    "vmacc.vx v0, t4, v11\n\t"
+                    "vmacc.vx v1, t5, v12\n\t"
+                    "vmacc.vx v0, t6, v13\n\t"
+                    "vmacc.vx v1, a7, v14\n\t"
+                    "vadd.vv v0, v0, v1\n\t"
+                    "vfcvt.f.x.v v0, v0\n\t"
+                    "vfmv.f.s %[ftmp], v0\n\t"
+                    "fmadd.s %[sumf], %[d], %[ftmp], %[sumf]"
+                    : [q6] "+&r" (q6), [q6h] "=&r" (q6h)
+                    , [scale] "+&r" (scale)
+                    , [sumf] "+&f" (sumf), [ftmp] "=&f" (ftmp)
+                    : [qh] "r" (qh), [q8] "r" (q8)
                     , [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
-                    , [mask] "r" (0x30)
+                    , [mask] "r" (0x30), [d] "f" (d)
                     : "memory"
                     , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
                     , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
                     , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
                     , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+                    , "t0", "t1", "t2", "t3", "t4", "t5", "t6", "a7"
+                    , "a6", "a5", "a4", "a3"
                 );
-                q6 += 64;   qh += 32;   q8 += 128;   scale += 8;
+                qh += 32;   q8 += 128;
             }
-
-            sumf += d * sum_t;
-
         }
         break;
     default:
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 0d5d3a3440..78ec189d4c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -3221,6 +3221,13 @@ void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
         uint16x8_t v_y = vec_convert_to_fp16(v_yd, 0);
         vec_xst(v_y, 0, (ggml_fp16_t *)(y + i));
     }
+#elif defined(__riscv_zvfh)
+    for (int vl; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
+        vfloat16m1_t vy = __riscv_vfncvt_f_f_w_f16m1(vx, vl);
+        __riscv_vse16_v_f16m1((_Float16 *)&y[i], vy, vl);
+    }
 #endif
     for (; i < n; ++i) {
         y[i] = GGML_CPU_FP32_TO_FP16(x[i]);
diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp
index 0652155cf7..437192d525 100644
--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -85,15 +85,21 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
         // reduce sum1,sum2 to sum1
         GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
     #elif defined(__riscv_v_intrinsic)
-        vfloat32m1_t vsum = __riscv_vfmv_v_f_f32m1(0.0f, 1);
-        for (int i = 0, avl; i < n; i += avl) {
-            avl = __riscv_vsetvl_e32m8(n - i);
-            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
-            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
-            vfloat32m8_t prod = __riscv_vfmul_vv_f32m8(ax, ay, avl);
-            vsum = __riscv_vfredusum_vs_f32m8_f32m1(prod, vsum, avl);
+        int vl = __riscv_vsetvlmax_e32m8();
+        vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+        vfloat32m8_t vsum;
+        vfloat32m8_t ax;
+        vfloat32m8_t ay;
+        vsum = __riscv_vfmv_v_f_f32m8_tu(vsum, 0.0f, vl);
+        for (int i = 0; i < n; i += vl) {
+            vl = __riscv_vsetvl_e32m8(n - i);
+            ax = __riscv_vle32_v_f32m8_tu(ax, &x[i], vl);
+            ay = __riscv_vle32_v_f32m8_tu(ay, &y[i], vl);
+            vsum = __riscv_vfmacc_vv_f32m8_tu(vsum, ax, ay, vl);
         }
-        sumf += __riscv_vfmv_f_s_f32m1_f32(vsum);
+        vl = __riscv_vsetvlmax_e32m8();
+        vs = __riscv_vfredusum_vs_f32m8_f32m1(vsum, vs, vl);
+        sumf += __riscv_vfmv_f_s_f32m1_f32(vs);
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -208,7 +214,7 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
     ggml_float sumf = 0.0;
 
 
-#if defined(GGML_SIMD) && !defined(__riscv_v_intrinsic)
+#if defined(GGML_SIMD)
     #if defined(__ARM_FEATURE_SVE)
         const int sve_register_length = svcntb() * 8; //get vector length
         const int ggml_f16_epr = sve_register_length / 16; // running when 16
@@ -271,6 +277,29 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
             sum1 = svmad_f16_x(pg, hx, hy, sum1);
         }
         GGML_F16x_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4);
+    #elif defined(__riscv_v_intrinsic)
+        #if defined(__riscv_zvfh)
+            int vl = __riscv_vsetvlmax_e32m2();
+            vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+            vfloat32m2_t vsum;
+            vfloat16m1_t ax;
+            vfloat16m1_t ay;
+            vsum = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vmv_v_x_u32m2(0, vl));
+            for (int i = 0; i < n; i += vl) {
+                vl = __riscv_vsetvl_e16m1(n - i);
+                ax = __riscv_vle16_v_f16m1_tu(ax, (const _Float16 *)&x[i], vl);
+                ay = __riscv_vle16_v_f16m1_tu(ay, (const _Float16 *)&y[i], vl);
+                vsum = __riscv_vfwmacc_vv_f32m2_tu(vsum, ax, ay, vl);
+            }
+            vl = __riscv_vsetvlmax_e32m1();
+            vfloat32m1_t ac0 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(vsum, 0), __riscv_vget_v_f32m2_f32m1(vsum, 1), vl);
+            vs = __riscv_vfredusum_vs_f32m1_f32m1(ac0, vs, vl);
+            sumf += __riscv_vfmv_f_s_f32m1_f32(vs);
+        #else
+            for (int i = 0; i < n; ++i) {
+                sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
+            }
+        #endif // __riscv_zvfh
     #else
         const int np = (n & ~(GGML_F16_STEP - 1));
 
@@ -302,7 +331,7 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
     for (int i = 0; i < n; ++i) {
         sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
     }
-#endif
+#endif // GGML_SIMD
 
     *s = sumf;
 }
@@ -361,6 +390,14 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, vmulq_f32(ggml_v_silu(vld1q_f32(x + i)), vld1q_f32(g + i)));
     }
+#elif defined(__riscv_v_intrinsic)
+    for (int vl; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
+        vfloat32m2_t vg = __riscv_vle32_v_f32m2(&g[i], vl);
+        vfloat32m2_t vy = __riscv_vfmul_vv_f32m2(ggml_v_silu_m2(vx, vl), vg, vl);
+        __riscv_vse32_v_f32m2(&y[i], vy, vl);
+    }
 #endif
     for (; i < n; ++i) {
         y[i] = ggml_silu_f32(x[i]) * g[i];
diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h
index 1346e7d7e0..ef334d089d 100644
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -1269,6 +1269,14 @@ inline static vfloat32m2_t ggml_v_expf_m2(vfloat32m2_t x, int vl) {
         vl);
 }
 
+// computes silu x/(1+exp(-x)) in single precision vector
+inline static vfloat32m2_t ggml_v_silu_m2(vfloat32m2_t x, int vl) {
+    const vfloat32m2_t neg_x = __riscv_vfneg_v_f32m2(x, vl);
+    const vfloat32m2_t exp_neg_x = ggml_v_expf_m2(neg_x, vl);
+    const vfloat32m2_t one_plus_exp_neg_x = __riscv_vfadd_vf_f32m2(exp_neg_x, 1.0f, vl);
+    return __riscv_vfdiv_vv_f32m2(x, one_plus_exp_neg_x, vl);
+}
+
 #endif // __ARM_NEON / __AVX2__ / __SSE2__ / __riscv_v_intrinsic
 
 inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {

From 5eae9348835037046f33fafd852df7c952c95209 Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Wed, 3 Sep 2025 16:46:01 +0800
Subject: [PATCH 268/287] CANN: Add RoPE contiguous check for 310I DUP device
 (#15735)

---
 ggml/src/ggml-cann/ggml-cann.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index bd2fcd3761..64fb2beff0 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2417,7 +2417,11 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if (mode & GGML_ROPE_TYPE_VISION) {
                 return false;
             }
-
+#ifdef ASCEND_310P
+            if(!ggml_is_contiguous(op->src[0])){
+                return false;
+            }
+#endif
             return true;
         }
         case GGML_OP_UPSCALE: {

From 40a751ea9a94364da73537b86502a808ebe1fc3a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Sep 2025 12:50:47 +0200
Subject: [PATCH 269/287] model-conversion : remove hardcoded /bin/bash
 shebangs [no ci] (#15765)

* model-conversion : remove hardcoded /bin/bash shebangs [no ci]

This commit updates the bash scripts to use env instead of using
hardcoded /bin/bash in the shebang line.

The motivation for this is that some systems may have bash installed
in a different location, and using /usr/bin/env bash ensures that
the script will use the first bash interpreter found in the user's
PATH, making the scripts more portable across different environments.

* model-conversion : rename script to .py [no ci]

This commit renames run-casual-gen-embeddings-org.sh to
run-casual-gen-embeddings-org.py to reflect its Python nature.
---
 examples/model-conversion/Makefile                              | 2 +-
 .../scripts/causal/compare-embeddings-logits.sh                 | 2 +-
 examples/model-conversion/scripts/causal/convert-model.sh       | 2 +-
 ...l-gen-embeddings-org.sh => run-casual-gen-embeddings-org.py} | 0
 .../scripts/causal/run-converted-model-embeddings-logits.sh     | 2 +-
 examples/model-conversion/scripts/causal/run-converted-model.sh | 2 +-
 .../scripts/embedding/compare-embeddings-logits.sh              | 2 +-
 examples/model-conversion/scripts/embedding/convert-model.sh    | 2 +-
 .../model-conversion/scripts/embedding/run-converted-model.sh   | 2 +-
 .../scripts/utils/create-collection-add-model.sh                | 2 ++
 .../model-conversion/scripts/utils/curl-embedding-server.sh     | 2 +-
 .../model-conversion/scripts/utils/inspect-converted-model.sh   | 2 +-
 examples/model-conversion/scripts/utils/perplexity-gen.sh       | 2 +-
 .../model-conversion/scripts/utils/perplexity-run-simple.sh     | 2 +-
 examples/model-conversion/scripts/utils/perplexity-run.sh       | 2 +-
 examples/model-conversion/scripts/utils/quantize.sh             | 2 +-
 examples/model-conversion/scripts/utils/run-embedding-server.sh | 2 +-
 17 files changed, 17 insertions(+), 15 deletions(-)
 rename examples/model-conversion/scripts/causal/{run-casual-gen-embeddings-org.sh => run-casual-gen-embeddings-org.py} (100%)

diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
index 03b928afda..ac7a414729 100644
--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -63,7 +63,7 @@ causal-verify-logits: causal-run-original-model causal-run-converted-model
 	@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
 
 causal-run-original-embeddings:
-	@./scripts/causal/run-casual-gen-embeddings-org.sh
+	@./scripts/causal/run-casual-gen-embeddings-org.py
 
 causal-run-converted-embeddings:
 	@./scripts/causal/run-converted-model-embeddings-logits.sh
diff --git a/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh b/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
index 287158f638..c53c89d48a 100755
--- a/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
+++ b/examples/model-conversion/scripts/causal/compare-embeddings-logits.sh
@@ -1,4 +1,4 @@
-#/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/causal/convert-model.sh b/examples/model-conversion/scripts/causal/convert-model.sh
index 9d95025950..32ffe132e7 100755
--- a/examples/model-conversion/scripts/causal/convert-model.sh
+++ b/examples/model-conversion/scripts/causal/convert-model.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh b/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py
similarity index 100%
rename from examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.sh
rename to examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py
diff --git a/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh b/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
index 64709f1798..fa16a02c65 100755
--- a/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
+++ b/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/causal/run-converted-model.sh b/examples/model-conversion/scripts/causal/run-converted-model.sh
index e2762729e7..f5f567d4ff 100755
--- a/examples/model-conversion/scripts/causal/run-converted-model.sh
+++ b/examples/model-conversion/scripts/causal/run-converted-model.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
index 35b5d71984..1401dcb43e 100755
--- a/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
+++ b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
@@ -1,4 +1,4 @@
-#/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/embedding/convert-model.sh b/examples/model-conversion/scripts/embedding/convert-model.sh
index 0609e35357..0929e42413 100755
--- a/examples/model-conversion/scripts/embedding/convert-model.sh
+++ b/examples/model-conversion/scripts/embedding/convert-model.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/embedding/run-converted-model.sh b/examples/model-conversion/scripts/embedding/run-converted-model.sh
index 5896090411..24b2810627 100755
--- a/examples/model-conversion/scripts/embedding/run-converted-model.sh
+++ b/examples/model-conversion/scripts/embedding/run-converted-model.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/utils/create-collection-add-model.sh b/examples/model-conversion/scripts/utils/create-collection-add-model.sh
index 4809da6cb6..485001b5fe 100644
--- a/examples/model-conversion/scripts/utils/create-collection-add-model.sh
+++ b/examples/model-conversion/scripts/utils/create-collection-add-model.sh
@@ -1,4 +1,6 @@
 
+#!/usr/bin/env bash
+
 COLLECTION_SLUG=$(python ./create_collection.py --return-slug)
 echo "Created collection: $COLLECTION_SLUG"
 
diff --git a/examples/model-conversion/scripts/utils/curl-embedding-server.sh b/examples/model-conversion/scripts/utils/curl-embedding-server.sh
index b6898665fa..7ed69e1ea5 100755
--- a/examples/model-conversion/scripts/utils/curl-embedding-server.sh
+++ b/examples/model-conversion/scripts/utils/curl-embedding-server.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 curl --request POST \
     --url http://localhost:8080/embedding \
     --header "Content-Type: application/json" \
diff --git a/examples/model-conversion/scripts/utils/inspect-converted-model.sh b/examples/model-conversion/scripts/utils/inspect-converted-model.sh
index e5b9324542..32d84826fa 100755
--- a/examples/model-conversion/scripts/utils/inspect-converted-model.sh
+++ b/examples/model-conversion/scripts/utils/inspect-converted-model.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 # First try command line argument, then environment variable, then file
 CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
diff --git a/examples/model-conversion/scripts/utils/perplexity-gen.sh b/examples/model-conversion/scripts/utils/perplexity-gen.sh
index 3db0b3fd27..4885acbae2 100755
--- a/examples/model-conversion/scripts/utils/perplexity-gen.sh
+++ b/examples/model-conversion/scripts/utils/perplexity-gen.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/utils/perplexity-run-simple.sh b/examples/model-conversion/scripts/utils/perplexity-run-simple.sh
index 69b3438f59..a2545436a5 100755
--- a/examples/model-conversion/scripts/utils/perplexity-run-simple.sh
+++ b/examples/model-conversion/scripts/utils/perplexity-run-simple.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/utils/perplexity-run.sh b/examples/model-conversion/scripts/utils/perplexity-run.sh
index 3bce7c8472..68b38e6628 100755
--- a/examples/model-conversion/scripts/utils/perplexity-run.sh
+++ b/examples/model-conversion/scripts/utils/perplexity-run.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/utils/quantize.sh b/examples/model-conversion/scripts/utils/quantize.sh
index 90460aa6b0..c25c5c21f3 100755
--- a/examples/model-conversion/scripts/utils/quantize.sh
+++ b/examples/model-conversion/scripts/utils/quantize.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 
diff --git a/examples/model-conversion/scripts/utils/run-embedding-server.sh b/examples/model-conversion/scripts/utils/run-embedding-server.sh
index 828fc47069..d30b765964 100755
--- a/examples/model-conversion/scripts/utils/run-embedding-server.sh
+++ b/examples/model-conversion/scripts/utils/run-embedding-server.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 #

From 2c8dac72eb6acd4e20c0da251535dfc46d35178b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Sep 2025 13:35:49 +0200
Subject: [PATCH 270/287] llama : fix incorrect model type for  Gemma 270M
 (#15764)

This commit fixes the model type for the Gemma 270M model in
llama_model.cpp which should be LLM_TYPE_270M. I incorrectly added this
previously as LLM_TYPE_537M which was wrong.

The motivation for this is that it causes the model to not be identified
properly when using tools like llama-bench. For example:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model                          |       size | ...
| ------------------------------ | ---------: | ...
| gemma3 ?B Q8_0                 | 271.81 MiB | ...
| gemma3 ?B Q8_0                 | 271.81 MiB | ...
```

With the changes in this commit the output will be:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model                          |       size | ...
| ------------------------------ | ---------: | ...
| gemma3 270M Q8_0               | 271.81 MiB | ...
| gemma3 270M Q8_0               | 271.81 MiB | ...
```
---
 src/llama-model.cpp | 2 +-
 src/llama-model.h   | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 58a0581e26..5e54ce25f1 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1110,7 +1110,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
-                    case 18: type = LLM_TYPE_537M; break;
+                    case 18: type = LLM_TYPE_270M; break;
                     case 26: type = LLM_TYPE_1B; break;
                     case 34: type = LLM_TYPE_4B; break;
                     case 48: type = LLM_TYPE_12B; break;
diff --git a/src/llama-model.h b/src/llama-model.h
index fa44d800d5..10b1767f27 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -39,7 +39,6 @@ enum llm_type {
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
-    LLM_TYPE_537M,
     LLM_TYPE_558M,
     LLM_TYPE_700M,
     LLM_TYPE_770M,

From cdedb70a998cea7052560fe0b0615a839443564d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 3 Sep 2025 18:16:26 +0300
Subject: [PATCH 271/287] sampling : optimize dist sampler (#15704)

ggml-ci
---
 src/llama-sampling.cpp | 67 ++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 65 insertions(+), 2 deletions(-)

diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index e8c0fc3418..2186f827bf 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -604,10 +604,73 @@ static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl*
 static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_dist *) smpl->ctx;
 
-    // sorting is not necessary here
-    llama_sampler_softmax_impl(cur_p, false);
+    // edge cases
+    if (cur_p->size == 0) {
+        cur_p->selected = -1;
+        return;
+    }
+
+    cur_p->selected = 0;
+
+    if (cur_p->size == 1) {
+        cur_p->data[0].p = 1.0f;
+        return;
+    }
+
+    // max logit for numerical stability
+    float max_l = cur_p->data[0].logit;
+    if (!cur_p->sorted) {
+        for (size_t i = 1; i < cur_p->size; ++i) {
+            max_l = std::max(max_l, cur_p->data[i].logit);
+        }
+    }
+
+    // apply softmax to obtain the probabilities
+    double sum_cum = 0.0f;
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        float p = expf(cur_p->data[i].logit - max_l);
+        cur_p->data[i].p = p;
+        sum_cum += p;
+    }
+
+#if 1
+    // sample from the obtained probabilities and normalize the probs in a single pass
+    // this is ~3x faster on Mac with full gpt-oss vocab than the version below
+    //
+    std::uniform_real_distribution<double> dist(0.0f, 1.0f);
+    const double rnd = dist(ctx->rng);
+
+          double sum_run = 0.0f;
+    const double sum_tgt = sum_cum*rnd;
+
+    bool found = false;
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        if (!found) {
+            // accumulate probs until we reach the target sum
+            sum_run += cur_p->data[i].p;
+            if (sum_run >= sum_tgt) {
+                cur_p->selected = i;
+                found = true;
+            }
+        }
+
+        // normalize probs
+        cur_p->data[i].p /= sum_cum;
+    }
+
+    // fallback to the last token (don't think this can happen)
+    assert(found);
+    if (!found) {
+        cur_p->selected = cur_p->size - 1;
+    }
+#else
+    // for clarity, this is the same as above but does one pass for normalization and one extra pass for sampling
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        cur_p->data[i].p /= sum_cum;
+    }
 
     cur_p->selected = llama_sample_dist(cur_p, ctx->rng);
+#endif
 }
 
 static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sampler * smpl) {

From 407c23786dd0d3a503e9429eead96e611d3950c9 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Sep 2025 18:28:36 +0200
Subject: [PATCH 272/287] model-conversion : fix pyright errors (#15770)

This commit addresses type errors reported by pyright in the model
conversion scripts.
---
 .../scripts/causal/run-casual-gen-embeddings-org.py          | 5 +++--
 examples/model-conversion/scripts/utils/inspect-org-model.py | 4 ++--
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py b/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py
index 2fb54ab990..55ad821385 100755
--- a/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py
+++ b/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py
@@ -3,11 +3,10 @@
 import argparse
 import os
 import importlib
-import sys
 import torch
 import numpy as np
 
-from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
 from pathlib import Path
 
 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
@@ -43,6 +42,8 @@ if unreleased_model_name:
         model = model_class.from_pretrained(model_path)
     except (ImportError, AttributeError) as e:
         print(f"Failed to import or load model: {e}")
+        print("Falling back to AutoModelForCausalLM")
+        model = AutoModelForCausalLM.from_pretrained(model_path)
 else:
     model = AutoModelForCausalLM.from_pretrained(model_path)
 print(f"Model class: {type(model)}")
diff --git a/examples/model-conversion/scripts/utils/inspect-org-model.py b/examples/model-conversion/scripts/utils/inspect-org-model.py
index bc6f45a5fb..ea14947fd2 100755
--- a/examples/model-conversion/scripts/utils/inspect-org-model.py
+++ b/examples/model-conversion/scripts/utils/inspect-org-model.py
@@ -40,7 +40,7 @@ if os.path.exists(index_path):
         file_path = os.path.join(model_path, file_name)
         print(f"\n--- From {file_name} ---")
 
-        with safe_open(file_path, framework="pt") as f:
+        with safe_open(file_path, framework="pt") as f:  # type: ignore
             for tensor_name in sorted(tensor_names):
                 tensor = f.get_tensor(tensor_name)
                 print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
@@ -49,7 +49,7 @@ elif os.path.exists(single_file_path):
     # Single file model (original behavior)
     print("Single-file model detected")
 
-    with safe_open(single_file_path, framework="pt") as f:
+    with safe_open(single_file_path, framework="pt") as f:  # type: ignore
         keys = f.keys()
         print("Tensors in model:")
         for key in sorted(keys):

From 661ae31c9c68201577e70278285b349a5a662caf Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Wed, 3 Sep 2025 19:59:16 +0200
Subject: [PATCH 273/287] CUDA: Optimize `rms_norm_f32` kernel and its fused
 variants, giving 1-6% perf E2E (#15715)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add fastdiv, use it in modulo and use modulo in rms_norm_f32

Fastdiv is much faster way to do integer division, which was identified
as bottleneck in rms_norm_f32

* Support more `block_size` values in `rms_norm_f32`

This makes us more flexible in selecting the optimal threads w.r.t
paralellizing across a col vs. launch-overheads of threads and mio
throttles

* Update ggml/src/ggml-cuda/common.cuh

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Replace modulo with fastmodulo in `rms_norm_f32`

* Use `BinPackArguments=true` for formating function calls

Will file a separate PR to adjust .clang-format file

* Update ggml/src/ggml-cuda/common.cuh

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Use uint3 for both `fastdiv` and `fastmodulo`

The compiler seems to reliably optimize away the unused .z component in
the fastdiv use-case, see https://godbolt.org/z/rx8KPrKr3

* More constrained type declarations

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Rename fastdiv and fastmodulo variables to shared variable name

As suggest by JohannesGaessler, this increases clarity of the intended
use

* Pack fastdiv/fastmodulo constants into uint2/uint3 objects

By packing constants to be used together into a struct, we are less
likely to make errors.

* Rename function parameter of fastmodulo

`modulo_consts` is more fitting/descriptive

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/common.cuh |  32 ++++++
 ggml/src/ggml-cuda/norm.cu    | 182 ++++++++++++++++++----------------
 2 files changed, 129 insertions(+), 85 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 85bc9e933b..a2dc26eab7 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -563,6 +563,38 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 #endif // CUDART_VERSION >= 12050
 }
 
+// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
+// Precompute mp (m' in the paper) and L such that division
+// can be computed using a multiply (high 32b of 64b result)
+// and a shift:
+//
+// n/d = (mulhi(n, mp) + n) >> L;
+static const uint3 init_fastdiv_values(uint32_t d) {
+    // compute L = ceil(log2(d));
+    uint32_t L = 0;
+    while (L < 32 && (uint32_t{ 1 } << L) < d) {
+        L++;
+    }
+
+    uint32_t mp = (uint32_t) ((uint64_t{ 1 } << 32) * ((uint64_t{ 1 } << L) - d) / d + 1);
+    // pack divisor as well to reduce error surface
+    return make_uint3(mp, L, d);
+}
+
+static __device__ __forceinline__ uint32_t fastdiv(uint32_t n, const uint3 fastdiv_values) {
+    // expects fastdiv_values to contain <mp, L, divisor> in <x, y, z>
+    // fastdiv_values.z is unused and optimized away by the compiler.
+    // Compute high 32 bits of n * mp
+    const uint32_t hi = __umulhi(n, fastdiv_values.x);
+    // add n, apply bit shift
+    return (hi + n) >> fastdiv_values.y;
+}
+
+static __device__ __forceinline__ uint32_t fastmodulo(uint32_t n, const uint3 fastdiv_values) {
+    // expects  fastdiv_values to contain <mp, L, divisor> in <x, y, z> (see init_fastdiv_values)
+    return n - fastdiv(n, fastdiv_values) * fastdiv_values.z;
+}
+
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v);
 
 static __device__ __forceinline__ float get_alibi_slope(
diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
index d5157d958b..4f153c5718 100644
--- a/ggml/src/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -105,29 +105,29 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
 }
 
 template <int block_size, bool do_multiply = false, bool do_add = false>
-static __global__ void rms_norm_f32(const float * x, float *       dst,
+static __global__ void rms_norm_f32(const float * x,
+                                    float *       dst,
                                     const int     ncols,
                                     const int64_t stride_row,
                                     const int64_t stride_channel,
                                     const int64_t stride_sample,
                                     const float   eps,
-                                    const float * mul                = nullptr,
-                                    const int64_t mul_stride_row     = 0,
-                                    const int64_t mul_stride_channel = 0,
-                                    const int64_t mul_stride_sample  = 0,
-                                    const int     mul_ncols          = 0,
-                                    const int     mul_nrows          = 0,
-                                    const int     mul_nchannels      = 0,
-                                    const int     mul_nsamples       = 0,
-                                    const float * add                = nullptr,
-                                    const int64_t add_stride_row     = 0,
-                                    const int64_t add_stride_channel = 0,
-                                    const int64_t add_stride_sample  = 0,
-                                    const int     add_ncols          = 0,
-                                    const int     add_nrows          = 0,
-                                    const int     add_nchannels      = 0,
-                                    const int     add_nsamples       = 0) {
-
+                                    const float * mul                  = nullptr,
+                                    const int64_t mul_stride_row       = 0,
+                                    const int64_t mul_stride_channel   = 0,
+                                    const int64_t mul_stride_sample    = 0,
+                                    const uint3   mul_ncols_packed     = make_uint3(0, 0, 0),
+                                    const uint3   mul_nrows_packed     = make_uint3(0, 0, 0),
+                                    const uint3   mul_nchannels_packed = make_uint3(0, 0, 0),
+                                    const uint3   mul_nsamples_packed  = make_uint3(0, 0, 0),
+                                    const float * add                  = nullptr,
+                                    const int64_t add_stride_row       = 0,
+                                    const int64_t add_stride_channel   = 0,
+                                    const int64_t add_stride_sample    = 0,
+                                    const uint3   add_ncols_packed     = make_uint3(0, 0, 0),
+                                    const uint3   add_nrows_packed     = make_uint3(0, 0, 0),
+                                    const uint3   add_nchannels_packed = make_uint3(0, 0, 0),
+                                    const uint3   add_nsamples_packed  = make_uint3(0, 0, 0)) {
     const int nrows     = gridDim.x;
     const int nchannels = gridDim.y;
 
@@ -142,16 +142,16 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
     dst += ((sample*nchannels + channel)*nrows + row)*ncols;
 
     if constexpr (do_multiply) {
-        const int mul_row = row % mul_nrows;
-        const int mul_channel = channel % mul_nchannels;
-        const int mul_sample = sample % mul_nsamples;
-        mul += mul_sample*mul_stride_sample + mul_channel*mul_stride_channel + mul_row*mul_stride_row;
+        const uint32_t mul_row     = fastmodulo(row, mul_nrows_packed);
+        const uint32_t mul_channel = fastmodulo(channel, mul_nchannels_packed);
+        const uint32_t mul_sample  = fastmodulo(sample, mul_nsamples_packed);
+        mul += mul_sample * mul_stride_sample + mul_channel * mul_stride_channel + mul_row * mul_stride_row;
     }
 
     if constexpr (do_add) {
-        const int add_row     = row % add_nrows;
-        const int add_channel = channel % add_nchannels;
-        const int add_sample  = sample % add_nsamples;
+        const int add_row     = fastmodulo(row, add_nrows_packed);
+        const int add_channel = fastmodulo(channel, add_nchannels_packed);
+        const int add_sample  = fastmodulo(sample, add_nsamples_packed);
         add += add_sample * add_stride_sample + add_channel * add_stride_channel + add_row * add_stride_row;
     }
 
@@ -165,15 +165,18 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
     // sum up partial sums
     tmp = warp_reduce_sum(tmp);
     if constexpr (block_size > WARP_SIZE) {
-        static_assert(block_size == 1024, "unexpected block_size");
+        static_assert((block_size <= 1024) && (block_size % 32 == 0), "unexpected block_size");
         __shared__ float s_sum[32];
-        const int warp_id = threadIdx.x / WARP_SIZE;
-        const int lane_id = threadIdx.x % WARP_SIZE;
+        const int        warp_id = tid / WARP_SIZE;
+        const int        lane_id = tid % WARP_SIZE;
         if (lane_id == 0) {
             s_sum[warp_id] = tmp;
         }
         __syncthreads();
-        tmp = s_sum[lane_id];
+        tmp = 0.0f;
+        if (lane_id < (block_size / WARP_SIZE)) {
+            tmp = s_sum[lane_id];
+        }
         tmp = warp_reduce_sum(tmp);
     }
 
@@ -182,12 +185,12 @@ static __global__ void rms_norm_f32(const float * x, float *       dst,
 
     for (int col = tid; col < ncols; col += block_size) {
         if constexpr (do_multiply && do_add) {
-            const int mul_col = col % mul_ncols;
-            const int add_col = col % add_ncols;
-            dst[col] = scale * x[col] * mul[mul_col] + add[add_col];
+            const int mul_col = fastmodulo(col, mul_ncols_packed);
+            const int add_col = fastmodulo(col, add_ncols_packed);
+            dst[col]          = scale * x[col] * mul[mul_col] + add[add_col];
         } else if constexpr (do_multiply) {
-            const int mul_col = col % mul_ncols;
-            dst[col] = scale * x[col] * mul[mul_col];
+            const int mul_col = fastmodulo(col, mul_ncols_packed);
+            dst[col]          = scale * x[col] * mul[mul_col];
         } else {
             dst[col] = scale * x[col];
         }
@@ -354,77 +357,86 @@ static void rms_norm_f32_cuda(
         const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, cudaStream_t stream) {
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        rms_norm_f32<WARP_SIZE, false><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
+        const dim3 block_dims(256, 1, 1);
+        rms_norm_f32<256, false><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
         rms_norm_f32<1024, false><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
     }
 }
 
-static void rms_norm_mul_f32_cuda(const float * x,
-                                  const float * mul,
-                                  const float * add,
-                                  float *       dst,
-                                  const int     ncols,
-                                  const int     nrows,
-                                  const int     nchannels,
-                                  const int     nsamples,
-                                  const int64_t stride_row,
-                                  const int64_t stride_channel,
-                                  const int64_t stride_sample,
-                                  const int64_t mul_stride_row,
-                                  const int64_t mul_stride_channel,
-                                  const int64_t mul_stride_sample,
-                                  const int     mul_ncols,
-                                  const int     mul_nrows,
-                                  const int     mul_nchannels,
-                                  const int     mul_nsamples,
-                                  const int64_t add_stride_row,
-                                  const int64_t add_stride_channel,
-                                  const int64_t add_stride_sample,
-                                  const int     add_ncols,
-                                  const int     add_nrows,
-                                  const int     add_nchannels,
-                                  const int     add_nsamples,
-                                  const float   eps,
-                                  cudaStream_t  stream) {
+static void rms_norm_mul_f32_cuda(const float *  x,
+                                  const float *  mul,
+                                  const float *  add,
+                                  float *        dst,
+                                  const int      ncols,
+                                  const int      nrows,
+                                  const int      nchannels,
+                                  const int      nsamples,
+                                  const int64_t  stride_row,
+                                  const int64_t  stride_channel,
+                                  const int64_t  stride_sample,
+                                  const int64_t  mul_stride_row,
+                                  const int64_t  mul_stride_channel,
+                                  const int64_t  mul_stride_sample,
+                                  const uint32_t mul_ncols,
+                                  const uint32_t mul_nrows,
+                                  const uint32_t mul_nchannels,
+                                  const uint32_t mul_nsamples,
+                                  const int64_t  add_stride_row,
+                                  const int64_t  add_stride_channel,
+                                  const int64_t  add_stride_sample,
+                                  const uint32_t add_ncols,
+                                  const uint32_t add_nrows,
+                                  const uint32_t add_nchannels,
+                                  const uint32_t add_nsamples,
+                                  const float    eps,
+                                  cudaStream_t   stream) {
     const dim3 blocks_num(nrows, nchannels, nsamples);
     if (mul == nullptr) {
         rms_norm_f32_cuda(x, dst, ncols, nrows, nchannels, nsamples, stride_row, stride_channel, stride_sample, eps, stream);
         return;
     }
     if (add == nullptr) {
+        const uint3 mul_ncols_packed     = init_fastdiv_values(mul_ncols);
+        const uint3 mul_nrows_packed     = init_fastdiv_values(mul_nrows);
+        const uint3 mul_nchannels_packed = init_fastdiv_values(mul_nchannels);
+        const uint3 mul_nsamples_packed  = init_fastdiv_values(mul_nsamples);
         if (ncols < 1024) {
-            const dim3 block_dims(WARP_SIZE, 1, 1);
-            rms_norm_f32<WARP_SIZE, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
-                ncols, stride_row, stride_channel, stride_sample, eps,
-                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
-                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+            const dim3 block_dims(256, 1, 1);
+            rms_norm_f32<256, true><<<blocks_num, block_dims, 0, stream>>>(
+                x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
-                ncols, stride_row, stride_channel, stride_sample, eps,
-                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
-                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples);
+            rms_norm_f32<1024, true><<<blocks_num, block_dims, 0, stream>>>(
+                x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed);
         }
     } else {
+        const uint3 mul_ncols_packed     = init_fastdiv_values(mul_ncols);
+        const uint3 mul_nrows_packed     = init_fastdiv_values(mul_nrows);
+        const uint3 mul_nchannels_packed = init_fastdiv_values(mul_nchannels);
+        const uint3 mul_nsamples_packed  = init_fastdiv_values(mul_nsamples);
+
+        const uint3 add_ncols_packed     = init_fastdiv_values(add_ncols);
+        const uint3 add_nrows_packed     = init_fastdiv_values(add_nrows);
+        const uint3 add_nchannels_packed = init_fastdiv_values(add_nchannels);
+        const uint3 add_nsamples_packed  = init_fastdiv_values(add_nsamples);
         if (ncols < 1024) {
-            const dim3 block_dims(WARP_SIZE, 1, 1);
-            rms_norm_f32<WARP_SIZE, true, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
-                ncols, stride_row, stride_channel, stride_sample, eps,
-                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
-                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
-                add, add_stride_row, add_stride_channel, add_stride_sample,
-                add_ncols, add_nrows, add_nchannels, add_nsamples);
+            const dim3 block_dims(256, 1, 1);
+            rms_norm_f32<256, true, true><<<blocks_num, block_dims, 0, stream>>>(
+                x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
+                add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
+                add_nchannels_packed, add_nsamples_packed);
         } else {
             const dim3 block_dims(1024, 1, 1);
-            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, 0, stream>>>(x, dst,
-                ncols, stride_row, stride_channel, stride_sample, eps,
-                mul, mul_stride_row, mul_stride_channel, mul_stride_sample,
-                mul_ncols, mul_nrows, mul_nchannels, mul_nsamples,
-                add, add_stride_row, add_stride_channel, add_stride_sample,
-                add_ncols, add_nrows, add_nchannels, add_nsamples);
+            rms_norm_f32<1024, true, true><<<blocks_num, block_dims, 0, stream>>>(
+                x, dst, ncols, stride_row, stride_channel, stride_sample, eps, mul, mul_stride_row, mul_stride_channel,
+                mul_stride_sample, mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed, add,
+                add_stride_row, add_stride_channel, add_stride_sample, add_ncols_packed, add_nrows_packed,
+                add_nchannels_packed, add_nsamples_packed);
         }
     }
 }

From 0014fb4add3a7d000c14b763538045c6170f57d9 Mon Sep 17 00:00:00 2001
From: Shin-myoung-serp <relent95@naver.com>
Date: Thu, 4 Sep 2025 03:22:55 +0900
Subject: [PATCH 274/287] ggml vulkan: add hardsigmoid and hardswish operations
 (#15762)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 22 +++++++++++++++++++
 .../vulkan-shaders/hardsigmoid.comp           | 22 +++++++++++++++++++
 .../ggml-vulkan/vulkan-shaders/hardswish.comp | 22 +++++++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  4 ++++
 4 files changed, 70 insertions(+)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/hardsigmoid.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/hardswish.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 7189fc1cfa..ea34bd26ca 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -529,6 +529,8 @@ struct vk_device_struct {
     vk_pipeline pipeline_relu[2];
     vk_pipeline pipeline_tanh[2];
     vk_pipeline pipeline_sigmoid[2];
+    vk_pipeline pipeline_hardsigmoid[2];
+    vk_pipeline pipeline_hardswish[2];
 
     vk_pipeline pipeline_geglu[2];
     vk_pipeline pipeline_reglu[2];
@@ -3261,6 +3263,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
     CREATE_UNARY(relu)
     CREATE_UNARY(tanh)
     CREATE_UNARY(sigmoid)
+    CREATE_UNARY(hardsigmoid)
+    CREATE_UNARY(hardswish)
 #undef CREATE_UNARY
 
 #define CREATE_GLU(name)  \
@@ -7533,6 +7537,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                 return ctx->device->pipeline_tanh[dst->type == GGML_TYPE_F16];
             case GGML_UNARY_OP_SIGMOID:
                 return ctx->device->pipeline_sigmoid[dst->type == GGML_TYPE_F16];
+            case GGML_UNARY_OP_HARDSIGMOID:
+                return ctx->device->pipeline_hardsigmoid[dst->type == GGML_TYPE_F16];
+            case GGML_UNARY_OP_HARDSWISH:
+                return ctx->device->pipeline_hardswish[dst->type == GGML_TYPE_F16];
             default:
                 break;
         }
@@ -10201,6 +10209,8 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_UNARY_OP_RELU:
         case GGML_UNARY_OP_TANH:
         case GGML_UNARY_OP_SIGMOID:
+        case GGML_UNARY_OP_HARDSIGMOID:
+        case GGML_UNARY_OP_HARDSWISH:
             break;
         default:
             return false;
@@ -10571,6 +10581,8 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_UNARY_OP_RELU:
         case GGML_UNARY_OP_TANH:
         case GGML_UNARY_OP_SIGMOID:
+        case GGML_UNARY_OP_HARDSIGMOID:
+        case GGML_UNARY_OP_HARDSWISH:
             ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun);
             break;
         default:
@@ -10813,6 +10825,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
         case GGML_UNARY_OP_RELU:
         case GGML_UNARY_OP_TANH:
         case GGML_UNARY_OP_SIGMOID:
+        case GGML_UNARY_OP_HARDSIGMOID:
+        case GGML_UNARY_OP_HARDSWISH:
             buf = tensor->buffer;
             break;
         default:
@@ -11764,6 +11778,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 case GGML_UNARY_OP_RELU:
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_SIGMOID:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
                     return ggml_is_contiguous(op->src[0]) &&
                            (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                            (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
@@ -12580,6 +12596,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         case GGML_UNARY_OP_SIGMOID:
             tensor_clone = ggml_sigmoid(ggml_ctx, src_clone[0]);
             break;
+        case GGML_UNARY_OP_HARDSIGMOID:
+            tensor_clone = ggml_hardsigmoid(ggml_ctx, src_clone[0]);
+            break;
+        case GGML_UNARY_OP_HARDSWISH:
+            tensor_clone = ggml_hardswish(ggml_ctx, src_clone[0]);
+            break;
         default:
             std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
             GGML_ABORT("fatal error");
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/hardsigmoid.comp b/ggml/src/ggml-vulkan/vulkan-shaders/hardsigmoid.comp
new file mode 100644
index 0000000000..1da252cc66
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/hardsigmoid.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float x = float(data_a[i]);
+    data_d[i] = D_TYPE(min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/hardswish.comp b/ggml/src/ggml-vulkan/vulkan-shaders/hardswish.comp
new file mode 100644
index 0000000000..3afc588274
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/hardswish.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float x = float(data_a[i]);
+    data_d[i] = D_TYPE(x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)));
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 1263a70e4f..613498d0d5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -657,6 +657,10 @@ void process_shaders() {
     string_to_spv("tanh_f32",       "tanh.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
     string_to_spv("sigmoid_f16",    "sigmoid.comp",     {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("sigmoid_f32",    "sigmoid.comp",     {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("hardsigmoid_f16","hardsigmoid.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("hardsigmoid_f32","hardsigmoid.comp", {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("hardswish_f16",  "hardswish.comp",   {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("hardswish_f32",  "hardswish.comp",   {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
     for (auto rte : {false, true}) {
         std::string suffix = rte ? "_rte" : "";

From 8227695d7a3e5b357cb37fad263f00a8ca6db710 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Wed, 3 Sep 2025 20:24:50 +0200
Subject: [PATCH 275/287] vulkan : update
 ggml_vk_instance_validation_ext_available (#15666)

* vulkan : update ggml_vk_instance_validation_ext_available

This commit updates ggml_vk_instance_validation_ext_available() to
check for VK_EXT_validation_features instead of
VK_KHR_portability_enumeration.

Based on how the returned boolean is used later in the code (to enable
both the validation layer and the VK_EXT_validation_features extension),
it appears the function may have been intended to check for the
validation layer features extension.

* remove try/catch

This was a left over from a previous iteration where I was explicitly
quering for a specific validation layer first, which would throw.

* update warning message about validation layers
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ea34bd26ca..55be80f412 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -4271,7 +4271,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     }
 }
 
-static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+static bool ggml_vk_instance_validation_ext_available();
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
 
 static bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
@@ -4292,7 +4292,7 @@ static void ggml_vk_instance_init() {
     vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version };
 
     const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
-    const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
+    const bool validation_ext = ggml_vk_instance_validation_ext_available();
 #ifdef __APPLE__
     const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
 #endif
@@ -12212,22 +12212,23 @@ ggml_backend_reg_t ggml_backend_vk_reg() {
 }
 
 // Extension availability
-static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+static bool ggml_vk_instance_validation_ext_available() {
 #ifdef GGML_VULKAN_VALIDATE
-    bool portability_enumeration_ext = false;
-    // Check for portability enumeration extension for MoltenVK support
-    for (const auto& properties : instance_extensions) {
-        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
-            return true;
+    // Check if validation layer provides the extension
+    const std::string layer_name = "VK_LAYER_KHRONOS_validation";
+    for (const auto& layer : vk::enumerateInstanceLayerProperties()) {
+        if (layer_name == layer.layerName.data()) {
+            for (const auto& ext : vk::enumerateInstanceExtensionProperties(layer_name)) {
+                if (strcmp("VK_EXT_validation_features", ext.extensionName.data()) == 0) {
+                    return true;
+                }
+            }
         }
     }
-    if (!portability_enumeration_ext) {
-        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
-    }
+
+    std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_validation_features not found." << std::endl;
 #endif
     return false;
-
-    UNUSED(instance_extensions);
 }
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
 #ifdef __APPLE__

From 0fce7a1248b74148c1eb0d368b7e18e8bcb96809 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Wed, 3 Sep 2025 13:33:15 -0500
Subject: [PATCH 276/287] vulkan: don't use std::string in load_shaders, to
 improve compile time (#15724)

* vulkan: don't use std::string in load_shaders, to improve compile time

* keep the string version for those calls that use it
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 55be80f412..2f86b22c4a 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2342,7 +2342,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     }
 
     std::vector<std::future<void>> compiles;
-    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint,
+    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const char *name, size_t spv_size, const void* spv_data, const char *entrypoint,
                                               uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
                                               uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
 
@@ -2379,6 +2379,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
                                       parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
     };
 
+    auto const &ggml_vk_create_pipeline2 = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const char *entrypoint,
+                                              uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
+                                              uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
+        return ggml_vk_create_pipeline(device, pipeline, name.c_str(), spv_size, spv_data, entrypoint,
+                                       parameter_count, push_constant_size, wg_denoms, specialization_constants,
+                                       align, disable_robustness, require_full_subgroups, required_subgroup_size);
+    };
+
     auto const &fa_wg_denoms = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows) -> std::array<uint32_t, 3> {
         return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows)[0], 1, 1};
     };
@@ -3114,9 +3122,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
         if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
-            ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
+            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
         } else {
-            ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
+            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
         }
     }
     ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 12 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
@@ -3200,7 +3208,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     bool rte = device->float_controls_rte_fp16;
 #define CREATE_BINARY(name, namemod, spec, bindings) \
     for (int s0 : {0,1}) for (int s1 : {0,1}) for (int d : {0,1}) \
-        ggml_vk_create_pipeline(device, device->pipeline_ ## name ## namemod[s0][s1][d], \
+        ggml_vk_create_pipeline2(device, device->pipeline_ ## name ## namemod[s0][s1][d], \
                                 #name + get_suffix(s0, s1, d) + #namemod, name ## _len[s0][s1][d][rte], name ## _data[s0][s1][d][rte], \
                                 "main", (bindings), sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1);
 
@@ -3218,8 +3226,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     if (device->multi_add) {
         for (uint32_t i = 0; i < MAX_FUSED_ADDS; ++i) {
-            ggml_vk_create_pipeline(device, device->pipeline_multi_add[i],     "multi_add_f32_"     + std::to_string(i+1), multi_add_f32_len,     multi_add_f32_data,     "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
-            ggml_vk_create_pipeline(device, device->pipeline_multi_add_rms[i], "multi_add_rms_f32_" + std::to_string(i+1), multi_add_rms_f32_len, multi_add_rms_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
+            ggml_vk_create_pipeline2(device, device->pipeline_multi_add[i],     "multi_add_f32_"     + std::to_string(i+1), multi_add_f32_len,     multi_add_f32_data,     "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
+            ggml_vk_create_pipeline2(device, device->pipeline_multi_add_rms[i], "multi_add_rms_f32_" + std::to_string(i+1), multi_add_rms_f32_len, multi_add_rms_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
         }
     }
 
@@ -3313,7 +3321,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     }
 
     for (uint32_t i = 0; i < num_argsort_pipelines; ++i) {
-        ggml_vk_create_pipeline(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1u<<i, 1, 1}, {1u<<i, i}, 1, true);
+        ggml_vk_create_pipeline2(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1u<<i, 1, 1}, {1u<<i, i}, 1, true);
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);

From dff7551bfdbdd6e57c13e523d7dcca317640e907 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Wed, 3 Sep 2025 22:55:10 +0200
Subject: [PATCH 277/287] vulkan: fix mmv subgroup16 selection (#15775)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2f86b22c4a..3cd5af1cdc 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2937,9 +2937,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     const bool use_subgroups = device->subgroup_arithmetic && device->architecture != vk_device_architecture::AMD_GCN;
     // Ensure a subgroup size >= 16 is available
-    const bool use_subgroups16 = use_subgroups &&
-                                    (!device->subgroup_size_control && device->subgroup_size >= 16 ||
-                                    device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16);
+    const bool use_subgroups16 = use_subgroups && subgroup_min_size_16;
 
     const uint32_t subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16) ? 16 : device->subgroup_size;
     const uint32_t subgroup_size16 = std::max(subgroup_size, 16u);

From 239b60e8986bbcb944f57311a02ac994431bf652 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Thu, 4 Sep 2025 11:03:02 +0800
Subject: [PATCH 278/287] CANN: fix acl_rstd allocation size in
 ggml_cann_rms_norm (#15760)

Fixes #15330

Adjust the allocation size of acl_rstd. The parameter `dims` is set to 3 according to the CANN documentation.

Co-authored-by: Yuchuan <yuchuan-cao@users.noreply.github.com>
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 80b9a932db..5c6163ad44 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -975,18 +975,19 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     );
 
     // build rstd, zero...
-    size_t acl_rstd_nb[GGML_MAX_DIMS];
+    int64_t acl_rstd_ne[] = {src->ne[1], src->ne[2], src->ne[3]};
+    size_t acl_rstd_nb[GGML_MAX_DIMS - 1];
     acl_rstd_nb[0] = sizeof(float);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * src->ne[i - 1];
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1];
     }
     aclTensor* acl_rstd = get_f32_cache_acl_tensor(
         ctx,
         &ctx.rms_norm_zero_tensor_cache.cache,
         ctx.rms_norm_zero_tensor_cache.size,
-        src->ne,
+        acl_rstd_ne,
         acl_rstd_nb,
-        GGML_MAX_DIMS,
+        GGML_MAX_DIMS - 1,
         0.0f      // value
     );
 

From 820bc9853100708011036e10f40b923968dd9b66 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Thu, 4 Sep 2025 08:30:28 +0200
Subject: [PATCH 279/287] opencl: add hs=40 to FA (#15758)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index a9a91ca585..fc54c90f7b 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -1339,7 +1339,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
 
         if (!kernel_src_f16.empty() && !kernel_src_f32.empty() && !kernel_src_f32_f16.empty()) {
             const struct { int dk; int dv; int bm; int bn; } fa_dims[] = {
-                { 64,  64, 64, 64}, { 80,  80, 64, 32}, { 96,  96, 64, 32},
+                { 40,  40, 32, 32}, { 64,  64, 64, 64}, { 80,  80, 64, 32}, { 96,  96, 64, 32},
                 {112, 112, 32, 32}, {128, 128, 32, 32}, {192, 128, 16, 16},
                 {192, 192, 16, 16}, {256, 256, 16, 16},
             };
@@ -2784,7 +2784,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 const int dv = v->ne[0];
 
                 const struct { int dk; int dv; } supported_dims[] = {
-                    { 64,  64}, { 80,  80}, { 96,  96},
+                    { 40,  40}, { 64,  64}, { 80,  80}, { 96,  96},
                     {112, 112}, {128, 128}, {192, 128},
                     {192, 192}, {256, 256},
                 };

From 5421f63ab08ca1e1a093662a5ccd0117e461185f Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Thu, 4 Sep 2025 15:12:30 +0800
Subject: [PATCH 280/287] CANN: Fix precision issue on 310I DUO multi-devices
 (#15784)

---
 docs/backend/CANN.md             | 14 +++++---------
 ggml/src/ggml-cann/aclnn_ops.cpp |  2 +-
 ggml/src/ggml-cann/common.h      |  2 +-
 ggml/src/ggml-cann/ggml-cann.cpp | 26 +++++++++++++++++++++-----
 4 files changed, 28 insertions(+), 16 deletions(-)

diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md
index 2d8866e3bd..357253f43a 100755
--- a/docs/backend/CANN.md
+++ b/docs/backend/CANN.md
@@ -293,17 +293,14 @@ We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers fr
 
 ## Environment variable setup
 
-### GGML_CANN_ASYNC_MODE
-
-Enables asynchronous operator submission. Disabled by default.
-
 ### GGML_CANN_MEM_POOL
 
-Specifies the memory pool management strategy:
+Specifies the memory pool management strategy, Default is vmm.
 
 - vmm: Utilizes a virtual memory manager pool. If hardware support for VMM is unavailable, falls back to the legacy (leg) memory pool.
 
 - prio: Employs a priority queue-based memory pool management.
+
 - leg: Uses a fixed-size buffer pool.
 
 ### GGML_CANN_DISABLE_BUF_POOL_CLEAN
@@ -312,9 +309,8 @@ Controls automatic cleanup of the memory pool. This option is only effective whe
 
 ### GGML_CANN_WEIGHT_NZ
 
-Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
+Converting the matmul weight format from ND to NZ to improve performance. Enabled by default.
 
-### GGML_CANN_DISABLE_ACL_GRAPH
+### GGML_CANN_ACL_GRAPH
 
-When this variable is set, ACL graph execution is disabled and operators are executed in an op-by-op (eager) mode.
-This mode is mainly intended for debugging or for cases where the overhead of graph construction and execution is not desirable.
+Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default.
diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 5c6163ad44..2d81fbd5a1 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -1956,7 +1956,7 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
     aclTensor* acl_weight_tensor;
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (weight_to_nz && is_matmul_weight(weight)) {
         int64_t acl_stride[2] = {1, transpose_ne[1]};
 
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index a041a157c3..e295f4ab47 100755
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -420,7 +420,7 @@ struct ggml_backend_cann_context {
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
 #ifdef USE_ACL_GRAPH
-        acl_graph_mode = !(parse_bool(get_env("GGML_CANN_DISABLE_ACL_GRAPH").value_or("")));
+        acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on"));
         GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n",
               __func__, device,
               acl_graph_mode ? "GRAPH" : "EAGER",
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 64fb2beff0..1aa2913a61 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1196,7 +1196,7 @@ static void ggml_backend_cann_buffer_set_tensor(
     // Why aclrtSynchronizeDevice?
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size,
                               ACL_MEMCPY_HOST_TO_DEVICE));
@@ -1279,6 +1279,10 @@ static bool ggml_backend_cann_buffer_cpy_tensor(
                                   ACL_MEMCPY_DEVICE_TO_DEVICE));
             return true;
         } else {
+#ifdef ASCEND_310P
+            // TODO: Support 310p P2P copy
+            return false;
+#endif
             // Different device but can access by peer.
             int32_t canAccessPeer = 0;
             ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device,
@@ -1439,7 +1443,7 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(
     int64_t ne0 = tensor->ne[0];
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
 
     // last line must bigger than 32, because every single op deal at
     // least 32 bytes.
@@ -2000,6 +2004,8 @@ static bool ggml_backend_cann_cpy_tensor_async(
     GGML_ASSERT(ggml_backend_is_cann(backend_src) ||
                 ggml_backend_is_cann(backend_dst));
 
+    GGML_ASSERT(!is_matmul_weight((const ggml_tensor*)src));
+
     if (!ggml_backend_buffer_is_cann(src->buffer) ||
         !ggml_backend_buffer_is_cann(dst->buffer)) {
         return false;
@@ -2020,6 +2026,10 @@ static bool ggml_backend_cann_cpy_tensor_async(
         return true;
     }
     if (backend_src != backend_dst) {
+#ifdef ASCEND_310P
+        // TODO: Support 310p P2P copy
+        return false;
+#endif
         ggml_backend_cann_buffer_context* buf_ctx_src =
             (ggml_backend_cann_buffer_context*)buf_src->context;
         ggml_backend_cann_buffer_context* buf_ctx_dst =
@@ -2036,7 +2046,6 @@ static bool ggml_backend_cann_cpy_tensor_async(
         }
 
         // need open both directions for memcpyasync between devices.
-        ggml_cann_set_device(cann_ctx_dst->device);
         ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_src->device, 0));
         ggml_cann_set_device(cann_ctx_src->device);
         ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_dst->device, 0));
@@ -2047,8 +2056,15 @@ static bool ggml_backend_cann_cpy_tensor_async(
                                    ACL_MEMCPY_DEVICE_TO_DEVICE,
                                    cann_ctx_src->stream()));
 
-        //TODO: workaround for Event didn`t work here.
-        aclrtSynchronizeStream(cann_ctx_src->stream());
+        // record event on src stream after the copy
+        if (!cann_ctx_src->copy_event) {
+            ACL_CHECK(aclrtCreateEventWithFlag(&cann_ctx_src->copy_event, ACL_EVENT_SYNC));
+        }
+        ACL_CHECK(aclrtRecordEvent(cann_ctx_src->copy_event, cann_ctx_src->stream()));
+
+        // wait on dst stream for the copy to complete
+        ggml_cann_set_device(cann_ctx_dst->device);
+        ACL_CHECK(aclrtStreamWaitEvent(cann_ctx_dst->stream(), cann_ctx_src->copy_event));
     } else {
         // src and dst are on the same backend
         ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,

From 0a1b3982cd0bd18730d50a693053b88c13fd04a6 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 4 Sep 2025 16:38:49 +0800
Subject: [PATCH 281/287] ggml: add ops for WAN video model (cuda && cpu)
 (#15669)

* add conv3d support

* add ggml_pad_ext for cpu & cuda backend

* cuda/cpu: add im2col_3d support

* cuda: make im2col a little faster

* fix cuda pad/scale/im2col3d

* make im2col_3d faster

* gguf: support loading tensors which n_dims > GGML_MAX_DIMS

* fix cuda get_rows

* avoid ggml_conv_3d conflict

* correct GGML_OP_COUNT assertion

* avoid build failure

* avoid build failure on MacOS

* cuda: remove unnecessary MIN define

* fix cpu im2col_3d

* adjust the code style

* cuda: use simpler loop in get_rows

* add test_im2col_3d to test-backend-ops

* test-backend-ops.cpp: remove trailing whitespace

* cpu: im2col_3d support non continuous src

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>

* fix test_im2col_3d

* remove unused variables

* cuda: get_rows: dfloat2 -> float2

* add test_pad_ext to test-backend-ops.cpp

* add gguf_init_from_file_ext impl

* Revert "gguf: support loading tensors which n_dims > GGML_MAX_DIMS"

This reverts commit d8377a0a37f314bd3713fe043b4333ad661610c1.

* Revert "add gguf_init_from_file_ext impl"

This reverts commit d9f1d13208c68ef83b3538201ac7f31614fb1994.

* update ggml_backend_vk_device_supports_op

* fix ggml_backend_vk_device_supports_op

* update other backend supports op for ggml_pad_ext

* metal/opencl/sycl/vulkan: fix GGML_OP_PAD check in supports_op

---------

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
---
 ggml/include/ggml.h                  |  51 +++++-
 ggml/src/ggml-cann/aclnn_ops.cpp     |  13 +-
 ggml/src/ggml-cpu/ggml-cpu.c         |   5 +
 ggml/src/ggml-cpu/ops.cpp            | 222 ++++++++++++++++++++++++++-
 ggml/src/ggml-cpu/ops.h              |   1 +
 ggml/src/ggml-cuda/getrows.cu        |  80 +++++-----
 ggml/src/ggml-cuda/ggml-cuda.cu      |   4 +
 ggml/src/ggml-cuda/im2col.cu         | 129 ++++++++++++++++
 ggml/src/ggml-cuda/im2col.cuh        |   1 +
 ggml/src/ggml-cuda/pad.cu            |  69 ++++++---
 ggml/src/ggml-cuda/scale.cu          |  19 +--
 ggml/src/ggml-metal/ggml-metal.m     |   3 +
 ggml/src/ggml-opencl/ggml-opencl.cpp |   4 +-
 ggml/src/ggml-sycl/ggml-sycl.cpp     |   3 +
 ggml/src/ggml-vulkan/ggml-vulkan.cpp |   3 +
 ggml/src/ggml.c                      | 128 ++++++++++++++-
 tests/test-backend-ops.cpp           | 114 +++++++++++++-
 17 files changed, 759 insertions(+), 90 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 7e9c3c8c7a..c01b98ac78 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -511,6 +511,7 @@ extern "C" {
         GGML_OP_CONV_TRANSPOSE_1D,
         GGML_OP_IM2COL,
         GGML_OP_IM2COL_BACK,
+        GGML_OP_IM2COL_3D,
         GGML_OP_CONV_2D,
         GGML_OP_CONV_3D,
         GGML_OP_CONV_2D_DW,
@@ -1870,6 +1871,41 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
+    GGML_API struct ggml_tensor * ggml_im2col_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int64_t               IC,
+            int                   s0, // stride width
+            int                   s1, // stride height
+            int                   s2, // stride depth
+            int                   p0, // padding width
+            int                   p1, // padding height
+            int                   p2, // padding depth
+            int                   d0, // dilation width
+            int                   d1, // dilation height
+            int                   d2, // dilation depth
+            enum ggml_type        dst_type);
+
+    // a: [OC*IC, KD, KH, KW]
+    // b: [N*IC, ID, IH, IW]
+    // result: [N*OC, OD, OH, OW]
+    GGML_API struct ggml_tensor * ggml_conv_3d(
+                struct ggml_context * ctx,
+                struct ggml_tensor  * a,
+                struct ggml_tensor  * b,
+                int64_t               IC,
+                int                   s0, // stride width
+                int                   s1, // stride height
+                int                   s2, // stride depth
+                int                   p0, // padding width
+                int                   p1, // padding height
+                int                   p2, // padding depth
+                int                   d0, // dilation width
+                int                   d1, // dilation height
+                int                   d2  // dilation depth
+        );
+
     // kernel size is a->ne[0] x a->ne[1]
     // stride is equal to kernel size
     // padding is zero
@@ -1941,7 +1977,7 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
-    GGML_API struct ggml_tensor * ggml_conv_3d(
+    GGML_API struct ggml_tensor * ggml_conv_3d_direct(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
             struct ggml_tensor  * b,   // input  [W, H, D, C * N]
@@ -2048,6 +2084,19 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    GGML_API struct ggml_tensor * ggml_pad_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  lp0,
+            int                  rp0,
+            int                  lp1,
+            int                  rp1,
+            int                  lp2,
+            int                  rp2,
+            int                  lp3,
+            int                  rp3
+            );
+
     // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
     GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
             struct ggml_context * ctx,
diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 2d81fbd5a1..ac2e2e1adf 100755
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -589,9 +589,16 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // the position of elements in the array means which dirction to padding,
     // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
     //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
-    int64_t paddings[] = {
-        0, dst->ne[0] - src->ne[0], 0, dst->ne[1] - src->ne[1],
-        0, dst->ne[2] - src->ne[2], 0, dst->ne[3] - src->ne[3]};
+    const int32_t lp0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t rp0 = ggml_get_op_params_i32(dst, 1);
+    const int32_t lp1 = ggml_get_op_params_i32(dst, 2);
+    const int32_t rp1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t lp2 = ggml_get_op_params_i32(dst, 4);
+    const int32_t rp2 = ggml_get_op_params_i32(dst, 5);
+    const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
+    const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
+
+    int64_t paddings[] = {lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3};
     aclnn_pad(ctx, acl_src, acl_dst, paddings);
     ggml_cann_release_resources(ctx, acl_src, acl_dst);
 }
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 78ec189d4c..0d35d9333e 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1876,6 +1876,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_im2col_back_f32(params, tensor);
             } break;
+        case GGML_OP_IM2COL_3D:
+            {
+                ggml_compute_forward_im2col_3d(params, tensor);
+            } break;
         case GGML_OP_CONV_2D:
             {
                 ggml_compute_forward_conv_2d(params, tensor);
@@ -2255,6 +2259,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_IM2COL:
         case GGML_OP_IM2COL_BACK:
+        case GGML_OP_IM2COL_3D:
         case GGML_OP_CONV_2D:
         case GGML_OP_CONV_3D:
         case GGML_OP_CONV_2D_DW:
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 8c1f794885..0bb767e01a 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -7027,6 +7027,209 @@ void ggml_compute_forward_im2col_back_f32(
     }
 }
 
+
+// ggml_compute_forward_im2col_3d_f16
+// src0: kernel [OC*IC, KD, KH, KW]
+// src1: image [N*IC, ID, IH, IW]
+// dst:  result [N*OD, OH, OW, IC * KD * KH * KW]
+static void ggml_compute_forward_im2col_3d_f16(
+        const ggml_compute_params * params,
+              ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t s2 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t p1 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t p2 = ((const int32_t *)(dst->op_params))[5];
+    const int32_t d0 = ((const int32_t *)(dst->op_params))[6];
+    const int32_t d1 = ((const int32_t *)(dst->op_params))[7];
+    const int32_t d2 = ((const int32_t *)(dst->op_params))[8];
+    const int32_t IC = ((const int32_t *)(dst->op_params))[9];
+
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t N  = ne13 / IC;
+    const int64_t ID = ne12;
+    const int64_t IH = ne11;
+    const int64_t IW = ne10;
+
+    const int64_t OC = ne03 / IC;
+    GGML_UNUSED(OC);
+    const int64_t KD = ne02;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+
+    const int64_t OD = ne3 / N;
+    const int64_t OH = ne2;
+    const int64_t OW = ne1;
+    const int64_t OH_OW = OH*OW;
+    const int64_t KD_KH_KW = KD*KH*KW;
+    const int64_t KH_KW = KH*KW;
+    const int64_t IC_KD_KH_KW = IC*KD*KH*KW;
+
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // im2col: [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+    {
+        ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data;
+
+        for (int64_t in = 0; in < N; in++) {
+            for (int64_t iod = 0; iod < OD; iod++) {
+                for (int64_t ioh = 0; ioh < OH; ioh++) {
+                    for (int64_t iow = 0; iow < OW; iow++) {
+                        for (int64_t iic = ith; iic < IC; iic += nth) {
+
+                            // micro kernel
+                            ggml_fp16_t * dst_data = wdata + (in*OD*OH_OW + iod*OH_OW + ioh*OW + iow)*IC_KD_KH_KW; // [IC, KD, KH, KW]
+                            const float * const src_data = (const float *) ((const char *)src1->data + (in*IC + iic)*nb13); // [ID, IH, IW]
+
+                            for (int64_t ikd = 0; ikd < KD; ikd++) {
+                                for (int64_t ikh = 0; ikh < KH; ikh++) {
+                                    for (int64_t ikw = 0; ikw < KW; ikw++) {
+                                        const int64_t iiw = iow*s0 + ikw*d0 - p0;
+                                        const int64_t iih = ioh*s1 + ikh*d1 - p1;
+                                        const int64_t iid = iod*s2 + ikd*d2 - p2;
+
+                                        if (iid < 0 || iid >= ID || iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
+                                            dst_data[iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw] = 0;
+                                        } else {
+                                            const float * const s = (const float *) ((const char *)src_data + iid*nb12 + iih*nb11 + iiw*nb10); // [ID, IH, IW]
+                                            dst_data[iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw] = GGML_CPU_FP32_TO_FP16(*s);
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+// ggml_compute_forward_im2col_3d_f32
+// src0: kernel [OC*IC, KD, KH, KW]
+// src1: image [N*IC, ID, IH, IW]
+// dst:  result [N*OD, OH, OW, IC * KD * KH * KW]
+static void ggml_compute_forward_im2col_3d_f32(
+        const ggml_compute_params * params,
+              ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t s2 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t p1 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t p2 = ((const int32_t *)(dst->op_params))[5];
+    const int32_t d0 = ((const int32_t *)(dst->op_params))[6];
+    const int32_t d1 = ((const int32_t *)(dst->op_params))[7];
+    const int32_t d2 = ((const int32_t *)(dst->op_params))[8];
+    const int32_t IC = ((const int32_t *)(dst->op_params))[9];
+
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t N  = ne13 / IC;
+    const int64_t ID = ne12;
+    const int64_t IH = ne11;
+    const int64_t IW = ne10;
+
+    const int64_t OC = ne03 / IC;
+    GGML_UNUSED(OC);
+    const int64_t KD = ne02;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+
+    const int64_t OD = ne3 / N;
+    const int64_t OH = ne2;
+    const int64_t OW = ne1;
+
+    const int64_t OH_OW = OH*OW;
+    const int64_t KD_KH_KW = KD*KH*KW;
+    const int64_t KH_KW = KH*KW;
+    const int64_t IC_KD_KH_KW = IC*KD*KH*KW;
+
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // im2col: [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+    {
+        float * const wdata = (float *) dst->data;
+
+        for (int64_t in = 0; in < N; in++) {
+            for (int64_t iod = 0; iod < OD; iod++) {
+                for (int64_t ioh = 0; ioh < OH; ioh++) {
+                    for (int64_t iow = 0; iow < OW; iow++) {
+                        for (int64_t iic = ith; iic < IC; iic += nth) {
+
+                            // micro kernel
+                            float * dst_data = wdata + (in*OD*OH_OW + iod*OH_OW + ioh*OW + iow)*IC_KD_KH_KW; // [IC, KD, KH, KW]
+                            const float * const src_data = (const float *) ((const char *)src1->data + (in*IC + iic)*nb13); // [ID, IH, IW]
+
+                            for (int64_t ikd = 0; ikd < KD; ikd++) {
+                                for (int64_t ikh = 0; ikh < KH; ikh++) {
+                                    for (int64_t ikw = 0; ikw < KW; ikw++) {
+                                        const int64_t iiw = iow*s0 + ikw*d0 - p0;
+                                        const int64_t iih = ioh*s1 + ikh*d1 - p1;
+                                        const int64_t iid = iod*s2 + ikd*d2 - p2;
+
+                                        if (iid < 0 || iid >= ID || iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
+                                            dst_data[iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw] = 0;
+                                        } else {
+                                            const float * const s = (const float *) ((const char *)src_data + iid*nb12 + iih*nb11 + iiw*nb10); // [ID, IH, IW]
+                                            dst_data[iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw] = *s;
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+
+void ggml_compute_forward_im2col_3d(
+        const ggml_compute_params * params,
+              ggml_tensor * dst) {
+    switch (dst->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_im2col_3d_f16(params, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_im2col_3d_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params, int64_t m, int64_t n, int64_t k,
                               void * a, void * b, float * c) {
     const ggml_type_traits * traits = ggml_get_type_traits(type);
@@ -8014,6 +8217,15 @@ static void ggml_compute_forward_pad_f32(
     GGML_TENSOR_UNARY_OP_LOCALS
 
     float * dst_ptr = (float *) dst->data;
+    const int32_t lp0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t rp0 = ggml_get_op_params_i32(dst, 1);
+    const int32_t lp1 = ggml_get_op_params_i32(dst, 2);
+    const int32_t rp1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t lp2 = ggml_get_op_params_i32(dst, 4);
+    const int32_t rp2 = ggml_get_op_params_i32(dst, 5);
+    const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
+    const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
+
 
     // TODO: optimize
 
@@ -8022,10 +8234,12 @@ static void ggml_compute_forward_pad_f32(
             for (int64_t i0 = 0; i0 < ne0; ++i0) {
                 for (int64_t i3 = 0; i3 < ne3; ++i3) {
                     const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
-
-                    const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-
-                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                    if ((i0 >= lp0 && i0 < ne0 - rp0) \
+                         && (i1 >= lp1 && i1 < ne1 - rp1) \
+                         && (i2 >= lp2 && i2 < ne2 - rp2) \
+                         && (i3 >= lp3 && i3 < ne3 - rp3)) {
+                        const int64_t src_idx = (i3 - lp3)*nb03 + (i2 - lp2)*nb02 + (i1 - lp1)*nb01 + (i0 - lp0)*nb00;
+                        const float * src_ptr = (const float *)((char *) src0->data + src_idx);
                         dst_ptr[dst_idx] = *src_ptr;
                     } else {
                         dst_ptr[dst_idx] = 0;
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index d0ea83843b..9824a03b45 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -69,6 +69,7 @@ void ggml_compute_forward_clamp(const struct ggml_compute_params * params, struc
 void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_im2col_3d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_3d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index 3ec0e957ab..83d02474f5 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -2,6 +2,8 @@
 #include "dequantize.cuh"
 #include "convert.cuh"
 
+#define MAX_GRIDDIM_Y 65535
+
 template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void k_get_rows(
         const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
@@ -11,32 +13,29 @@ static __global__ void k_get_rows(
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
-    const int i00 = (blockIdx.y * blockDim.x + threadIdx.x)*2;
-    const int i10 =  blockIdx.x;
-    const int i11 =  blockIdx.z / ne12;
-    const int i12 =  blockIdx.z % ne12;
+    for (int64_t i00 = 2*(blockIdx.y*blockDim.x + threadIdx.x); i00 < ne00; i00 += gridDim.y*blockDim.x) {
+        // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
+        const int i10 =  blockIdx.x;
+        const int i11 =  blockIdx.z / ne12;
+        const int i12 =  blockIdx.z % ne12;
 
-    if (i00 >= ne00) {
-        return;
+        const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+        dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+        const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+        const int ib   =  i00/qk;      // block index
+        const int iqs  = (i00%qk)/qr;  // quant index
+        const int iybs = i00 - i00%qk; // dst block start index
+        const int y_offset = qr == 1 ? 1 : qk/2;
+
+        // dequantize
+        float2 v;
+        dequantize_kernel(src0_row, ib, iqs, v);
+
+        dst_row[iybs + iqs + 0]        = ggml_cuda_cast<dst_t>(v.x);
+        dst_row[iybs + iqs + y_offset] = ggml_cuda_cast<dst_t>(v.y);
     }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03;
-
-    const int ib   =  i00/qk;      // block index
-    const int iqs  = (i00%qk)/qr;  // quant index
-    const int iybs = i00 - i00%qk; // dst block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    float2 v;
-    dequantize_kernel(src0_row, ib, iqs, v);
-
-    dst_row[iybs + iqs + 0]        = ggml_cuda_cast<dst_t>(v.x);
-    dst_row[iybs + iqs + y_offset] = ggml_cuda_cast<dst_t>(v.y);
 }
 
 template<typename src0_t, typename dst_t>
@@ -48,22 +47,23 @@ static __global__ void k_get_rows_float(
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
-    const int i00 = blockIdx.y * blockDim.x + threadIdx.x;
-    const int i10 = blockIdx.x;
-    const int i11 = blockIdx.z / ne12;
-    const int i12 = blockIdx.z % ne12;
+    for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) {
+        // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
+        const int i10 = blockIdx.x;
+        const int i11 = blockIdx.z / ne12;
+        const int i12 = blockIdx.z % ne12;
 
-    if (i00 >= ne00) {
-        return;
+        if (i00 >= ne00) {
+            return;
+        }
+
+        const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+        dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+        const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
+
+        dst_row[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
     }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
-
-    dst_row[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
 }
 
 template<typename grad_t, typename dst_t>
@@ -98,7 +98,7 @@ static void get_rows_cuda_q(
         cudaStream_t stream) {
     const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
     const int block_num_y = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
-    const dim3 block_nums(ne10, block_num_y, ne11*ne12);
+    const dim3 block_nums(ne10, MIN(block_num_y, MAX_GRIDDIM_Y), ne11*ne12);
 
     // strides in elements
     // const size_t s0 = nb0 / sizeof(dst_t);
@@ -131,7 +131,7 @@ static void get_rows_cuda_float(
         cudaStream_t stream) {
     const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
     const int block_num_y = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
-    const dim3 block_nums(ne10, block_num_y, ne11*ne12);
+    const dim3 block_nums(ne10, MIN(block_num_y, MAX_GRIDDIM_Y), ne11*ne12);
 
     // strides in elements
     // const size_t s0 = nb0 / sizeof(dst_t);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index e06f95f081..0c01eb6fa8 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2452,6 +2452,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
+        case GGML_OP_IM2COL_3D:
+            ggml_cuda_op_im2col_3d(ctx, dst);
+            break;
         case GGML_OP_CONV_2D:
             ggml_cuda_op_conv2d(ctx, dst);
             break;
@@ -3559,6 +3562,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
         }
         case GGML_OP_IM2COL:
+        case GGML_OP_IM2COL_3D:
         case GGML_OP_CONV_2D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_CONV_TRANSPOSE_2D:
diff --git a/ggml/src/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu
index 16bb9bec97..7737d6a5d5 100644
--- a/ggml/src/ggml-cuda/im2col.cu
+++ b/ggml/src/ggml-cuda/im2col.cu
@@ -112,3 +112,132 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
     }
 }
+
+// [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+template <typename T>
+static  __global__ void im2col_3d_kernel(
+        const float * src, T * dst,
+        int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
+        int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+        int64_t OH_OW, int64_t KD_KH_KW, int64_t ID_IH_IW, int64_t KH_KW, int64_t IH_IW, int64_t IC_ID_IH_IW,
+        int64_t IC_KD_KH_KW, int64_t OW_KD_KH_KW, int64_t OD_OH_OW_IC_KD_KH_KW, int64_t OH_OW_IC_KD_KH_KW,
+        int64_t OW_IC_KD_KH_KW, int64_t N_OD_OH, int64_t OD_OH,
+        int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2) {
+    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i >= IC_KD_KH_KW) {
+        return;
+    }
+
+    const int64_t iic = i / KD_KH_KW;
+    const int64_t ikd = (i - iic * KD_KH_KW) / KH_KW;
+    const int64_t ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / KW;
+    const int64_t ikw = i % KW;
+
+    const int64_t  iow = blockIdx.y;
+    for (int64_t iz = blockIdx.z; iz < N_OD_OH; iz+=MAX_GRIDDIM_Z) {
+        const int64_t in  = iz / OD_OH;
+        const int64_t iod = (iz - in*OD_OH) / OH;
+        const int64_t ioh = iz % OH;
+
+        const int64_t iiw = iow * s0 + ikw * d0 - p0;
+        const int64_t iih = ioh * s1 + ikh * d1 - p1;
+        const int64_t iid = iod * s2 + ikd * d2 - p2;
+
+        const int64_t offset_dst = in*OD_OH_OW_IC_KD_KH_KW + iod*OH_OW_IC_KD_KH_KW + ioh*OW_IC_KD_KH_KW + iow*IC_KD_KH_KW + iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw;
+
+        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
+            dst[offset_dst] = 0.0f;
+        } else {
+            const int64_t offset_src = in*IC_ID_IH_IW + iic*ID_IH_IW + iid*IH_IW + iih*IW + iiw;
+            dst[offset_dst] = src[offset_src];
+        }
+    }
+}
+
+// [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+template <typename T>
+static void im2col_3d_cuda(const float * src, T* dst,
+    int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
+    int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
+    const int64_t OH_OW = OH*OW;
+    const int64_t KD_KH_KW = KD*KH*KW;
+    const int64_t ID_IH_IW = ID*IH*IW;
+    const int64_t KH_KW = KH*KW;
+    const int64_t IH_IW = IH*IW;
+    const int64_t IC_KD_KH_KW = IC*KD*KH*KW;
+    const int64_t OW_KD_KH_KW = OW*KD*KH*KW;
+    const int64_t N_OD_OH = N*OD*OH;
+    const int64_t OD_OH = OD*OH;
+    const int64_t IC_ID_IH_IW = IC*ID*IH*IW;
+    const int64_t OD_OH_OW_IC_KD_KH_KW = OD*OH*OW*IC*KD*KH*KW;
+    const int64_t OH_OW_IC_KD_KH_KW = OH*OW*IC*KD*KH*KW;
+    const int64_t OW_IC_KD_KH_KW = OW*IC*KD*KH*KW;
+    const int64_t num_blocks = (IC_KD_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    dim3 block_nums(num_blocks, OW, MIN(N_OD_OH, MAX_GRIDDIM_Z));
+    im2col_3d_kernel<<<block_nums, MIN(IC_KD_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                                                                                           OH_OW, KD_KH_KW, ID_IH_IW, KH_KW, IH_IW, IC_ID_IH_IW,
+                                                                                           IC_KD_KH_KW, OW_KD_KH_KW, OD_OH_OW_IC_KD_KH_KW,
+                                                                                           OH_OW_IC_KD_KH_KW, OW_IC_KD_KH_KW, N_OD_OH, OD_OH,
+                                                                                           s0, s1, s2, p0, p1, p2, d0, d1, d2);
+}
+
+static void im2col_3d_cuda_f16(const float * src, half * dst,
+    int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
+    int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
+
+    im2col_3d_cuda<half>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+}
+
+static void im2col_3d_cuda_f32(const float * src, float * dst,
+    int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
+    int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
+
+    im2col_3d_cuda<float>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+}
+
+void ggml_cuda_op_im2col_3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t s2 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t p1 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t p2 = ((const int32_t *)(dst->op_params))[5];
+    const int32_t d0 = ((const int32_t *)(dst->op_params))[6];
+    const int32_t d1 = ((const int32_t *)(dst->op_params))[7];
+    const int32_t d2 = ((const int32_t *)(dst->op_params))[8];
+    const int32_t IC = ((const int32_t *)(dst->op_params))[9];
+
+    const int64_t N  = ne13 / IC;
+    const int64_t ID = ne12;
+    const int64_t IH = ne11;
+    const int64_t IW = ne10;
+
+    const int64_t OC = ne03 / IC;
+    const int64_t KD = ne02;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+
+    const int64_t OD = ne3 / N;
+    const int64_t OH = ne2;
+    const int64_t OW = ne1;
+
+    if(dst->type == GGML_TYPE_F16) {
+        im2col_3d_cuda_f16(src1_d, (half *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+    } else {
+        im2col_3d_cuda_f32(src1_d, (float *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+    }
+}
diff --git a/ggml/src/ggml-cuda/im2col.cuh b/ggml/src/ggml-cuda/im2col.cuh
index 1ce8fae4d9..2da1223d63 100644
--- a/ggml/src/ggml-cuda/im2col.cuh
+++ b/ggml/src/ggml-cuda/im2col.cuh
@@ -3,3 +3,4 @@
 #define CUDA_IM2COL_BLOCK_SIZE 256
 
 void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_im2col_3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu
index 77432b0468..29aef33c1a 100644
--- a/ggml/src/ggml-cuda/pad.cu
+++ b/ggml/src/ggml-cuda/pad.cu
@@ -1,36 +1,50 @@
 #include "pad.cuh"
 
-static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
-    // blockIdx.z: idx of ne2*ne3, aka ne02*ne03
-    // blockIdx.y: idx of ne1
-    // blockIDx.x: idx of ne0 / BLOCK_SIZE
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
+static __global__ void pad_f32(const float * src, float * dst,
+                               const int lp0, const int rp0, const int lp1, const int rp1,
+                               const int lp2, const int rp2, const int lp3, const int rp3,
+                               const int ne0, const int ne1, const int ne2, const int ne3) {
+    // blockIdx.z: i3*ne2+i2
+    // blockIdx.y: i1
+    // blockIDx.x: i0 / CUDA_PAD_BLOCK_SIZE
+    // gridDim.y:  ne1
+    int i0 = threadIdx.x + blockIdx.x * blockDim.x;
+    int i1 = blockIdx.y;
+    int i2 = blockIdx.z % ne2;
+    int i3 = blockIdx.z / ne2;
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
         return;
     }
 
     // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (nidx < ne00 && blockIdx.y < (unsigned)ne01 && blockIdx.z < (unsigned)(ne02*ne03)) {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne00 +
-            blockIdx.z * ne00 * ne01;
-        dst[offset_dst] = x[offset_src];
+    const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+    if ((i0 >= lp0 && i0 < ne0 - rp0) &&
+        (i1 >= lp1 && i1 < ne1 - rp1) &&
+        (i2 >= lp2 && i2 < ne2 - rp2) &&
+        (i3 >= lp3 && i3 < ne3 - rp3)) {
+        const int64_t i00 = i0 - lp0;
+        const int64_t i01 = i1 - lp1;
+        const int64_t i02 = i2 - lp2;
+        const int64_t i03 = i3 - lp3;
+        const int64_t ne02 = ne2 - lp2 - rp2;
+        const int64_t ne01 = ne1 - lp1 - rp1;
+        const int64_t ne00 = ne0 - lp0 - rp0;
+
+        const int64_t src_idx = i03*(ne00*ne01*ne02) + i02*(ne00*ne01) + i01*ne00 + i00;
+
+        dst[dst_idx] = src[src_idx];
     } else {
-        dst[offset_dst] = 0.0f;
+        dst[dst_idx] = 0.0f;
     }
 }
 
-static void pad_f32_cuda(const float * x, float * dst,
-    const int ne00, const int ne01, const int ne02, const int ne03,
+static void pad_f32_cuda(const float * src, float * dst,
+    const int lp0, const int rp0, const int lp1, const int rp1,
+    const int lp2, const int rp2, const int lp3, const int rp3,
     const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
     int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
     dim3 gridDim(num_blocks, ne1, ne2*ne3);
-    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
+    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, ne2, ne3);
 }
 
 void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -41,9 +55,18 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int32_t lp0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t rp0 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t lp1 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t rp1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t lp2 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t rp2 = ((const int32_t*)(dst->op_params))[5];
+    const int32_t lp3 = ((const int32_t*)(dst->op_params))[6];
+    const int32_t rp3 = ((const int32_t*)(dst->op_params))[7];
 
     pad_f32_cuda(src0_d, dst_d,
-        src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
-        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
+                 lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
+                 dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
 }
diff --git a/ggml/src/ggml-cuda/scale.cu b/ggml/src/ggml-cuda/scale.cu
index 2ee9e58899..0ddeff6a17 100644
--- a/ggml/src/ggml-cuda/scale.cu
+++ b/ggml/src/ggml-cuda/scale.cu
@@ -1,18 +1,19 @@
 #include "scale.cuh"
 
-static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+#define MAX_GRIDDIM_X 0x7FFFFFFF
 
-    if (i >= k) {
-        return;
+static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int64_t nelements) {
+    int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
+    int64_t stride = (int64_t)blockDim.x * (int64_t)gridDim.x;
+
+    for (int64_t i = tid; i < nelements; i += stride) {
+        dst[i] = scale * x[i] + bias;
     }
-
-    dst[i] = scale * x[i] + bias;
 }
 
-static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
-    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, k);
+static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int64_t nelements, cudaStream_t stream) {
+    const int64_t num_blocks = (nelements + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
+    scale_f32<<<MIN(MAX_GRIDDIM_X, num_blocks), CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, nelements);
 }
 
 void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 3d16a1dcd4..9b4006d987 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -1886,7 +1886,10 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_UPSCALE:
             return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
         case GGML_OP_POOL_2D:
+            return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_PAD:
+            return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) &&
+                   (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index fc54c90f7b..727163b7fd 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -2701,7 +2701,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
         case GGML_OP_PAD:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
-                   op->src[0]->ne[3] == 1 && op->ne[3] == 1;
+                   op->src[0]->ne[3] == 1 && op->ne[3] == 1 &&
+                   (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) &&
+                   (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_UPSCALE:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         case GGML_OP_CONV_2D:
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 18ff4e0b0c..877fbf7e86 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4398,7 +4398,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_POOL_2D:
         case GGML_OP_ACC:
+            return true;
         case GGML_OP_PAD:
+            return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) &&
+                   (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_RWKV_WKV6:
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 3cd5af1cdc..cd1c66ba7b 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -12076,7 +12076,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_ACC:
         case GGML_OP_CONCAT:
         case GGML_OP_SCALE:
+            return true;
         case GGML_OP_PAD:
+            return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) &&
+                   (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_ROLL:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index d76ea58f78..f35c337952 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -974,6 +974,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CONV_TRANSPOSE_1D",
     "IM2COL",
     "IM2COL_BACK",
+    "IM2COL_3D",
     "CONV_2D",
     "CONV_3D",
     "CONV_2D_DW",
@@ -1018,7 +1019,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
+static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1077,6 +1078,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "conv_transpose_1d(x)",
     "im2col(x)",
     "im2col_back(x)",
+    "im2col_3d(x)",
     "conv_2d(x)",
     "conv_3d(x)",
     "conv_2d_dw(x)",
@@ -1121,7 +1123,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
+static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4361,6 +4363,91 @@ struct ggml_tensor * ggml_conv_2d(
     return result;
 }
 
+// a: [OC*IC, KD, KH, KW]
+// b: [N*IC, ID, IH, IW]
+// result: [N*OD, OH, OW, IC * KD * KH * KW]
+struct ggml_tensor * ggml_im2col_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int64_t               IC,
+        int                   s0, // stride width
+        int                   s1, // stride height
+        int                   s2, // stride depth
+        int                   p0, // padding width
+        int                   p1, // padding height
+        int                   p2, // padding depth
+        int                   d0, // dilation width
+        int                   d1, // dilation height
+        int                   d2, // dilation depth
+        enum ggml_type        dst_type) {
+    const int64_t N = b->ne[3] / IC;
+    const int64_t ID = b->ne[2];
+    const int64_t IH = b->ne[1];
+    const int64_t IW = b->ne[0];
+
+    const int64_t OC = a->ne[3] / IC;
+    UNUSED(OC);
+    const int64_t KD = a->ne[2];
+    const int64_t KH = a->ne[1];
+    const int64_t KW = a->ne[0];
+    const int64_t OD = ggml_calc_conv_output_size(ID, KD, s2, p2, d2);
+    const int64_t OH = ggml_calc_conv_output_size(IH, KH, s1, p1, d1);
+    const int64_t OW = ggml_calc_conv_output_size(IW, KW, s0, p0, d0);
+
+    GGML_ASSERT((OD > 0)  && "b too small compared to a");
+    GGML_ASSERT((OH > 0)  && "b too small compared to a");
+    GGML_ASSERT((OW > 0)  && "b too small compared to a");
+
+
+    const int64_t ne[4] = {KW*KH*KD*IC, OW, OH, OD*N};
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne);
+    int32_t params[] = { s0, s1, s2, p0, p1, p2, d0, d1, d2, (int32_t)IC};
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op     = GGML_OP_IM2COL_3D;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
+// a: [OC*IC, KD, KH, KW]
+// b: [N*IC, ID, IH, IW]
+// result: [N*OC, OD, OH, OW]
+struct ggml_tensor * ggml_conv_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int64_t               IC,
+        int                   s0, // stride width
+        int                   s1, // stride height
+        int                   s2, // stride depth
+        int                   p0, // padding width
+        int                   p1, // padding height
+        int                   p2, // padding depth
+        int                   d0, // dilation width
+        int                   d1, // dilation height
+        int                   d2  // dilation depth
+        ) {
+    struct ggml_tensor * im2col = ggml_im2col_3d(ctx, a, b, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, a->type); // [N*OD, OH, OW, IC * KD * KH * KW]
+
+    int64_t OC = a->ne[3] / IC;
+    int64_t N = b->ne[3] / IC;
+    struct ggml_tensor * result =
+        ggml_mul_mat(ctx,
+                ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N*OD, OH, OW, IC * KD * KH * KW] => [N*OD*OH*OW, IC * KD * KH * KW]
+                ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2] * IC), OC));                          // [OC*IC, KD, KH, KW] => [OC, IC * KD * KH * KW]
+
+    int64_t OD = im2col->ne[3] / N;
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1]*im2col->ne[2], OD, N, OC); // [OC, N*OD*OH*OW] => [OC, N, OD, OH*OW]
+    result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OD, OH*OW]
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], OD, OC * N); // [N*OC, OD, OH, OW]
+
+    return result;
+}
+
 // ggml_conv_2d_sk_p0
 
 struct ggml_tensor * ggml_conv_2d_sk_p0(
@@ -4482,9 +4569,9 @@ struct ggml_tensor * ggml_conv_2d_direct(
     return result;
 }
 
-// ggml_conv_3d
+// ggml_conv_3d_direct
 
-struct ggml_tensor * ggml_conv_3d(
+struct ggml_tensor * ggml_conv_3d_direct(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
@@ -4710,11 +4797,36 @@ struct ggml_tensor * ggml_pad(
         int                   p1,
         int                   p2,
         int                   p3) {
+    return ggml_pad_ext(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3);
+}
+
+struct ggml_tensor * ggml_pad_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  lp0,
+            int                  rp0,
+            int                  lp1,
+            int                  rp1,
+            int                  lp2,
+            int                  rp2,
+            int                  lp3,
+            int                  rp3
+            ) {
     struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
-            a->ne[0] + p0,
-            a->ne[1] + p1,
-            a->ne[2] + p2,
-            a->ne[3] + p3);
+            a->ne[0] + lp0 + rp0,
+            a->ne[1] + lp1 + rp1,
+            a->ne[2] + lp2 + rp2,
+            a->ne[3] + lp3 + rp3);
+
+    ggml_set_op_params_i32(result, 0, lp0);
+    ggml_set_op_params_i32(result, 1, rp0);
+    ggml_set_op_params_i32(result, 2, lp1);
+    ggml_set_op_params_i32(result, 3, rp1);
+    ggml_set_op_params_i32(result, 4, lp2);
+    ggml_set_op_params_i32(result, 5, rp2);
+    ggml_set_op_params_i32(result, 6, lp3);
+    ggml_set_op_params_i32(result, 7, rp3);
+
 
     result->op     = GGML_OP_PAD;
     result->src[0] = a;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 3a58621094..89b812f1ab 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -297,6 +297,8 @@ static std::string var_to_str(ggml_scale_mode mode) {
 #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k)
 #define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l)
 #define VARS_TO_STR13(a, b, c, d, e, f, g, h, i, j, k, l, m) VAR_TO_STR(a) + "," + VARS_TO_STR12(b, c, d, e, f, g, h, i, j, k, l, m)
+#define VARS_TO_STR14(a, b, c, d, e, f, g, h, i, j, k, l, m, n) VAR_TO_STR(a) + "," + VARS_TO_STR13(b, c, d, e, f, g, h, i, j, k, l, m, n)
+#define VARS_TO_STR15(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o) VAR_TO_STR(a) + "," + VARS_TO_STR14(b, c, d, e, f, g, h, i, j, k, l, m, n, o)
 
 #ifdef GGML_USE_SYCL
 static bool inline _isinf(float f) {
@@ -4023,6 +4025,56 @@ struct test_im2col : public test_case {
     }
 };
 
+// GGML_OP_IM2COL_3D
+struct test_im2col_3d : public test_case {
+    const ggml_type type_input;
+    const ggml_type type_kernel;
+    const ggml_type dst_type;
+    const std::array<int64_t, 4> ne_input;
+    const std::array<int64_t, 4> ne_kernel;
+    // stride
+    const int s0;
+    const int s1;
+    const int s2;
+    // padding
+    const int p0;
+    const int p1;
+    const int p2;
+    // dilation
+    const int d0;
+    const int d1;
+    const int d2;
+
+    const int64_t IC;
+
+    std::string vars() override {
+        return VARS_TO_STR15(type_input, type_kernel, dst_type, ne_input, ne_kernel, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2);
+    }
+
+    test_im2col_3d(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32,
+                std::array<int64_t, 4> ne_input = {10, 10, 10, 9}, // [OC*IC, KD, KH, KW]
+                std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [N*IC, ID, IH, IW]
+                int64_t IC = 3,
+                int s0 = 1, int s1 = 1, int s2 = 1,
+                int p0 = 1, int p1 = 1, int p2 = 1,
+                int d0 = 1, int d1 = 1, int d2 = 1)
+        : type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), s2(s2), p0(p0), p1(p1), p2(p2), d0(d0), d1(d1), d2(d2), IC(IC) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
+        ggml_set_param(input);
+        ggml_set_name(input, "input");
+
+        ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
+        ggml_set_name(kernel, "kernel");
+
+        ggml_tensor * out = ggml_im2col_3d(ctx, kernel, input, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, dst_type);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // CONV_2D
 struct test_conv_2d : public test_case {
     const std::array<int64_t, 4> ne_input;
@@ -4221,7 +4273,7 @@ struct test_conv_3d : public test_case {
         ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel);
         ggml_set_name(kernel, "kernel");
 
-        ggml_tensor * out = ggml_conv_3d(ctx, kernel, input, s0, s1, s2, p0, p1, p2, d0, d1, d2, (int)IC, (int)N, (int)OC);
+        ggml_tensor * out = ggml_conv_3d_direct(ctx, kernel, input, s0, s1, s2, p0, p1, p2, d0, d1, d2, (int)IC, (int)N, (int)OC);
         ggml_set_name(out, "out");
         return out;
     }
@@ -4640,6 +4692,39 @@ struct test_pad : public test_case {
     }
 };
 
+struct test_pad_ext : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const int lp0;
+    const int rp0;
+    const int lp1;
+    const int rp1;
+    const int lp2;
+    const int rp2;
+    const int lp3;
+    const int rp3;
+
+    std::string vars() override {
+        return VARS_TO_STR10(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    }
+
+    test_pad_ext(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {512, 512, 3, 1},
+            int lp0 = 1, int rp0 = 1, int lp1 = 1, int rp1 = 1,
+            int lp2 = 1, int rp2 = 1, int lp3 = 1, int rp3 = 1)
+        : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // GGML_OP_PAD_REFLECT_1D
 struct test_pad_reflect_1d : public test_case {
     const ggml_type type;
@@ -5623,6 +5708,32 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2560}, {3, 3, 2, 2560}, 1, 1, 1, 1, 1, 1, true));
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {5, 5, 1, 32}, {3, 4, 1, 32}, 1, 1, 0, 0, 1, 1, true));
 
+    // im2col 3D
+    test_cases.emplace_back(new test_im2col_3d(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32));
+    test_cases.emplace_back(new test_im2col_3d(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
+    test_cases.emplace_back(new test_im2col_3d(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+    for (int s0 : {1, 3}) {
+        for (int s1 : {1, 3}) {
+            for (int s2 : {1, 3}) {
+                for (int p0 : {0, 3}) {
+                    for (int p1 : {0, 3}) {
+                        for (int p2 : {0, 3}) {
+                            for (int d0 : {1, 3}) {
+                                for (int d1 : {1, 3}) {
+                                    for (int d2 : {1, 3}) {
+                                        test_cases.emplace_back(new test_im2col_3d(
+                                            GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 20, 10, 3}, {3, 3, 3, 3},
+                                            3, s0, s1, s2, p0, p1, p2, d0, d1, d2));
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
 // Conv_2D test cases
 #ifdef DETAILED_TESTS
     // Probably we do not have enough time to execute these in the pipeline.
@@ -6340,6 +6451,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {9, 9, 1280, 1}));
     test_cases.emplace_back(new test_acc());
     test_cases.emplace_back(new test_pad());
+    test_cases.emplace_back(new test_pad_ext());
     test_cases.emplace_back(new test_pad_reflect_1d());
     test_cases.emplace_back(new test_roll());
     test_cases.emplace_back(new test_arange());

From badb80cadbc40e047b30c43611aba575fc8d6845 Mon Sep 17 00:00:00 2001
From: Eric Curtin <ecurtin@redhat.com>
Date: Thu, 4 Sep 2025 10:49:44 +0100
Subject: [PATCH 282/287] Document the new max GPU layers default in help
 (#15771)

This is a key change, just letting users know.

Signed-off-by: Eric Curtin <ericcurtin17@gmail.com>
---
 common/arg.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index fcee0c4470..7507c81155 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2466,7 +2466,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
-        "number of layers to store in VRAM",
+        string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
         [](common_params & params, int value) {
             params.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {

From a68d9144262f1d0ef4f6ba7ad4a7e73e977ba78c Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 4 Sep 2025 11:50:23 +0200
Subject: [PATCH 283/287] server: add exceed_context_size_error type (#15780)

* server: add exceed_context_size_error type

* change error code to 400
---
 tools/server/server.cpp                       | 36 ++++++++++++++-----
 .../server/tests/unit/test_chat_completion.py | 17 +++++++++
 2 files changed, 45 insertions(+), 8 deletions(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index e0302e2f2f..44487eca59 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -86,6 +86,7 @@ enum error_type {
     ERROR_TYPE_PERMISSION,
     ERROR_TYPE_UNAVAILABLE, // custom error
     ERROR_TYPE_NOT_SUPPORTED, // custom error
+    ERROR_TYPE_EXCEED_CONTEXT_SIZE, // custom error
 };
 
 static bool server_task_type_need_embd(server_task_type task_type) {
@@ -1224,6 +1225,10 @@ static json format_error_response(const std::string & message, const enum error_
             type_str = "unavailable_error";
             code = 503;
             break;
+        case ERROR_TYPE_EXCEED_CONTEXT_SIZE:
+            type_str = "exceed_context_size_error";
+            code = 400;
+            break;
     }
     return json {
         {"code", code},
@@ -1237,12 +1242,21 @@ struct server_task_result_error : server_task_result {
     error_type err_type = ERROR_TYPE_SERVER;
     std::string err_msg;
 
+    // for ERROR_TYPE_EXCEED_CONTEXT_SIZE
+    int32_t n_prompt_tokens = 0;
+    int32_t n_ctx           = 0;
+
     virtual bool is_error() override {
         return true;
     }
 
     virtual json to_json() override {
-        return format_error_response(err_msg, err_type);
+        json res = format_error_response(err_msg, err_type);
+        if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
+            res["n_prompt_tokens"] = n_prompt_tokens;
+            res["n_ctx"]           = n_ctx;
+        }
+        return res;
     }
 };
 
@@ -2605,16 +2619,22 @@ struct server_context {
     }
 
     void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
-        send_error(slot.id_task, error, type);
+        send_error(slot.id_task, error, type, slot.n_prompt_tokens, slot.n_ctx);
     }
 
-    void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+    void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER, const int32_t n_prompt_tokens = 0, const int32_t n_ctx = 0) {
         SRV_ERR("task id = %d, error: %s\n", id_task, error.c_str());
 
+        if (type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
+            GGML_ASSERT(n_ctx > 0 && n_prompt_tokens > 0);
+        }
+
         auto res = std::make_unique<server_task_result_error>();
-        res->id       = id_task;
-        res->err_type = type;
-        res->err_msg  = error;
+        res->id              = id_task;
+        res->err_type        = type;
+        res->err_msg         = error;
+        res->n_prompt_tokens = n_prompt_tokens;
+        res->n_ctx           = n_ctx;
 
         queue_results.send(std::move(res));
     }
@@ -3286,7 +3306,7 @@ struct server_context {
 
                             if (slot.n_prompt_tokens > slot.n_ctx) {
                                 slot.release();
-                                send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_SERVER);
+                                send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                 continue;
                             }
                         } else {
@@ -3296,7 +3316,7 @@ struct server_context {
                                 //       context shift should be applied only during the generation phase
                                 if (slot.n_prompt_tokens >= slot.n_ctx) {
                                     slot.release();
-                                    send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_INVALID_REQUEST);
+                                    send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                     continue;
                                 }
                             }
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index 509c024b75..22dbfdd9b4 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -385,3 +385,20 @@ def test_logit_bias():
     output_text = res.choices[0].message.content
     assert output_text
     assert all(output_text.find(" " + tok + " ") == -1 for tok in exclude)
+
+def test_context_size_exceeded():
+    global server
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "messages": [
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ] * 100, # make the prompt too long
+    })
+    assert res.status_code == 400
+    assert "error" in res.body
+    assert res.body["error"]["type"] == "exceed_context_size_error"
+    assert res.body["error"]["n_prompt_tokens"] > 0
+    assert server.n_ctx is not None
+    assert server.n_slots is not None
+    assert res.body["error"]["n_ctx"] == server.n_ctx // server.n_slots

From c1c354e44c06d259679bb5bb7a8fa9f0b28480e4 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Thu, 4 Sep 2025 20:20:14 +0800
Subject: [PATCH 284/287] CANN: Refactor ND to NZ workspace to be per-device
 (#15763)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CANN:Refactor ND to NZ workspace to be per-device in Ascend backend

- Replaced the previous single global ND→NZ workspace with a per-device
  cache using unordered_map keyed by device ID.
- Functions `release_nz_workspace`, `relloc_nz_workspace`, and
  `get_nz_workspace` now manage workspace independently for each device,
  preventing memory conflicts in multi-device / pipeline parallel scenarios.
- This change fixes potential precision issues caused by workspace
  overwrites when multiple devices perform ND→NZ conversions concurrently.

Co-authored-by: hipudding <huafengchun@gmail.com>

* refactor

Signed-off-by: noemotiovon <757486878@qq.com>

* rename

Signed-off-by: noemotiovon <757486878@qq.com>

* fix review comments

Signed-off-by: noemotiovon <757486878@qq.com>

---------

Signed-off-by: noemotiovon <757486878@qq.com>
Co-authored-by: hipudding <huafengchun@gmail.com>
---
 ggml/src/ggml-cann/ggml-cann.cpp | 83 +++++++++++++++++++++++---------
 1 file changed, 60 insertions(+), 23 deletions(-)

diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 1aa2913a61..756ad8dfad 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1116,30 +1116,65 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor(
     return GGML_STATUS_SUCCESS;
 }
 
-// ND to NZ Workspace Cache Management. Thread-safety: Not guaranteed
-namespace {
-    void* g_nz_workspace = nullptr;
-    size_t g_nz_workspace_allocated = 0;
+/**
+ * @brief Workspace for caching NZ buffers per device.
+ *
+ * This struct manages a device buffer used in NZ computations. It supports
+ * allocation, reallocation, and clearing of cached memory. The struct is
+ * designed to be used with a global array, one per device.
+ */
+struct ggml_cann_nz_workspace {
+    void*  ptr;       // Pointer to allocated device buffer
+    size_t allocated; // Size of currently allocated buffer in bytes
 
-    void release_nz_workspace() {
-        if (g_nz_workspace) {
-            aclrtFree(g_nz_workspace);
-            g_nz_workspace = nullptr;
-            g_nz_workspace_allocated = 0;
+    /**
+     * @brief Constructor. Initializes the workspace with no allocated memory.
+     */
+    ggml_cann_nz_workspace() : ptr(nullptr), allocated(0) {}
+
+    /**
+     * @brief Free cached memory and reset the workspace.
+     *
+     * If a buffer has been allocated, this function releases it using
+     * aclrtFree and resets internal state.
+     */
+    void clear() {
+        if (ptr) {
+            ACL_CHECK(aclrtFree(ptr));
+            ptr = nullptr;
+            allocated = 0;
         }
     }
 
-    void relloc_nz_workspace(size_t new_size) {
-        if (new_size > g_nz_workspace_allocated) {
-        if (g_nz_workspace) {
-            aclrtFree(g_nz_workspace);
-            g_nz_workspace = nullptr;
+    /**
+     * @brief Allocate or reallocate the workspace buffer.
+     *
+     * If the requested size is larger than the currently allocated size,
+     * the old buffer will be freed and a new buffer of the requested size
+     * will be allocated on the device.
+     *
+     * @param new_size Size in bytes to allocate for the workspace.
+     */
+    void realloc(size_t new_size) {
+        if (new_size > allocated) {
+            clear();
+            ACL_CHECK(aclrtMalloc(&ptr, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
+            allocated = new_size;
         }
-        ACL_CHECK(aclrtMalloc(&g_nz_workspace, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
-        g_nz_workspace_allocated = new_size;
     }
-    }
-}
+
+    /**
+     * @brief Get the device buffer pointer.
+     *
+     * @return Pointer to the allocated buffer, or nullptr if not allocated.
+     */
+    void* get() const { return ptr; }
+};
+
+/**
+ * @brief Global array of NZ workspaces, one per device.
+ */
+static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES];
 
 /**
  * @brief Convert tensor weights to NZ format using Ascend CANN API.
@@ -1149,13 +1184,13 @@ namespace {
  * improve performance on certain hardware.
  *
  * @param tensor Pointer to the input ggml_tensor containing the weights.
- * @param data Pointer to the raw data buffer for the tensor weights.
  * @param offset Byte offset within the tensor data buffer where weights start.
+ * @param device device id.
  *
  * @note The workspace buffer used in this function is managed globally and reused
  *       across calls. This reduces overhead from repeated memory allocation and deallocation.
  */
-static void weight_format_to_nz(ggml_tensor *tensor, size_t offset) {
+static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) {
     aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
                                     tensor->nb, 2, ACL_FORMAT_ND, offset);
     uint64_t workspaceSize = 0;
@@ -1165,7 +1200,9 @@ static void weight_format_to_nz(ggml_tensor *tensor, size_t offset) {
     ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed,
                                                     &workspaceSize, &executor));
     // Avoid frequent malloc/free of the workspace.
-    relloc_nz_workspace(workspaceSize);
+    g_nz_workspaces[device].realloc(workspaceSize);
+
+    void* g_nz_workspace = g_nz_workspaces[device].get();
 
     ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr));
     ACL_CHECK(aclDestroyTensor(weightTransposed));
@@ -1203,7 +1240,7 @@ static void ggml_backend_cann_buffer_set_tensor(
         if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
             GGML_ASSERT(tensor->ne[2] == 1);
             GGML_ASSERT(tensor->ne[3] == 1);
-            weight_format_to_nz(tensor, offset);
+            weight_format_to_nz(tensor, offset, ctx->device);
         }
     } else {
         void *transform_buffer = malloc(size);
@@ -2262,7 +2299,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
     ggml_cann_set_device(cann_ctx->device);
-    release_nz_workspace();
+    g_nz_workspaces[cann_ctx->device].clear();
 
 #ifdef USE_ACL_GRAPH
     bool use_cann_graph = true;

From d1e2adba65208d6de3d7f6f23b00c562ff5bb777 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 4 Sep 2025 15:40:44 +0200
Subject: [PATCH 285/287] llama : set n_outputs to 1 to avoid 0 outputs
 mean-pooling (#15791)

* llama : set n_outputs to 1 to avoid 0 outputs mean-pooling

This commit modifies the llama_context constructor to set n_outputs to
1.

The motivation for this is that when using pooling, and specifically
mean pooling, for embeddings having n_outputs set to 0 can lead to the
following error:
```console
$ build/bin/llama-embedding -m models/nomic-embed-text-1.5-Q4_K_M.gguf \
   --pooling mean -p "Hello, how are you?"
...
llama_context:        CPU  output buffer size =     0.12 MiB
/home/danbev/work/ai/llama.cpp/ggml/src/ggml.c:3023: GGML_ASSERT(ggml_can_mul_mat(a, b)) failed
0x0000743c96d107e3 in __GI___wait4 (pid=292978, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30	../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
30	in ../sysdeps/unix/sysv/linux/wait4.c
196	        waitpid(child_pid, NULL, 0);
230	        ggml_print_backtrace();
3023	    GGML_ASSERT(ggml_can_mul_mat(a, b));
1823	                cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
18983	    llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
1399	    auto * gf = model.build_graph(gparams);
292	            auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
2329	        auto * ctx = new llama_context(*model, params);
913	    llama_context * lctx = llama_init_from_model(model, cparams);
105	    common_init_result llama_init = common_init_from_params(params);
[Inferior 1 (process 292976) detached]
Aborted (core dumped)
```

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* add comment about not reserving graphs with zero outputs

* add assert in graph_reserve to ensure n_outputs >= 1

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 src/llama-context.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 2de6fcf0cb..6b3188be4b 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -285,6 +285,9 @@ llama_context::llama_context(
         const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
 
+        // avoid reserving graphs with zero outputs
+        n_outputs = 1;
+
         LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
 
         // resolve automatic Flash Attention use
@@ -1368,6 +1371,7 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
 
 ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
     LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
+    GGML_ASSERT(n_outputs >= 1);
 
     if (n_tokens % n_seqs != 0) {
         n_tokens = ((n_tokens + (n_seqs - 1)) / n_seqs) * n_seqs; // round to next multiple of n_seqs

From 856ed0947f27b4ec3ad269fceda0402fbab263d3 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 4 Sep 2025 09:53:22 -0600
Subject: [PATCH 286/287] metal : Add template specialization for mul_mm_id w/
 ne20 == 10 (#15799)

Branch: GGMLMetalNE20

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---
 ggml/src/ggml-metal/ggml-metal.m     | 3 +++
 ggml/src/ggml-metal/ggml-metal.metal | 1 +
 2 files changed, 4 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 9b4006d987..c1a0a2bef1 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -407,6 +407,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_10,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,
@@ -1439,6 +1440,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4,       mul_mm_id_map0_f16_ne20_4,       has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6,       mul_mm_id_map0_f16_ne20_6,       has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8,       mul_mm_id_map0_f16_ne20_8,       has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_10,      mul_mm_id_map0_f16_ne20_10,      has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16,      mul_mm_id_map0_f16_ne20_16,      has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F16,               mul_mm_id_f32_f16,               has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F16,               mul_mm_id_f16_f16,               has_simdgroup_mm);
@@ -3979,6 +3981,7 @@ static int ggml_metal_encode_node(
                             case 4:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_4 ].pipeline; break;
                             case 6:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_6 ].pipeline; break;
                             case 8:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_8 ].pipeline; break;
+                            case 10: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_10].pipeline; break;
                             case 16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MAP0_F16_NE20_16].pipeline; break;
                             default: GGML_ABORT("missing specialization for ne20 = %d", (int) ne20);
                         }
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 9c5933d24a..2d56c62674 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -7618,6 +7618,7 @@ template [[host_name("kernel_mul_mm_id_map0_f16_ne20_2" )]] kernel kernel_mul_mm
 template [[host_name("kernel_mul_mm_id_map0_f16_ne20_4" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<4>;
 template [[host_name("kernel_mul_mm_id_map0_f16_ne20_6" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<6>;
 template [[host_name("kernel_mul_mm_id_map0_f16_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>;
+template [[host_name("kernel_mul_mm_id_map0_f16_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>;
 template [[host_name("kernel_mul_mm_id_map0_f16_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>;
 
 template<typename T, typename T4x4, typename simdgroup_T8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>

From fb15d649ed14ab447eeab911e0c9d21e35fb243e Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 4 Sep 2025 18:10:29 +0200
Subject: [PATCH 287/287] llama : add support for EmbeddingGemma 300m (#15798)

This commit add support for the EmbeddingGemma 300m. This model supports
sliding window attention (SWA) and a new swq_type is introduced to
support symmetric SWA masking.

This commit also extracts the code from the function
llama_is_masked_swa in llama-impl.h, so that the logic can be shared
by both llm_graph_input_attn_no_cache::set_input and
llama_kv_cache::set_input_kq_mask.

With this commit the EmbeddingGemma 300m model can be converted to
to GGUF and used with llama.cpp.

Once the model has been uploaded to HuggingFace it can be used like
this:
```console
./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-GGUF:Q8_0
```
---
 convert_hf_to_gguf.py          |   9 ++
 gguf-py/gguf/constants.py      |  20 +++++
 gguf-py/gguf/tensor_mapping.py |  14 +++
 src/llama-arch.cpp             |  22 +++++
 src/llama-arch.h               |   1 +
 src/llama-graph.cpp            |  59 ++++++++++--
 src/llama-graph.h              |   8 ++
 src/llama-hparams.cpp          |  37 ++++++++
 src/llama-hparams.h            |   9 +-
 src/llama-kv-cache-iswa.cpp    |   4 +-
 src/llama-kv-cache.cpp         |  27 +-----
 src/llama-kv-cache.h           |   3 -
 src/llama-memory-hybrid.cpp    |   2 -
 src/llama-memory-hybrid.h      |   1 -
 src/llama-model.cpp            | 159 ++++++++++++++++++++++++++++++++-
 15 files changed, 328 insertions(+), 47 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 908435d7e5..717b8a6595 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -5122,6 +5122,15 @@ class Gemma3Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("Gemma3TextModel")
+class EmbeddingGemma(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+
 @ModelBase.register("Gemma3ForConditionalGeneration")
 class Gemma3VisionModel(MmprojModel):
     def set_gguf_parameters(self):
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 6156d35c2a..c922bacf6e 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -340,6 +340,7 @@ class MODEL_ARCH(IntEnum):
     GEMMA2           = auto()
     GEMMA3           = auto()
     GEMMA3N          = auto()
+    GEMMA_EMBEDDING  = auto()
     STARCODER2       = auto()
     RWKV6            = auto()
     RWKV6QWEN2       = auto()
@@ -674,6 +675,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.GEMMA2:           "gemma2",
     MODEL_ARCH.GEMMA3:           "gemma3",
     MODEL_ARCH.GEMMA3N:          "gemma3n",
+    MODEL_ARCH.GEMMA_EMBEDDING:  "gemma-embedding",
     MODEL_ARCH.STARCODER2:       "starcoder2",
     MODEL_ARCH.RWKV6:            "rwkv6",
     MODEL_ARCH.RWKV6QWEN2:       "rwkv6qwen2",
@@ -1719,6 +1721,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.LAUREL_R,
         MODEL_TENSOR.LAUREL_POST_NORM,
     ],
+    MODEL_ARCH.GEMMA_EMBEDDING: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.STARCODER2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 497f48809f..b0c3d65e95 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -14,6 +14,7 @@ class TensorNameMap:
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414 plamo2 granite-hybrid
+            "embed_tokens",                              # embeddinggemma
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert nomic-bert
             "language_model.embedding.word_embeddings",  # persimmon
@@ -141,6 +142,7 @@ class TensorNameMap:
             "rwkv.blocks.{bid}.ln1",                                # rwkv6
             "model.layers.{bid}.ln1",                               # rwkv7
             "model.layers.{bid}.input_layernorm",                   # llama4
+            "layers.{bid}.input_layernorm",                         # embeddinggemma
             "transformer_encoder.{bid}.attention_norm",             # neobert
             "model.layers.{bid}.operator_norm",                     # lfm2
             "model.transformer.blocks.{bid}.attn_norm",             # llada
@@ -179,6 +181,7 @@ class TensorNameMap:
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
             "model.layers.{bid}.self_attn.q_proj",                       # llama-hf nemotron olmoe olmo2 phimoe
+            "layers.{bid}.self_attn.q_proj",                             # embeddinggemma
             "model.layers.{bid}.self_attn.q_proj_no_perm",               # llama-custom
             "layers.{bid}.attention.wq",                                 # llama-pth
             "encoder.layer.{bid}.attention.self.query",                  # bert
@@ -197,6 +200,7 @@ class TensorNameMap:
         # Attention key
         MODEL_TENSOR.ATTN_K: (
             "model.layers.{bid}.self_attn.k_proj",                     # llama-hf nemotron olmoe olmo2 phimoe
+            "layers.{bid}.self_attn.k_proj",                           # embeddinggemma
             "model.layers.{bid}.self_attn.k_proj_no_perm",             # llama-custom
             "layers.{bid}.attention.wk",                               # llama-pth
             "encoder.layer.{bid}.attention.self.key",                  # bert
@@ -216,6 +220,7 @@ class TensorNameMap:
         # Attention value
         MODEL_TENSOR.ATTN_V: (
             "model.layers.{bid}.self_attn.v_proj",                       # llama-hf nemotron olmoe olmo2 phimoe
+            "layers.{bid}.self_attn.v_proj",                             # embeddinggemma
             "layers.{bid}.attention.wv",                                 # llama-pth
             "encoder.layer.{bid}.attention.self.value",                  # bert
             "transformer.layer.{bid}.attention.v_lin",                   # distillbert
@@ -239,6 +244,7 @@ class TensorNameMap:
             "transformer.h.{bid}.self_attention.dense",                     # falcon
             "h.{bid}.self_attention.dense",                                 # bloom
             "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2 phimoe
+            "layers.{bid}.self_attn.o_proj",                                # embeddinggemma
             "model.layers.{bid}.self_attn.out_proj",                        # lfm2
             "model.layers.{bid}.self_attn.linear_attn",                     # deci
             "layers.{bid}.attention.wo",                                    # llama-pth
@@ -277,6 +283,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.ATTN_POST_NORM: (
             "model.layers.{bid}.post_attention_layernorm",       # gemma2 olmo2    # ge
+            "layers.{bid}.post_attention_layernorm",             # embeddinggemma
             "model.layers.{bid}.post_self_attn_layernorm",       # glm-4-0414
             "model.layers.layers.{bid}.post_mixer_norm.weight",  # plamo2
         ),
@@ -320,12 +327,14 @@ class TensorNameMap:
         # Post feed-forward norm
         MODEL_TENSOR.FFN_PRE_NORM: (
             "model.layers.{bid}.pre_feedforward_layernorm", # gemma2
+            "layers.{bid}.pre_feedforward_layernorm",       # embeddinggemma
             "model.layers.{bid}.pre_ff_layernorm.weight",
         ),
 
         # Post feed-forward norm
         MODEL_TENSOR.FFN_POST_NORM: (
             "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2
+            "layers.{bid}.post_feedforward_layernorm",       # embeddinggemma
             "model.layers.{bid}.post_mlp_layernorm", # glm-4-0414
             "model.layers.layers.{bid}.post_mlp_norm.weight", # plamo2
             "model.layers.{bid}.feed_forward.up_proj",
@@ -362,6 +371,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.dense_h_to_4h",                  # falcon
             "h.{bid}.mlp.dense_h_to_4h",                              # bloom
             "model.layers.{bid}.mlp.up_proj",                         # llama-hf refact nemotron olmo2
+            "layers.{bid}.mlp.up_proj",                               # embeddinggemma
             "layers.{bid}.feed_forward.w3",                           # llama-pth
             "encoder.layer.{bid}.intermediate.dense",                 # bert
             "transformer.layer.{bid}.ffn.lin1",                       # distillbert
@@ -421,6 +431,7 @@ class TensorNameMap:
         # Feed-forward gate
         MODEL_TENSOR.FFN_GATE: (
             "model.layers.{bid}.mlp.gate_proj",           # llama-hf refact olmo2
+            "layers.{bid}.mlp.gate_proj",                 # embeddinggemma
             "layers.{bid}.feed_forward.w1",               # llama-pth
             "transformer.h.{bid}.mlp.w2",                 # qwen
             "transformer.h.{bid}.mlp.c_fc2",              # jais
@@ -461,6 +472,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.dense_4h_to_h",                  # falcon
             "h.{bid}.mlp.dense_4h_to_h",                              # bloom
             "model.layers.{bid}.mlp.down_proj",                       # llama-hf nemotron olmo2
+            "layers.{bid}.mlp.down_proj",                             # embeddinggemma
             "layers.{bid}.feed_forward.w2",                           # llama-pth
             "encoder.layer.{bid}.output.dense",                       # bert
             "transformer.layer.{bid}.ffn.lin2",                       # distillbert
@@ -513,6 +525,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.query_layernorm",                   # hunyuan
             "model.layers.{bid}.self_attn.q_norm",                            # cohere olmoe chameleon olmo2
+            "layers.{bid}.self_attn.q_norm",                                  # embeddinggemma
             "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
             "transformer.layers.{bid}.attn.q_norm",                           # openelm
@@ -525,6 +538,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.key_layernorm",                     # hunyuan
             "model.layers.{bid}.self_attn.k_norm",                            # cohere olmoe chameleon olmo2
+            "layers.{bid}.self_attn.k_norm",                                  # embeddinggemma
             "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
             "transformer.layers.{bid}.attn.k_norm",                           # openelm
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index d5c8477f4a..d92bf2dd85 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -45,6 +45,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_GEMMA2,           "gemma2"           },
     { LLM_ARCH_GEMMA3,           "gemma3"           },
     { LLM_ARCH_GEMMA3N,          "gemma3n"          },
+    { LLM_ARCH_GEMMA_EMBEDDING,  "gemma-embedding"  },
     { LLM_ARCH_STARCODER2,       "starcoder2"       },
     { LLM_ARCH_MAMBA,            "mamba"            },
     { LLM_ARCH_MAMBA2,           "mamba2"           },
@@ -1038,6 +1039,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_LAUREL_POST_NORM,     "blk.%d.laurel_post_norm" },
         },
     },
+    {
+        LLM_ARCH_GEMMA_EMBEDDING,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
     {
         LLM_ARCH_STARCODER2,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 86c119692d..41f377923f 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -49,6 +49,7 @@ enum llm_arch {
     LLM_ARCH_GEMMA2,
     LLM_ARCH_GEMMA3,
     LLM_ARCH_GEMMA3N,
+    LLM_ARCH_GEMMA_EMBEDDING,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
     LLM_ARCH_MAMBA2,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 49ea5da7cb..7ce2960eb3 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -258,6 +258,36 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+static void print_mask(float * data, int64_t n_tokens, int64_t n_kv, int64_t n_swa, llama_swa_type swa_type) {
+    LLAMA_LOG_DEBUG("%s: === Attention mask ===\n", __func__);
+    const char * swa_type_str = (swa_type == LLAMA_SWA_TYPE_NONE) ? "LLAMA_SWA_TYPE_NONE" :
+                          (swa_type == LLAMA_SWA_TYPE_STANDARD) ? "LLAMA_SWA_TYPE_STANDARD" :
+                          (swa_type == LLAMA_SWA_TYPE_CHUNKED) ? "LLAMA_SWA_TYPE_CHUNKED" :
+                          (swa_type == LLAMA_SWA_TYPE_SYMMETRIC) ? "LLAMA_SWA_TYPE_SYMMETRIC" : "unknown";
+    LLAMA_LOG_DEBUG("%s: n_swa : %d, n_kv: %d, swq_type: %s\n", __func__, (int)n_swa, (int)n_kv, swa_type_str);
+    LLAMA_LOG_DEBUG("%s: '0' = can attend, '∞' = masked\n", __func__);
+    LLAMA_LOG_DEBUG("%s: Rows = query tokens, Columns = key/value tokens\n\n", __func__);
+
+    LLAMA_LOG_DEBUG("    ");
+    for (int j = 0; j < std::min((int64_t)20, n_kv); ++j) {
+        LLAMA_LOG_DEBUG("%2d", j);
+    }
+    LLAMA_LOG_DEBUG("\n");
+
+    for (int i = 0; i < std::min((int64_t)20, n_tokens); ++i) {
+        LLAMA_LOG_DEBUG(" %2d ", i);
+        for (int j = 0; j < std::min((int64_t)20, n_kv); ++j) {
+            float val = data[i * n_kv + j];
+            if (val == -INFINITY) {
+                LLAMA_LOG_DEBUG(" ∞");
+            } else {
+                LLAMA_LOG_DEBUG(" 0");
+            }
+        }
+        LLAMA_LOG_DEBUG("\n");
+    }
+}
+
 void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     const int64_t n_kv     = ubatch->n_tokens;
     const int64_t n_tokens = ubatch->n_tokens;
@@ -277,21 +307,32 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
                 for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) {
                     const llama_seq_id s0 = ubatch->seq_id[i0][0];
 
-                    // TODO: reimplement this like in llama_kv_cache
-                    if (s0 == s1 && (!cparams.causal_attn || ubatch->pos[i0] <= ubatch->pos[i1])) {
-                        if (hparams.use_alibi) {
-                            f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
-                        } else {
-                            f = 0.0f;
-                        }
-                        break;
+                    if (s0 != s1) {
+                        continue; // skip different sequences
+                    }
+
+                    if (cparams.causal_attn && ubatch->pos[i0] > ubatch->pos[i1]) {
+                        continue; // skip future tokens for causal attention
+                    }
+
+                    if (hparams.is_masked_swa(ubatch->pos[i0], ubatch->pos[i1])) {
+                        continue; // skip masked tokens for SWA
+                    }
+
+                    // TODO: reimplement this like in llama_kv_cache_unified
+                    if (hparams.use_alibi) {
+                        f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
+                    } else {
+                        f = 0.0f;
                     }
                 }
-
                 data[h*(n_kv*n_tokens) + i1*n_kv + i0] = f;
             }
         }
     }
+    if (debug) {
+        print_mask(data, n_tokens, n_kv, hparams.n_swa, hparams.swa_type);
+    }
 }
 
 void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 3c85333fde..ca90fdf613 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -78,6 +78,11 @@ struct llm_graph_params;
 
 class llm_graph_input_i {
 public:
+    llm_graph_input_i() {
+        const char * LLAMA_GRAPH_INPUT_DEBUG = getenv("LLAMA_GRAPH_INPUT_DEBUG");
+        debug = LLAMA_GRAPH_INPUT_DEBUG ? atoi(LLAMA_GRAPH_INPUT_DEBUG) : 0;
+    }
+
     virtual ~llm_graph_input_i() = default;
 
     virtual void set_input(const llama_ubatch * ubatch) = 0;
@@ -90,6 +95,9 @@ public:
         GGML_UNUSED(params);
         return false;
     }
+protected:
+    // env: LLAMA_GRAPH_INPUT_DEBUG
+    int debug = 0;
 };
 
 using llm_graph_input_ptr = std::unique_ptr<llm_graph_input_i>;
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 91636572da..4b7a65362b 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -1,6 +1,7 @@
 #include "llama-hparams.h"
 
 #include "ggml.h"
+#include <cassert>
 
 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
     if (dense_first) {
@@ -178,3 +179,39 @@ uint32_t llama_hparams::n_layer_kv() const {
 
     return res;
 }
+
+bool llama_hparams::is_masked_swa(llama_pos p0, llama_pos p1) const {
+    assert(p0 >= 0 && p1 >= 0);
+
+    switch (swa_type) {
+        case LLAMA_SWA_TYPE_NONE:
+            {
+            } break;
+        case LLAMA_SWA_TYPE_STANDARD:
+            {
+                if (p1 - p0 >= (int32_t) n_swa) {
+                    return true;
+                }
+            } break;
+        case LLAMA_SWA_TYPE_CHUNKED:
+            {
+                const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
+
+                if (p0 < pos_chunk_start) {
+                    return true;
+                }
+            } break;
+        case LLAMA_SWA_TYPE_SYMMETRIC:
+            {
+                const int32_t half_n_swa = (int32_t) n_swa / 2;
+                const int32_t pos_diff = p1 - p0;
+
+                // Mask if outside the symmetric window
+                if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
+                    return true;
+                }
+            } break;
+    }
+
+    return false;
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 60415f0c20..06d1e51db0 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -16,9 +16,10 @@ enum llama_expert_gating_func_type {
 };
 
 enum llama_swa_type {
-    LLAMA_SWA_TYPE_NONE     = 0,
-    LLAMA_SWA_TYPE_STANDARD = 1,
-    LLAMA_SWA_TYPE_CHUNKED  = 2,
+    LLAMA_SWA_TYPE_NONE      = 0,
+    LLAMA_SWA_TYPE_STANDARD  = 1,
+    LLAMA_SWA_TYPE_CHUNKED   = 2,
+    LLAMA_SWA_TYPE_SYMMETRIC = 3,
 };
 
 struct llama_hparams_posnet {
@@ -227,6 +228,8 @@ struct llama_hparams {
 
     // number of layers for which has_kv() returns true
     uint32_t n_layer_kv() const;
+
+    bool is_masked_swa(llama_pos p0, llama_pos p1) const;
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
diff --git a/src/llama-kv-cache-iswa.cpp b/src/llama-kv-cache-iswa.cpp
index d7342914c6..7c51a1981e 100644
--- a/src/llama-kv-cache-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -60,14 +60,14 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
     kv_base = std::make_unique<llama_kv_cache>(
             model, type_k, type_v,
             v_trans, offload, unified, size_base, n_seq_max, n_pad,
-            0, LLAMA_SWA_TYPE_NONE, filter_base, reuse);
+            0, filter_base, reuse);
 
     LLAMA_LOG_INFO("%s: creating     SWA KV cache, size = %u cells\n", __func__, size_swa);
 
     kv_swa = std::make_unique<llama_kv_cache>(
             model, type_k, type_v,
             v_trans, offload, unified, size_swa, n_seq_max, n_pad,
-            hparams.n_swa, hparams.swa_type, filter_swa, reuse);
+            hparams.n_swa, filter_swa, reuse);
 }
 
 void llama_kv_cache_iswa::clear(bool data) {
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index f1c6918738..1564faedf4 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -27,11 +27,10 @@ llama_kv_cache::llama_kv_cache(
                  uint32_t   n_seq_max,
                  uint32_t   n_pad,
                  uint32_t   n_swa,
-           llama_swa_type   swa_type,
     const layer_filter_cb & filter,
     const  layer_reuse_cb & reuse) :
     model(model), hparams(model.hparams), v_trans(v_trans),
-    n_seq_max(n_seq_max), n_stream(unified ? 1 : n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
+    n_seq_max(n_seq_max), n_stream(unified ? 1 : n_seq_max), n_pad(n_pad), n_swa(n_swa) {
 
     GGML_ASSERT(kv_size % n_pad == 0);
 
@@ -1393,29 +1392,7 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
 }
 
 bool llama_kv_cache::is_masked_swa(llama_pos p0, llama_pos p1) const {
-    assert(p0 >= 0 && p1 >= 0);
-
-    switch (swa_type) {
-        case LLAMA_SWA_TYPE_NONE:
-            {
-            } break;
-        case LLAMA_SWA_TYPE_STANDARD:
-            {
-                if (p1 - p0 >= (int32_t) n_swa) {
-                    return true;
-                }
-            } break;
-        case LLAMA_SWA_TYPE_CHUNKED:
-            {
-                const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
-
-                if (p0 < pos_chunk_start) {
-                    return true;
-                }
-            } break;
-    }
-
-    return false;
+    return hparams.is_masked_swa(p0, p1);
 }
 
 void llama_kv_cache::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 07d29bb818..55ee355b22 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -89,7 +89,6 @@ public:
                      uint32_t   n_seq_max,
                      uint32_t   n_pad,
                      uint32_t   n_swa,
-               llama_swa_type   swa_type,
         const layer_filter_cb & filter,
         const  layer_reuse_cb & reuse);
 
@@ -212,8 +211,6 @@ private:
     // env: LLAMA_KV_CACHE_DEBUG
     int debug = 0;
 
-    const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
-
     std::vector<ggml_context_ptr>        ctxs;
     std::vector<ggml_backend_buffer_ptr> bufs;
 
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index ba61ebaa88..38f92da118 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -17,7 +17,6 @@ llama_memory_hybrid::llama_memory_hybrid(
                  uint32_t   kv_size,
                  uint32_t   n_pad,
                  uint32_t   n_swa,
-           llama_swa_type   swa_type,
                             /* recurrent */
                 ggml_type   type_r,
                 ggml_type   type_s,
@@ -41,7 +40,6 @@ llama_memory_hybrid::llama_memory_hybrid(
         n_seq_max,
         n_pad,
         n_swa,
-        swa_type,
         filter_attn == nullptr ?
             [&](int32_t il) { return !hparams.is_recurrent(il); }
             : filter_attn,
diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h
index 11a3565178..0eb63f5ef8 100644
--- a/src/llama-memory-hybrid.h
+++ b/src/llama-memory-hybrid.h
@@ -27,7 +27,6 @@ public:
                  uint32_t   kv_size,
                  uint32_t   n_pad,
                  uint32_t   n_swa,
-           llama_swa_type   swa_type,
                             /* recurrent */
                 ggml_type   type_r,
                 ggml_type   type_s,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 5e54ce25f1..afc4cb4809 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1142,6 +1142,26 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_GEMMA_EMBEDDING:
+            {
+                hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
+                hparams.set_swa_pattern(6);
+
+                hparams.causal_attn = false; // embeddings do not use causal attention
+                hparams.rope_freq_base_train_swa  = 10000.0f;
+                hparams.rope_freq_scale_train_swa = 1.0f;
+
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type);
+
+                switch (hparams.n_layer) {
+                    case 24: type = LLM_TYPE_0_3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+                hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k));
+
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3484,6 +3504,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     }
                 } break;
             case LLM_ARCH_GEMMA3:
+            case LLM_ARCH_GEMMA_EMBEDDING:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -11045,6 +11066,136 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
     }
 };
 
+struct llm_build_gemma_embedding_iswa : public llm_graph_context {
+    llm_build_gemma_embedding_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_k;
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+        if (ubatch.token) {
+            inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+            cb(inpL, "inp_scaled", -1);
+        }
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+            const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+            // norm
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
+                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+            }
+
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            cur = build_norm(cur,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = build_norm(sa_out,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = build_norm(cur,
+                    model.layers[il].ffn_post_norm, NULL,
+                    LLM_NORM_RMS, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, sa_out);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 // TODO: move up next to build_starcoder
 struct llm_build_starcoder2 : public llm_graph_context {
     llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
@@ -18481,6 +18632,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
         case LLM_ARCH_NOMIC_BERT_MOE:
         case LLM_ARCH_NEO_BERT:
         case LLM_ARCH_WAVTOKENIZER_DEC:
+        case LLM_ARCH_GEMMA_EMBEDDING:
         case LLM_ARCH_DREAM:
         case LLM_ARCH_LLADA:
             {
@@ -18529,7 +18681,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         /* attn_kv_size      */ cparams.n_ctx,
                         /* attn_n_pad        */ padding,
                         /* attn_n_swa        */ hparams.n_swa,
-                        /* attn_swa_type     */ hparams.swa_type,
                         /* recurrent_type_k  */ GGML_TYPE_F32,
                         /* recurrent_type_v  */ GGML_TYPE_F32,
                         /* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
@@ -18599,7 +18750,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                                 cparams.n_seq_max,
                                 padding,
                                 hparams.n_swa,
-                                hparams.swa_type,
                                 nullptr,
                                 nullptr);
                     }
@@ -18761,6 +18911,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_gemma3n_iswa>(*this, params);
             } break;
+        case LLM_ARCH_GEMMA_EMBEDDING:
+            {
+                llm = std::make_unique<llm_build_gemma_embedding_iswa>(*this, params);
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 llm = std::make_unique<llm_build_starcoder2>(*this, params);
@@ -19161,6 +19315,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_GEMMA2:
         case LLM_ARCH_GEMMA3:
         case LLM_ARCH_GEMMA3N:
+        case LLM_ARCH_GEMMA_EMBEDDING:
         case LLM_ARCH_STARCODER2:
         case LLM_ARCH_OPENELM:
         case LLM_ARCH_GPTNEOX: