From b5b8fa1c8b3b27683b2965a22f9985eec683d384 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 23 Jan 2026 12:03:42 +0100 Subject: [PATCH 001/206] chat : fix translategemma crash on common_chat_format_example (#19019) --- common/chat.cpp | 45 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/common/chat.cpp b/common/chat.cpp index 6853f4ad47..aba26e97a1 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -2650,6 +2650,45 @@ static common_chat_params common_chat_params_init_exaone_moe(const common_chat_t return data; } +static common_chat_params common_chat_params_init_translate_gemma(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + // This template does not support tools or reasoning + // we just need to transform the messages into the correct schema + + templates_params inputs_new = inputs; + json & messages = inputs_new.messages; + + GGML_ASSERT(messages.is_array()); + for (auto & message : messages) { + if (message.contains("role") && message["role"].get() != "user") { + continue; + } + if (!message.contains("content")) { + message["content"] = json::array(); + } + if (message.contains("content") && !message["content"].is_array()) { + auto content_str = message["content"].get(); + // default to en-GB if not specified (to make common_chat_format_example works) + auto src_lang = message.contains("source_lang_code") ? message["source_lang_code"].get() : "en-GB"; + auto tgt_lang = message.contains("target_lang_code") ? message["target_lang_code"].get() : "en-GB"; + message["content"] = json::array({ + json{ + {"type", "text"}, + {"text", content_str}, + {"source_lang_code", src_lang}, + {"target_lang_code", tgt_lang}, + } + }); + } + } + + data.prompt = apply(tmpl, inputs_new, std::nullopt, std::nullopt); + data.format = COMMON_CHAT_FORMAT_GENERIC; + + return data; +} + 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); @@ -3045,6 +3084,12 @@ static common_chat_params common_chat_templates_apply_jinja( return common_chat_params_init_solar_open(tmpl, params); } + // TranslateGemma + if (src.find("[source_lang_code]") != std::string::npos && + src.find("[target_lang_code]") != std::string::npos) { + return common_chat_params_init_translate_gemma(tmpl, params); + } + // Plain handler (no tools) if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { return common_chat_params_init_without_tools(tmpl, params); From cb6caca191b9a3a9a4eaa13dd9e465225d127034 Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Fri, 23 Jan 2026 20:54:10 +0800 Subject: [PATCH 002/206] [SYCL] use malloc to support both iGPU and dGPU in same time (#18992) * use malloc to support both iGPU and dGPU in same time * support windows --------- Co-authored-by: Neo Zhang Jianyu --- ggml/src/ggml-sycl/ggml-sycl.cpp | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index bb8acc922b..ce2f0d41c9 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -1157,13 +1157,28 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_ GGML_UNUSED(buft); } +inline void * aligned_malloc_host(size_t alignment, size_t size) { +#ifdef _WIN32 + return _aligned_malloc(size, alignment); +#else + return aligned_alloc(alignment, size); +#endif +} + +inline void free_aligned_mem_host(void * memblock) { +#ifdef _WIN32 + _aligned_free(memblock); +#else + free(memblock); +#endif +} + static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_sycl_host_free(buffer->context); + free_aligned_mem_host((void *)buffer->context); } static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_sycl_host_malloc(size); - + void * ptr = aligned_malloc_host(TENSOR_ALIGNMENT, size); if (ptr == nullptr) { // fallback to cpu buffer return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); From 557515be1e93ed8939dd8a7c7d08765fdbe8be31 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 23 Jan 2026 18:22:34 +0200 Subject: [PATCH 003/206] graph : utilize `ggml_build_forward_select()` to avoid reallocations (#18898) * graph : avoid branches between embedding and token inputs * models : make deepstack graphs (e.g. Qwen3 VL) have constant topology * ci : enable -DGGML_SCHED_NO_REALLOC=ON for server CI * cont : pad token embeddings to n_embd_inp --- .github/workflows/server.yml | 4 +-- src/llama-context.cpp | 2 +- src/llama-graph.cpp | 65 ++++++++++++++++++++++++++---------- src/llama-graph.h | 9 +++-- src/models/gemma3n-iswa.cpp | 4 +-- src/models/qwen3vl-moe.cpp | 19 +++-------- src/models/qwen3vl.cpp | 19 +++-------- 7 files changed, 69 insertions(+), 53 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 9d9d6884d4..9f1ef48c82 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -72,7 +72,7 @@ jobs: - name: Build id: cmake_build run: | - cmake -B build -DLLAMA_BUILD_BORINGSSL=ON + cmake -B build -DLLAMA_BUILD_BORINGSSL=ON -DGGML_SCHED_NO_REALLOC=ON cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server - name: Python setup @@ -108,7 +108,7 @@ jobs: - name: Build id: cmake_build run: | - cmake -B build -DLLAMA_BUILD_BORINGSSL=ON + cmake -B build -DLLAMA_BUILD_BORINGSSL=ON -DGGML_SCHED_NO_REALLOC=ON cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server - name: Python setup diff --git a/src/llama-context.cpp b/src/llama-context.cpp index a6d5ddfa33..a35cf5a94b 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2903,7 +2903,7 @@ void llama_context::opt_epoch_iter( }; ctx_compute_opt = ggml_init(params); } - ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits()); + ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_inp_tokens(), res->get_logits()); ggml_opt_alloc(opt_ctx, train); res->set_inputs(&ubatch); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 5ebd0cf8aa..f9ed87cce1 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -23,7 +23,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) { } if (ubatch->embd) { - const int64_t n_embd = embd->ne[0]; + GGML_ASSERT(n_embd == embd->ne[0]); + const int64_t n_tokens = ubatch->n_tokens; ggml_backend_tensor_set(embd, ubatch->embd, 0, n_tokens*n_embd*ggml_element_size(embd)); @@ -33,8 +34,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) { bool llm_graph_input_embd::can_reuse(const llm_graph_params & params) { bool res = true; - res &= (!tokens && !params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens); - res &= (!embd && !params.ubatch.embd) || (embd && embd->ne[1] == params.ubatch.n_tokens); + res &= (!params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens); + res &= (!params.ubatch.embd) || (embd && embd->ne[1] == params.ubatch.n_tokens); return res; } @@ -634,7 +635,8 @@ int64_t llm_graph_result::get_max_nodes() const { } void llm_graph_result::reset() { - t_tokens = nullptr; + t_inp_tokens = nullptr; + t_inp_embd = nullptr; t_logits = nullptr; t_embd = nullptr; t_embd_pooled = nullptr; @@ -1338,17 +1340,29 @@ ggml_tensor * llm_graph_context::build_moe_ffn( // input embeddings with optional lora ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const { - const int64_t n_embd = hparams.n_embd_inp(); + const int64_t n_embd_inp = hparams.n_embd_inp(); + const int64_t n_embd = hparams.n_embd; - auto inp = std::make_unique(); + assert(n_embd_inp >= n_embd); - ggml_tensor * cur = nullptr; + auto inp = std::make_unique(n_embd_inp); - if (ubatch.token) { - inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens); - //cb(inp->tokens, "inp_tokens", -1); - ggml_set_input(inp->tokens); - res->t_tokens = inp->tokens; + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens); + cb(inp->tokens, "inp_tokens", -1); + ggml_set_input(inp->tokens); + res->t_inp_tokens = inp->tokens; + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd_inp, ubatch.n_tokens); + cb(inp->embd, "inp_embd", -1); + ggml_set_input(inp->embd); + + // select one of the 2 inputs, based on the batch contents + // ref: https://github.com/ggml-org/llama.cpp/pull/18550 + std::array inps; + + // token embeddings path (ubatch.token != nullptr) + { + auto & cur = inps[0]; cur = ggml_get_rows(ctx0, tok_embd, inp->tokens); @@ -1369,19 +1383,36 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const { cur = ggml_add(ctx0, cur, inpL_delta); } - } else { - inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ubatch.n_tokens); - ggml_set_input(inp->embd); + + if (n_embd_inp != n_embd) { + cur = ggml_pad(ctx0, cur, hparams.n_embd_inp() - n_embd, 0, 0, 0); + } + } + + // vector embeddings path (ubatch.embd != nullptr) + { + auto & cur = inps[1]; cur = inp->embd; } + assert(ggml_are_same_shape (inps[0], inps[1])); + assert(ggml_are_same_stride(inps[0], inps[1])); + + ggml_tensor * cur = ggml_build_forward_select(gf, inps.data(), inps.size(), ubatch.token ? 0 : 1); + + if (n_embd_inp != n_embd) { + cur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0); + } + + res->t_inp_embd = cur; + // For Granite architecture if (hparams.f_embedding_scale != 0.0f) { cur = ggml_scale(ctx0, cur, hparams.f_embedding_scale); } - cb(cur, "inp_embd", -1); + cb(cur, "embd", -1); res->add_input(std::move(inp)); @@ -1480,7 +1511,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const { //} const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd_inp(); - const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train; + const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train; cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc); ggml_set_input(cur); diff --git a/src/llama-graph.h b/src/llama-graph.h index 93d32522d1..242a046d56 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -106,7 +106,7 @@ using llm_graph_input_ptr = std::unique_ptr; class llm_graph_input_embd : public llm_graph_input_i { public: - llm_graph_input_embd() = default; + llm_graph_input_embd(int64_t n_embd) : n_embd(n_embd) {} virtual ~llm_graph_input_embd() = default; void set_input(const llama_ubatch * ubatch) override; @@ -115,6 +115,8 @@ public: ggml_tensor * tokens = nullptr; // I32 [n_batch] ggml_tensor * embd = nullptr; // F32 [n_embd, n_batch] + + const int64_t n_embd = 0; }; class llm_graph_input_pos : public llm_graph_input_i { @@ -566,7 +568,7 @@ public: virtual ~llm_graph_result() = default; - ggml_tensor * get_tokens() const { return t_tokens; } + ggml_tensor * get_inp_tokens() const { return t_inp_tokens; } ggml_tensor * get_logits() const { return t_logits; } ggml_tensor * get_embd() const { return t_embd; } ggml_tensor * get_embd_pooled() const { return t_embd_pooled; } @@ -593,7 +595,8 @@ public: void set_params(const llm_graph_params & params); // important graph nodes - ggml_tensor * t_tokens = nullptr; + ggml_tensor * t_inp_tokens = nullptr; + ggml_tensor * t_inp_embd = nullptr; // [n_embd_inp, n_tokens] ggml_tensor * t_logits = nullptr; ggml_tensor * t_embd = nullptr; ggml_tensor * t_embd_pooled = nullptr; diff --git a/src/models/gemma3n-iswa.cpp b/src/models/gemma3n-iswa.cpp index 51acab1490..7db6d3bf4e 100644 --- a/src/models/gemma3n-iswa.cpp +++ b/src/models/gemma3n-iswa.cpp @@ -245,12 +245,12 @@ ggml_tensor * llm_build_gemma3n_iswa::view_2d_slice(ggml_tensor * x, int idx) { // equivalent to get_per_layer_inputs() in python code // output shape: [n_embd_altup, n_layer, n_tokens] ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() { - auto inp = std::make_unique(); + auto inp = std::make_unique(n_embd); ggml_tensor * inp_per_layer; if (ubatch.token) { inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens); ggml_set_input(inp->tokens); - res->t_tokens = inp->tokens; + res->t_inp_tokens = inp->tokens; inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens); inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens); inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_altup)); diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vl-moe.cpp index f72f80a837..e5e1a2150c 100644 --- a/src/models/qwen3vl-moe.cpp +++ b/src/models/qwen3vl-moe.cpp @@ -2,7 +2,8 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const size_t n_deepstack_layers = hparams.n_deepstack_layers; - const int64_t n_embd = hparams.n_embd; + + const int64_t n_embd = hparams.n_embd; const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -16,17 +17,6 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); - std::vector deepstack_features(n_deepstack_layers, nullptr); - - if (ubatch.embd) { - // Image input: split main embd and deepstack embds - ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0); - for (size_t i = 0; i < n_deepstack_layers; i++) { - deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float)); - } - inpL = inpL_main; - } - // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); @@ -120,8 +110,9 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ cur = build_cvec(cur, il); cb(cur, "l_out", il); - if (ubatch.embd && (size_t)il < n_deepstack_layers) { - cur = ggml_add(ctx0, cur, deepstack_features[il]); + if (il < (int) n_deepstack_layers) { + ggml_tensor * ds = ggml_view_2d(ctx0, res->t_inp_embd, n_embd, n_tokens, res->t_inp_embd->nb[1], (il + 1) * n_embd * sizeof(float)); + cur = ggml_add(ctx0, cur, ds); cb(cur, "deepstack_out", il); } diff --git a/src/models/qwen3vl.cpp b/src/models/qwen3vl.cpp index 0bae52239c..0f8315b324 100644 --- a/src/models/qwen3vl.cpp +++ b/src/models/qwen3vl.cpp @@ -2,7 +2,8 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const size_t n_deepstack_layers = hparams.n_deepstack_layers; - const int64_t n_embd = hparams.n_embd; + + const int64_t n_embd = hparams.n_embd; const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -16,17 +17,6 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_ int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); - std::vector deepstack_features(n_deepstack_layers, nullptr); - - if (ubatch.embd) { - // Image input: split main embd and deepstack embds - ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0); - for (size_t i = 0; i < n_deepstack_layers; i++) { - deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float)); - } - inpL = inpL_main; - } - // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); @@ -113,8 +103,9 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_ cur = build_cvec(cur, il); cb(cur, "l_out", il); - if (ubatch.embd && (size_t)il < n_deepstack_layers) { - cur = ggml_add(ctx0, cur, deepstack_features[il]); + if (il < (int) n_deepstack_layers) { + ggml_tensor * ds = ggml_view_2d(ctx0, res->t_inp_embd, n_embd, n_tokens, res->t_inp_embd->nb[1], (il + 1) * n_embd * sizeof(float)); + cur = ggml_add(ctx0, cur, ds); cb(cur, "deepstack_out", il); } From 8af1f5f430baaab1719db8f0a259bcc2a1cfdaa0 Mon Sep 17 00:00:00 2001 From: nullname Date: Sat, 24 Jan 2026 14:02:07 +0800 Subject: [PATCH 004/206] ggml-hexagon: flash-attn opt (#19025) * optimize flash attention kernel by improving score computation and online softmax update * wip * Refactor online softmax update in flash attention kernel for improved performance * Optimize flash attention kernel by replacing float array with HVX_Vector for score computation * wip --- ggml/src/ggml-hexagon/htp/flash-attn-ops.c | 54 +++++++++++++--------- 1 file changed, 32 insertions(+), 22 deletions(-) diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c index 1de47d0f3d..c7cb2a4e0b 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c @@ -2,9 +2,9 @@ #pragma clang diagnostic ignored "-Wunused-function" #pragma clang diagnostic ignored "-Wunused-but-set-variable" +#include #include #include - #include #include @@ -111,7 +111,7 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict hvx_vec_store_u(r, 4, rsum); } -// MAD: y (F32) += x (F16) * v (float) +// MAD: y (F32) += x (F16) * s (float) static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, int n, float s) { const HVX_Vector * restrict ptr_x = (const HVX_Vector *) x; HVX_Vector * restrict ptr_y = (HVX_Vector *) y; @@ -318,9 +318,12 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in uint32_t ic = 0; // Process in blocks of 32 (VLEN_FP32) - for (; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32) { + static_assert(FLASH_ATTN_BLOCK_SIZE / VLEN_FP32 == 4, "FLASH_ATTN_BLOCK_SIZE changed, fix HVX_Vector_x4 usage"); + HVX_Vector_x4 scores_x4; + HVX_Vector v_max = hvx_vec_splat_f32(-INFINITY); + for (uint32_t iv = 0; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32, ++iv) { // 1. Compute scores - float __attribute__((aligned(VLEN))) scores_arr[VLEN_FP32]; + float __attribute__((aligned(VLEN))) scores_arr[FLASH_ATTN_BLOCK_SIZE]; for (int j = 0; j < VLEN_FP32; ++j) { const uint32_t cur_ic = ic + j; const uint8_t * k_ptr = k_base + cur_ic * size_k_row_padded; @@ -356,36 +359,43 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in scores = Q6_Vsf_equals_Vqf32(scores); } - // 4. Online Softmax Update - HVX_Vector v_max = hvx_vec_reduce_max_f32(scores); - float m_block = hvx_vec_get_f32(v_max); + scores_x4.v[iv] = scores; + v_max = Q6_Vsf_vmax_VsfVsf(scores, v_max); + } + { + // 4. Online Softmax Update + v_max = hvx_vec_reduce_max_f32(v_max); + float m_block = hvx_vec_get_f32(v_max); float M_old = M; float M_new = (m_block > M) ? m_block : M; M = M_new; - float ms = expf(M_old - M_new); - + const float ms = expf(M_old - M_new); hvx_scale_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms); - S = S * ms; HVX_Vector M_new_vec = hvx_vec_splat_f32(M_new); - HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_new_vec); - HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted)); + HVX_Vector p_sum_vec = hvx_vec_splat_f32(0.0f); + for (uint32_t ic2 = 0, iv = 0; ic2 + VLEN_FP32 <= current_block_size; ic2 += VLEN_FP32, ++iv) { + HVX_Vector scores = scores_x4.v[iv]; + HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_new_vec); + HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted)); - HVX_Vector p_sum_vec = hvx_vec_reduce_sum_f32(P); - float p_sum = hvx_vec_get_f32(p_sum_vec); - S += p_sum; + p_sum_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(p_sum_vec, P)); - // 5. Accumulate V - float __attribute__((aligned(VLEN))) p_arr[VLEN_FP32]; - *(HVX_Vector*)p_arr = P; + // 5. Accumulate V + float __attribute__((aligned(VLEN))) p_arr[VLEN_FP32]; + *(HVX_Vector*)p_arr = P; - for (int j = 0; j < VLEN_FP32; ++j) { - const uint32_t cur_ic = ic + j; - const uint8_t * v_ptr = v_base + cur_ic * size_v_row_padded; - hvx_mad_f32_f16_aa(VKQ32, v_ptr, DV, p_arr[j]); + for (int j = 0; j < VLEN_FP32; ++j) { + const uint32_t cur_ic = ic2 + j; + const uint8_t * v_ptr = v_base + cur_ic * size_v_row_padded; + hvx_mad_f32_f16_aa(VKQ32, v_ptr, DV, p_arr[j]); + } } + + p_sum_vec = hvx_vec_reduce_sum_f32(p_sum_vec); + S = S * ms + hvx_vec_get_f32(p_sum_vec); } // Leftover From 81ab64f3c858c0db8c7c3a6bccd4cbbe624f52a3 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Sat, 24 Jan 2026 14:25:20 +0800 Subject: [PATCH 005/206] ggml-cuda: enable cuda-graphs for `n-cpu-moe` (#18934) * ggml-cuda: add split-wise cuda graph * add n-cpu-moe compare_llama_bench.py * fix hip/musa builds --- ggml/src/ggml-cuda/common.cuh | 38 ++++++++++++- ggml/src/ggml-cuda/ggml-cuda.cu | 95 ++++++++++++++++++++------------- ggml/src/ggml-cuda/mean.cu | 17 +++--- scripts/compare-llama-bench.py | 6 +-- 4 files changed, 105 insertions(+), 51 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 179522d835..09a491a836 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -1327,10 +1327,44 @@ struct ggml_backend_cuda_context { cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } }; cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; - std::unique_ptr cuda_graph; - int curr_stream_no = 0; +#ifdef USE_CUDA_GRAPH + // Map from first_node_ptr to cuda_graph - allows multiple graphs per context + // when the computation is split across CPU/GPU (e.g., with --n-cpu-moe) + std::unordered_map> cuda_graphs; + + ggml_cuda_graph * cuda_graph(const void * first_node_ptr) { + auto it = cuda_graphs.find(first_node_ptr); + if (it == cuda_graphs.end()) { + cuda_graphs[first_node_ptr] = std::make_unique(); + return cuda_graphs[first_node_ptr].get(); + } + return it->second.get(); + } + + // Check if any CUDA graph is enabled for this context (used by kernels that need to know + // if graphs are in use without having access to the specific graph key) + bool any_cuda_graph_enabled() const { + for (const auto & [key, graph] : cuda_graphs) { + if (graph && graph->is_enabled()) { + return true; + } + } + return false; + } + + // Check if any CUDA graph has an instance for this context + bool any_cuda_graph_has_instance() const { + for (const auto & [key, graph] : cuda_graphs) { + if (graph && graph->instance != nullptr) { + return true; + } + } + return false; + } +#endif // USE_CUDA_GRAPH + explicit ggml_backend_cuda_context(int device) : device(device), name(GGML_CUDA_NAME + std::to_string(device)) { diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index cda422defb..99f0919a51 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -2969,18 +2969,25 @@ static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_ return true; } +static const void * ggml_cuda_graph_get_key(ggml_cgraph * cgraph) { + return cgraph->nodes[0]; +} + static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) { bool res = false; - if (cuda_ctx->cuda_graph->instance == nullptr) { + const void * graph_key = ggml_cuda_graph_get_key(cgraph); + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); + + if (graph->instance == nullptr) { res = true; } // Check if the graph size has changed - if (cuda_ctx->cuda_graph->props.size() != (size_t)cgraph->n_nodes + cgraph->n_leafs) { + if (graph->props.size() != (size_t)cgraph->n_nodes + cgraph->n_leafs) { res = true; - cuda_ctx->cuda_graph->props.resize(cgraph->n_nodes + cgraph->n_leafs); + graph->props.resize(cgraph->n_nodes + cgraph->n_leafs); } // Loop over nodes in GGML graph to determine if CUDA graph update is required @@ -2988,37 +2995,38 @@ static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx for (int i = 0; i < cgraph->n_nodes; i++) { bool props_match = true; if (!res) { - props_match = ggml_cuda_graph_node_properties_match(cgraph->nodes[i], &cuda_ctx->cuda_graph->props[i]); + props_match = ggml_cuda_graph_node_properties_match(cgraph->nodes[i], &graph->props[i]); } if (!props_match) { res = true; } - ggml_cuda_graph_node_set_properties(&cuda_ctx->cuda_graph->props[i], cgraph->nodes[i]); + ggml_cuda_graph_node_set_properties(&graph->props[i], cgraph->nodes[i]); } for (int i = 0; i < cgraph->n_leafs; i++) { - bool props_match= true; + bool props_match = true; if (!res) { - props_match = ggml_cuda_graph_node_properties_match(cgraph->leafs[i], &cuda_ctx->cuda_graph->props[cgraph->n_nodes + i]); + props_match = ggml_cuda_graph_node_properties_match(cgraph->leafs[i], &graph->props[cgraph->n_nodes + i]); } if (!props_match) { res = true; } - ggml_cuda_graph_node_set_properties(&cuda_ctx->cuda_graph->props[cgraph->n_nodes + i], cgraph->leafs[i]); + ggml_cuda_graph_node_set_properties(&graph->props[cgraph->n_nodes + i], cgraph->leafs[i]); } return res; } -static void ggml_cuda_graph_update_executable(ggml_backend_cuda_context * cuda_ctx) { +static void ggml_cuda_graph_update_executable(ggml_backend_cuda_context * cuda_ctx, const void * graph_key) { + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); #if CUDART_VERSION >= 12000 cudaGraphExecUpdateResultInfo result_info; - cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); + cudaError_t stat = cudaGraphExecUpdate(graph->instance, graph->graph, &result_info); #else cudaGraphNode_t errorNode; cudaGraphExecUpdateResult result_info; - cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &errorNode, &result_info); + cudaError_t stat = cudaGraphExecUpdate(graph->instance, graph->graph, &errorNode, &result_info); #endif // CUDART_VERSION >= 12000 if (stat == cudaErrorGraphExecUpdateFailure) { @@ -3029,14 +3037,14 @@ static void ggml_cuda_graph_update_executable(ggml_backend_cuda_context * cuda_c // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate (void)cudaGetLastError(); - CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance)); - cuda_ctx->cuda_graph->instance = nullptr; - CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); + CUDA_CHECK(cudaGraphExecDestroy(graph->instance)); + graph->instance = nullptr; + CUDA_CHECK(cudaGraphInstantiate(&graph->instance, graph->graph, NULL, NULL, 0)); } else { GGML_ASSERT(stat == cudaSuccess); } } -#endif +#endif // USE_CUDA_GRAPH static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope, const ggml_tensor * view, @@ -3241,7 +3249,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, return false; } -static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required) { +static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required, const void * graph_key) { bool graph_evaluated_or_captured = false; // flag used to determine whether it is an integrated_gpu @@ -3695,13 +3703,14 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud } #ifdef USE_CUDA_GRAPH + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture - if (cuda_ctx->cuda_graph->graph != nullptr) { - CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph)); - cuda_ctx->cuda_graph->graph = nullptr; + if (graph->graph != nullptr) { + CUDA_CHECK(cudaGraphDestroy(graph->graph)); + graph->graph = nullptr; } - CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph)); + CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &graph->graph)); graph_evaluated_or_captured = true; // CUDA graph has been captured std::lock_guard lock(ggml_cuda_lock); @@ -3714,40 +3723,39 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud } if (use_cuda_graph) { - if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph. - CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); + if (graph->instance == nullptr) { // Create executable graph from captured graph. + CUDA_CHECK(cudaGraphInstantiate(&graph->instance, graph->graph, NULL, NULL, 0)); } if (cuda_graph_update_required) { // Update graph executable - ggml_cuda_graph_update_executable(cuda_ctx); + ggml_cuda_graph_update_executable(cuda_ctx, graph_key); } // Launch graph - CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream())); + CUDA_CHECK(cudaGraphLaunch(graph->instance, cuda_ctx->stream())); #else graph_evaluated_or_captured = true; #endif // USE_CUDA_GRAPH } } -static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx) { +static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, const void * graph_key) { #ifdef USE_CUDA_GRAPH + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); - if (cuda_ctx->cuda_graph == nullptr) { - cuda_ctx->cuda_graph.reset(new ggml_cuda_graph()); - } - - if (cuda_ctx->cuda_graph->graph == nullptr) { + if (graph->graph == nullptr) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) { - if (!cuda_ctx->cuda_graph->disable_due_to_gpu_arch) { + if (!graph->disable_due_to_gpu_arch) { GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__); } - cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; + graph->disable_due_to_gpu_arch = true; } } - return cuda_ctx->cuda_graph->is_enabled(); + return graph->is_enabled(); #else GGML_UNUSED(cuda_ctx); + GGML_UNUSED(graph_key); return false; #endif // USE_CUDA_GRAPH } @@ -3759,15 +3767,19 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, bool use_cuda_graph = false; bool cuda_graph_update_required = false; + const void * graph_key = nullptr; #ifdef USE_CUDA_GRAPH - use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx); + graph_key = ggml_cuda_graph_get_key(cgraph); - if (cuda_ctx->cuda_graph->is_enabled()) { + use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx, graph_key); + + ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); + if (graph->is_enabled()) { cuda_graph_update_required = ggml_cuda_graph_update_required(cuda_ctx, cgraph); use_cuda_graph = ggml_cuda_graph_check_compability(cgraph); - cuda_ctx->cuda_graph->record_update(use_cuda_graph, cuda_graph_update_required); + graph->record_update(use_cuda_graph, cuda_graph_update_required); } #endif // USE_CUDA_GRAPH @@ -3781,7 +3793,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed)); } - ggml_cuda_graph_evaluate_and_capture(cuda_ctx, cgraph, use_cuda_graph, cuda_graph_update_required); + ggml_cuda_graph_evaluate_and_capture(cuda_ctx, cgraph, use_cuda_graph, cuda_graph_update_required, graph_key); return GGML_STATUS_SUCCESS; } @@ -3814,7 +3826,14 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev static void ggml_backend_cuda_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context; - const bool use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx); +#ifdef USE_CUDA_GRAPH + const void * graph_key = ggml_cuda_graph_get_key(cgraph); + const bool use_cuda_graph = ggml_cuda_graph_set_enabled(cuda_ctx, graph_key); +#else + const bool use_cuda_graph = false; + GGML_UNUSED(cuda_ctx); + GGML_UNUSED(cgraph); +#endif static bool enable_graph_optimization = [] { const char * env = getenv("GGML_CUDA_GRAPH_OPT"); diff --git a/ggml/src/ggml-cuda/mean.cu b/ggml/src/ggml-cuda/mean.cu index 60542fc19d..49af538995 100644 --- a/ggml/src/ggml-cuda/mean.cu +++ b/ggml/src/ggml-cuda/mean.cu @@ -31,14 +31,15 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { #endif // USE_CUDA_GRAPH if ((nrows == 1) && #ifdef USE_CUDA_GRAPH - // CUDA_GRAPHS_DISABLED - ((ncols > 65536) && - ((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) || - ctx.cuda_graph->is_enabled())) || - // CUDA_GRAPHS ENABLED - ((ncols > 32768) && - !((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) || - ctx.cuda_graph->is_enabled()))) { + // Determine if CUDA graphs are effectively disabled for this context + // (no graph instance exists and we're not capturing, OR graphs are explicitly enabled) + (((ncols > 65536) && + (((!ctx.any_cuda_graph_has_instance()) && (iscapturing == cudaStreamCaptureStatusNone)) || + ctx.any_cuda_graph_enabled())) || + // CUDA graphs are enabled - use lower threshold + ((ncols > 32768) && + !(((!ctx.any_cuda_graph_has_instance()) && (iscapturing == cudaStreamCaptureStatusNone)) || + ctx.any_cuda_graph_enabled())))) { #else (ncols > 65536)) { #endif // USE_CUDA_GRAPH diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index c45c83fdb5..9541b89eb9 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -29,7 +29,7 @@ LLAMA_BENCH_DB_FIELDS = [ "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", "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", - "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", + "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", "n_cpu_moe" ] LLAMA_BENCH_DB_TYPES = [ @@ -38,7 +38,7 @@ LLAMA_BENCH_DB_TYPES = [ "TEXT", "INTEGER", "INTEGER", "TEXT", "TEXT", "INTEGER", "TEXT", "INTEGER", "INTEGER", "INTEGER", "TEXT", "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", - "TEXT", "INTEGER", "INTEGER", "REAL", "REAL", + "TEXT", "INTEGER", "INTEGER", "REAL", "REAL", "INTEGER", ] # All test-backend-ops SQL fields @@ -59,7 +59,7 @@ assert len(TEST_BACKEND_OPS_DB_FIELDS) == len(TEST_BACKEND_OPS_DB_TYPES) # Properties by which to differentiate results per commit for llama-bench: LLAMA_BENCH_KEY_PROPERTIES = [ - "cpu_info", "gpu_info", "backends", "n_gpu_layers", "tensor_buft_overrides", "model_filename", "model_type", + "cpu_info", "gpu_info", "backends", "n_gpu_layers", "n_cpu_moe", "tensor_buft_overrides", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth" ] From 8f91ca54ec0b22f3ff3a495f32be8e8300638cdf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 24 Jan 2026 10:09:36 +0100 Subject: [PATCH 006/206] CUDA: re-use MLA K data for V in MMA FA (#19057) --- ggml/src/ggml-cuda/fattn-common.cuh | 74 ++++++++++++++-------------- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 63 +++++++++++------------ ggml/src/ggml-cuda/fattn.cu | 5 ++ 3 files changed, 72 insertions(+), 70 deletions(-) diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index a781fb91f5..40c7725784 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -782,12 +782,7 @@ void launch_fattn( const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; - // TODO: make this more generic by removing the notion of "MLA". - // for example "is V a view of K?" so we can skip loading it. - // V strides should be driven by V itself and avoid assumption of the data layout - const bool is_mla = V->op == GGML_OP_VIEW && V->src[0] == K; - - GGML_ASSERT(V || is_mla); + const bool V_is_K_view = V->op == GGML_OP_VIEW && V->src[0] == K && V->data == K->data; const ggml_tensor * mask = dst->src[3]; const ggml_tensor * sinks = dst->src[4]; @@ -797,9 +792,9 @@ void launch_fattn( GGML_ASSERT(Q->type == GGML_TYPE_F32); GGML_ASSERT(KQV->type == GGML_TYPE_F32); - GGML_ASSERT( Q->nb[0] == ggml_element_size(Q)); - GGML_ASSERT( K->nb[0] == ggml_element_size(K)); - GGML_ASSERT(!V || V->nb[0] == ggml_element_size(V)); + GGML_ASSERT(Q->nb[0] == ggml_element_size(Q)); + GGML_ASSERT(K->nb[0] == ggml_element_size(K)); + GGML_ASSERT(V->nb[0] == ggml_element_size(V)); GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); @@ -820,10 +815,10 @@ void launch_fattn( size_t nb12 = K->nb[2]; size_t nb13 = K->nb[3]; - const char * V_data = V ? (const char *) V->data : nullptr; - size_t nb21 = V ? V->nb[1] : nb11; - size_t nb22 = V ? V->nb[2] : nb12; - size_t nb23 = V ? V->nb[3] : nb13; + const char * V_data = (const char *) V->data; + size_t nb21 = V->nb[1]; + size_t nb22 = V->nb[2]; + size_t nb23 = V->nb[3]; if (need_f16_K && K->type != GGML_TYPE_F16) { const size_t bs = ggml_blck_size(K->type); @@ -852,32 +847,39 @@ void launch_fattn( K_data = (char *) K_f16.ptr; } - if (V && need_f16_V && V->type != GGML_TYPE_F16) { - const size_t bs = ggml_blck_size(V->type); - const size_t ts = ggml_type_size(V->type); - - V_f16.alloc(ggml_nelements(V)); - if (ggml_is_contiguously_allocated(V)) { - to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); - to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); - V_data = (char *) V_f16.ptr; - - nb21 = nb21*bs*sizeof(half)/ts; - nb22 = nb22*bs*sizeof(half)/ts; - nb23 = nb23*bs*sizeof(half)/ts; + if (need_f16_V && V->type != GGML_TYPE_F16) { + if (V_is_K_view) { + V_data = K_data; + nb21 = nb11; + nb22 = nb12; + nb23 = nb13; } else { - GGML_ASSERT(V->nb[0] == ts); - to_fp16_nc_cuda_t to_fp16 = ggml_get_to_fp16_nc_cuda(V->type); - const int64_t s01 = nb21 / ts; - const int64_t s02 = nb22 / ts; - const int64_t s03 = nb23 / ts; - to_fp16(V_data, V_f16.ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3], s01, s02, s03, main_stream); + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); - nb21 = V->ne[0] * sizeof(half); - nb22 = V->ne[1] * nb21; - nb23 = V->ne[2] * nb22; + V_f16.alloc(ggml_nelements(V)); + if (ggml_is_contiguously_allocated(V)) { + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); + to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); + V_data = (char *) V_f16.ptr; + + nb21 = nb21*bs*sizeof(half)/ts; + nb22 = nb22*bs*sizeof(half)/ts; + nb23 = nb23*bs*sizeof(half)/ts; + } else { + GGML_ASSERT(V->nb[0] == ts); + to_fp16_nc_cuda_t to_fp16 = ggml_get_to_fp16_nc_cuda(V->type); + const int64_t s01 = nb21 / ts; + const int64_t s02 = nb22 / ts; + const int64_t s03 = nb23 / ts; + to_fp16(V_data, V_f16.ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3], s01, s02, s03, main_stream); + + nb21 = V->ne[0] * sizeof(half); + nb22 = V->ne[1] * nb21; + nb23 = V->ne[2] * nb22; + } + V_data = (char *) V_f16.ptr; } - V_data = (char *) V_f16.ptr; } const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 203569e345..3e7d67b40d 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -400,7 +400,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask( } template static __device__ __forceinline__ void flash_attn_ext_f16_iter( const float2 * const __restrict__ Q_f2, @@ -442,8 +442,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( constexpr int stride_tile_Q = DKQ/2 + 4; constexpr int stride_tile_K = nbatch_K2 + 4; - static_assert(!mla || nbatch_K2 >= nbatch_V2, "bad nbatch_K2, nbatch_V2 for MLA"); - constexpr int stride_tile_V = mla ? stride_tile_K : nbatch_V2 + 4; + constexpr int stride_tile_V = V_is_K_view ? stride_tile_K : nbatch_V2 + 4; const int k_VKQ_0 = kb0 * nbatch_fa; #if defined(TURING_MMA_AVAILABLE) @@ -456,7 +455,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( if constexpr (nstages > 1) { static_assert(!oob_check, "OOB check incompatible with multi-stage pipeline"); - static_assert(!mla, "multi-stage loading not implemented for MLA"); + static_assert(!V_is_K_view, "K data reuse not implemented multi-stage loading"); static_assert(nbatch_K2 == DKQ/2, "batching not implemented for multi stage loading"); constexpr bool use_cp_async = true; cp_async_wait_all(); @@ -471,8 +470,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( } } + // For MLA K and V have the same data. + // Therefore, iterate over K in reverse and later re-use the data if possible. #pragma unroll - for (int k0_start = 0; k0_start < DKQ/2; k0_start += nbatch_K2) { + for (int k0_start = (DKQ/2-1) - (DKQ/2-1) % nbatch_K2; k0_start >= 0; k0_start -= nbatch_K2) { const int k0_stop = k0_start + nbatch_K2 < DKQ/2 ? k0_start + nbatch_K2 : DKQ/2; const int k0_diff = k0_stop - k0_start; @@ -776,6 +777,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( } if constexpr (nstages > 1) { + static_assert(!V_is_K_view, "K data reuse not implemented multi-stage loading"); // Preload K tile for next iteration: constexpr bool use_cp_async = true; cp_async_wait_all(); @@ -791,11 +793,6 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( } - // For MLA K and V have the same data. - // Therefore, iterate over V in reverse and re-use the data if possible. - static_assert(!mla || nstages <= 1, "combination of MLA and multi-stage loading not implemented"); - // constexpr int reusable_cutoff = mla ? (DV - 1) - (DV - 1) % (2*nbatch_K2) : DV; - constexpr int reusable_cutoff = DV; // TODO implement properly #if defined(AMD_WMMA_AVAILABLE) && !defined(LDMATRIX_TRANS_AVAILABLE) T_A_VKQ A_identity; make_identity_mat(A_identity); @@ -803,12 +800,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( // Calculate VKQ tile, need to use logical rather than physical elements for i0 due to transposition of V: #pragma unroll - for (int i0_stop = DV; i0_stop > 0; i0_stop -= 2*nbatch_V2) { - const int i0_start = i0_stop - 2*nbatch_V2 > 0 ? i0_stop - 2*nbatch_V2 : 0; - const int i0_diff = i0_stop - i0_start; + for (int i0_start = 0; i0_start < DV; i0_start += 2*nbatch_V2) { + static_assert(DV % (2*nbatch_V2) == 0, "bad loop size"); + const int i0_stop = i0_start + 2*nbatch_V2; + const int i0_diff = i0_stop - i0_start; if constexpr (nstages <= 1) { - if (i0_start < reusable_cutoff) { + if (!V_is_K_view || i0_stop > 2*nbatch_K2) { constexpr bool use_cp_async = nstages == 1; flash_attn_ext_f16_load_tile (V_h2 + int64_t(k_VKQ_0)*stride_V + i0_start/2, tile_V, i0_diff/2, stride_V, k_VKQ_sup); @@ -818,7 +816,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( __syncthreads(); } } - const half2 * tile_V_i = i0_start < reusable_cutoff ? tile_V : tile_V + (i0_start - reusable_cutoff)/2; + const half2 * tile_V_i = !V_is_K_view || i0_stop > 2*nbatch_K2 ? tile_V : tile_V + i0_start/2; #if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J; @@ -921,7 +919,7 @@ template struct mma_tile_sizes { }; #endif // defined(TURING_MMA_AVAILABLE) -template +template static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const float2 * const __restrict__ Q_f2, const half2 * const __restrict__ K_h2, @@ -975,8 +973,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( constexpr int stride_tile_Q = DKQ/2 + 4; constexpr int stride_tile_K = nbatch_K2 + 4; - static_assert(!mla || nbatch_K2 >= nbatch_V2, "bad nbatch_K2, nbatch_V2 for MLA"); - constexpr int stride_tile_V = mla ? stride_tile_K : nbatch_V2 + 4; + constexpr int stride_tile_V = V_is_K_view ? stride_tile_K : nbatch_V2 + 4; constexpr int stride_tile_KV_max = stride_tile_K > stride_tile_V ? stride_tile_K : stride_tile_V; extern __shared__ half2 tile_Q[]; @@ -1080,7 +1077,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( constexpr bool last_iter = false; constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter - (Q_f2, K_h2, V_h2, mask_h, 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, @@ -1089,7 +1086,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( constexpr bool last_iter = true; const int k_VKQ_sup = ne11 - kb0*nbatch_fa; flash_attn_ext_f16_iter - (Q_f2, K_h2, V_h2, mask_h, 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, @@ -1100,7 +1097,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( constexpr bool last_iter = false; constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter - (Q_f2, K_h2, V_h2, mask_h, 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, @@ -1109,7 +1106,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( constexpr bool last_iter = true; constexpr int k_VKQ_sup = nbatch_fa; flash_attn_ext_f16_iter - (Q_f2, K_h2, V_h2, mask_h, 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, @@ -1457,7 +1454,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( #endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) } -template +template __launch_bounds__(ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols1*ncols2), ggml_cuda_fattn_mma_get_occupancy(DKQ, DV, ncols1*ncols2)) static __global__ void flash_attn_ext_f16( const char * __restrict__ Q, @@ -1509,8 +1506,6 @@ static __global__ void flash_attn_ext_f16( } #endif // defined(AMD_WMMA_AVAILABLE) - static_assert(!mla || DKQ >= DV, "MLA needs DKQ >= DV"); - constexpr int ncols = ncols1 * ncols2; constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols); constexpr int nthreads = ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols); @@ -1523,7 +1518,7 @@ static __global__ void flash_attn_ext_f16( const int stride_K = nb11 / sizeof(half2); const int stride_mask = nb31 / sizeof(half); - const int stride_V = mla ? stride_K : nb21 / sizeof(half2); + const int stride_V = V_is_K_view ? stride_K : nb21 / sizeof(half2); const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1; @@ -1553,7 +1548,7 @@ static __global__ void flash_attn_ext_f16( (const half *) (mask + nb33*(sequence % ne33)); float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2); - const half2 * V_h2 = mla ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); + const half2 * V_h2 = V_is_K_view ? K_h2 : (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, head0, n_head_log2, m0, m1) : 1.0f; @@ -1564,12 +1559,12 @@ static __global__ void flash_attn_ext_f16( constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. if (kb0_start == 0) { constexpr bool needs_fixup = false; // CUDA block is working on an entire tile. - flash_attn_ext_f16_process_tile + flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); } else { constexpr bool needs_fixup = true; // CUDA block is missing the beginning of a tile. - flash_attn_ext_f16_process_tile + flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); } @@ -1597,7 +1592,7 @@ static __global__ void flash_attn_ext_f16( (const half *) (mask + nb33*(sequence % ne33)); float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2); - const half2 * V_h2 = mla ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); + const half2 * V_h2 = V_is_K_view ? K_h2 : (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, head0, n_head_log2, m0, m1) : 1.0f; @@ -1608,7 +1603,7 @@ 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 + flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); #else @@ -1644,7 +1639,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml const int cols_per_warp = std::min(ncols, get_cols_per_warp(cc)); const int nwarps = nthreads / WARP_SIZE; - constexpr bool mla = DKQ == 576; + constexpr bool V_is_K_view = DKQ == 576; // Guaranteed by the kernel selection logic in fattn.cu const size_t nbytes_shared_KV_1stage = nbatch_fa * std::max(nbatch_K2 + 4, nbatch_V2 + 4) * sizeof(half2); const size_t nbytes_shared_KV_2stage = nbatch_fa * (nbatch_K2 + 4 + nbatch_V2 + 4) * sizeof(half2); @@ -1669,7 +1664,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml fattn_kernel_t fattn_kernel; if (logit_softcap == 0.0f) { constexpr bool use_logit_softcap = false; - fattn_kernel = flash_attn_ext_f16; + fattn_kernel = flash_attn_ext_f16; #if !defined(GGML_USE_MUSA) static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; @@ -1680,7 +1675,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml #endif // !defined(GGML_USE_MUSA) } else { constexpr bool use_logit_softcap = true; - fattn_kernel = flash_attn_ext_f16; + fattn_kernel = flash_attn_ext_f16; #if !defined(GGML_USE_MUSA) static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 87f07a2f93..ba2b96bc32 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -247,6 +247,8 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const } } + const bool V_is_K_view = V->op == GGML_OP_VIEW && V->src[0] == K && V->data == K->data; + const int cc = ggml_cuda_info().devices[device].cc; switch (K->ne[0]) { @@ -269,6 +271,9 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const if (!gqa_opt_applies || gqa_ratio % 4 != 0) { return BEST_FATTN_KERNEL_NONE; } + if (!V_is_K_view) { + return BEST_FATTN_KERNEL_NONE; + } break; default: return BEST_FATTN_KERNEL_NONE; From bb02f74c612064947e51d23269a1cf810b67c9a7 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 24 Jan 2026 17:58:45 +0100 Subject: [PATCH 007/206] chat: fix language input for translategemma (#19052) * chat: fix language input for translategemma * Update common/chat.cpp Co-authored-by: Aldehir Rojas --------- Co-authored-by: Aldehir Rojas --- common/chat.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/common/chat.cpp b/common/chat.cpp index aba26e97a1..eeb38ad06a 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -2659,6 +2659,10 @@ static common_chat_params common_chat_params_init_translate_gemma(const common_c templates_params inputs_new = inputs; json & messages = inputs_new.messages; + // default to chat_template_kwargs, or en-GB if not specified + std::string default_src_lang = inputs.extra_context.value("source_lang_code", "en-GB"); + std::string default_tgt_lang = inputs.extra_context.value("target_lang_code", "en-GB"); + GGML_ASSERT(messages.is_array()); for (auto & message : messages) { if (message.contains("role") && message["role"].get() != "user") { @@ -2670,8 +2674,10 @@ static common_chat_params common_chat_params_init_translate_gemma(const common_c if (message.contains("content") && !message["content"].is_array()) { auto content_str = message["content"].get(); // default to en-GB if not specified (to make common_chat_format_example works) - auto src_lang = message.contains("source_lang_code") ? message["source_lang_code"].get() : "en-GB"; - auto tgt_lang = message.contains("target_lang_code") ? message["target_lang_code"].get() : "en-GB"; + auto src_lang = message.contains("source_lang_code") + ? message["source_lang_code"].get() : default_src_lang; + auto tgt_lang = message.contains("target_lang_code") + ? message["target_lang_code"].get() : default_tgt_lang; message["content"] = json::array({ json{ {"type", "text"}, From 4e5b83b226919c146fbfa754641a6772bcb2d722 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 24 Jan 2026 21:57:51 +0100 Subject: [PATCH 008/206] GGUF: check that tensor size is representable (#19072) --- ggml/src/gguf.cpp | 8 ++++++++ tests/test-gguf.cpp | 19 +++++++++++++++---- 2 files changed, 23 insertions(+), 4 deletions(-) diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp index bfab5c4d60..ed0d7f2cae 100644 --- a/ggml/src/gguf.cpp +++ b/ggml/src/gguf.cpp @@ -585,6 +585,14 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par break; } + // check that the size of the tensor in bytes is representable + if (ok && uint64_t(ggml_nelements(&info.t)/ggml_blck_size(info.t.type)) > SIZE_MAX/ggml_type_size(info.t.type)) { + GGML_LOG_ERROR("%s: tensor '%s' with shape (%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") has a size in bytes > %zu\n", + __func__, info.t.name, info.t.ne[0], info.t.ne[1], info.t.ne[2], info.t.ne[3], SIZE_MAX); + ok = false; + break; + } + // calculate byte offsets given the tensor shape and type info.t.nb[0] = type_size; info.t.nb[1] = info.t.nb[0]*(info.t.ne[0]/blck_size); diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp index 3f0c312e2f..84b7f3bc49 100644 --- a/tests/test-gguf.cpp +++ b/tests/test-gguf.cpp @@ -1,9 +1,11 @@ #include "ggml.h" #include "ggml-backend.h" #include "../ggml/src/ggml-impl.h" +#include "gguf.h" #include #include +#include #include #include #include @@ -34,6 +36,7 @@ enum handcrafted_file_type { HANDCRAFTED_TENSORS_BAD_N_DIMS = 20 + offset_has_tensors, HANDCRAFTED_TENSORS_BAD_SHAPE = 30 + offset_has_tensors, HANDCRAFTED_TENSORS_NE_TOO_BIG = 40 + offset_has_tensors, + HANDCRAFTED_TENSORS_NBYTES_TOO_BIG = 45 + offset_has_tensors, HANDCRAFTED_TENSORS_BAD_TYPE = 50 + offset_has_tensors, HANDCRAFTED_TENSORS_BAD_OFFSET = 60 + offset_has_tensors, HANDCRAFTED_TENSORS_DUPLICATE_NAME = 70 + offset_has_tensors, @@ -69,6 +72,7 @@ static std::string handcrafted_file_type_name(const enum handcrafted_file_type h case HANDCRAFTED_TENSORS_BAD_N_DIMS: return "TENSORS_BAD_N_DIMS"; case HANDCRAFTED_TENSORS_BAD_SHAPE: return "TENSORS_BAD_SHAPE"; case HANDCRAFTED_TENSORS_NE_TOO_BIG: return "TENSORS_NE_TOO_BIG"; + case HANDCRAFTED_TENSORS_NBYTES_TOO_BIG: return "TENSORS_NBYTES_TOO_BIG"; case HANDCRAFTED_TENSORS_BAD_TYPE: return "TENSORS_BAD_TYPE"; case HANDCRAFTED_TENSORS_BAD_OFFSET: return "TENSORS_BAD_OFFSET"; case HANDCRAFTED_TENSORS_DUPLICATE_NAME: return "TENSORS_DUPLICATE_NAME"; @@ -326,7 +330,7 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft uint64_t offset = 0; for (int i = 0; i < int(tensor_configs.size()); ++i) { - const ggml_type type = tensor_configs[i].first; + const ggml_type type = hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG ? GGML_TYPE_I64 : tensor_configs[i].first; const std::array shape = tensor_configs[i].second; std::string name = "my_tensor"; @@ -343,7 +347,7 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft } helper_write(file, name.data(), name.length()); - uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_TOO_BIG ? 2 : 1; + uint32_t n_dims = (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG || hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG) ? 2 : 1; for (int i = GGML_MAX_DIMS-1; i >= 1; --i) { if (shape[i] != 1) { n_dims = i + 1; @@ -358,13 +362,19 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft } if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) { + const int64_t bad_dim = -1; for (uint32_t j = 0; j < n_dims; ++j) { - const int64_t bad_dim = -1; helper_write(file, bad_dim); } } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){ + const int64_t big_dim = 4*int64_t(INT32_MAX); + for (uint32_t j = 0; j < n_dims; ++j) { + helper_write(file, big_dim); + } + } else if (hft == HANDCRAFTED_TENSORS_NBYTES_TOO_BIG){ + const size_t big_ne = SIZE_MAX/ggml_type_size(type); + const int64_t big_dim = GGML_PAD(int64_t(1.01f*std::pow(big_ne, 1.0f/n_dims)) + 1, ggml_blck_size(type)); for (uint32_t j = 0; j < n_dims; ++j) { - const int64_t big_dim = 4*int64_t(INT32_MAX); helper_write(file, big_dim); } } else { @@ -682,6 +692,7 @@ static std::pair test_handcrafted_file(const unsigned int seed) { HANDCRAFTED_TENSORS_BAD_N_DIMS, HANDCRAFTED_TENSORS_BAD_SHAPE, HANDCRAFTED_TENSORS_NE_TOO_BIG, + HANDCRAFTED_TENSORS_NBYTES_TOO_BIG, HANDCRAFTED_TENSORS_BAD_TYPE, HANDCRAFTED_TENSORS_BAD_OFFSET, HANDCRAFTED_TENSORS_DUPLICATE_NAME, From e9fd8dcab45d6cd147874e32565923bdfd0efbdb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 24 Jan 2026 22:13:08 +0100 Subject: [PATCH 009/206] llama-fit-params: keep explicit --ctx-size 0 (#19070) --- common/arg.cpp | 4 ++++ include/llama.h | 1 + src/llama.cpp | 8 ++++++-- tools/fit-params/fit-params.cpp | 2 +- 4 files changed, 12 insertions(+), 3 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 163c9b71b0..98477e8117 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1231,6 +1231,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), [](common_params & params, int value) { params.n_ctx = value; + if (value == 0) { + // disable context reduction in llama_params_fit if the user explicitly requests the full context size: + params.fit_params_min_ctx = UINT32_MAX; + } } ).set_env("LLAMA_ARG_CTX_SIZE")); add_opt(common_arg( diff --git a/include/llama.h b/include/llama.h index 280745713e..1507107f1a 100644 --- a/include/llama.h +++ b/include/llama.h @@ -489,6 +489,7 @@ extern "C" { // - returns true if the parameters could be successfully modified to fit device memory // - this function is NOT thread safe because it modifies the global llama logger state // - only parameters that have the same value as in llama_default_model_params are modified + // with the exception of the context size which is modified if and only if equal to 0 LLAMA_API enum llama_params_fit_status llama_params_fit( const char * path_model, struct llama_model_params * mparams, diff --git a/src/llama.cpp b/src/llama.cpp index f1096d960e..11b75fcff9 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -311,8 +311,12 @@ static void llama_params_fit_impl( __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); } } else { - LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n", - __func__, hp_nct, n_ctx_min); + if (n_ctx_min == UINT32_MAX) { + LLAMA_LOG_INFO("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct); + } else { + LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n", + __func__, hp_nct, n_ctx_min); + } } } else { LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx); diff --git a/tools/fit-params/fit-params.cpp b/tools/fit-params/fit-params.cpp index f9d9cb34c7..0176be06e7 100644 --- a/tools/fit-params/fit-params.cpp +++ b/tools/fit-params/fit-params.cpp @@ -36,7 +36,7 @@ int main(int argc, char ** argv) { LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__); common_log_flush(common_log_main()); - printf("-c %" PRIu32 " -ngl %" PRIu32, cparams.n_ctx, mparams.n_gpu_layers); + printf("-c %" PRIu32 " -ngl %" PRIi32, cparams.n_ctx, mparams.n_gpu_layers); size_t nd = llama_max_devices(); while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) { From 9981c3013018b1e955f2e44dbe514032496d3f6e Mon Sep 17 00:00:00 2001 From: Bartowski <3266127+bartowski1182@users.noreply.github.com> Date: Sat, 24 Jan 2026 20:36:47 -0500 Subject: [PATCH 010/206] convert : fix conversion for inheriting models that were bypassing modify_tensors (#19064) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add undo_permute = False where needed * Replace super().modify_tensors with ModelBase * Add one more ModelBase.modify_tensors * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- convert_hf_to_gguf.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 8cc4963fb2..d8bc95fa6a 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2736,7 +2736,7 @@ class AfmoeModel(LlamaModel): data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" - yield from super().modify_tensors(data_torch, merged_name, bid) + yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid) return else: @@ -2745,7 +2745,7 @@ class AfmoeModel(LlamaModel): if name.endswith(".expert_bias"): name = name.replace(".expert_bias", ".expert_bias.bias") - yield from super().modify_tensors(data_torch, name, bid) + yield from ModelBase.modify_tensors(self, data_torch, name, bid) @ModelBase.register( @@ -8918,7 +8918,7 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel): return Mamba2Model.modify_tensors(self, data_torch, name, bid) elif bid in self._attn_layers: return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) - yield from super().modify_tensors(data_torch, name, bid) + yield from ModelBase.modify_tensors(self, data_torch, name, bid) def set_gguf_parameters(self): """This method merges params from both parents and some that are @@ -9050,33 +9050,33 @@ class NemotronHModel(GraniteHybridModel): if self.is_moe and bid is not None: if name.endswith("mixer.gate.e_score_correction_bias"): new_name = name.replace("e_score_correction_bias", "e_score_correction.bias") - yield from super().modify_tensors(data_torch, new_name, bid) + yield from ModelBase.modify_tensors(self, data_torch, new_name, bid) return if name.endswith("mixer.dt_bias"): new_name = name.replace("dt_bias", "dt.bias") - yield from super().modify_tensors(data_torch, new_name, bid) + yield from ModelBase.modify_tensors(self, data_torch, new_name, bid) return if name.endswith("mixer.conv1d.weight"): squeezed_data = data_torch.squeeze() - yield from super().modify_tensors(squeezed_data, name, bid) + yield from ModelBase.modify_tensors(self, squeezed_data, name, bid) return if name.endswith("mixer.A_log"): transformed_data = -torch.exp(data_torch) reshaped_data = transformed_data.squeeze().reshape(-1, 1) - yield from super().modify_tensors(reshaped_data, name, bid) + yield from ModelBase.modify_tensors(self, reshaped_data, name, bid) return if name.endswith("mixer.D"): reshaped_data = data_torch.squeeze().reshape(-1, 1) - yield from super().modify_tensors(reshaped_data, name, bid) + yield from ModelBase.modify_tensors(self, reshaped_data, name, bid) return if name.endswith("mixer.norm.weight"): reshaped_data = data_torch.reshape(self.n_group, -1) - yield from super().modify_tensors(reshaped_data, name, bid) + yield from ModelBase.modify_tensors(self, reshaped_data, name, bid) return if name.find("mixer.experts") != -1: @@ -9101,7 +9101,7 @@ class NemotronHModel(GraniteHybridModel): data_torch = torch.stack(datas, dim=0) merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" - yield from super().modify_tensors(data_torch, merged_name, bid) + yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid) return else: return @@ -10731,7 +10731,7 @@ class CogVLMModel(LlamaModel): if name.startswith("model.vision."): return - yield from super().modify_tensors(data_torch, name, bid) + yield from ModelBase.modify_tensors(self, data_torch, name, bid) @ModelBase.register("JanusForConditionalGeneration") From 16639ba2178fc1ccbae912d678d8b515dc4dc322 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sun, 25 Jan 2026 07:31:42 +0100 Subject: [PATCH 011/206] common : use two decimal places for float arg help messages (#19048) * common : use two decimal places for float arg help messages This commit updates the help messages for various command-line arguments in arg.cpp to display floating-point default values with two decimal places instead of one. The motivation for this changes is that currently only having one decimal place means that values generated using --help or llama-gen-docs will not display the correct values. For example, currently the value of top-p in tools/server/README.md is `0.9`, but the default value is actually '0.95'. And running llama-gen-docs does not update this value as it uses the output from the help message, which shows only one decimal place, so the values look like they are unchanged. * docs : run llama-gen-docs to update docs --- common/arg.cpp | 42 +++++++++++++++---------------- tools/cli/README.md | 48 +++++++++++++++++------------------ tools/completion/README.md | 44 ++++++++++++++++---------------- tools/server/README.md | 51 +++++++++++++++++++------------------- 4 files changed, 94 insertions(+), 91 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 98477e8117..04fd375d56 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1577,7 +1577,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--temp"}, "N", - string_format("temperature (default: %.1f)", (double)params.sampling.temp), + string_format("temperature (default: %.2f)", (double)params.sampling.temp), [](common_params & params, const std::string & value) { params.sampling.temp = std::stof(value); params.sampling.temp = std::max(params.sampling.temp, 0.0f); @@ -1594,7 +1594,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam().set_env("LLAMA_ARG_TOP_K")); add_opt(common_arg( {"--top-p"}, "N", - string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), + string_format("top-p sampling (default: %.2f, 1.0 = disabled)", (double)params.sampling.top_p), [](common_params & params, const std::string & value) { params.sampling.top_p = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P; @@ -1602,7 +1602,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--min-p"}, "N", - string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p), + string_format("min-p sampling (default: %.2f, 0.0 = disabled)", (double)params.sampling.min_p), [](common_params & params, const std::string & value) { params.sampling.min_p = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P; @@ -1610,14 +1610,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--top-nsigma"}, "N", - string_format("top-n-sigma sampling (default: %.1f, -1.0 = disabled)", params.sampling.top_n_sigma), + string_format("top-n-sigma sampling (default: %.2f, -1.0 = disabled)", params.sampling.top_n_sigma), [](common_params & params, const std::string & value) { params.sampling.top_n_sigma = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--xtc-probability"}, "N", - string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability), + string_format("xtc probability (default: %.2f, 0.0 = disabled)", (double)params.sampling.xtc_probability), [](common_params & params, const std::string & value) { params.sampling.xtc_probability = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY; @@ -1625,7 +1625,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--xtc-threshold"}, "N", - string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), + string_format("xtc threshold (default: %.2f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), [](common_params & params, const std::string & value) { params.sampling.xtc_threshold = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD; @@ -1633,7 +1633,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--typical"}, "N", - string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sampling.typ_p), + string_format("locally typical sampling, parameter p (default: %.2f, 1.0 = disabled)", (double)params.sampling.typ_p), [](common_params & params, const std::string & value) { params.sampling.typ_p = std::stof(value); } @@ -1652,7 +1652,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--repeat-penalty"}, "N", - string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), + string_format("penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), [](common_params & params, const std::string & value) { params.sampling.penalty_repeat = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT; @@ -1660,21 +1660,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--presence-penalty"}, "N", - string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_present), + string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present), [](common_params & params, const std::string & value) { params.sampling.penalty_present = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--frequency-penalty"}, "N", - string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_freq), + string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq), [](common_params & params, const std::string & value) { params.sampling.penalty_freq = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--dry-multiplier"}, "N", - string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sampling.dry_multiplier), + string_format("set DRY sampling multiplier (default: %.2f, 0.0 = disabled)", (double)params.sampling.dry_multiplier), [](common_params & params, const std::string & value) { params.sampling.dry_multiplier = std::stof(value); } @@ -1755,14 +1755,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--dynatemp-range"}, "N", - string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), + string_format("dynamic temperature range (default: %.2f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), [](common_params & params, const std::string & value) { params.sampling.dynatemp_range = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--dynatemp-exp"}, "N", - string_format("dynamic temperature exponent (default: %.1f)", (double)params.sampling.dynatemp_exponent), + string_format("dynamic temperature exponent (default: %.2f)", (double)params.sampling.dynatemp_exponent), [](common_params & params, const std::string & value) { params.sampling.dynatemp_exponent = std::stof(value); } @@ -1778,7 +1778,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--mirostat-lr"}, "N", - string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta), + string_format("Mirostat learning rate, parameter eta (default: %.2f)", (double)params.sampling.mirostat_eta), [](common_params & params, const std::string & value) { params.sampling.mirostat_eta = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA; @@ -1786,7 +1786,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--mirostat-ent"}, "N", - string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau), + string_format("Mirostat target entropy, parameter tau (default: %.2f)", (double)params.sampling.mirostat_tau), [](common_params & params, const std::string & value) { params.sampling.mirostat_tau = std::stof(value); params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU; @@ -1920,28 +1920,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_env("LLAMA_ARG_YARN_ORIG_CTX")); add_opt(common_arg( {"--yarn-ext-factor"}, "N", - string_format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), + string_format("YaRN: extrapolation mix factor (default: %.2f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), [](common_params & params, const std::string & value) { params.yarn_ext_factor = std::stof(value); } ).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); add_opt(common_arg( {"--yarn-attn-factor"}, "N", - string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor), + string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.2f)", (double)params.yarn_attn_factor), [](common_params & params, const std::string & value) { params.yarn_attn_factor = std::stof(value); } ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); add_opt(common_arg( {"--yarn-beta-slow"}, "N", - string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow), + string_format("YaRN: high correction dim or alpha (default: %.2f)", (double)params.yarn_beta_slow), [](common_params & params, const std::string & value) { params.yarn_beta_slow = std::stof(value); } ).set_env("LLAMA_ARG_YARN_BETA_SLOW")); add_opt(common_arg( {"--yarn-beta-fast"}, "N", - string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast), + string_format("YaRN: low correction dim or beta (default: %.2f)", (double)params.yarn_beta_fast), [](common_params & params, const std::string & value) { params.yarn_beta_fast = std::stof(value); } @@ -3335,14 +3335,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN")); add_opt(common_arg( {"--draft-p-split"}, "P", - string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split), + string_format("speculative decoding split probability (default: %.2f)", (double)params.speculative.p_split), [](common_params & params, const std::string & value) { params.speculative.p_split = std::stof(value); } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT")); add_opt(common_arg( {"--draft-p-min"}, "P", - string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min), + string_format("minimum speculative decoding probability (greedy) (default: %.2f)", (double)params.speculative.p_min), [](common_params & params, const std::string & value) { params.speculative.p_min = std::stof(value); } diff --git a/tools/cli/README.md b/tools/cli/README.md index 3b6f0708ed..4a15cbad9d 100644 --- a/tools/cli/README.md +++ b/tools/cli/README.md @@ -45,10 +45,10 @@ | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | | `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | | `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | -| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | -| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | -| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | -| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.00)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_FAST) | | `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | | `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | | `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | @@ -109,30 +109,30 @@ | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--temp N` | temperature (default: 0.8) | +| `--temp N` | temperature (default: 0.80) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | -| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | -| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | -| `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) | -| `--adaptive-decay N` | adaptive-p: EMA decay for adaptation; effective history length ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99) | -| `--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) | +| `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) | +| `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) | +| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) | +| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | -| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | -| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | -| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | -| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) | +| `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) | +| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.00, 0.0 = disabled) | | `--dry-base N` | set DRY sampling base value (default: 1.75) | | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | | `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | -| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | -| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | +| `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) | +| `--adaptive-decay N` | adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable.
(valid range 0.0 to 0.99) (default: 0.90) | +| `--dynatemp-range N` | dynamic temperature range (default: 0.00, 0.0 = disabled) | +| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.00) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | -| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | -| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.10) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.00) | | `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | @@ -173,12 +173,12 @@ | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(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:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(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)
(env: LLAMA_ARG_THINK_BUDGET) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | -| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | | `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | | `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)
(env: LLAMA_ARG_DRAFT_MIN) | -| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)
(env: LLAMA_ARG_DRAFT_P_MIN) | | `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | | `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | diff --git a/tools/completion/README.md b/tools/completion/README.md index a16be3f684..3ca3e68454 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -128,10 +128,10 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | | `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | | `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | -| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | -| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | -| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | -| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.00)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_FAST) | | `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | | `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | | `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | @@ -192,28 +192,30 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--temp N` | temperature (default: 0.8) | +| `--temp N` | temperature (default: 0.80) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | -| `--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) | +| `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) | +| `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) | +| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) | +| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | -| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | -| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | -| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | -| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) | +| `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) | +| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.00, 0.0 = disabled) | | `--dry-base N` | set DRY sampling base value (default: 1.75) | | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | | `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | -| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | -| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | +| `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) | +| `--adaptive-decay N` | adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable.
(valid range 0.0 to 0.99) (default: 0.90) | +| `--dynatemp-range N` | dynamic temperature range (default: 0.00, 0.0 = disabled) | +| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.00) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | -| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | -| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.10) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.00) | | `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | @@ -251,8 +253,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: disabled)
(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:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(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)
(env: LLAMA_ARG_THINK_BUDGET) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | -| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | diff --git a/tools/server/README.md b/tools/server/README.md index f113f9cb75..d132830171 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -63,10 +63,10 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | | `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | | `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | -| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | -| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | -| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | -| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.00)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.00)
(env: LLAMA_ARG_YARN_BETA_FAST) | | `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | | `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | | `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | @@ -126,30 +126,30 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--temp N` | temperature (default: 0.8) | +| `--temp N` | temperature (default: 0.80) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | -| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | -| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | -| `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) | -| `--adaptive-decay N` | adaptive-p: EMA decay for adaptation; effective history length ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99) | -| `--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) | +| `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) | +| `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) | +| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) | +| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | -| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | -| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | -| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | -| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) | +| `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) | +| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.00, 0.0 = disabled) | | `--dry-base N` | set DRY sampling base value (default: 1.75) | | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | | `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | -| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | -| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | +| `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) | +| `--adaptive-decay N` | adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable.
(valid range 0.0 to 0.99) (default: 0.90) | +| `--dynatemp-range N` | dynamic temperature range (default: 0.00, 0.0 = disabled) | +| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.00) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | -| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | -| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.10) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.00) | | `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | @@ -199,7 +199,8 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'
(env: LLAMA_CHAT_TEMPLATE_KWARGS) | | `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | | `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | -| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE) | +| `--cache-prompt, --no-cache-prompt` | whether to enable prompt caching (default: enabled)
(env: LLAMA_ARG_CACHE_PROMPT) | +| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | | `--slots, --no-slots` | expose slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | @@ -212,8 +213,8 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(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:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(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)
(env: LLAMA_ARG_THINK_BUDGET) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | -| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, 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, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
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

(env: LLAMA_ARG_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: 0.10, 0.0 = disabled) | | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | @@ -222,7 +223,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) | | `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | | `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)
(env: LLAMA_ARG_DRAFT_MIN) | -| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)
(env: LLAMA_ARG_DRAFT_P_MIN) | | `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | | `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | From 24bc23830313ce13080ae393309cef080f2141c2 Mon Sep 17 00:00:00 2001 From: Jakkala Mahesh <155058658+MaheshJakkala@users.noreply.github.com> Date: Sun, 25 Jan 2026 12:40:52 +0530 Subject: [PATCH 012/206] llama: fix integer type consistency in split helpers (#18894) * llama: fix integer type consistency in split helpers * llama: apply minor style fixes * llama: remove trailing whitespace --- include/llama.h | 4 ++-- src/llama.cpp | 58 +++++++++++++++++++++++++++++++++++++------------ 2 files changed, 46 insertions(+), 16 deletions(-) diff --git a/include/llama.h b/include/llama.h index 1507107f1a..c3360ae57c 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1476,12 +1476,12 @@ extern "C" { /// @details Build a split GGUF final path for this chunk. /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf" // Returns the split_path length. - LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count); + LLAMA_API int32_t llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int32_t split_no, int32_t split_count); /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match. /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0" // Returns the split_prefix length. - LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count); + LLAMA_API int32_t llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int32_t split_no, int32_t split_count); // Print system information LLAMA_API const char * llama_print_system_info(void); diff --git a/src/llama.cpp b/src/llama.cpp index 11b75fcff9..6da90d6f1f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1095,25 +1095,55 @@ int32_t llama_chat_apply_template( // model split // -int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) { +int32_t llama_split_path( + char * split_path, + size_t maxlen, + const char * path_prefix, + int32_t split_no, + int32_t split_count) { + static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf"; - if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) { - return strlen(split_path); + + const int written = snprintf( + split_path, + maxlen, + SPLIT_PATH_FORMAT, + path_prefix, + split_no + 1, + split_count + ); + + if (written < 0 || (size_t) written >= maxlen) { + return 0; } - return 0; + + return (int32_t) written; } -int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count) { - std::string str_split_path(split_path); - char postfix[32]; - snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count); - std::string str_postfix(postfix); +int32_t llama_split_prefix( + char * split_prefix, + size_t maxlen, + const char * split_path, + int32_t split_no, + int32_t split_count) { - // check if split_prefix ends with postfix - int size_prefix = str_split_path.size() - str_postfix.size(); - if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) { - snprintf(split_prefix, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path); - return size_prefix; + const std::string str_split_path(split_path); + + char postfix[32]; + snprintf(postfix, sizeof(postfix), "-%05d-of-%05d.gguf", split_no + 1, split_count); + + const std::string str_postfix(postfix); + if (str_split_path.size() <= str_postfix.size()) { + return 0; + } + + const size_t size_prefix = str_split_path.size() - str_postfix.size(); + + if (str_split_path.compare(size_prefix, std::string::npos, str_postfix) == 0) { + const size_t copy_len = std::min(size_prefix + 1, maxlen); + snprintf(split_prefix, copy_len, "%s", split_path); + + return (int32_t) size_prefix; } return 0; From 1243f93a2de868e16a9e52af55b7ab930110c04e Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Sun, 25 Jan 2026 15:11:19 +0800 Subject: [PATCH 013/206] readme: update RWKV7 model links (#19061) Signed-off-by: Molly Sophia --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 91a8f25d1c..0783e43e5c 100644 --- a/README.md +++ b/README.md @@ -132,6 +132,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) +- [x] [RWKV-7](https://huggingface.co/collections/shoumenchougou/rwkv7-gxx-gguf) - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) - [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1) - [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct) From 080b161995218bb40bfc03a6446c9e2b4c7e81e0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Jan 2026 09:12:50 +0200 Subject: [PATCH 014/206] completion : fix prompt cache for recurrent models (#19045) --- src/llama-context.cpp | 1 + tools/completion/completion.cpp | 88 ++++++++++++++++++--------------- 2 files changed, 48 insertions(+), 41 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index a35cf5a94b..fb817e1385 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2559,6 +2559,7 @@ size_t llama_context::state_write_data(llama_io_write_i & io) { } } + // [TAG_CONTEXT_STATE_LOGITS] // write logits { LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__); diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp index a9eda119d7..f368a2f4c6 100644 --- a/tools/completion/completion.cpp +++ b/tools/completion/completion.cpp @@ -342,44 +342,51 @@ int main(int argc, char ** argv) { return 1; } - // debug message about similarity of saved session, if applicable - size_t n_matching_session_tokens = 0; - if (!session_tokens.empty()) { - for (llama_token id : session_tokens) { - if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { - break; + bool session_do_save = false; + + { + size_t n_match = 0; + + if (!session_tokens.empty()) { + for (llama_token id : session_tokens) { + if (n_match >= embd_inp.size() || id != embd_inp[n_match]) { + break; + } + n_match++; + } + if (params.prompt.empty() && n_match == embd_inp.size()) { + LOG_INF("%s: using full prompt from session file\n", __func__); + } else if (n_match >= embd_inp.size()) { + LOG_INF("%s: session file has exact match for prompt!\n", __func__); + } else if (n_match < (embd_inp.size() / 2)) { + LOG_WRN("%s: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", + __func__, n_match, embd_inp.size()); + } else { + LOG_INF("%s: session file matches %zu / %zu tokens of prompt\n", + __func__, n_match, embd_inp.size()); + } + + if (session_tokens.size() == n_match) { + // [TAG_CONTEXT_STATE_LOGITS] + // in this case, we are going to reuse the logits from the session + // if we ever decide to remove the logits from the session, we need to handle this somehow + // ref: https://github.com/ggml-org/llama.cpp/pull/18862#issuecomment-3756330941 + } + + // remove any "future" tokens that we might have inherited from the previous session + if (session_tokens.size() > n_match) { + if (!llama_memory_seq_rm(mem, -1, n_match, -1)) { + LOG_WRN("%s: unable to resuse common prefix (for example, when the memory is recurrent)\n", __func__); + llama_memory_clear(mem, true); + session_tokens.clear(); + n_match = 0; + } else { + session_tokens.resize(n_match); + } } - n_matching_session_tokens++; - } - if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) { - LOG_INF("%s: using full prompt from session file\n", __func__); - } else if (n_matching_session_tokens >= embd_inp.size()) { - LOG_INF("%s: session file has exact match for prompt!\n", __func__); - } else if (n_matching_session_tokens < (embd_inp.size() / 2)) { - LOG_WRN("%s: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", - __func__, n_matching_session_tokens, embd_inp.size()); - } else { - LOG_INF("%s: session file matches %zu / %zu tokens of prompt\n", - __func__, n_matching_session_tokens, embd_inp.size()); } - // remove any "future" tokens that we might have inherited from the previous session - if (!llama_memory_seq_rm(mem, -1, n_matching_session_tokens, -1)) { - LOG_INF("%s: unable to resuse common prefix\n", __func__); - n_matching_session_tokens = 0; - llama_memory_seq_rm(mem, -1, -1, -1); - } - } - - LOG_DBG("recalculate the cached logits (check): embd_inp.size() %zu, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu\n", - embd_inp.size(), n_matching_session_tokens, embd_inp.size(), session_tokens.size()); - - // if we will use the cache for the full prompt without reaching the end of the cache, force - // reevaluation of the last token to recalculate the cached logits - if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { - LOG_DBG("recalculate the cached logits (do): session_tokens.resize( %zu )\n", embd_inp.size() - 1); - - session_tokens.resize(embd_inp.size() - 1); + session_do_save = !path_session.empty() && n_match < embd_inp.size() && !params.prompt_cache_ro; } // number of tokens to keep when resetting context @@ -521,10 +528,9 @@ int main(int argc, char ** argv) { is_interacting = params.interactive_first; } - bool is_antiprompt = false; - bool input_echo = true; - bool display = true; - bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size(); + bool is_antiprompt = false; + bool input_echo = true; + bool display = true; int n_past = 0; int n_remain = params.n_predict; @@ -700,8 +706,8 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed && !is_interacting) { // optionally save the session on first sample (for faster prompt loading next time) - if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { - need_to_save_session = false; + if (session_do_save) { + session_do_save = false; llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); LOG_DBG("saved session to %s\n", path_session.c_str()); From 70d860824a72c3daaea678b74d5051fdd46d9c38 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 25 Jan 2026 13:05:05 +0100 Subject: [PATCH 015/206] convert : fix Gemma3N, GraniteMoe and Ernie4.5Moe (#19084) * fix Gemma3N and Ernie4.5Moe * fix GraniteMoe --- convert_hf_to_gguf.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d8bc95fa6a..b56a99c5fa 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3799,7 +3799,7 @@ class Ernie4_5MoeModel(Ernie4_5Model): merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" yield from super().modify_tensors(data_torch, merged_name, bid) else: - yield from super().modify_tensors(data_torch, name, bid) + yield from ModelBase.modify_tensors(self, data_torch, name, bid) def prepare_tensors(self): super().prepare_tensors() @@ -6153,7 +6153,7 @@ class Gemma3nVisionAudioModel(ConformerAudioModel): if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"): data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1] - yield from super().modify_tensors(data_torch, new_name, bid) + yield from ModelBase.modify_tensors(self, data_torch, new_name, bid) @ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration") @@ -6253,7 +6253,7 @@ class Gemma3NModel(Gemma3Model): # Continue with normal processing name = name.replace("language_model.", "") - yield from super().modify_tensors(data_torch, name, bid) + yield from ModelBase.modify_tensors(self, data_torch, name, bid) return if "altup_unembed_projections" in name: @@ -6270,7 +6270,7 @@ class Gemma3NModel(Gemma3Model): raise ValueError(f"Unknown name: {name}") out = self._stack_matrices(self._altup_unembd) if out is not None: - yield from super().modify_tensors(out, "model.altup_unembed_projections.weight", bid) + yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid) return else: return @@ -6287,7 +6287,7 @@ class Gemma3NModel(Gemma3Model): raise ValueError(f"Unknown name: {name}") out = self._stack_matrices(self._altup_proj) if out is not None: - yield from super().modify_tensors(out, "model.altup_projections.weight", bid) + yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid) return else: return @@ -8803,8 +8803,8 @@ class GraniteMoeModel(GraniteModel): ffn_dim = self.hparams["intermediate_size"] assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size" gate, up = data_torch.split(ffn_dim, dim=-2) - yield from super().modify_tensors(gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid) - yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid) + yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid) + yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid) has_experts = bool(self.hparams.get('num_local_experts')) @@ -8813,15 +8813,15 @@ class GraniteMoeModel(GraniteModel): assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size" gate, up = data_torch.split(ffn_dim, dim=-2) if has_experts: - yield from super().modify_tensors(gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid) - yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid) + yield from ModelBase.modify_tensors(self, gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid) + yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid) return - yield from super().modify_tensors(gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid) - yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid) + yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid) + yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid) return if not has_experts and name.endswith("shared_mlp.output_linear.weight"): - yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid) + yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid) return yield from super().modify_tensors(data_torch, name, bid) From d9c6ce46f747189cd6238ca7699253613f77c016 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Jan 2026 15:48:56 +0200 Subject: [PATCH 016/206] kv-cache : support V-less cache (#19067) * kv-cache : support V-less cache * cuda : better check for V_is_K_view * cuda : improve V_is_K_view check * graph : add comments * hparams : refactor --- ggml/src/ggml-cuda/fattn-common.cuh | 2 +- ggml/src/ggml-cuda/fattn.cu | 2 +- src/llama-context.cpp | 8 +- src/llama-graph.cpp | 117 ++++++++++++++++++++++++++-- src/llama-graph.h | 48 ++++++++++++ src/llama-hparams.cpp | 19 ++++- src/llama-hparams.h | 14 +++- src/llama-kv-cache.cpp | 36 +++++++-- src/llama-model-saver.cpp | 4 +- src/llama-model.cpp | 30 ++++--- src/models/deepseek2.cpp | 19 ++--- 11 files changed, 246 insertions(+), 53 deletions(-) diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 40c7725784..13c5b0a459 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -782,7 +782,7 @@ void launch_fattn( const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; - const bool V_is_K_view = V->op == GGML_OP_VIEW && V->src[0] == K && V->data == K->data; + const bool V_is_K_view = V->view_src && V->view_offs == 0 && (V->view_src == K || V->view_src == K->view_src); const ggml_tensor * mask = dst->src[3]; const ggml_tensor * sinks = dst->src[4]; diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index ba2b96bc32..a5e6624181 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -247,7 +247,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const } } - const bool V_is_K_view = V->op == GGML_OP_VIEW && V->src[0] == K && V->data == K->data; + const bool V_is_K_view = V->view_src && V->view_offs == 0 && (V->view_src == K || V->view_src == K->view_src); const int cc = ggml_cuda_info().devices[device].cc; diff --git a/src/llama-context.cpp b/src/llama-context.cpp index fb817e1385..72211db17b 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -793,7 +793,7 @@ float * llama_context::get_embeddings_ith(int32_t i) { throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs)); } - const uint32_t n_embd_out = model.hparams.get_n_embd_out(); + const uint32_t n_embd_out = model.hparams.n_embd_out(); return embd + j*n_embd_out; } catch (const std::exception & err) { LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what()); @@ -1279,7 +1279,7 @@ int llama_context::encode(const llama_batch & batch_inp) { { // extract token embeddings GGML_ASSERT(embd != nullptr); - const uint32_t n_embd_out = hparams.get_n_embd_out(); + const uint32_t n_embd_out = hparams.n_embd_out(); GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd_size); ggml_backend_tensor_get_async(backend_embd, t_embd, embd, 0, n_tokens*n_embd_out*sizeof(float)); @@ -1688,7 +1688,7 @@ int llama_context::decode(const llama_batch & batch_inp) { { // extract token embeddings GGML_ASSERT(embd != nullptr); - const uint32_t n_embd_out = hparams.get_n_embd_out(); + const uint32_t n_embd_out = hparams.n_embd_out(); float * embd_out = embd + n_outputs_prev*n_embd_out; if (n_outputs) { @@ -1821,7 +1821,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba const auto n_batch = cparams.n_batch; const auto n_vocab = vocab.n_tokens(); - const auto n_embd_out = hparams.get_n_embd_out(); + const auto n_embd_out = hparams.n_embd_out(); bool has_logits = true; bool has_embd = cparams.embeddings; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index f9ed87cce1..16d42c4ae3 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -407,6 +407,27 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) { return res; } +void llm_graph_input_attn_k::set_input(const llama_ubatch * ubatch) { + mctx->set_input_k_idxs(self_k_idxs, ubatch); + + mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); +} + +bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) { + const auto * mctx = static_cast(params.mctx); + + this->mctx = mctx; + + bool res = true; + + res &= self_k_idxs->ne[0] == params.ubatch.n_tokens; + + res &= self_kq_mask->ne[0] == mctx->get_n_kv(); + res &= self_kq_mask->ne[1] == params.ubatch.n_tokens; + + return res; +} + 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); @@ -1596,11 +1617,6 @@ ggml_tensor * llm_graph_context::build_attn_mha( v = ggml_transpose(ctx0, v); } - // TODO: update llama_kv_cache to not store V cache in the MLA case and automatically return a view of K - if (v_mla) { - v = ggml_view_4d(ctx0, k, v->ne[0], v->ne[1], v->ne[2], v->ne[3], k->nb[1], k->nb[2], k->nb[3], 0); - } - // this can happen when KV cache is not used (e.g. an embedding model with non-causal attn) if (k->type == GGML_TYPE_F32) { k = ggml_cast(ctx0, k, GGML_TYPE_F16); @@ -1823,9 +1839,11 @@ ggml_tensor * llm_graph_context::build_attn( ggml_tensor * v_cur, ggml_tensor * kq_b, ggml_tensor * sinks, - ggml_tensor * v_mla, + ggml_tensor * v_mla, // TODO: remove float kq_scale, int il) const { + GGML_ASSERT(v_mla == nullptr); + // these nodes are added to the graph together so that they are not reordered // by doing so, the number of splits in the graph is reduced // expand k later to enable rope fusion which directly writes into k-v cache @@ -1868,6 +1886,93 @@ ggml_tensor * llm_graph_context::build_attn( return cur; } +static std::unique_ptr build_attn_inp_k_impl( + ggml_context * ctx0, + const llama_ubatch & ubatch, + const llama_hparams & hparams, + const llama_cparams & cparams, + const llama_kv_cache_context * mctx_cur) { + + auto inp = std::make_unique(hparams, cparams, mctx_cur); + + { + 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; + const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq; + + inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch); + + inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream); + ggml_set_input(inp->self_kq_mask); + + inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; + } + + return inp; +} + +llm_graph_input_attn_k * llm_graph_context::build_attn_inp_k() const { + const auto * mctx_cur = static_cast(mctx); + + auto inp = build_attn_inp_k_impl(ctx0, ubatch, hparams, cparams, mctx_cur); + + return (llm_graph_input_attn_k *) res->add_input(std::move(inp)); +} + +ggml_tensor * llm_graph_context::build_attn( + llm_graph_input_attn_k * 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 * 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 + // by doing so, the number of splits in the graph is reduced + // expand k later to enable rope fusion which directly writes into k-v cache + ggml_build_forward_expand(gf, q_cur); + ggml_build_forward_expand(gf, v_cur); + ggml_build_forward_expand(gf, k_cur); + + const auto * mctx_cur = inp->mctx; + + // store to KV cache + { + const auto & k_idxs = inp->get_k_idxs(); + + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il)); + } + + const auto & kq_mask = inp->get_kq_mask(); + + ggml_tensor * q = q_cur; + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = ggml_view_4d(ctx0, k, v_cur->ne[0], k->ne[1], k->ne[2], k->ne[3], k->nb[1], k->nb[2], k->nb[3], 0); + + 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) { + cur = build_lora_mm(wo, cur); + if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) { + // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators + ggml_mul_mat_set_prec(cur, GGML_PREC_F32); + } + } + + if (wo_b) { + cur = ggml_add(ctx0, cur, wo_b); + } + + return cur; +} + ggml_tensor * llm_graph_context::build_attn( llm_graph_input_attn_kv_iswa * inp, ggml_tensor * wo, diff --git a/src/llama-graph.h b/src/llama-graph.h index 242a046d56..4090d8116c 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -317,6 +317,39 @@ public: const llama_kv_cache_context * mctx; }; +// V-less input for the KV cache +// ref: https://github.com/ggml-org/llama.cpp/pull/19067 +class llm_graph_input_attn_k : public llm_graph_input_i { +public: + llm_graph_input_attn_k( + const llama_hparams & hparams, + const llama_cparams & cparams, + const llama_kv_cache_context * mctx) : + hparams(hparams), + cparams(cparams), + mctx(mctx) { + } + ~llm_graph_input_attn_k() = default; + + void set_input(const llama_ubatch * ubatch) override; + + bool can_reuse(const llm_graph_params & params) override; + + ggml_tensor * get_k_idxs() const { return self_k_idxs; } + + ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; } + + ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch] + + ggml_tensor * self_kq_mask = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream] + + const llama_hparams hparams; + const llama_cparams cparams; + + const llama_kv_cache_context * mctx; +}; + class llm_graph_input_attn_kv_iswa : public llm_graph_input_i { public: llm_graph_input_attn_kv_iswa( @@ -833,6 +866,21 @@ struct llm_graph_context { 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] // TODO: remove + float kq_scale, + int il) const; + + llm_graph_input_attn_k * build_attn_inp_k() const; + + ggml_tensor * build_attn( + llm_graph_input_attn_k * 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] + 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-hparams.cpp b/src/llama-hparams.cpp index 5f1df995f3..392f9160ce 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -72,8 +72,8 @@ uint32_t llama_hparams::n_embd_inp() const { return n_embd_inp; } -uint32_t llama_hparams::get_n_embd_out() const { - return n_embd_out > 0 ? n_embd_out : n_embd; +uint32_t llama_hparams::n_embd_out() const { + return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd; } uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const { @@ -175,6 +175,21 @@ bool llama_hparams::is_swa(uint32_t il) const { GGML_ABORT("fatal error"); } +bool llama_hparams::is_mla() const { + assert((n_embd_head_k_mla_impl == 0 && n_embd_head_v_mla_impl == 0) || + (n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0)); + + return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0; +} + +uint32_t llama_hparams::n_embd_head_k_mla() const { + return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k; +} + +uint32_t llama_hparams::n_embd_head_v_mla() const { + return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v; +} + 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) { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 2bf8665520..caed0ec1b7 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -53,8 +53,8 @@ struct llama_hparams { uint32_t n_rel_attn_bkts = 0; // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA - uint32_t n_embd_head_k_mla = 0; - uint32_t n_embd_head_v_mla = 0; + uint32_t n_embd_head_k_mla_impl = 0; + uint32_t n_embd_head_v_mla_impl = 0; // for WavTokenizer struct llama_hparams_posnet posnet; @@ -164,7 +164,7 @@ struct llama_hparams { uint32_t n_cls_out = 1; // output embedding dimension (0 = use n_embd) - uint32_t n_embd_out = 0; + uint32_t n_embd_out_impl = 0; // llama4 smallthinker uint32_t n_moe_layer_step = 0; @@ -239,7 +239,7 @@ struct llama_hparams { uint32_t n_embd_inp() const; // dimension of output embeddings - uint32_t get_n_embd_out() const; + uint32_t n_embd_out() const; // dimension of key embeddings across all k-v heads uint32_t n_embd_k_gqa(uint32_t il = 0) const; @@ -269,6 +269,12 @@ struct llama_hparams { bool is_swa(uint32_t il) const; + // note: currently only support if either all or none of the layers are MLA + bool is_mla() const; + + uint32_t n_embd_head_k_mla() const; + uint32_t n_embd_head_v_mla() const; + bool has_kv(uint32_t il) const; // number of layers for which has_kv() returns true diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index a7327c4987..f3c9b49f30 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -97,6 +97,8 @@ llama_kv_cache::llama_kv_cache( __func__, hparams.n_embd_v_gqa_max()); } + const bool is_mla = hparams.is_mla(); + 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); @@ -130,18 +132,21 @@ llama_kv_cache::llama_kv_cache( throw std::runtime_error("failed to create ggml context for kv cache"); } - ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream); - ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream); + const bool has_k = true; + const bool has_v = !is_mla; - ggml_format_name(k, "cache_k_l%d", il); - ggml_format_name(v, "cache_v_l%d", il); + ggml_tensor * k = has_k ? ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream) : nullptr; + ggml_tensor * v = has_v ? ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream) : nullptr; + + has_k && ggml_format_name(k, "cache_k_l%d", il); + has_v && ggml_format_name(v, "cache_v_l%d", il); std::vector k_stream; std::vector v_stream; for (uint32_t s = 0; s < n_stream; ++s) { - k_stream.push_back(ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2])); - v_stream.push_back(ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2])); + k_stream.push_back(has_k ? ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2]) : nullptr); + v_stream.push_back(has_v ? ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]) : nullptr); } map_layer_ids[il] = layers.size(); @@ -647,7 +652,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co const auto & layer = layers[il]; ggml_backend_tensor_copy(layer.k_stream[ssrc], layer.k_stream[sdst]); - ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]); + + if (layer.v_stream[ssrc]) { + ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]); + } } } } @@ -1516,7 +1524,7 @@ size_t llama_kv_cache::size_v_bytes() const { size_t size_v_bytes = 0; for (const auto & layer : layers) { - size_v_bytes += ggml_nbytes(layer.v); + size_v_bytes += layer.v ? ggml_nbytes(layer.v) : 0; } return size_v_bytes; @@ -1798,6 +1806,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); auto * v = layer.v_stream[cr.strm]; + if (!v) { + continue; + } // Write value type const int32_t v_type_i = (int32_t) v->type; @@ -1824,6 +1835,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); auto * v = layer.v_stream[cr.strm]; + if (!v) { + continue; + } // Write value type const int32_t v_type_i = (int32_t) v->type; @@ -2027,6 +2041,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); auto * v = layer.v_stream[strm]; + if (!v) { + continue; + } // Read type of value int32_t v_type_i_ref; @@ -2068,6 +2085,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); auto * v = layer.v_stream[strm]; + if (!v) { + continue; + } // Read type of value int32_t v_type_i_ref; diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index ae27c71ce2..36e353074e 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -146,8 +146,8 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_VOCAB_SIZE, vocab.n_tokens()); add_kv(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); add_kv(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); - if (hparams.n_embd_out > 0) { - add_kv(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out); + if (hparams.n_embd_out_impl > 0) { + add_kv(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out_impl); } add_kv(LLM_KV_BLOCK_COUNT, hparams.n_layer); add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index b58b35a426..32f49e7996 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -512,7 +512,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); - ml.get_key(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out, false); + ml.get_key(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out_impl, false); ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer); ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); @@ -1697,15 +1697,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_DEEPSEEK2: { // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B - bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); + const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); if (!is_lite) { ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); } ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla, false); - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla, false); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); @@ -4909,14 +4910,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_DEEPSEEK2: { - // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B - const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); - - const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); + const bool is_mla = hparams.is_mla(); // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA - const int64_t n_embd_head_k_mla = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k; - const int64_t n_embd_head_v_mla = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v; + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); const int64_t n_embd_head_qk_rope = hparams.n_rot; const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; @@ -4941,13 +4939,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) { auto & layer = layers[i]; layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - if (!is_lite) { + if (q_lora_rank > 0) { layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); } layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); - if (!is_lite) { + if (q_lora_rank > 0) { layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0); } else { @@ -6597,7 +6595,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } // for LFM2-ColBert-350M - dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.get_n_embd_out()}, TENSOR_NOT_REQUIRED); + dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.n_embd_out()}, TENSOR_NOT_REQUIRED); } break; case LLM_ARCH_SMALLTHINKER: { @@ -7316,8 +7314,8 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead); LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q); LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv); - LLAMA_LOG_INFO("%s: n_embd_head_k_mla = %d\n", __func__, hparams.n_embd_head_k_mla); - LLAMA_LOG_INFO("%s: n_embd_head_v_mla = %d\n", __func__, hparams.n_embd_head_v_mla); + LLAMA_LOG_INFO("%s: n_embd_head_k_mla = %d\n", __func__, hparams.n_embd_head_k_mla()); + LLAMA_LOG_INFO("%s: n_embd_head_v_mla = %d\n", __func__, hparams.n_embd_head_v_mla()); LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared); LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); @@ -8162,7 +8160,7 @@ int32_t llama_model_n_embd_inp(const llama_model * model) { } int32_t llama_model_n_embd_out(const llama_model * model) { - return model->hparams.get_n_embd_out(); + return model->hparams.n_embd_out(); } int32_t llama_model_n_layer(const llama_model * model) { diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index c404c1946d..297dca5136 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -2,14 +2,11 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B - bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); - - const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); + const bool is_mla = hparams.is_mla(); // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA - const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k; - const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v; + const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v = hparams.n_embd_head_v_mla(); const int64_t n_embd_head_qk_rope = hparams.n_rot; const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; @@ -43,7 +40,8 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv(); + auto * inp_attn_kv = !is_mla ? build_attn_inp_kv() : nullptr; + auto * inp_attn_k = is_mla ? build_attn_inp_k() : nullptr; ggml_tensor * inp_out_ids = build_inp_out_ids(); @@ -57,6 +55,9 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr // self_attention { ggml_tensor * q = NULL; + + const bool is_lite = model.layers[il].wq; + if (!is_lite) { q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); cb(q, "q", il); @@ -145,7 +146,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr } // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group) - cur = build_attn(inp_attn, + cur = build_attn(inp_attn_k, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il); } else { @@ -182,7 +183,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr } // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups) - cur = build_attn(inp_attn, + cur = build_attn(inp_attn_kv, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); } From bcb43163aed6a8986cf3d66e90848c9c258d4936 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Sun, 25 Jan 2026 23:25:58 +0800 Subject: [PATCH 017/206] ggml-cpu: Use tiled FA for prompt-processing (#19012) * ggml-cpu: Use tiled FA for prompt-processing the FA performance is gimped on CPU on long contexts because it essentially uses a vector kernel. This PR adds a tiled FA for PP. Perf tuning for tile sizes done on a AMD EPYC single-socket 64-c machine. * fix out of bounds for mask * skip rows where there are all masks * skip tile if mask is inf * store mask in worksize * check inf tile earlier --- ggml/src/ggml-cpu/common.h | 8 + ggml/src/ggml-cpu/ggml-cpu.c | 9 +- ggml/src/ggml-cpu/ops.cpp | 290 ++++++++++++++++++++++++++++++++++- 3 files changed, 303 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-cpu/common.h b/ggml/src/ggml-cpu/common.h index 6adca5437f..1057b5bb15 100644 --- a/ggml/src/ggml-cpu/common.h +++ b/ggml/src/ggml-cpu/common.h @@ -6,6 +6,9 @@ #include "ggml-impl.h" #include "simd-mappings.h" +#define GGML_FA_TILE_Q 32 +#define GGML_FA_TILE_KV 16 + #ifdef __cplusplus #include @@ -84,4 +87,9 @@ static std::pair get_thread_range(const struct ggml_compute_pa return {ir0, ir1}; } +struct ggml_fa_tile_config { + static constexpr size_t Q = GGML_FA_TILE_Q; + static constexpr size_t KV = GGML_FA_TILE_KV; +}; + #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 4c7a75e768..b1de2ae871 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -14,6 +14,7 @@ #include "vec.h" #include "ops.h" #include "ggml.h" +#include "common.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -2866,10 +2867,12 @@ struct ggml_cplan ggml_graph_plan( } break; case GGML_OP_FLASH_ATTN_EXT: { - const int64_t ne10 = node->src[1]->ne[0]; // DK - const int64_t ne20 = node->src[2]->ne[0]; // DV + const int64_t DK = node->src[1]->ne[0]; + const int64_t DV = node->src[2]->ne[0]; - cur = sizeof(float)*(1*ne10 + 2*ne20)*n_tasks; // 1x head size K + 2x head size V (per thread) + // Tiled flash attention scratch (tile sizes defined in common.h) + // Per-thread: Q_q + KQ + mask + VKQ32 + V32 + padding + cur = sizeof(float)*(GGML_FA_TILE_Q*DK + 2*GGML_FA_TILE_Q*GGML_FA_TILE_KV + GGML_FA_TILE_Q*DV + GGML_FA_TILE_KV*DV)*n_tasks; } break; case GGML_OP_FLASH_ATTN_BACK: { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 387e2fe42c..48c8964361 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -8164,6 +8164,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( // online softmax / attention // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf + for (int64_t ic = 0; ic < nek1; ++ic) { const float mv = mp ? slope*GGML_CPU_FP16_TO_FP32(mp[ic]) : 0.0f; if (mv == -INFINITY) { @@ -8271,6 +8272,280 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( } } +static void ggml_compute_forward_flash_attn_ext_tiled( + const ggml_compute_params * params, + ggml_tensor * dst, + int ir0, int ir1) { + 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]; + const ggml_tensor * sinks = dst->src[4]; + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int64_t DK = nek0; + const int64_t DV = nev0; + const int64_t N = neq1; + + GGML_ASSERT(ne0 == DV); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == DK); + GGML_ASSERT(nek0 == DK); + GGML_ASSERT(nev0 == DV); + + GGML_ASSERT(neq1 == N); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(k->type == v->type); + const ggml_type kv_type = k->type; + + const auto * kv_type_traits_cpu = ggml_get_type_traits_cpu(kv_type); + const ggml_from_float_t kv_from_float = kv_type_traits_cpu->from_float; + const ggml_vec_dot_t kv_vec_dot = kv_type_traits_cpu->vec_dot; + const size_t kv_type_size = ggml_type_size(kv_type); + + // broadcast factors + const int64_t rk2 = neq2/nek2; + const int64_t rk3 = neq3/nek3; + + const int64_t rv2 = neq2/nev2; + const int64_t rv3 = neq3/nev3; + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + int ith = params->ith; + + static constexpr int Q_TILE_SZ = ggml_fa_tile_config::Q; + static constexpr int KV_TILE_SZ = ggml_fa_tile_config::KV; + + GGML_ASSERT(nek1 % KV_TILE_SZ == 0 && "KV sequence length must be divisible by KV_TILE_SZ"); + + int ir = ir0; + while (ir < ir1) { + // q indices for the start of this tile + const int iq3 = ir/(neq2*neq1); + const int iq2 = (ir - iq3*neq2*neq1)/neq1; + const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + + // Number of valid rows in this tile: + // - limited by tile size (Q_TILE_SZ) + // - limited by chunk boundary (ir1 - ir) + // - limited by head boundary (neq1 - iq1) to avoid crossing into next head + const int tile_rows = MIN(Q_TILE_SZ, MIN((int)(ir1 - ir), (int)(neq1 - iq1))); + GGML_ASSERT(tile_rows > 0); + + const uint32_t h = iq2; // head index + 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; + + float S[Q_TILE_SZ]; + float M[Q_TILE_SZ]; + + for (int i = 0 ; i < Q_TILE_SZ; ++i) { + S[i] = 0.; + M[i] = -INFINITY; + } + + // Per-thread scratch layout: + // Q_q: Q_TILE_SZ * DK (converted Q tile in KV type) + // KQ: Q_TILE_SZ * KV_TILE_SZ (attention scores in float) + // mask: Q_TILE_SZ * KV_TILE_SZ (mask in float) + // VKQ32: Q_TILE_SZ * DV (FP32 output accumulator) + // V32: KV_TILE_SZ * DV (F32 buffer for V tile - used for f166 conversion) + float * base = (float *) params->wdata + ith*(Q_TILE_SZ*DK + 2*Q_TILE_SZ*KV_TILE_SZ + Q_TILE_SZ*DV + KV_TILE_SZ*DV + CACHE_LINE_SIZE_F32); + + void * Q_q = base; + float * KQ = (float *)((char *)base + Q_TILE_SZ * DK * sizeof(float)); + float * mask32 = KQ + Q_TILE_SZ * KV_TILE_SZ; + float * VKQ32 = mask32 + Q_TILE_SZ * KV_TILE_SZ; + float * V32 = VKQ32 + Q_TILE_SZ * DV; // F32 buffer for V tile + + memset(VKQ32, 0, Q_TILE_SZ * DV * sizeof(float)); + memset(mask32, 0, Q_TILE_SZ * KV_TILE_SZ * sizeof(float)); + + // k indices + const int ik3 = iq3 / rk3; + const int ik2 = iq2 / rk2; + + // v indices + const int iv3 = iq3 / rv3; + const int iv2 = iq2 / rv2; + + for (int tq = 0; tq < tile_rows; tq++) { + const float * pq = (const float *) ((char *) q->data + ((iq1 + tq)*nbq1 + iq2*nbq2 + iq3*nbq3)); + kv_from_float(pq, (char *)Q_q + tq * DK * kv_type_size, DK); + } + // Zero-pad remaining rows + for (int tq = tile_rows; tq < Q_TILE_SZ; tq++) { + memset((char *)Q_q + tq * DK * kv_type_size, 0, DK * kv_type_size); + } + + for (int64_t ic = 0; ic < nek1; ic += KV_TILE_SZ) { + + // skip the tile entirely if all the masks are -inf + if (mask) { + bool can_skip = true; + for (int tq = 0; tq < tile_rows; tq++) { + const ggml_fp16_t * mp_row = (const ggml_fp16_t *)((const char *) mask->data + (iq1 + tq)*mask->nb[1] + (iq2%mask->ne[2])*mask->nb[2] + (iq3%mask->ne[3])*mask->nb[3]); + for (int tk = 0; tk < KV_TILE_SZ; tk++) { + mask32[tq * KV_TILE_SZ + tk] = slope * GGML_CPU_FP16_TO_FP32(mp_row[ic + tk]); + if (mask32[tq * KV_TILE_SZ + tk] != -INFINITY) { + can_skip = false; + } + } + } + + if (can_skip) { + continue; + } + } + + for (int tq = 0; tq < Q_TILE_SZ; tq++) { + const void * q_row = (const char *)Q_q + tq * DK * kv_type_size; + for (int tk = 0; tk < KV_TILE_SZ; tk++) { + const void * k_row = (const char *) k->data + ((ic + tk)*nbk1 + ik2*nbk2 + ik3*nbk3); + float s; + kv_vec_dot(DK, &s, 0, k_row, 0, q_row, 0, 1); + KQ[tq * KV_TILE_SZ + tk] = s * scale; + } + } + + if (logit_softcap != 0.0f) { + ggml_vec_tanh_f32(Q_TILE_SZ * KV_TILE_SZ, KQ, KQ); + ggml_vec_scale_f32(Q_TILE_SZ * KV_TILE_SZ, KQ, logit_softcap); + } + + if (mask) { + ggml_vec_add_f32(tile_rows * KV_TILE_SZ, KQ, KQ, mask32); + } + + bool skip[Q_TILE_SZ] = {}; + + for (int tq = 0; tq < Q_TILE_SZ; tq++) { + float * kq_row = KQ + tq * KV_TILE_SZ; + + float tile_max; + ggml_vec_max_f32(KV_TILE_SZ, &tile_max, kq_row); + + if (tile_max == -INFINITY) { + skip[tq] = true; + continue; + } + + const float Mold = M[tq]; + const float Mnew = fmaxf(Mold, tile_max); + + if (Mnew > Mold) { + const float ms = expf(Mold - Mnew); + ggml_vec_scale_f32(DV, VKQ32 + tq * DV, ms); + S[tq] *= ms; + } + M[tq] = Mnew; + + + S[tq] += ggml_vec_soft_max_f32(KV_TILE_SZ, kq_row, kq_row, Mnew); + } + + // Convert V tile to F32 first (if F16), then do MAD + // On x86, ggml_vec_mad_f16 internall converts F16<->F32 on every load/store, so pre-converting is faster. + // TODO: on ARM, native f16 should be faster + if (kv_type == GGML_TYPE_F16) { + for (int tk = 0; tk < KV_TILE_SZ; tk++) { + const ggml_fp16_t * v_row = (const ggml_fp16_t *)((const char *) v->data + ((ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3)); + ggml_fp16_to_fp32_row(v_row, V32 + tk * DV, DV); + } + for (int tq = 0; tq < Q_TILE_SZ; tq++) { + if (skip[tq]) continue; + float * vkq_row = VKQ32 + tq * DV; + for (int tk = 0; tk < KV_TILE_SZ; tk++) { + const float p = KQ[tq * KV_TILE_SZ + tk]; + ggml_vec_mad_f32(DV, vkq_row, V32 + tk * DV, p); + } + } + } else { + for (int tq = 0; tq < Q_TILE_SZ; tq++) { + if (skip[tq]) continue; + float * vkq_row = VKQ32 + tq * DV; + for (int tk = 0; tk < KV_TILE_SZ; tk++) { + const float p = KQ[tq * KV_TILE_SZ + tk]; + const float * v_row = (const float *)((const char *) v->data + ((ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3)); + ggml_vec_mad_f32(DV, vkq_row, v_row, p); + } + } + } + } + + // sinks (apply only to valid rows in the tile) + if (sinks) { + const float s = ((float *)((char *) sinks->data))[h]; + + for (int tq = 0; tq < tile_rows; tq++) { + float ms = 1.0f; + float vs = 1.0f; + + if (s > M[tq]) { + ms = expf(M[tq] - s); + ggml_vec_scale_f32(DV, VKQ32 + tq * DV, ms); + } else { + vs = expf(s - M[tq]); + } + + S[tq] = S[tq] * ms + vs; + } + } + + for (int tq = 0; tq < tile_rows; tq++) { + // V /= S + const float S_inv = S[tq] == 0.0f ? 0.0f : 1.0f / S[tq]; + ggml_vec_scale_f32(DV, VKQ32 + tq * DV, S_inv); + + // dst indices + const int i1 = iq1 + tq; + const int i2 = iq2; + const int i3 = iq3; + + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32 + tq * DV, nb1); + } + + ir += tile_rows; + } +} + static void ggml_compute_forward_flash_attn_ext_f16( const ggml_compute_params * params, ggml_tensor * dst) { @@ -8343,6 +8618,15 @@ static void ggml_compute_forward_flash_attn_ext_f16( // The number of elements in each chunk const int64_t dr = (nr + nchunk - 1) / nchunk; + static constexpr int64_t KV_TILE_SZ = ggml_fa_tile_config::KV; + static constexpr int64_t Q_TILE_SZ = ggml_fa_tile_config::Q; + const bool kv_is_f32_or_f16 = (k->type == GGML_TYPE_F32 || k->type == GGML_TYPE_F16); + const bool use_tiled = (q->type == GGML_TYPE_F32 && + kv_is_f32_or_f16 && + k->type == v->type && + nek1 % KV_TILE_SZ == 0 && + neq1 >= Q_TILE_SZ); // Only use tiled for batch >= tile size + // The first chunk comes from our thread_id, the rest will get auto-assigned. int current_chunk = ith; @@ -8350,7 +8634,11 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int64_t ir0 = dr * current_chunk; const int64_t ir1 = MIN(ir0 + dr, nr); - ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1); + if (use_tiled) { + ggml_compute_forward_flash_attn_ext_tiled(params, dst, ir0, ir1); + } else { + ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1); + } current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1); } From 0bf56369384ff1af2731cd1168785e283a1ba266 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 25 Jan 2026 18:03:34 +0100 Subject: [PATCH 018/206] convert : yield Gemma3N custom_map tensors directly (#19091) --- convert_hf_to_gguf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index b56a99c5fa..6e6e618989 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -6145,7 +6145,8 @@ class Gemma3nVisionAudioModel(ConformerAudioModel): if name.startswith("model.vision_tower.timm_model.blocks."): # Double-indexed block tensors through custom logic - new_name = self.custom_map(name) + yield (self.custom_map(name), data_torch) + return else: # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py new_name = self.map_tensor_name(name) From 0440bfd1605333726ea0fb7a836942660bf2f9a6 Mon Sep 17 00:00:00 2001 From: ccbinn Date: Mon, 26 Jan 2026 02:07:19 +0800 Subject: [PATCH 019/206] metal : fix recommendedMaxWorkingSetSize availability on legacy iOS/macOS (#19088) Co-authored-by: chenbin11 --- ggml/src/ggml-metal/ggml-metal-device.m | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index eb4e2c209c..7f9c384c34 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -785,8 +785,12 @@ ggml_metal_device_t ggml_metal_device_init(void) { dev->props.op_offload_min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32; dev->props.max_buffer_size = dev->mtl_device.maxBufferLength; - dev->props.max_working_set_size = dev->mtl_device.recommendedMaxWorkingSetSize; dev->props.max_theadgroup_memory_size = dev->mtl_device.maxThreadgroupMemoryLength; + if (@available(macOS 10.12, iOS 16.0, *)) { + dev->props.max_working_set_size = dev->mtl_device.recommendedMaxWorkingSetSize; + } else { + dev->props.max_working_set_size = dev->mtl_device.maxBufferLength; + } strncpy(dev->props.name, [[dev->mtl_device name] UTF8String], sizeof(dev->props.name) - 1); From 0c21677e43044d27f6f7a7f9f95c67f7c4b3fdb4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 25 Jan 2026 21:19:47 +0100 Subject: [PATCH 020/206] CUDA: faster FA for GQA > 1 but not power of 2 (#19092) --- ggml/src/ggml-cuda/fattn-common.cuh | 22 +++--- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 30 +++++---- ggml/src/ggml-cuda/fattn.cu | 67 ++++++++++++++++--- ...ttn-mma-f16-instance-ncols1_1-ncols2_32.cu | 5 ++ ...ttn-mma-f16-instance-ncols1_2-ncols2_32.cu | 5 ++ .../template-instances/generate_cu_files.py | 6 +- 6 files changed, 99 insertions(+), 36 deletions(-) create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu create mode 100644 ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 13c5b0a459..1f5f1b9206 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -643,9 +643,10 @@ static __global__ void flash_attn_stream_k_fixup( const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; + const int iter_z = (ne02 + (ncols2 - 1)) / ncols2; - const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; - const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; + const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; const bool did_not_have_any_data = kbc0 == kbc0_stop; const bool wrote_beginning_of_tile = kbc0 % iter_k == 0; @@ -654,15 +655,15 @@ static __global__ void flash_attn_stream_k_fixup( return; } - const int sequence = kbc0 / (iter_k*iter_j*(ne02/ncols2)); - const int head = (kbc0 - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j); - const int jt = (kbc0 - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*head) / iter_k; // j index of current tile. + const int sequence = kbc0 / (iter_k*iter_j*iter_z); + const int zt = (kbc0 - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); + const int jt = (kbc0 - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. - if (jt*ncols1 + j >= ne01) { + if (jt*ncols1 + j >= ne01 || zt*ncols2 + c >= ne02) { return; } - dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + head*(ncols2*D) + (j*ne02 + c)*D + tid; + dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + zt*(ncols2*D) + (j*ne02 + c)*D + tid; // Load the partial result that needs a fixup: float dst_val = 0.0f; @@ -681,7 +682,7 @@ static __global__ void flash_attn_stream_k_fixup( int bidx = bidx0 - 1; int kbc_stop = kbc0; while(true) { - const int kbc = int64_t(bidx)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc = int64_t(bidx)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; if (kbc == kbc_stop) { // Did not have any data. bidx--; kbc_stop = kbc; @@ -883,7 +884,8 @@ void launch_fattn( } const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); - const int ntiles_total = ntiles_x * (Q->ne[2] / ncols2) * Q->ne[3]; + const int ntiles_z = ((Q->ne[2] + ncols2 - 1) / ncols2); + const int ntiles_total = ntiles_x * ntiles_z * Q->ne[3]; // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or @@ -958,7 +960,7 @@ void launch_fattn( blocks_num.x = ntiles_x; blocks_num.y = parallel_blocks; - blocks_num.z = (Q->ne[2]/ncols2)*Q->ne[3]; + blocks_num.z = ntiles_z*Q->ne[3]; if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 3e7d67b40d..9004d46904 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -940,6 +940,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int stride_V, const int stride_mask, const int jt, + const int zt, const int kb0_start, const int kb0_stop) { #if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) @@ -1022,7 +1023,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int j = jc / ncols2; const int c = jc % ncols2; - if (jt*ncols1 + j < int(ne01.z)) { + if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt*ncols2 + c < ne02)) { #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); @@ -1408,7 +1409,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int j_dst = jc_dst / ncols2; const int c_dst = jc_dst % ncols2; - if (!is_fixup && jt*ncols1 + j_dst >= int(ne01.z)) { + if (!is_fixup && ((ncols1 > 1 && jt*ncols1 + j_dst >= int(ne01.z)) || (ncols2 > 1 && zt*ncols2 + c_dst >= ne02))) { continue; } @@ -1522,10 +1523,11 @@ static __global__ void flash_attn_ext_f16( const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1; + const int iter_z = (ne02 + (ncols2 - 1)) / ncols2; // kbc == k block continuous, current index in continuous ijk space. - int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; - const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; + const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined. // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup). @@ -1536,9 +1538,9 @@ static __global__ void flash_attn_ext_f16( int kb0_stop = min(iter_k, kb0_start + kbc_stop - kbc); while (kbc < kbc_stop && kb0_stop == iter_k) { - const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2)); - 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 int sequence = kbc / (iter_k*iter_j*iter_z); + const int zt = (kbc - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); // head in units of ncols2 + const int jt = (kbc - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. const int head0 = zt * ncols2; @@ -1561,12 +1563,12 @@ static __global__ void flash_attn_ext_f16( constexpr bool needs_fixup = false; // CUDA block is working on an entire tile. flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); + ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); } else { constexpr bool needs_fixup = true; // CUDA block is missing the beginning of a tile. flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); + ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); } kbc += iter_k; @@ -1580,9 +1582,9 @@ static __global__ void flash_attn_ext_f16( return; } - const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2)); - 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 int sequence = kbc / (iter_k*iter_j*iter_z); + const int zt = (kbc - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); // head in units of ncols2 + const int jt = (kbc - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. const int head0 = zt * ncols2; @@ -1605,7 +1607,7 @@ static __global__ void flash_attn_ext_f16( constexpr bool needs_fixup = false; flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); + ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); #else GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, max_bias, m0, m1, n_head_log2, logit_softcap, @@ -1739,3 +1741,5 @@ extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 16); extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 4); extern DECL_FATTN_MMA_F16_CASE(576, 512, 8, 4); extern DECL_FATTN_MMA_F16_CASE(576, 512, 16, 4); +extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 32); +extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 32); diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index a5e6624181..2f5dbd13a3 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -18,9 +18,11 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con } } - if ((turing_mma_available(cc) || amd_wmma_available(cc)) && Q->ne[1] <= 16/ncols2) { - ggml_cuda_flash_attn_ext_mma_f16_case(ctx, dst); - return; + if constexpr (ncols2 <= 16) { + if ((turing_mma_available(cc) || amd_wmma_available(cc)) && Q->ne[1] <= 16/ncols2) { + ggml_cuda_flash_attn_ext_mma_f16_case(ctx, dst); + return; + } } if (ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING || amd_wmma_available(cc) || Q->ne[1] <= 32/ncols2) { @@ -33,6 +35,7 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con template static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; @@ -60,17 +63,38 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); const int gqa_ratio = Q->ne[2] / K->ne[2]; - if (use_gqa_opt && gqa_ratio % 8 == 0) { + // On Volta the GQA optimizations aren't as impactful vs. minimizing wasted compute: + if (cc == GGML_CUDA_CC_VOLTA) { + if (use_gqa_opt && gqa_ratio % 8 == 0) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 4 == 0) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 2 == 0) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); + return; + } + + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio > 4) { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); return; } - if (use_gqa_opt && gqa_ratio % 4 == 0) { + if (use_gqa_opt && gqa_ratio > 2) { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); return; } - if (use_gqa_opt && gqa_ratio % 2 == 0) { + if (use_gqa_opt && gqa_ratio > 1) { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ctx, dst); return; } @@ -79,6 +103,7 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con } static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; @@ -121,8 +146,30 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); const int gqa_ratio = Q->ne[2] / K->ne[2]; - GGML_ASSERT(gqa_ratio % 4 == 0); - if (gqa_ratio % 16 == 0) { + if (gqa_ratio == 20) { // GLM 4.7 Flash + if (cc >= GGML_CUDA_CC_BLACKWELL) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); + break; + } + if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { + if (Q->ne[1] <= 4) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); + break; + } + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); + break; + } + if (cc >= GGML_CUDA_CC_TURING) { + if (Q->ne[1] <= 4) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 32>(ctx, dst); + break; + } + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); + break; + } + // Volta: + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); + } else if (gqa_ratio % 16 == 0) { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); } else { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); @@ -234,7 +281,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const // The effective batch size for the kernel can be increased by gqa_ratio. // The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded, - bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; for (const ggml_tensor * t : {Q, K, V, mask}) { if (t == nullptr || ggml_is_quantized(t->type)) { continue; @@ -268,7 +315,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const if (V->ne[0] != 512) { return BEST_FATTN_KERNEL_NONE; } - if (!gqa_opt_applies || gqa_ratio % 4 != 0) { + if (!gqa_opt_applies) { return BEST_FATTN_KERNEL_NONE; } if (!V_is_K_view) { diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu new file mode 100644 index 0000000000..1f554d81e5 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-mma-f16.cuh" + +DECL_FATTN_MMA_F16_CASE(576, 512, 1, 32); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu new file mode 100644 index 0000000000..264751d65e --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-mma-f16.cuh" + +DECL_FATTN_MMA_F16_CASE(576, 512, 2, 32); diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py index 10be71ab57..e382df1ae2 100755 --- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py +++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py @@ -71,7 +71,7 @@ for type_k in TYPES_KV: f.write(SOURCE_FATTN_VEC.format(type_k=type_k, type_v=type_v)) for ncols in [8, 16, 32, 64]: - for ncols2 in [1, 2, 4, 8, 16]: + for ncols2 in [1, 2, 4, 8, 16, 32]: if ncols2 > ncols: continue ncols1 = ncols // ncols2 @@ -83,9 +83,9 @@ for ncols in [8, 16, 32, 64]: continue if head_size_kq == 72: continue - if head_size_kq != 576 and ncols2 == 16: + if head_size_kq != 576 and ncols2 in (16, 32): continue - if head_size_kq == 576 and ncols2 not in (4, 16): + if head_size_kq == 576 and ncols2 not in (4, 16, 32): continue head_size_v = head_size_kq if head_size_kq != 576 else 512 f.write(SOURCE_FATTN_MMA_CASE.format(ncols1=ncols1, ncols2=ncols2, head_size_kq=head_size_kq, head_size_v=head_size_v)) From 56f3ebf38ea5757447e7ea167cda15e1eebf9601 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Jan 2026 11:24:30 +0200 Subject: [PATCH 021/206] model : add correct type for GLM 4.7 Flash (#19106) --- src/llama-model.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 32f49e7996..cc784e1cb0 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1737,6 +1737,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { switch (hparams.n_layer) { case 27: type = LLM_TYPE_16B; break; + case 47: type = LLM_TYPE_30B_A3B; break; case 60: type = LLM_TYPE_236B; break; case 61: type = LLM_TYPE_671B; break; default: type = LLM_TYPE_UNKNOWN; From 142cbe2ac68978e5dec3a2e19c1b64ef1c5740b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Mon, 26 Jan 2026 15:22:49 +0100 Subject: [PATCH 022/206] ci : use new 1vCPU runner for lightweight jobs (#19107) * use new 1vCPU runner for lightweight jobs * pyright is too heavy, look into ty some day use new pip-install input --- .github/workflows/check-vendor.yml | 2 +- .github/workflows/close-issue.yml | 2 +- .github/workflows/editorconfig.yml | 2 +- .github/workflows/gguf-publish.yml | 2 +- .github/workflows/labeler.yml | 2 +- .github/workflows/pre-tokenizer-hashes.yml | 2 +- .github/workflows/python-check-requirements.yml | 2 +- .github/workflows/python-lint.yml | 2 +- .github/workflows/python-type-check.yml | 4 +--- .github/workflows/update-ops-docs.yml | 2 +- .github/workflows/winget.yml | 2 +- 11 files changed, 11 insertions(+), 13 deletions(-) diff --git a/.github/workflows/check-vendor.yml b/.github/workflows/check-vendor.yml index b9e8ac7658..1671ed7b8b 100644 --- a/.github/workflows/check-vendor.yml +++ b/.github/workflows/check-vendor.yml @@ -19,7 +19,7 @@ on: jobs: check-vendor: - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - name: Checkout diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index 8fb5310d0b..ec3df08b2d 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -10,7 +10,7 @@ permissions: jobs: close-issues: - runs-on: ubuntu-latest + runs-on: ubuntu-slim permissions: issues: write pull-requests: write diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index a5cd590017..702dc89f5b 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -20,7 +20,7 @@ concurrency: jobs: editorconfig: - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - uses: actions/checkout@v6 - uses: editorconfig-checker/action-editorconfig-checker@v2 diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml index 5bdab0f157..0e95766459 100644 --- a/.github/workflows/gguf-publish.yml +++ b/.github/workflows/gguf-publish.yml @@ -21,7 +21,7 @@ on: jobs: deploy: - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - uses: actions/checkout@v6 diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml index 42f00c0cd8..eab20c6881 100644 --- a/.github/workflows/labeler.yml +++ b/.github/workflows/labeler.yml @@ -7,7 +7,7 @@ jobs: permissions: contents: read pull-requests: write - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - uses: actions/checkout@v6 with: diff --git a/.github/workflows/pre-tokenizer-hashes.yml b/.github/workflows/pre-tokenizer-hashes.yml index 8120df0e36..7126b62b69 100644 --- a/.github/workflows/pre-tokenizer-hashes.yml +++ b/.github/workflows/pre-tokenizer-hashes.yml @@ -12,7 +12,7 @@ on: jobs: pre-tokenizer-hashes: - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - name: Checkout repository diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml index 08cdcb9d01..1219b87459 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -20,7 +20,7 @@ concurrency: jobs: python-check-requirements: - runs-on: ubuntu-latest + runs-on: ubuntu-slim name: check-requirements steps: - name: Check out source repository diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index 91dc4d78a4..8d1dd7a7d5 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -15,7 +15,7 @@ concurrency: jobs: flake8-lint: - runs-on: ubuntu-latest + runs-on: ubuntu-slim name: Lint steps: - name: Check out source repository diff --git a/.github/workflows/python-type-check.yml b/.github/workflows/python-type-check.yml index 54d5fab5ba..e801a9f42e 100644 --- a/.github/workflows/python-type-check.yml +++ b/.github/workflows/python-type-check.yml @@ -29,9 +29,7 @@ jobs: uses: actions/setup-python@v6 with: python-version: "3.11" - - name: Install Python dependencies - # TODO: use a venv - run: pip install -r requirements/requirements-all.txt + pip-install: -r requirements/requirements-all.txt - name: Type-check with Pyright uses: jakebailey/pyright-action@v2 with: diff --git a/.github/workflows/update-ops-docs.yml b/.github/workflows/update-ops-docs.yml index 40447db4e4..2ab06eb981 100644 --- a/.github/workflows/update-ops-docs.yml +++ b/.github/workflows/update-ops-docs.yml @@ -14,7 +14,7 @@ on: jobs: update-ops-docs: - runs-on: ubuntu-latest + runs-on: ubuntu-slim steps: - name: Checkout repository diff --git a/.github/workflows/winget.yml b/.github/workflows/winget.yml index 7506091647..e3981f30b9 100644 --- a/.github/workflows/winget.yml +++ b/.github/workflows/winget.yml @@ -8,7 +8,7 @@ on: jobs: update: name: Update Winget Package - runs-on: ubuntu-latest + runs-on: ubuntu-slim if: github.repository_owner == 'ggml-org' steps: From 8f80d1b254aef70a0959e314be368d05debe7294 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Jan 2026 20:18:34 +0200 Subject: [PATCH 023/206] graph : fix nkvo offload with FA (#19105) --- src/llama-context.cpp | 7 ------- src/llama-graph.cpp | 5 +++++ 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 72211db17b..0b2b05c419 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2173,13 +2173,6 @@ llm_graph_cb llama_context::graph_get_cb() const { ggml_set_name(cur, name); } - if (!cparams.offload_kqv) { - if (strcmp(name, "kqv_merged_cont") == 0) { - // all nodes between the KV store and the attention output are run on the CPU - ggml_backend_sched_set_tensor_backend(sched.get(), cur, backend_cpu); - } - } - // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends // FIXME: fix in ggml_backend_sched const bool full_offload = model.n_gpu_layers() > model.hparams.n_layer; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 16d42c4ae3..b3198b7e3a 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1630,6 +1630,11 @@ ggml_tensor * llm_graph_context::build_attn_mha( hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f); cb(cur, LLAMA_TENSOR_NAME_FATTN, il); + if (!cparams.offload_kqv) { + // all nodes between the KV store and the attention output are run on the CPU + ggml_backend_sched_set_tensor_backend(sched, cur, backend_cpu); + } + ggml_flash_attn_ext_add_sinks(cur, sinks); ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32); From b0311c16d2f650a8bd5af652549075b458bd713a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 26 Jan 2026 23:24:58 +0100 Subject: [PATCH 024/206] CUDA: fix padding of GQA to power of 2 in FA (#19115) --- ggml/src/ggml-cuda/fattn-common.cuh | 43 +++++++++-------- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 69 +++++++++++++++------------- tests/test-backend-ops.cpp | 4 +- 3 files changed, 64 insertions(+), 52 deletions(-) diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 1f5f1b9206..3d7daccfdf 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -629,8 +629,8 @@ static __global__ void flash_attn_mask_to_KV_max( template // D == head size __launch_bounds__(D, 1) static __global__ void flash_attn_stream_k_fixup( - float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne03, const int ne11, - const int nbatch_fa) { + float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne03, + const int ne11, const int ne12, const int nbatch_fa) { constexpr int ncols = ncols1*ncols2; const int bidx0 = blockIdx.x; @@ -641,12 +641,14 @@ static __global__ void flash_attn_stream_k_fixup( const float * dst_fixup_data = ((const float *) dst_fixup) + gridDim.x*(2*2*ncols); - const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; - const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; - const int iter_z = (ne02 + (ncols2 - 1)) / ncols2; + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; - const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; + const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; + const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; + const int iter_z_gqa = (gqa_ratio + (ncols2 - 1)) / ncols2; + + const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x; + const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x; const bool did_not_have_any_data = kbc0 == kbc0_stop; const bool wrote_beginning_of_tile = kbc0 % iter_k == 0; @@ -655,15 +657,19 @@ static __global__ void flash_attn_stream_k_fixup( return; } - const int sequence = kbc0 / (iter_k*iter_j*iter_z); - const int zt = (kbc0 - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); - const int jt = (kbc0 - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. + // z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index + const int sequence = kbc0 /(iter_k*iter_j*iter_z_gqa*ne12); + const int z_KV = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa); + const int zt_gqa = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j); + const int jt = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k; - if (jt*ncols1 + j >= ne01 || zt*ncols2 + c >= ne02) { + const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index. + + if (jt*ncols1 + j >= ne01 || zt_gqa*ncols2 + c >= gqa_ratio) { return; } - dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + zt*(ncols2*D) + (j*ne02 + c)*D + tid; + dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + zt_Q*D + (j*ne02 + c)*D + tid; // Load the partial result that needs a fixup: float dst_val = 0.0f; @@ -682,7 +688,7 @@ static __global__ void flash_attn_stream_k_fixup( int bidx = bidx0 - 1; int kbc_stop = kbc0; while(true) { - const int kbc = int64_t(bidx)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; + const int kbc = int64_t(bidx)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x; if (kbc == kbc_stop) { // Did not have any data. bidx--; kbc_stop = kbc; @@ -883,9 +889,10 @@ void launch_fattn( } } - const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); - const int ntiles_z = ((Q->ne[2] + ncols2 - 1) / ncols2); - const int ntiles_total = ntiles_x * ntiles_z * Q->ne[3]; + const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); + const int gqa_ratio = Q->ne[2] / K->ne[2]; + const int ntiles_z_gqa = ((gqa_ratio + ncols2 - 1) / ncols2); + const int ntiles_total = ntiles_x * ntiles_z_gqa * K->ne[2] * Q->ne[3]; // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or @@ -960,7 +967,7 @@ void launch_fattn( blocks_num.x = ntiles_x; blocks_num.y = parallel_blocks; - blocks_num.z = ntiles_z*Q->ne[3]; + blocks_num.z = ntiles_z_gqa*K->ne[2]*Q->ne[3]; if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); @@ -1014,7 +1021,7 @@ void launch_fattn( flash_attn_stream_k_fixup <<>> - ((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], Q->ne[3], K->ne[1], nbatch_fa); + ((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], Q->ne[3], K->ne[1], K->ne[2], nbatch_fa); } } else if (parallel_blocks > 1) { const dim3 block_dim_combine(DV, 1, 1); diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 9004d46904..0b8ef90794 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -933,6 +933,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const float logit_softcap, const uint3 ne01, const int ne02, + const int gqa_ratio, const int ne11, const int stride_Q1, const int stride_Q2, @@ -940,7 +941,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int stride_V, const int stride_mask, const int jt, - const int zt, + const int zt_gqa, const int kb0_start, const int kb0_stop) { #if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) @@ -1023,7 +1024,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int j = jc / ncols2; const int c = jc % ncols2; - if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt*ncols2 + c < ne02)) { + if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt_gqa*ncols2 + c < gqa_ratio)) { #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); @@ -1409,7 +1410,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int j_dst = jc_dst / ncols2; const int c_dst = jc_dst % ncols2; - if (!is_fixup && ((ncols1 > 1 && jt*ncols1 + j_dst >= int(ne01.z)) || (ncols2 > 1 && zt*ncols2 + c_dst >= ne02))) { + if (!is_fixup && ((ncols1 > 1 && jt*ncols1 + j_dst >= int(ne01.z)) || (ncols2 > 1 && zt_gqa*ncols2 + c_dst >= gqa_ratio))) { continue; } @@ -1448,7 +1449,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } #else GGML_UNUSED_VARS(Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dstk_fixup, - scale, slope, logit_softcap, ne01, ne02, + scale, slope, logit_softcap, ne01, ne02, gqa_ratio, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); NO_DEVICE_CODE; @@ -1521,13 +1522,13 @@ static __global__ void flash_attn_ext_f16( const int stride_V = V_is_K_view ? stride_K : nb21 / sizeof(half2); - const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; - const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1; - const int iter_z = (ne02 + (ncols2 - 1)) / ncols2; + const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; + const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1; + const int iter_z_gqa = (gqa_ratio + (ncols2 - 1)) / ncols2; // kbc == k block continuous, current index in continuous ijk space. - int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; - const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*iter_z*ne03) / gridDim.x; + int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x; + const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x; // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined. // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup). @@ -1538,22 +1539,24 @@ static __global__ void flash_attn_ext_f16( int kb0_stop = min(iter_k, kb0_start + kbc_stop - kbc); while (kbc < kbc_stop && kb0_stop == iter_k) { - const int sequence = kbc / (iter_k*iter_j*iter_z); - const int zt = (kbc - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); // head in units of ncols2 - const int jt = (kbc - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. + // z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index + const int sequence = kbc /(iter_k*iter_j*iter_z_gqa*ne12); + const int z_KV = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa); + const int zt_gqa = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j); + const int jt = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k; - const int head0 = zt * ncols2; + const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index. - 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 float2 * Q_f2 = (const float2 *) (Q + nb03*sequence + nb02*zt_Q); + const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*z_KV); const half * mask_h = ncols2 == 1 && !mask ? nullptr : (const half *) (mask + nb33*(sequence % ne33)); - float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2); + float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + zt_Q) * (DV/2); - const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); - const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr; + const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV); + const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr; - const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f; + const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f; if (KV_max) { kb0_stop = min(kb0_stop, KV_max[sequence*iter_j + jt] / nbatch_fa); @@ -1563,12 +1566,12 @@ static __global__ void flash_attn_ext_f16( constexpr bool needs_fixup = false; // CUDA block is working on an entire tile. flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); + ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop); } else { constexpr bool needs_fixup = true; // CUDA block is missing the beginning of a tile. flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); + ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop); } kbc += iter_k; @@ -1582,22 +1585,24 @@ static __global__ void flash_attn_ext_f16( return; } - const int sequence = kbc / (iter_k*iter_j*iter_z); - const int zt = (kbc - iter_k*iter_j*iter_z*sequence) / (iter_k*iter_j); // head in units of ncols2 - const int jt = (kbc - iter_k*iter_j*iter_z*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile. + // z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index. + const int sequence = kbc /(iter_k*iter_j*iter_z_gqa*ne12); + const int z_KV = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa); + const int zt_gqa = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j); + const int jt = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k; - const int head0 = zt * ncols2; + const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index. - 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 float2 * Q_f2 = (const float2 *) (Q + nb03*sequence + nb02*zt_Q); + const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*z_KV); const half * mask_h = ncols2 == 1 && !mask ? nullptr : (const half *) (mask + nb33*(sequence % ne33)); - float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2); + float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + zt_Q) * (DV/2); - const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); - const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr; + const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV); + const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr; - const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f; + const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f; if (KV_max) { kb0_stop = min(kb0_stop, KV_max[sequence*iter_j + jt] / nbatch_fa); @@ -1607,7 +1612,7 @@ static __global__ void flash_attn_ext_f16( constexpr bool needs_fixup = false; flash_attn_ext_f16_process_tile (Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap, - ne01, ne02, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt, kb0_start, kb0_stop); + ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop); #else GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, max_bias, m0, m1, n_head_log2, logit_softcap, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 146d05f53b..d4c1f525c6 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -8216,8 +8216,8 @@ static std::vector> make_test_cases_eval() { for (int nh : { 4, }) { for (int nr3 : { 1, 3, }) { if (hsk > 64 && nr3 > 1) continue; // skip broadcast for large head sizes - for (int nr2 : { 1, 4, 16 }) { - if (nr2 == 16 && hsk != 128) continue; + for (int nr2 : { 1, 4, 12 }) { + if (nr2 == 12 && hsk != 128) continue; //for (int kv : { 1, 17, 31, 33, 61, 113, 65, 127, 129, 130, 255, 260, 371, 380, 407, 512, 1024, }) { for (int kv : { 113, 512, 1024, }) { if (nr2 != 1 && kv != 512) continue; From 94eeb5967c129365f50ca8462a7595ea319430d9 Mon Sep 17 00:00:00 2001 From: lhez Date: Mon, 26 Jan 2026 19:36:24 -0800 Subject: [PATCH 025/206] opencl: add flattened q6_K mv (#19054) * opencl: flatten `q6_K` and add `kernel_mul_mv_q6_K_f32_flat` * opencl: clean up * opencl: refactor q6_K mv - put loop body in `block_q_6_K_dot_y_flat` * opencl: tweak the workgroup size a bit * opencl: output 4 values per subgroup for `kernel_mul_mv_q6_K_f32_flat` * opencl: proper alignment for q6_K * opencl: boundary handling for flattened q6_K mv * opencl: rename q6_K mv kernel file * opencl: put flattened q6_K mv in its own file * opencl: use lower k in file name * opencl: use K in variable names --- ggml/src/ggml-opencl/CMakeLists.txt | 3 +- ggml/src/ggml-opencl/ggml-opencl.cpp | 260 +++++++++++++++++- ggml/src/ggml-opencl/kernels/cvt.cl | 70 +++++ .../{mul_mv_q6_k.cl => mul_mv_q6_k_f32.cl} | 0 .../kernels/mul_mv_q6_k_f32_flat.cl | 194 +++++++++++++ 5 files changed, 518 insertions(+), 9 deletions(-) rename ggml/src/ggml-opencl/kernels/{mul_mv_q6_k.cl => mul_mv_q6_k_f32.cl} (100%) create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32_flat.cl diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 79039c30e1..0259474b6e 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -85,7 +85,8 @@ set(GGML_OPENCL_KERNELS mul_mv_q4_0_f32_8x_flat mul_mv_q4_0_f32_1d_8x_flat mul_mv_q4_0_f32_1d_16x_flat - mul_mv_q6_k + mul_mv_q6_k_f32 + mul_mv_q6_k_f32_flat mul_mv_q8_0_f32 mul_mv_q8_0_f32_flat mul_mv_mxfp4_f32 diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 27b2761ef1..678e40965a 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -533,8 +533,10 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mat_q4_0_f32_8x_flat; cl_kernel kernel_convert_block_q4_0_noshuffle; cl_kernel kernel_restore_block_q4_0_noshuffle; + cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K; 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_q6_K_f32_flat; cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat; cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat; cl_kernel kernel_solve_tri_f32; @@ -892,6 +894,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err)); + CL_CHECK((backend_ctx->kernel_convert_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err)); + CL_CHECK((backend_ctx->kernel_restore_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err)); GGML_LOG_CONT("."); } @@ -1114,14 +1118,14 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } - // mul_mv_q6_k + // mul_mv_q6_k_f32 { #ifdef GGML_OPENCL_EMBED_KERNELS const std::string kernel_src { - #include "mul_mv_q6_k.cl.h" + #include "mul_mv_q6_k_f32.cl.h" }; #else - const std::string kernel_src = read_file("mul_mv_q6_k.cl"); + const std::string kernel_src = read_file("mul_mv_q6_k_f32.cl"); #endif backend_ctx->program_mul_mv_q6_K = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); @@ -1130,6 +1134,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // mul_mv_q6_k_f32_flat + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "mul_mv_q6_k_f32_flat.cl.h" + }; +#else + const std::string kernel_src = read_file("mul_mv_q6_k_f32_flat.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q6_K_f32_flat", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // mul_mv_q8_0_f32 { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2919,6 +2940,50 @@ struct ggml_tensor_extra_cl_q8_0 { } }; +struct ggml_tensor_extra_cl_q6_K { + // Lower 4 bits of quantized weights. + cl_mem ql = nullptr; + // Upper 2 bits of quantized weights. + cl_mem qh = nullptr; + // Scales for each block. + cl_mem s = nullptr; + // Scales for each super block. + cl_mem d = nullptr; + + size_t size_ql = 0; + size_t size_qh = 0; + size_t size_s = 0; + size_t size_d = 0; + + ~ggml_tensor_extra_cl_q6_K() { + reset(); + } + + void reset() { + if (ql != nullptr) { + CL_CHECK(clReleaseMemObject(ql)); + ql = nullptr; + } + if (qh != nullptr) { + CL_CHECK(clReleaseMemObject(qh)); + qh = nullptr; + } + if (s != nullptr) { + CL_CHECK(clReleaseMemObject(s)); + s = nullptr; + } + if (d != nullptr) { + CL_CHECK(clReleaseMemObject(d)); + d = nullptr; + } + + size_ql = 0; + size_qh = 0; + size_s = 0; + size_d = 0; + } +}; + //------------------------------------------------------------------------------ // Backend API //------------------------------------------------------------------------------ @@ -3465,6 +3530,12 @@ struct ggml_backend_opencl_buffer_context { for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) { delete e; } + for (ggml_tensor_extra_cl_q6_K * e : temp_tensor_extras_q6_K) { + delete e; + } + for (ggml_tensor_extra_cl_q6_K * e : temp_tensor_extras_q6_K_in_use) { + delete e; + } } ggml_tensor_extra_cl * ggml_opencl_alloc_temp_tensor_extra() { @@ -3527,6 +3598,21 @@ struct ggml_backend_opencl_buffer_context { return extra; } + ggml_tensor_extra_cl_q6_K * ggml_opencl_alloc_temp_tensor_extra_q6_K() { + ggml_tensor_extra_cl_q6_K * extra; + if (temp_tensor_extras_q6_K.empty()) { + extra = new ggml_tensor_extra_cl_q6_K(); + } else { + extra = temp_tensor_extras_q6_K.back(); + temp_tensor_extras_q6_K.pop_back(); + } + + temp_tensor_extras_q6_K_in_use.push_back(extra); + + extra->reset(); + return extra; + } + void reset() { for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) { temp_tensor_extras.push_back(e); @@ -3547,6 +3633,11 @@ struct ggml_backend_opencl_buffer_context { temp_tensor_extras_q8_0.push_back(e); } temp_tensor_extras_q8_0_in_use.clear(); + + for (ggml_tensor_extra_cl_q6_K * e : temp_tensor_extras_q6_K_in_use) { + temp_tensor_extras_q6_K.push_back(e); + } + temp_tensor_extras_q6_K_in_use.clear(); } // Pools for extras. Available extras are in `temp_tensor_extras`. Extras @@ -3562,6 +3653,8 @@ struct ggml_backend_opencl_buffer_context { std::vector temp_tensor_extras_mxfp4_in_use; std::vector temp_tensor_extras_q8_0; std::vector temp_tensor_extras_q8_0_in_use; + std::vector temp_tensor_extras_q6_K; + std::vector temp_tensor_extras_q6_K_in_use; // The buffer_context is initially created by ggml_backend_buft_alloc_buffer // before any tensor is initialized (at the beginning of alloc_tensor_range). @@ -4068,6 +4161,92 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, return; } + if (tensor->type == GGML_TYPE_Q6_K) { + ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra; + GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized"); + + // Allocate the new extra and create aliases from the original. + ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; + ggml_tensor_extra_cl_q6_K * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q6_K(); + + size_t size_ql = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2; + size_t size_qh = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/4; + size_t size_s = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/16; + size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t); + GGML_ASSERT(size_ql + size_qh + size_s + size_d == ggml_nbytes(tensor) && + "Incorrect tensor size"); + + cl_int err; + cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, + ggml_nbytes(tensor), NULL, &err); + CL_CHECK(err); + CL_CHECK(clEnqueueWriteBuffer( + queue, data_device, CL_TRUE, 0, + ggml_nbytes(tensor), data, 0, NULL, NULL)); + + cl_buffer_region region; + + // Subbuffer for ql + region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment); + region.size = size_ql; + extra->ql = clCreateSubBuffer( + extra_orig->data_device, CL_MEM_READ_WRITE, + CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + auto previous_origin = region.origin; + + // Subbuffer for qh + region.origin = align_to(previous_origin + size_ql, backend_ctx->alignment); + region.size = size_qh; + extra->qh = clCreateSubBuffer( + extra_orig->data_device, CL_MEM_READ_WRITE, + CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + previous_origin = region.origin; + + // Subbuffer for scales + region.origin = align_to(previous_origin + size_qh, backend_ctx->alignment); + region.size = size_s; + extra->s = clCreateSubBuffer( + extra_orig->data_device, CL_MEM_READ_WRITE, + CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + previous_origin = region.origin; + + // Create subbuffer for d. + region.origin = align_to(previous_origin + size_s, backend_ctx->alignment); + region.size = size_d; + extra->d = clCreateSubBuffer( + extra_orig->data_device, CL_MEM_READ_WRITE, + CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); + CL_CHECK(err); + previous_origin = region.origin; + + // Flatten the weights + cl_kernel kernel = backend_ctx->kernel_convert_block_q6_K; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->ql)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->s)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->d)); + + size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + cl_event evt; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + CL_CHECK(clReleaseMemObject(data_device)); + + extra->size_ql = size_ql; + extra->size_qh = size_qh; + extra->size_s = size_s; + extra->size_d = size_d; + + tensor->extra = extra; + return; + } #endif // GGML_OPENCL_SOA_Q ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra; @@ -4277,6 +4456,34 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; size_t local_work_size[] = {1, 1, 1}; + cl_event evt; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_work_size, local_work_size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + CL_CHECK(clEnqueueReadBuffer( + queue, data_device, CL_TRUE, offset, + size, data, 0, NULL, NULL)); + CL_CHECK(clReleaseMemObject(data_device)); + return; + } + if (tensor->type == GGML_TYPE_Q6_K) { + ggml_tensor_extra_cl_q6_K * extra = (ggml_tensor_extra_cl_q6_K *)tensor->extra; + + cl_int err; + cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, + ggml_nbytes(tensor), NULL, &err); + CL_CHECK(err); + + cl_kernel kernel = backend_ctx->kernel_restore_block_q6_K; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->ql)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qh)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->s)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device)); + + size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; + size_t local_work_size[] = {1, 1, 1}; + cl_event evt; CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt)); @@ -7765,6 +7972,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra; ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra; ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra; + ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra; #endif const int ne00 = src0 ? src0->ne[0] : 0; @@ -8648,14 +8856,49 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: +#ifdef GGML_OPENCL_SOA_Q + kernel = backend_ctx->kernel_mul_mv_q6_K_f32_flat; + + if (backend_ctx->gpu_family == INTEL) { + nth0 = 16; + nth1 = 2; + ndst = 4; + } else if (backend_ctx->gpu_family == ADRENO) { + nth0 = 64; + nth1 = 2; + ndst = 4; + } else { + GGML_ASSERT(false && "TODO: Unknown GPU"); + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q6_K->ql)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q6_K->qh)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q6_K->s)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q6_K->d)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra1->data_device)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset1)); + 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), &ne10)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne12)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne0)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne1)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &r2)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &r3)); +#else kernel = backend_ctx->kernel_mul_mv_q6_K_f32; if (backend_ctx->gpu_family == INTEL) { - nth0 = 2; - nth1 = 16; + nth0 = 16; + nth1 = 2; + ndst = 1; } else if (backend_ctx->gpu_family == ADRENO) { - nth0 = 2; - nth1 = 64; + nth0 = 64; + nth1 = 2; + ndst = 1; } else { GGML_ASSERT(false && "TODO: Unknown GPU"); } @@ -8675,6 +8918,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne1)); CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &r2)); CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3)); +#endif // GGML_OPENCL_SOA_Q break; case GGML_TYPE_MXFP4: { #ifdef GGML_OPENCL_SOA_Q @@ -8777,7 +9021,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co } else if (src0t == GGML_TYPE_Q5_K) { GGML_ASSERT(false && "not implemented"); } else if (src0t == GGML_TYPE_Q6_K) { - size_t global_work_size[] = {(size_t)(ne01+1)/2*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13}; + size_t global_work_size[] = {(size_t)(ne01+ndst*nth1-1)/(ndst*nth1)*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13}; size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1}; backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl index 513a4d3e28..adf576a839 100644 --- a/ggml/src/ggml-opencl/kernels/cvt.cl +++ b/ggml/src/ggml-opencl/kernels/cvt.cl @@ -46,6 +46,16 @@ struct block_q4_0 uint8_t qs[QK4_0 / 2]; }; +//------------------------------------------------------------------------------ +// block_q6_K +//------------------------------------------------------------------------------ +struct block_q6_K { + uint8_t ql[QK_K/2]; // quants, lower 4 bits + uint8_t qh[QK_K/4]; // quants, upper 2 bits + int8_t scales[QK_K/16]; // scales, quantized with 8 bits + half d; // super-block scale +}; + //------------------------------------------------------------------------------ // kernel_convert_block_q4_0 // Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA). @@ -263,3 +273,63 @@ kernel void kernel_restore_block_q8_0( b->qs[i] = q[i]; } } + +//------------------------------------------------------------------------------ +// kernel_convert_block_q6_K +// Convert the block_q6_K format to 3 separate arrays (AOS -> SOA). +// This kernel does not deshuffle the bits. +// Each thread processes a super block. +//------------------------------------------------------------------------------ +kernel void kernel_convert_block_q6_K( + global struct block_q6_K * src0, + global uchar * dst_ql, + global uchar * dst_qh, + global char * dst_s, + global half * dst_d +) { + global struct block_q6_K * b = (global struct block_q6_K *) src0 + get_global_id(0); + global uchar * ql = (global uchar *) dst_ql + QK_K/2*get_global_id(0); + global uchar * qh = (global uchar *) dst_qh + QK_K/4*get_global_id(0); + global char * s = (global char *) dst_s + QK_K/16*get_global_id(0); + global half * d = (global half *) dst_d + get_global_id(0); + + *d = b->d; + + for (int i = 0; i < QK_K/2; ++i) { + ql[i] = b->ql[i]; + } + for (int i = 0; i < QK_K/4; ++i) { + qh[i] = b->qh[i]; + } + for (int i = 0; i < QK_K/16; ++i) { + s[i] = b->scales[i]; + } +} + +// Restore block_q6_K from flattened arrays. +// Each thread processes a super block. +kernel void kernel_restore_block_q6_K( + global uchar * dst_ql, + global uchar * dst_qh, + global char * dst_s, + global half * dst_d, + global struct block_q6_K * dst +) { + global struct block_q6_K * b = (global struct block_q6_K *) dst + get_global_id(0); + global uchar * ql = (global uchar *) dst_ql + QK_K/2*get_global_id(0); + global uchar * qh = (global uchar *) dst_qh + QK_K/4*get_global_id(0); + global char * s = (global char *) dst_s + QK_K/16*get_global_id(0); + global half * d = (global half *) dst_d + get_global_id(0); + + b->d = *d; + + for (int i = 0; i < QK_K/2; ++i) { + b->ql[i] = ql[i]; + } + for (int i = 0; i < QK_K/4; ++i) { + b->qh[i] = qh[i]; + } + for (int i = 0; i < QK_K/16; ++i) { + b->scales[i] = s[i]; + } +} diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q6_k.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32.cl similarity index 100% rename from ggml/src/ggml-opencl/kernels/mul_mv_q6_k.cl rename to ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32.cl diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32_flat.cl new file mode 100644 index 0000000000..86fe09c6dd --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32_flat.cl @@ -0,0 +1,194 @@ +#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 + +//------------------------------------------------------------------------------ +// kernel_mul_mv_q6_K_f32_flat +//------------------------------------------------------------------------------ +#define Q6_K_MASK1 0x03 +#define Q6_K_MASK2 0x0C +#define Q6_K_MASK3 0x30 +#define Q6_K_MASK4 0xC0 + +#define QK_K 256 + +inline float block_q_6_K_dot_y_flat( + global uchar * blk_ql, + global uchar * blk_qh, + global char * blk_scales, + global half * blk_d, + global float * yy, + int ib, + int ip, + int is, + int l0 +) { + int y_offset = 128*ip + l0; + int q_offset_l = 64*ip + l0; + int q_offset_h = 32*ip + l0; + + global uchar * q1 = blk_ql + ib*128 + q_offset_l; + global uchar * q2 = q1 + QK_K/8; + global uchar * qh = blk_qh + ib*64 + q_offset_h; + global char * sc = blk_scales + ib*16 + is; + + global float * y = yy + ib * QK_K + y_offset; + + float dall = blk_d[ib]; + + float sumf = 0; + float4 sums = {0.f, 0.f, 0.f, 0.f}; + + sums.s0 += y[0+ 0] * ((float)((q1[0] & 0xF) | ((qh[0] & Q6_K_MASK1) << 4)) - 32.f); + sums.s1 += y[0+32] * ((float)((q2[0] & 0xF) | ((qh[0] & Q6_K_MASK2) << 2)) - 32.f); + sums.s2 += y[0+64] * ((float)((q1[0] >> 4) | ((qh[0] & Q6_K_MASK3) << 0)) - 32.f); + sums.s3 += y[0+96] * ((float)((q2[0] >> 4) | ((qh[0] & Q6_K_MASK4) >> 2)) - 32.f); + + sums.s0 += y[1+ 0] * ((float)((q1[1] & 0xF) | ((qh[1] & Q6_K_MASK1) << 4)) - 32.f); + sums.s1 += y[1+32] * ((float)((q2[1] & 0xF) | ((qh[1] & Q6_K_MASK2) << 2)) - 32.f); + sums.s2 += y[1+64] * ((float)((q1[1] >> 4) | ((qh[1] & Q6_K_MASK3) << 0)) - 32.f); + sums.s3 += y[1+96] * ((float)((q2[1] >> 4) | ((qh[1] & Q6_K_MASK4) >> 2)) - 32.f); + + sums.s0 += y[2+ 0] * ((float)((q1[2] & 0xF) | ((qh[2] & Q6_K_MASK1) << 4)) - 32.f); + sums.s1 += y[2+32] * ((float)((q2[2] & 0xF) | ((qh[2] & Q6_K_MASK2) << 2)) - 32.f); + sums.s2 += y[2+64] * ((float)((q1[2] >> 4) | ((qh[2] & Q6_K_MASK3) << 0)) - 32.f); + sums.s3 += y[2+96] * ((float)((q2[2] >> 4) | ((qh[2] & Q6_K_MASK4) >> 2)) - 32.f); + + sums.s0 += y[3+ 0] * ((float)((q1[3] & 0xF) | ((qh[3] & Q6_K_MASK1) << 4)) - 32.f); + sums.s1 += y[3+32] * ((float)((q2[3] & 0xF) | ((qh[3] & Q6_K_MASK2) << 2)) - 32.f); + sums.s2 += y[3+64] * ((float)((q1[3] >> 4) | ((qh[3] & Q6_K_MASK3) << 0)) - 32.f); + sums.s3 += y[3+96] * ((float)((q2[3] >> 4) | ((qh[3] & Q6_K_MASK4) >> 2)) - 32.f); + + sumf += dall * (sums.s0 * sc[0] + sums.s1 * sc[2] + sums.s2 * sc[4] + sums.s3 * sc[6]); + + return sumf; +} + +#undef N_DST +#undef N_SIMDGROUP +#undef N_SIMDWIDTH + +#ifdef INTEL_GPU +#define N_DST 4 +#define N_SIMDGROUP 2 +#define N_SIMDWIDTH 16 +#elif defined (ADRENO_GPU) +#define N_DST 4 +#define N_SIMDGROUP 2 +#define N_SIMDWIDTH 64 +#endif + +#define BLOCK_STRIDE (N_SIMDWIDTH/16) // number of blocks each subgroup processes + +#ifdef INTEL_GPU +REQD_SUBGROUP_SIZE_16 +#elif defined (ADRENO_GPU) +REQD_SUBGROUP_SIZE_64 +#endif +kernel void kernel_mul_mv_q6_K_f32_flat( + global uchar * src0_ql, + global uchar * src0_qh, + global char * src0_s, + global half * src0_d, + global float * src1, + ulong offset1, + global float * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne10, + int ne12, + int ne0, + int ne1, + int r2, + int r3 +) { + src1 = (global float*)((global char*)src1 + offset1); + dst = (global float*)((global char*)dst + offsetd); + + int nb = ne00/QK_K; + + int r0 = get_group_id(0); + int r1 = get_group_id(1); + int im = get_group_id(2); + + int i12 = im%ne12; + int i13 = im/ne12; + + int first_row = (N_SIMDGROUP * r0 + get_sub_group_id()) * N_DST; + + ulong offset_src0 = first_row*nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + ulong offset_src0_ql = offset_src0 * 128; + ulong offset_src0_qh = offset_src0 * 64; + ulong offset_src0_s = offset_src0 * 16; + ulong offset_src0_d = offset_src0; + + global uchar * blk_ql = (global uchar *) src0_ql + offset_src0_ql; + global uchar * blk_qh = (global uchar *) src0_qh + offset_src0_qh; + global char * blk_scales = (global char *) src0_s + offset_src0_s; + global half * blk_d = (global half *) src0_d + offset_src0_d; + global float * yy = (global float *) src1 + r1*ne10 + im*ne00*ne1; + + int tid = get_sub_group_local_id()/BLOCK_STRIDE; // first block_stride groups have tid=0 + int ix = get_sub_group_local_id()%BLOCK_STRIDE; // first block is 0..block_stride-1 + int ip = tid/8; // first or second half of (super) block (0 or 1) + int il = tid%8; // each half has 8 parts, one per scale + int n = 4; // 4 scales at a time (and 4 sums) + int l0 = n*il; // offset into half-block, 0..28 + int is = 8*ip + l0/16; // 0, 1, 8, 9 + + float4 sumf = 0; + + for (int ib = ix; ib < nb; ib += BLOCK_STRIDE) { + if (first_row + 0 < ne01) { + sumf.s0 += block_q_6_K_dot_y_flat(blk_ql + 0*nb*128, blk_qh + 0*nb*64, blk_scales + 0*nb*16, blk_d + 0*nb, yy, ib, ip, is, l0); + } + if (first_row + 1 < ne01) { + sumf.s1 += block_q_6_K_dot_y_flat(blk_ql + 1*nb*128, blk_qh + 1*nb*64, blk_scales + 1*nb*16, blk_d + 1*nb, yy, ib, ip, is, l0); + } + if (first_row + 2 < ne01) { + sumf.s2 += block_q_6_K_dot_y_flat(blk_ql + 2*nb*128, blk_qh + 2*nb*64, blk_scales + 2*nb*16, blk_d + 2*nb, yy, ib, ip, is, l0); + } + if (first_row + 3 < ne01) { + sumf.s3 += block_q_6_K_dot_y_flat(blk_ql + 3*nb*128, blk_qh + 3*nb*64, blk_scales + 3*nb*16, blk_d + 3*nb, yy, ib, ip, is, l0); + } + } + + float4 tot = (float4)( + sub_group_reduce_add(sumf.s0), + sub_group_reduce_add(sumf.s1), + sub_group_reduce_add(sumf.s2), + sub_group_reduce_add(sumf.s3) + ); + if (get_sub_group_local_id() == 0) { + if (first_row + 0 < ne01) { + dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0; + } + if (first_row + 1 < ne01) { + dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1; + } + if (first_row + 2 < ne01) { + dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2; + } + if (first_row + 3 < ne01) { + dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3; + } + } +} From 7afdfc9b844ce38179fc4f0e4caa8b5c9a98db43 Mon Sep 17 00:00:00 2001 From: shalinib-ibm Date: Tue, 27 Jan 2026 09:22:34 +0530 Subject: [PATCH 026/206] ggml-cpu: Enable FP16 MMA kernels on PPC (#19060) --- ggml/src/ggml-cpu/llamafile/sgemm.cpp | 81 +++++++++++++++++++-------- 1 file changed, 58 insertions(+), 23 deletions(-) diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 7dc36d4f8a..8f980c16b9 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -1797,10 +1797,27 @@ class tinyBLAS_Q0_AVX { } \ } \ +template +struct mma_instr; + +template<> +struct mma_instr { + static inline void outer_product(acc_t *acc, vec_t a, vec_t b) { + __builtin_mma_xvbf16ger2pp(acc, a, b); + } +}; + +template<> +struct mma_instr { + static inline void outer_product(acc_t *acc, vec_t a, vec_t b) { + __builtin_mma_xvf16ger2pp(acc, a, b); + } +}; + template -class tinyBLAS_BF16_PPC { +class tinyBLAS_HP16_PPC { public: - tinyBLAS_BF16_PPC(int64_t k, + tinyBLAS_HP16_PPC(int64_t k, const TA *A, int64_t lda, const TB *B, int64_t ldb, TC *C, int64_t ldc, @@ -2118,8 +2135,8 @@ class tinyBLAS_BF16_PPC { packNormal((A+(ii*lda)+l), lda, 4, 8, (uint8_t*)vec_A); packNormal((B+(jj*ldb)+l), ldb, 8, 8, (uint8_t*)vec_B); for (int x = 0; x < 4; x++) { - __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); - __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x], vec_B[x+4]); + mma_instr::outer_product(&acc_0, vec_A[x], vec_B[x]); + mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); } } SAVE_ACC(&acc_0, ii, jj); @@ -2135,8 +2152,8 @@ class tinyBLAS_BF16_PPC { packNormal((A+(ii*lda)+l), lda, 8, 8, (uint8_t*)vec_A); packNormal((B+(jj*ldb)+l), ldb, 8, 4, (uint8_t*)vec_B); for (int x = 0; x < 4; x++) { - __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); - __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x+4], vec_B[x]); + mma_instr::outer_product(&acc_0, vec_A[x], vec_B[x]); + mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); } } SAVE_ACC(&acc_0, ii, jj); @@ -2155,10 +2172,10 @@ class tinyBLAS_BF16_PPC { packNormal(A+(ii*lda)+l, lda, 8, 8, (uint8_t*)vec_A); packNormal(B+(jj*ldb)+l, ldb, 8, 8, (uint8_t*)vec_B); for (int x = 0; x < 4; x++) { - __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); - __builtin_mma_xvbf16ger2pp(&acc_1, (vec_t)vec_A[x], (vec_t)vec_B[x+4]); - __builtin_mma_xvbf16ger2pp(&acc_2, (vec_t)vec_A[x+4], (vec_t)vec_B[x]); - __builtin_mma_xvbf16ger2pp(&acc_3, (vec_t)vec_A[x+4], (vec_t)vec_B[x+4]); + mma_instr::outer_product(&acc_0, vec_A[x], vec_B[x]); + mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); + mma_instr::outer_product(&acc_2, vec_A[x+4], vec_B[x]); + mma_instr::outer_product(&acc_3, vec_A[x+4], vec_B[x+4]); } } @@ -2189,7 +2206,7 @@ class tinyBLAS_BF16_PPC { packNormal(A+(ii*lda)+l, lda, RM, 4, (uint8_t*)vec_A); packNormal(B+(jj*ldb)+l, ldb, RN, 4, (uint8_t*)vec_B); for (int x = 0; x<2; x++) { - __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + mma_instr::outer_product(&acc_0, vec_A[x], vec_B[x]); } } __builtin_mma_disassemble_acc(vec_C, &acc_0); @@ -2224,8 +2241,8 @@ class tinyBLAS_BF16_PPC { packNormal(A+(ii*lda)+l, lda, RM, 8, (uint8_t*)vec_A); packNormal(B+(jj*ldb)+l, ldb, RN, 8, (uint8_t*)vec_B); for (int x = 0; x<4; x++) { - __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); - __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x], vec_B[x+4]); + mma_instr::outer_product(&acc_0, vec_A[x], vec_B[x]); + mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); } } __builtin_mma_disassemble_acc(vec_C, &acc_0); @@ -3418,16 +3435,19 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 return tb.matmul(m, n); } #elif defined(__MMA__) - if ((k % 8)) - return false; - if(Btype == GGML_TYPE_BF16) { - tinyBLAS_BF16_PPC tb{ k, - (const ggml_bf16_t *)A, lda, - (const ggml_bf16_t *)B, ldb, - (float *)C, ldc, - params->ith, params->nth}; - tb.matmul(m, n); - return true; + if (k % 8) { + return false; + } + + if (Btype == GGML_TYPE_BF16) { + tinyBLAS_HP16_PPC tb{ k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc, + params->ith, params->nth }; + + tb.matmul(m, n); + return true; } #elif defined(__riscv_zvfbfwma) #if LMUL == 1 @@ -3516,6 +3536,21 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 #endif return tb.matmul(m, n); } +#elif defined(__MMA__) + if (k % 8) { + return false; + } + + if (Btype == GGML_TYPE_F16) { + tinyBLAS_HP16_PPC tb{ k, + (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc, + params->ith, params->nth }; + + tb.matmul(m, n); + return true; + } #endif return false; } From fc3cdf32ce5ea3017299d2afb947d3ba9844445a Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 27 Jan 2026 06:16:00 +0100 Subject: [PATCH 027/206] common : clarify HTTPS build options in error message (#19103) * common : clarify HTTPS build options in error message This commit updates the https error message to provide clearer instructions for users who encounter the "HTTPS is not supported" error. The motivation for this is that it might not be clear to users that only one of these options are needed to enable HTTPS support. The LLAMA_OPENSSL option is also added to the message to cover all possible build configurations. * clarify that OpenSSL is the default for HTTPS support --- common/http.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/common/http.h b/common/http.h index 7c683aafcf..e8ed56f952 100644 --- a/common/http.h +++ b/common/http.h @@ -60,10 +60,10 @@ static std::pair common_http_client(const std: #ifndef CPPHTTPLIB_OPENSSL_SUPPORT if (parts.scheme == "https") { throw std::runtime_error( - "HTTPS is not supported. Please rebuild with:\n" + "HTTPS is not supported. Please rebuild with one of:\n" " -DLLAMA_BUILD_BORINGSSL=ON\n" " -DLLAMA_BUILD_LIBRESSL=ON\n" - "or ensure dev files of an OpenSSL-compatible library are available when building." + " -DLLAMA_OPENSSL=ON (default, requires OpenSSL dev files installed)" ); } #endif From a83c73a18aaffba253ffd01e7cd3af41feaf8179 Mon Sep 17 00:00:00 2001 From: Gaurav Garg Date: Tue, 27 Jan 2026 06:52:44 +0000 Subject: [PATCH 028/206] [CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full (#19042) * [CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full With pipeline parallelism, during prompt processing, the CPU-side CUDA command buffer gets full, stalling the CPU. Due to this, enough work doesn't get submitted to the GPU, causing bubbles in the GPU timeline. Fix this by setting the CUDA environment variable CUDA_SCALE_LAUNCH_QUEUES to 4x to increase the command buffer size. * Set the env variable in the CUDA backend registry allocation * Add link to PR in code comment * Remove warning logs and update documentation --- docs/build.md | 8 ++++++++ ggml/src/ggml-cuda/ggml-cuda.cu | 10 ++++++++++ 2 files changed, 18 insertions(+) diff --git a/docs/build.md b/docs/build.md index fce9361b2d..4983cfcfea 100644 --- a/docs/build.md +++ b/docs/build.md @@ -248,6 +248,14 @@ You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf ``` +#### CUDA_SCALE_LAUNCH_QUEUES + +The environment variable [`CUDA_SCALE_LAUNCH_QUEUES`](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/environment-variables.html#cuda-scale-launch-queues) controls the size of CUDA's command buffer, which determines how many GPU operations can be queued before the CPU must wait for the GPU to catch up. A larger buffer reduces CPU-side stalls and allows more work to be queued on a GPU. + +**Default behavior:** llama.cpp automatically sets `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs. + +See PR [#19042](https://github.com/ggml-org/llama.cpp/pull/19042) for performance benchmarks and technical details. + ### Unified Memory The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`. diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 99f0919a51..e9df0ea4a7 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -4876,6 +4876,16 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { static std::mutex mutex; std::lock_guard lock(mutex); if (!initialized) { + // Set CUDA_SCALE_LAUNCH_QUEUES before any CUDA API call to improve multi-GPU pipeline parallelism performance + // PR: https://github.com/ggml-org/llama.cpp/pull/19042 + if (getenv("CUDA_SCALE_LAUNCH_QUEUES") == nullptr) { +#ifdef _WIN32 + _putenv_s("CUDA_SCALE_LAUNCH_QUEUES", "4x"); +#else + setenv("CUDA_SCALE_LAUNCH_QUEUES", "4x", 0); // don't overwrite if already set +#endif // _WIN32 + } + ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context; const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32; From be8890e7217982fc02da568bc3955e9416d0e5d0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= <1478977+Alcpz@users.noreply.github.com> Date: Tue, 27 Jan 2026 09:08:10 +0000 Subject: [PATCH 029/206] ggml-cpu: aarm64: q6_K repack gemm and gemv (and generic) implementations (i8mm) #18860 (#18888) * Boilerplate for q6_K repack * q6_K repack to q6_Kx8 implementation Signed-off-by: Alberto Cabrera * q6_K generic gemv and gemm * wip, gemm_q6_K 8x8 * Still WIP: loading of q8s, q6h and q6l * first working version of q6_K gemm * Moved q6 loads outside of sb block, Unrolled inner loop * Replaced modulo with mask * First implementation of GEMV * ggml_vdotq_s32 -> vdotq_s32 * Reduce width of accumulators in q6_K gemv * Bsums instead of calc bias. Preload scales to use vget_lane. Unroll. * Reuse scales in GEMM (same GEMV opt) * Added todos for bsum and different qh repack * Arch fallback * VSLIQ for merging qh adn ql * Removed TODO, already tested * Apply suggestions Co-authored-by: Georgi Gerganov * Removed unused import --------- Signed-off-by: Alberto Cabrera Co-authored-by: Georgi Gerganov --- ggml/src/ggml-cpu/arch-fallback.h | 15 + ggml/src/ggml-cpu/arch/arm/repack.cpp | 429 +++++++++++++++++++++++++- ggml/src/ggml-cpu/repack.cpp | 339 ++++++++++++++++++-- ggml/src/ggml-cpu/repack.h | 16 +- 4 files changed, 771 insertions(+), 28 deletions(-) diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h index 0a85a4cff3..427c1146e4 100644 --- a/ggml/src/ggml-cpu/arch-fallback.h +++ b/ggml/src/ggml-cpu/arch-fallback.h @@ -1,3 +1,4 @@ + #pragma once // Rename `_generic` functions if no native implementation is available. @@ -42,6 +43,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -53,6 +55,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +# define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 @@ -73,6 +76,7 @@ #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 @@ -80,6 +84,7 @@ #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 @@ -102,6 +107,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -113,6 +119,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 @@ -136,6 +143,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -147,6 +155,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 @@ -177,6 +186,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -187,6 +197,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 @@ -216,6 +227,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -227,6 +239,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 @@ -258,6 +271,7 @@ #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K +#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 @@ -269,6 +283,7 @@ #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index 883d862901..f40226494c 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -1055,10 +1055,10 @@ void ggml_gemv_q5_K_8x8_q8_K(int n, // FUSED BIAS: Compute and subtract bias immediately // bias = (bsums_lo * mins_lo + bsums_hi * mins_hi) * sb_min - int32x4_t bias = vmull_s16(bsums_vec_lo, group_mins_lo); - bias = vmlal_s16(bias, bsums_vec_hi, group_mins_hi); + int32x4_t bias = vmull_s16(bsums_vec_lo, group_mins_lo); + bias = vmlal_s16(bias, bsums_vec_hi, group_mins_hi); float32x4_t bias_f32 = vcvtq_f32_s32(bias); - acc_f32[i] = vmlsq_f32(acc_f32[i], sb_min, bias_f32); + acc_f32[i] = vmlsq_f32(acc_f32[i], sb_min, bias_f32); } } // for sb } // for b @@ -1072,6 +1072,208 @@ void ggml_gemv_q5_K_8x8_q8_K(int n, ggml_gemv_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_q6_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) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 8; + + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + constexpr int col_pairs = ncols_interleaved / 2; + const uint8x16_t m4b = vdupq_n_u8(0x0f); + const uint8x16_t mask_lo = vdupq_n_u8(0x03); + const uint8x16_t mask_hi = vdupq_n_u8(0x30); + + // 1x8 tile = 2 x 4 + float32x4_t acc_f32[2]; + + const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb); + + acc_f32[0] = vdupq_n_f32(0); + acc_f32[1] = vdupq_n_f32(0); + + for (int b = 0; b < nb; b++) { + float32x4_t q6_d_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d)); // d0 d1 d2 d3 + float32x4_t q6_d_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4)); // d4 d5 d6 d7 + float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d); + float32x4_t sb_scale_0 = vmulq_f32(q6_d_0, q8_d); + float32x4_t sb_scale_1 = vmulq_f32(q6_d_1, q8_d); + + int32x2_t acc[col_pairs]; + for (int i = 0; i < col_pairs; i++) { + acc[i] = vdup_n_s32(0); + } + + // Load all 16 scales once and widen to int16 (Q6_K has 16 scales per block) + // Reused for bias and dequantization later + int16_t q6_scales[16 * 8]; + for (int i = 0; i < 16; i++) { + int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8)); + vst1q_s16(q6_scales + i * 8, scales); + } + + // Compute bias per column using q8 bsums and preloaded scales to skip the -32 shift + int32x4_t bias_lo = vdupq_n_s32(0); + int32x4_t bias_hi = vdupq_n_s32(0); + + // Load bsums in chunks of 4 to process with vectorized operations + for (int i = 0; i < 16; i += 4) { + int16x4_t bsums_vec = vld1_s16(q8_ptr[b].bsums + i); + int16x4_t scales_lo_0 = vld1_s16(q6_scales + (i + 0) * 8); + int16x4_t scales_hi_0 = vld1_s16(q6_scales + (i + 0) * 8 + 4); + int16x4_t scales_lo_1 = vld1_s16(q6_scales + (i + 1) * 8); + int16x4_t scales_hi_1 = vld1_s16(q6_scales + (i + 1) * 8 + 4); + int16x4_t scales_lo_2 = vld1_s16(q6_scales + (i + 2) * 8); + int16x4_t scales_hi_2 = vld1_s16(q6_scales + (i + 2) * 8 + 4); + int16x4_t scales_lo_3 = vld1_s16(q6_scales + (i + 3) * 8); + int16x4_t scales_hi_3 = vld1_s16(q6_scales + (i + 3) * 8 + 4); + + bias_lo = vmlal_lane_s16(bias_lo, scales_lo_0, bsums_vec, 0); + bias_hi = vmlal_lane_s16(bias_hi, scales_hi_0, bsums_vec, 0); + bias_lo = vmlal_lane_s16(bias_lo, scales_lo_1, bsums_vec, 1); + bias_hi = vmlal_lane_s16(bias_hi, scales_hi_1, bsums_vec, 1); + bias_lo = vmlal_lane_s16(bias_lo, scales_lo_2, bsums_vec, 2); + bias_hi = vmlal_lane_s16(bias_hi, scales_hi_2, bsums_vec, 2); + bias_lo = vmlal_lane_s16(bias_lo, scales_lo_3, bsums_vec, 3); + bias_hi = vmlal_lane_s16(bias_hi, scales_hi_3, bsums_vec, 3); + } + bias_lo = vshlq_n_s32(bias_lo, 5); + bias_hi = vshlq_n_s32(bias_hi, 5); + + // Process two 128-value halves per superblock + for (int half = 0; half < 2; half++) { + const uint8_t * ql_base = q6_ptr[b].ql + half * 512; + const uint8_t * qh_base = q6_ptr[b].qh + half * 256; + + // A subblock (sb) is a set of weights that share the scale + // Since q6_K scales are per 16 elements + // num sbs -> 256 elements / (16 elements/scale * 2 elements/byte * 2 halves) + for (int sb = 0; sb < QK_K / 64; sb++) { + const int8_t * q8_base_l = q8_ptr[b].qs + half * 128 + sb * 16; + const int8_t * q8_base_h = q8_base_l + 64; + + // Load and duplicate q8 values (each register covers two interleaved columns of q6) + int8x16_t q8_l[2]; + int8x16_t q8_h[2]; + for (int i = 0; i < 2; i++) { + q8_l[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base_l + i * 8)); + q8_h[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base_h + i * 8)); + } + + // TODO: Test other qh repack patterns to reduce loads + const int ql_off_base = sb * QK_K / 2; + const int qh_off_base = ql_off_base & 255; // wraps after 256 bytes + + // Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1) + ggml_uint8x16x4_t q6_ql_0 = ggml_vld1q_u8_x4(ql_base + ql_off_base); + ggml_uint8x16x4_t q6_ql_1 = ggml_vld1q_u8_x4(ql_base + ql_off_base + 64); + ggml_uint8x16x4_t q6_qh_0 = ggml_vld1q_u8_x4(qh_base + qh_off_base); + ggml_uint8x16x4_t q6_qh_1 = ggml_vld1q_u8_x4(qh_base + qh_off_base + 64); + + // Adjust qh for subblocks 2 and 3 (shift right by 2) + if (sb > 1) { + q6_qh_0.val[0] = vshrq_n_u8(q6_qh_0.val[0], 2); + q6_qh_0.val[1] = vshrq_n_u8(q6_qh_0.val[1], 2); + q6_qh_0.val[2] = vshrq_n_u8(q6_qh_0.val[2], 2); + q6_qh_0.val[3] = vshrq_n_u8(q6_qh_0.val[3], 2); + q6_qh_1.val[0] = vshrq_n_u8(q6_qh_1.val[0], 2); + q6_qh_1.val[1] = vshrq_n_u8(q6_qh_1.val[1], 2); + q6_qh_1.val[2] = vshrq_n_u8(q6_qh_1.val[2], 2); + q6_qh_1.val[3] = vshrq_n_u8(q6_qh_1.val[3], 2); + } + + // Process column pairs (0-1, 2-3, 4-5, 6-7) + for (int cp = 0; cp < col_pairs; cp++) { + const uint8x16_t q6_qs_cp_0_l = q6_ql_0.val[cp]; + const uint8x16_t q6_qs_cp_1_l = q6_ql_1.val[cp]; + const uint8x16_t q6_qs_cp_0_h = q6_qh_0.val[cp]; + const uint8x16_t q6_qs_cp_1_h = q6_qh_1.val[cp]; + + // Extract high 2 bits for upper nibble reconstruction + const uint8x16_t q6_qs_cp_0_hh = vandq_u8(q6_qs_cp_0_h, mask_hi); + const uint8x16_t q6_qs_cp_1_hh = vandq_u8(q6_qs_cp_1_h, mask_hi); + + // q6 = (low4 | high2<<4), without -32 bias (handled via bsums) + const int8x16_t q6_l0 = vreinterpretq_s8_u8( + vsliq_n_u8(vandq_u8(q6_qs_cp_0_l, m4b), vandq_u8(q6_qs_cp_0_h, mask_lo), 4)); + const int8x16_t q6_l1 = vreinterpretq_s8_u8( + vsliq_n_u8(vandq_u8(q6_qs_cp_1_l, m4b), vandq_u8(q6_qs_cp_1_h, mask_lo), 4)); + const int8x16_t q6_h0 = + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_cp_0_l, 4), q6_qs_cp_0_hh)); + const int8x16_t q6_h1 = + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_cp_1_l, 4), q6_qs_cp_1_hh)); + + int32x4_t sb_acc_l = vdupq_n_s32(0); + sb_acc_l = vdotq_s32(sb_acc_l, q6_l0, q8_l[0]); + sb_acc_l = vdotq_s32(sb_acc_l, q6_l1, q8_l[1]); + + int32x4_t sb_acc_h = vdupq_n_s32(0); + sb_acc_h = vdotq_s32(sb_acc_h, q6_h0, q8_h[0]); + sb_acc_h = vdotq_s32(sb_acc_h, q6_h1, q8_h[1]); + + // Pairwise add to get per-column sums: [col0, col1] + int32x2_t sum_l = vpadd_s32(vget_low_s32(sb_acc_l), vget_high_s32(sb_acc_l)); + int32x2_t sum_h = vpadd_s32(vget_low_s32(sb_acc_h), vget_high_s32(sb_acc_h)); + + const int scale_idx_l = half * 8 + sb; + const int scale_idx_h = half * 8 + sb + 4; + + // Access scales using array indexing (scales are interleaved by column) + const int32x2_t scale_vec_l = { (int32_t) q6_scales[scale_idx_l * 8 + cp * 2], + (int32_t) q6_scales[scale_idx_l * 8 + cp * 2 + 1] }; + const int32x2_t scale_vec_h = { (int32_t) q6_scales[scale_idx_h * 8 + cp * 2], + (int32_t) q6_scales[scale_idx_h * 8 + cp * 2 + 1] }; + + // Accumulate scaled results + acc[cp] = vmla_s32(acc[cp], sum_l, scale_vec_l); + acc[cp] = vmla_s32(acc[cp], sum_h, scale_vec_h); + } + } + } // for half + + // Bias correction + acc[0] = vsub_s32(acc[0], vget_low_s32(bias_lo)); + acc[1] = vsub_s32(acc[1], vget_high_s32(bias_lo)); + acc[2] = vsub_s32(acc[2], vget_low_s32(bias_hi)); + acc[3] = vsub_s32(acc[3], vget_high_s32(bias_hi)); + + // Apply superblock scale (no mins for q6_K) + // acc[cp] has [c0, c1] + float32x2_t w_01 = vmul_f32(vcvt_f32_s32(acc[0]), vget_low_f32(sb_scale_0)); + float32x2_t w_23 = vmul_f32(vcvt_f32_s32(acc[1]), vget_high_f32(sb_scale_0)); + float32x2_t w_45 = vmul_f32(vcvt_f32_s32(acc[2]), vget_low_f32(sb_scale_1)); + float32x2_t w_67 = vmul_f32(vcvt_f32_s32(acc[3]), vget_high_f32(sb_scale_1)); + + acc_f32[0] = vaddq_f32(acc_f32[0], vcombine_f32(w_01, w_23)); + acc_f32[1] = vaddq_f32(acc_f32[1], vcombine_f32(w_45, w_67)); + } // for b + + int base = x * ncols_interleaved; + vst1q_f32(s + base, acc_f32[0]); + vst1q_f32(s + base + 4, acc_f32[1]); + } // for x + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + ggml_gemv_q6_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, @@ -3146,8 +3348,8 @@ void ggml_gemm_q5_K_8x8_q8_K(int n, const int8x16_t qs_lo_0 = vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(qs_cp_0, m4b), hbit_lo_0, 4)); int32x4_t acc_0 = sb_acc[0]; acc_0 = vmmlaq_s32(acc_0, qs_lo_0, q8s[0][0]); - int32x4_t acc_2 = sb_acc[2]; - acc_2 = vmmlaq_s32(acc_2, qs_lo_0, q8s[1][0]); + int32x4_t acc_2 = sb_acc[2]; + acc_2 = vmmlaq_s32(acc_2, qs_lo_0, q8s[1][0]); const int8x16_t qs_hi_0 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(qs_cp_0, 4), hbit_hi_0)); int32x4_t acc_1 = sb_acc[1]; acc_1 = vmmlaq_s32(acc_1, qs_hi_0, q8s[0][4]); @@ -3271,6 +3473,223 @@ void ggml_gemm_q5_K_8x8_q8_K(int n, ggml_gemm_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemm_q6_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) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 8; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + constexpr int q8_k_blocklen = 4; + const uint8x16_t m4b = vdupq_n_u8(0x0f); + const uint8x16_t mask_lo = vdupq_n_u8(0x03); + const uint8x16_t mask_hi = vdupq_n_u8(0x30); + const int8x16_t m32s = vdupq_n_s8(32); + + // 8 accumulators: 4 q8 rows × 2 col groups (0-3, 4-7) + float32x4_t acc_f32[blocklen]; + + for (int y = 0; y < nr / q8_k_blocklen; y++) { + const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb); + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb); + + for (int i = 0; i < blocklen; i++) { + acc_f32[i] = vdupq_n_f32(0); + } + + for (int b = 0; b < nb; b++) { + int32x4_t acc[8]; // rows 01 stored in [0][1][2][3], rows 23 stored in [4][5][6][7] + for (int i = 0; i < 8; i++) { + acc[i] = vdupq_n_s32(0); + } + + // Q6_K has simple 8-bit scales, 16 per block (one per 16 values) + // Reused for bias and dequantization later + int16_t q6_scales[16 * 8]; + for (int i = 0; i < 16; ++i) { + int16x8_t s16 = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8)); + vst1q_s16(q6_scales + i * 8, s16); + } + + // Process two 128-value halves per superblock + for (int half = 0; half < 2; half++) { + + const uint8_t * ql_base = q6_ptr[b].ql + half * 512; + const uint8_t * qh_base = q6_ptr[b].qh + half * 256; + + // A subblock (sb) is a set of weights that share the scale + // Since q6_K scales are per 16 elements + // num sbs -> 256 elements / (16 elements/scale * 2 elements/byte * 2 halves) + for (int sb = 0; sb < QK_K / 64; sb++) { + // Q6_K weight index increasing by 64 instead of 32 requires + // loading various q8 memory regions + const int8_t * q8_base_l = q8_ptr[b].qs + half * 512 + sb * 64; + const int8_t * q8_base_h = q8_ptr[b].qs + half * 512 + 256 + sb * 64; + + int8x16_t q8_l_01[2]; + int8x16_t q8_l_23[2]; + for (int i = 0; i < 2; i++) { + const int offset = i * 32; + q8_l_01[i] = vld1q_s8(q8_base_l + offset); // 0..7 & 8..15 (r01) + q8_l_23[i] = vld1q_s8(q8_base_l + offset + 16); // 0..7 & 8..15 (r23) + } + + int8x16_t q8_h_01[2]; + int8x16_t q8_h_23[2]; + for (int i = 0; i < 2; i++) { + const int offset = i * 32; + q8_h_01[i] = vld1q_s8(q8_base_h + offset); + q8_h_23[i] = vld1q_s8(q8_base_h + offset + 16); + } + + const int ql_off_base = sb * QK_K / 2; + + uint8x16_t q6_ql_0[4]; + uint8x16_t q6_ql_1[4]; + for (int k = 0; k < 4; k++) { + q6_ql_0[k] = vld1q_u8(ql_base + ql_off_base + 16 * k); + q6_ql_1[k] = vld1q_u8(ql_base + ql_off_base + 64 + 16 * k); + } + + const int qh_off_base = (sb * QK_K / 2) & 255; // wrap after 256 bytes + uint8x16_t q6_qh_0[4]; + uint8x16_t q6_qh_1[4]; + for (int k = 0; k < 4; k++) { + q6_qh_0[k] = vld1q_u8(qh_base + qh_off_base + 16 * k); + q6_qh_1[k] = vld1q_u8(qh_base + qh_off_base + 64 + 16 * k); + } + + // Adjust for the proper high bits (Sb 2 and 3) + if (sb > 1) { + for (int k = 0; k < 4; k++) { + q6_qh_0[k] = vshrq_n_u8(q6_qh_0[k], 2); + q6_qh_1[k] = vshrq_n_u8(q6_qh_1[k], 2); + } + } + + // Process column pairs (0-1, 2-3, 4-5, 6-7) + for (int cp = 0; cp < ncols_interleaved / 2; cp++) { + const uint8x16_t q6_qs_cp_0_l = q6_ql_0[cp]; + const uint8x16_t q6_qs_cp_1_l = q6_ql_1[cp]; + const uint8x16_t q6_qs_cp_0_h = q6_qh_0[cp]; + const uint8x16_t q6_qs_cp_1_h = q6_qh_1[cp]; + + // Extract high 2 bits for upper nibble reconstruction + const uint8x16_t q6_qs_cp_0_hh = vandq_u8(q6_qs_cp_0_h, mask_hi); + const uint8x16_t q6_qs_cp_1_hh = vandq_u8(q6_qs_cp_1_h, mask_hi); + + // q6 = (low4 | high2<<4) - 32 + // Use vsliq_n_u8 to combine shift-left-insert in one instruction (like Q5_K) + const int8x16_t q6_l0 = vsubq_s8( + vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_qs_cp_0_l, m4b), vandq_u8(q6_qs_cp_0_h, mask_lo), 4)), + m32s); + const int8x16_t q6_l1 = vsubq_s8( + vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_qs_cp_1_l, m4b), vandq_u8(q6_qs_cp_1_h, mask_lo), 4)), + m32s); + const int8x16_t q6_h0 = vsubq_s8( + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_cp_0_l, 4), q6_qs_cp_0_hh)), m32s); + const int8x16_t q6_h1 = vsubq_s8( + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_cp_1_l, 4), q6_qs_cp_1_hh)), m32s); + + // row pair 0, base_l + int32x4_t sb_acc_0l = vmmlaq_s32(vdupq_n_s32(0), q6_l0, q8_l_01[0]); + sb_acc_0l = vmmlaq_s32(sb_acc_0l, q6_l1, q8_l_01[1]); + // row pair 0, base_h + int32x4_t sb_acc_0h = vmmlaq_s32(vdupq_n_s32(0), q6_h0, q8_h_01[0]); + sb_acc_0h = vmmlaq_s32(sb_acc_0h, q6_h1, q8_h_01[1]); + // row pair 1, base_l + int32x4_t sb_acc_1l = vmmlaq_s32(vdupq_n_s32(0), q6_l0, q8_l_23[0]); + sb_acc_1l = vmmlaq_s32(sb_acc_1l, q6_l1, q8_l_23[1]); + // row pair 1, base_h + int32x4_t sb_acc_1h = vmmlaq_s32(vdupq_n_s32(0), q6_h0, q8_h_23[0]); + sb_acc_1h = vmmlaq_s32(sb_acc_1h, q6_h1, q8_h_23[1]); + + const int scale_idx_l = half * 8 + sb; + const int scale_idx_h = half * 8 + sb + 4; + + const int32x4_t scale_vec_l = { + q6_scales[scale_idx_l * 8 + cp * 2 + 0], + q6_scales[scale_idx_l * 8 + cp * 2 + 0], + q6_scales[scale_idx_l * 8 + cp * 2 + 1], + q6_scales[scale_idx_l * 8 + cp * 2 + 1], + }; + const int32x4_t scale_vec_h = { + q6_scales[scale_idx_h * 8 + cp * 2 + 0], + q6_scales[scale_idx_h * 8 + cp * 2 + 0], + q6_scales[scale_idx_h * 8 + cp * 2 + 1], + q6_scales[scale_idx_h * 8 + cp * 2 + 1], + }; + + acc[cp] = vmlaq_s32(acc[cp], sb_acc_0l, scale_vec_l); + acc[cp] = vmlaq_s32(acc[cp], sb_acc_0h, scale_vec_h); + acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc_1l, scale_vec_l); + acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc_1h, scale_vec_h); + } + } + } // for half + + // Reorder i8mm output to match memory layout + for (int i = 0; i < 8; i++) { + int32x2x2_t aux = vzip_s32(vget_low_s32(acc[i]), vget_high_s32(acc[i])); + acc[i] = vcombine_s32(aux.val[0], aux.val[1]); + } + int32x4_t reorder_acc[8] = { + vcombine_s32(vget_low_s32(acc[0]), vget_low_s32(acc[1])), + vcombine_s32(vget_low_s32(acc[2]), vget_low_s32(acc[3])), + vcombine_s32(vget_high_s32(acc[0]), vget_high_s32(acc[1])), + vcombine_s32(vget_high_s32(acc[2]), vget_high_s32(acc[3])), + vcombine_s32(vget_low_s32(acc[4]), vget_low_s32(acc[5])), + vcombine_s32(vget_low_s32(acc[6]), vget_low_s32(acc[7])), + vcombine_s32(vget_high_s32(acc[4]), vget_high_s32(acc[5])), + vcombine_s32(vget_high_s32(acc[6]), vget_high_s32(acc[7])), + }; + + // Apply superblock scale (no mins for q6_K) + for (int i = 0; i < q8_k_blocklen; i++) { + for (int j = 0; j < 2; j++) { + float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d[i]); + float32x4_t q6_d = vcvt_f32_f16(vld1_f16((const __fp16 *) (q6_ptr[b].d + j * 4))); + const float32x4_t scale = vmulq_f32(q6_d, q8_d); + + acc_f32[2 * i + j] = + vmlaq_f32(acc_f32[2 * i + j], vcvtq_f32_s32(reorder_acc[2 * i + j]), scale); + } + } + } // for b + + // Store results + for (int i = 0; i < q8_k_blocklen; i++) { + int row = y * q8_k_blocklen + i; + for (int j = 0; j < 2; j++) { + int col = x * ncols_interleaved + j * 4; + int offset = row * bs + col; + vst1q_f32(s + offset, acc_f32[2 * i + j]); + } + } + } // for x + } // for y + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + ggml_gemm_q6_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index 19e021e59a..24e8ab4618 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -703,6 +703,97 @@ void ggml_gemv_q5_K_8x8_q8_K_generic(int n, } } + +void ggml_gemv_q6_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) { + constexpr int qk = QK_K; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[8]; + + const block_q8_K * a_ptr = (const block_q8_K *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) { + sumf[j] = 0.0f; + } + + for (int l = 0; l < nb; l++) { + + + for (int k = 0; k < 16; k++) { + // k = 0.. 7 weights 0-63 low, 64-127 high + // k = 8..15 weights 128-191 low, 192-255 high + const int base_l = (k / 8) * 128 + (k % 8) * 8; + const int base_h = base_l + 64; + + const int scale_idx_l = base_l / 16; + const int scale_idx_h = base_h / 16; + + // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half + const int qh_shift_l = ((base_l % 128) / 32) * 2; + const int qh_shift_h = ((base_h % 128) / 32) * 2; + + // qh_half: offset to the correct 32-byte half (0 or 32) + const int qh_half_l = (base_l / 128) * 32; + const int qh_half_h = (base_h / 128) * 32; + + for (int j = 0; j < ncols_interleaved; j++) { + // Interleaved scales + const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j]; + const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j]; + + int sumi_l = 0; + int sumi_h = 0; + + for (int i = 0; i < blocklen; i++) { + const int ql_pos = k * 64 + j * 8 + i; + const int l_4 = b_ptr[l].ql[ql_pos] & 0xF; + const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF; + + // qh indexing with 8-byte interleaving (like q5_K) + const int qh_byte_l = qh_half_l + ((base_l + i) % 32); + const int qh_chunk_l = qh_byte_l / 8; + const int qh_pos_l = qh_byte_l % 8; + const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l; + const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3; + + const int qh_byte_h = qh_half_h + ((base_h + i) % 32); + const int qh_chunk_h = qh_byte_h / 8; + const int qh_pos_h = qh_byte_h % 8; + const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h; + const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3; + + const int q_l = ((hi_2_l << 4) | l_4) - 32; + const int q_h = ((hi_2_h << 4) | hi_4) - 32; + + const int8_t a_l = a_ptr[l].qs[base_l + i]; + const int8_t a_h = a_ptr[l].qs[base_h + i]; + + sumi_l += q_l * a_l; + sumi_h += q_h * a_h; + } + + sumf[j] += + (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d; + } + } + } + + for (int j = 0; j < ncols_interleaved; j++) { + s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + 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) { const int qk = QK8_0; const int nb = n / qk; @@ -1133,15 +1224,7 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, 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); float sumf[4][8]; float sum_minf[4][8]; @@ -1402,6 +1485,111 @@ void ggml_gemm_q5_K_8x8_q8_K_generic(int n, } } +void ggml_gemm_q6_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) { + const int qk = QK_K; + 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(bs); + + float sumf[4][8]; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb); + + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumf[m][j] = 0.0f; + } + } + + for (int l = 0; l < nb; l++) { + for (int k = 0; k < 16; k++) { + // k = 0.. 7 weights 0-63 low, 64-127 high + // k = 8..15 weights 128-191 low, 192-255 high + const int base_l = (k / 8) * 128 + (k % 8) * 8; + const int base_h = base_l + 64; + + const int scale_idx_l = base_l / 16; + const int scale_idx_h = base_h / 16; + + // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half + const int qh_shift_l = ((base_l % 128) / 32) * 2; + const int qh_shift_h = ((base_h % 128) / 32) * 2; + + // qh_half: offset to the correct 32-byte half (0 or 32) + const int qh_half_l = (base_l / 128) * 32; + const int qh_half_h = (base_h / 128) * 32; + + // Activation base indices for q8_Kx4 interleaved format + // Layout: 128-value halves (k/8), then 8-value sub-blocks (k%8) with stride 32 + const int q8_base = (k / 8) * 512 + (k % 8) * 32; + + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + // Interleaved scales + const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j]; + const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j]; + + int sumi_l = 0; + int sumi_h = 0; + + for (int i = 0; i < blocklen; i++) { + const int ql_pos = k * 64 + j * 8 + i; + const int l_4 = b_ptr[l].ql[ql_pos] & 0xF; + const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF; + + const int qh_idx_l = qh_half_l + ((base_l + i) % 32); + const int qh_chunk_l = qh_idx_l / 8; + const int qh_pos_l = qh_idx_l % 8; + const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l; + const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3; + + const int qh_idx_h = qh_half_h + ((base_h + i) % 32); + const int qh_chunk_h = qh_idx_h / 8; + const int qh_pos_h = qh_idx_h % 8; + const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h; + const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3; + + const int q_l = ((hi_2_l << 4) | l_4) - 32; + const int q_h = ((hi_2_h << 4) | hi_4) - 32; + + const int8_t q8_l = a_ptr[l].qs[q8_base + m * 8 + i]; + const int8_t q8_h = a_ptr[l].qs[q8_base + m * 8 + i + 256]; + + sumi_l += q_l * q8_l; + sumi_h += q_h * q8_h; + } + + sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * + 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]; + } + } + } + } +} + 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) { const int qk = QK8_0; const int nb = n / qk; @@ -1801,8 +1989,7 @@ static block_q2_Kx8 make_block_q2_Kx8(block_q2_K * in, unsigned int blck_size_in // Every 16 byte is packed such that it contains scales and mins for corresponding sub blocks from Q2_K structure // For eg - First 16 bytes contains 16 scales and 16 mins - each of first and second sub blocks from different Q2_K structures - for(int i = 0; i < 128; i++){ - + for (int i = 0; i < 128; i++) { // Index for selecting which q2k super block int src1 = (i % 16) / 2; // Index for selecting scale @@ -1902,6 +2089,52 @@ static block_q5_Kx8 make_block_q5_Kx8(block_q5_K * in, unsigned int blck_size_in return out; } +static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_interleave) { + block_q6_Kx8 out; + constexpr int n_blocks = 8; // Kx8 + for (int i = 0; i < n_blocks; i++) { + out.d[i] = in[i].d; + } + + const int end_ls = QK_K * 4 / blck_size_interleave; + // Interleave Q6_K quants by taking 8 bytes at a time + for (int i = 0; i < end_ls; ++i) { + int src_id = i % n_blocks; + int src_offset = (i / n_blocks) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elem_ls; + memcpy(&elem_ls, &in[src_id].ql[src_offset], sizeof(uint64_t)); + memcpy(&out.ql[dst_offset], &elem_ls, sizeof(uint64_t)); + } + + // Interleave high bits using same 8-byte pattern as low bits + const int end_hs = end_ls / 2; + for (int i = 0; i < end_hs; ++i) { + int src_id = i % n_blocks; + int src_offset = (i / n_blocks) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elem_hs; + memcpy(&elem_hs, &in[src_id].qh[src_offset], sizeof(uint64_t)); + memcpy(&out.qh[dst_offset], &elem_hs, sizeof(uint64_t)); + } + + // The below logic is designed so as to unpack and rearrange scales in Q6_K + // The output Q6_Kx8 structure interleaves the 8 bit scales in the same fashion as the quants + // Q6_K structure has an 8-bit scale per 16 elements -> 16 scales + // scales: [0 bl0 0 bl1 ... 0 bl7][1 bl0 ... 1 bl7] ... [15 bl0 ... 15 bl7] (bl = block) + constexpr int n_scales = QK_K / 16; + + for (int i = 0; i < n_blocks; i++) { + for (int j = 0; j < n_scales; j++) { + out.scales[j * n_blocks + i] = in[i].scales[j]; + } + } + + return out; +} + static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { GGML_ASSERT(t->type == GGML_TYPE_Q4_0); GGML_ASSERT(interleave_block == 4 || interleave_block == 8); @@ -1983,7 +2216,7 @@ static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block 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++ ) { + for (int i = 0; i < nrows_interleaved; i++) { dst_tmp[i] = src[x + i * nblocks]; } *dst++ = make_block_q2_Kx8(dst_tmp, interleave_block); @@ -2027,6 +2260,35 @@ static int repack_q5_K_to_q5_K_8_bl(struct ggml_tensor * t, return 0; } +static int repack_q6_K_to_q6_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q6_K); + GGML_ASSERT(interleave_block == 8); + constexpr int nrows_interleaved = 8; + + block_q6_Kx8 * dst = (block_q6_Kx8 *)t->data; + const block_q6_K * src = (const block_q6_K *) data; + block_q6_K dst_tmp[8]; + int nrow = ggml_nrows(t); + int nblocks = t->ne[0] / QK_K; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q6_K)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 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_q6_Kx8(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; +} + static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { GGML_ASSERT(t->type == GGML_TYPE_Q4_0); GGML_ASSERT(interleave_block == 8); @@ -2249,6 +2511,10 @@ template <> int repack(struct ggml_tensor * t, const void * da return repack_q5_K_to_q5_K_8_bl(t, 8, data, data_size); } +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q6_K_to_q6_K_8_bl(t, 8, data, data_size); +} + template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { return repack_iq4_nl_to_iq4_nl_4_bl(t, 4, data, data_size); } @@ -2286,7 +2552,14 @@ template <> void gemv(int n, float * s, size_t ggml_gemv_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc); } -template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { +template <> +void gemv(int n, + float * s, + size_t bs, + const void * vx, + const void * vy, + int nr, + int nc) { ggml_gemv_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } @@ -2302,6 +2575,10 @@ template <> void gemv(int n, float * s, size_t ggml_gemv_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); +} + template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc); } @@ -2330,7 +2607,14 @@ template <> void gemm(int n, float * s, size_t ggml_gemm_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc); } -template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { +template <> +void gemm(int n, + float * s, + size_t bs, + const void * vx, + const void * vy, + int nr, + int nc) { ggml_gemm_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc); } @@ -2350,6 +2634,10 @@ template <> void gemm(int n, float * s, size_t ggml_gemm_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); +} + template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc); } @@ -2714,20 +3002,19 @@ template (ne00, - (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, - src0_cur + src0_cur_start * nb01, - src1_col, 1, src0_cur_end - src0_cur_start); + gemv( + ne00, (float *) ((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, + src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); } } #undef MMID_MATRIX_ROW @@ -2743,7 +3030,6 @@ template q4_0_4x4_q8_0; static const ggml::cpu::repack::tensor_traits q4_0_4x8_q8_0; @@ -2756,6 +3042,9 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons // instance for Q5_K static const ggml::cpu::repack::tensor_traits q5_K_8x8_q8_K; + // instance for Q6_K + static const ggml::cpu::repack::tensor_traits q6_K_8x8_q8_K; + // instance for Q2 static const ggml::cpu::repack::tensor_traits q2_K_8x8_q8_K; @@ -2812,6 +3101,12 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &q5_K_8x8_q8_K; } } + } else if (cur->type == GGML_TYPE_Q6_K) { + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + if (cur->ne[1] % 8 == 0) { + return &q6_K_8x8_q8_K; + } + } } else if (cur->type == GGML_TYPE_IQ4_NL) { if (ggml_cpu_has_avx2()) { if (cur->ne[1] % 8 == 0) { diff --git a/ggml/src/ggml-cpu/repack.h b/ggml/src/ggml-cpu/repack.h index da87103157..855320eeeb 100644 --- a/ggml/src/ggml-cpu/repack.h +++ b/ggml/src/ggml-cpu/repack.h @@ -65,6 +65,16 @@ struct block_q5_Kx8 { static_assert(sizeof(block_q5_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 5, "wrong q5_K block size/padding"); +struct block_q6_Kx8 { + ggml_half d[8]; + int8_t scales[QK_K / 16 * 8]; + uint8_t ql[QK_K / 2 * 8]; // low bits of 6-bit quants (groups of 2) + uint8_t qh[QK_K / 4 * 8]; // high bits of 6-bit quants (groups of 4) +}; + +static_assert(sizeof(block_q6_Kx8) == sizeof(ggml_half) * 8 + QK_K / 16 * 8 + 3 * QK_K / 4 * 8, + "wrong q6_K block size/padding"); + struct block_q8_Kx4 { float d[4]; // delta int8_t qs[QK_K * 4]; // quants @@ -95,13 +105,14 @@ void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTR void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); void ggml_quantize_mat_q8_K_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); -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_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_gemv_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_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); +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_q4_K_8x4_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_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_q5_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_q6_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); @@ -111,6 +122,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo void ggml_gemm_q4_K_8x4_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_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_q5_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_q6_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); void ggml_gemv_q8_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); @@ -130,6 +142,7 @@ void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, void ggml_gemv_q4_K_8x4_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_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_q5_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_q6_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); @@ -139,6 +152,7 @@ void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, void ggml_gemm_q4_K_8x4_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_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_q5_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_q6_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); void ggml_gemv_q8_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); From c0204a0893a9e9408c4025e1648832d06fa1c564 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 27 Jan 2026 11:54:25 +0100 Subject: [PATCH 030/206] ci : revert slim runner for winget (#19129) --- .github/workflows/winget.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/winget.yml b/.github/workflows/winget.yml index e3981f30b9..7506091647 100644 --- a/.github/workflows/winget.yml +++ b/.github/workflows/winget.yml @@ -8,7 +8,7 @@ on: jobs: update: name: Update Winget Package - runs-on: ubuntu-slim + runs-on: ubuntu-latest if: github.repository_owner == 'ggml-org' steps: From a5bb8ba4c50257437630c136210396810741bbf7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 27 Jan 2026 14:28:56 +0100 Subject: [PATCH 031/206] CUDA: tune GLM 4.7 Flash FA kernel selection logic (#19097) --- ggml/src/ggml-cuda/fattn.cu | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 2f5dbd13a3..b061fdf9a2 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -148,6 +148,10 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg const int gqa_ratio = Q->ne[2] / K->ne[2]; if (gqa_ratio == 20) { // GLM 4.7 Flash if (cc >= GGML_CUDA_CC_BLACKWELL) { + if (Q->ne[1] <= 4 && K->ne[1] >= 65536) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); + break; + } ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); break; } @@ -161,6 +165,10 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg } if (cc >= GGML_CUDA_CC_TURING) { if (Q->ne[1] <= 4) { + if (K->ne[1] <= 16384) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); + break; + } ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 32>(ctx, dst); break; } From 68ac3acb435450d5ba1e62748e17671815313dc3 Mon Sep 17 00:00:00 2001 From: David Lima Date: Tue, 27 Jan 2026 10:48:51 -0300 Subject: [PATCH 032/206] docs: Remove duplicated word on CUDA build section (#19136) --- docs/build.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/build.md b/docs/build.md index 4983cfcfea..e088c519a7 100644 --- a/docs/build.md +++ b/docs/build.md @@ -144,7 +144,7 @@ We also have a [guide](./backend/CUDA-FEDORA.md) for setting up CUDA toolkit in - ***Necessary*** for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/). - (there are no supported CUDA packages for these systems) - ***Necessary*** for users that have a host that is not a: [Supported Nvidia CUDA Release Platform](https://developer.nvidia.com/cuda-downloads). - - (for example, you may have [Fedora 42 Beta](https://fedoramagazine.org/announcing-fedora-linux-42-beta/) as your your host operating system) + - (for example, you may have [Fedora 42 Beta](https://fedoramagazine.org/announcing-fedora-linux-42-beta/) as your host operating system) - ***Convenient*** For those running [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde), and want to keep their host system clean. - *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download) From 2b4cbd2834e427024bc7f935a1f232aecac6679b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Tue, 27 Jan 2026 19:50:42 +0100 Subject: [PATCH 033/206] jinja : implement mixed type object keys (#18955) * implement mixed type object keys * add tests * refactor * minor fixes * massive refactor * add more tests * forgotten tuples * fix array/object is_hashable * correct (albeit broken) jinja responses verified with transformers * improved hashing and equality * refactor hash function * more exhausive test case * clean up * cont * cont (2) * missing cstring --------- Co-authored-by: Xuan Son Nguyen --- common/jinja/runtime.cpp | 55 ++-- common/jinja/runtime.h | 26 +- common/jinja/string.cpp | 6 + common/jinja/string.h | 3 + common/jinja/utils.h | 100 ++++++++ common/jinja/value.cpp | 86 ++++--- common/jinja/value.h | 473 ++++++++++++++++++++++++++++------- tests/test-chat-template.cpp | 4 +- tests/test-jinja.cpp | 217 ++++++++++++++++ 9 files changed, 802 insertions(+), 168 deletions(-) diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp index e3e4ebf1ec..f234d9284f 100644 --- a/common/jinja/runtime.cpp +++ b/common/jinja/runtime.cpp @@ -44,6 +44,12 @@ static std::string get_line_col(const std::string & source, size_t pos) { return "line " + std::to_string(line) + ", column " + std::to_string(col); } +static void ensure_key_type_allowed(const value & val) { + if (!val->is_hashable()) { + throw std::runtime_error("Type: " + val->type() + " is not allowed as object key"); + } +} + // execute with error handling value statement::execute(context & ctx) { try { @@ -95,20 +101,10 @@ value identifier::execute_impl(context & ctx) { value object_literal::execute_impl(context & ctx) { auto obj = mk_val(); for (const auto & pair : val) { - value key_val = pair.first->execute(ctx); - if (!is_val(key_val) && !is_val(key_val)) { - throw std::runtime_error("Object literal: keys must be string or int values, got " + key_val->type()); - } - std::string key = key_val->as_string().str(); + value key = pair.first->execute(ctx); value val = pair.second->execute(ctx); - JJ_DEBUG("Object literal: setting key '%s' with value type %s", key.c_str(), val->type().c_str()); + JJ_DEBUG("Object literal: setting key '%s' with value type %s", key->as_string().str().c_str(), val->type().c_str()); obj->insert(key, val); - - if (is_val(key_val)) { - obj->val_obj.is_key_numeric = true; - } else if (obj->val_obj.is_key_numeric) { - throw std::runtime_error("Object literal: cannot mix numeric and non-numeric keys"); - } } return obj; } @@ -127,9 +123,9 @@ value binary_expression::execute_impl(context & ctx) { value right_val = right->execute(ctx); JJ_DEBUG("Executing binary expression %s '%s' %s", left_val->type().c_str(), op.value.c_str(), right_val->type().c_str()); if (op.value == "==") { - return mk_val(value_compare(left_val, right_val, value_compare_op::eq)); + return mk_val(*left_val == *right_val); } else if (op.value == "!=") { - return mk_val(!value_compare(left_val, right_val, value_compare_op::eq)); + return mk_val(!(*left_val == *right_val)); } auto workaround_concat_null_with_str = [&](value & res) -> bool { @@ -230,7 +226,7 @@ value binary_expression::execute_impl(context & ctx) { auto & arr = right_val->as_array(); bool member = false; for (const auto & item : arr) { - if (value_compare(left_val, item, value_compare_op::eq)) { + if (*left_val == *item) { member = true; break; } @@ -265,10 +261,9 @@ value binary_expression::execute_impl(context & ctx) { } } - // String in object - if (is_val(left_val) && is_val(right_val)) { - auto key = left_val->as_string().str(); - bool has_key = right_val->has_key(key); + // Value key in object + if (is_val(right_val)) { + bool has_key = right_val->has_key(left_val); if (op.value == "in") { return mk_val(has_key); } else if (op.value == "not in") { @@ -465,14 +460,8 @@ value for_statement::execute_impl(context & ctx) { JJ_DEBUG("%s", "For loop over object keys"); auto & obj = iterable_val->as_ordered_object(); for (auto & p : obj) { - auto tuple = mk_val(); - if (iterable_val->val_obj.is_key_numeric) { - tuple->push_back(mk_val(std::stoll(p.first))); - } else { - tuple->push_back(mk_val(p.first)); - } - tuple->push_back(p.second); - items.push_back(tuple); + auto tuple = mk_val(p); + items.push_back(std::move(tuple)); } if (ctx.is_get_stats) { iterable_val->stats.used = true; @@ -602,11 +591,13 @@ value set_statement::execute_impl(context & ctx) { auto rhs = val ? val->execute(ctx) : exec_statements(body, ctx); if (is_stmt(assignee)) { + // case: {% set my_var = value %} auto var_name = cast_stmt(assignee)->val; JJ_DEBUG("Setting global variable '%s' with value type %s", var_name.c_str(), rhs->type().c_str()); ctx.set_val(var_name, rhs); } else if (is_stmt(assignee)) { + // case: {% set a, b = value %} auto tuple = cast_stmt(assignee); if (!is_val(rhs)) { throw std::runtime_error("Cannot unpack non-iterable type in set: " + rhs->type()); @@ -625,6 +616,7 @@ value set_statement::execute_impl(context & ctx) { } } else if (is_stmt(assignee)) { + // case: {% set ns.my_var = value %} auto member = cast_stmt(assignee); if (member->computed) { throw std::runtime_error("Cannot assign to computed member"); @@ -767,22 +759,22 @@ value member_expression::execute_impl(context & ctx) { } JJ_DEBUG("Member expression on object type %s, property type %s", object->type().c_str(), property->type().c_str()); + ensure_key_type_allowed(property); value val = mk_val("object_property"); if (is_val(object)) { JJ_DEBUG("%s", "Accessing property on undefined object, returning undefined"); return val; + } else if (is_val(object)) { - if (!is_val(property)) { - throw std::runtime_error("Cannot access object with non-string: got " + property->type()); - } auto key = property->as_string().str(); - val = object->at(key, val); + val = object->at(property, val); if (is_val(val)) { val = try_builtin_func(ctx, key, object, true); } JJ_DEBUG("Accessed property '%s' value, got type: %s", key.c_str(), val->type().c_str()); + } else if (is_val(object) || is_val(object)) { if (is_val(property)) { int64_t index = property->as_int(); @@ -806,6 +798,7 @@ value member_expression::execute_impl(context & ctx) { auto key = property->as_string().str(); JJ_DEBUG("Accessing %s built-in '%s'", is_val(object) ? "array" : "string", key.c_str()); val = try_builtin_func(ctx, key, object, true); + } else { throw std::runtime_error("Cannot access property with non-string/non-number: got " + property->type()); } diff --git a/common/jinja/runtime.h b/common/jinja/runtime.h index dc7f4e471c..17a6dff5aa 100644 --- a/common/jinja/runtime.h +++ b/common/jinja/runtime.h @@ -79,18 +79,18 @@ struct context { } value get_val(const std::string & name) { - auto it = env->val_obj.unordered.find(name); - if (it != env->val_obj.unordered.end()) { - return it->second; - } else { - return mk_val(name); - } + value default_val = mk_val(name); + return env->at(name, default_val); } void set_val(const std::string & name, const value & val) { env->insert(name, val); } + void set_val(const value & name, const value & val) { + env->insert(name, val); + } + void print_vars() const { printf("Context Variables:\n%s\n", value_to_json(env, 2).c_str()); } @@ -344,9 +344,19 @@ struct array_literal : public expression { } }; -struct tuple_literal : public array_literal { - explicit tuple_literal(statements && val) : array_literal(std::move(val)) {} +struct tuple_literal : public expression { + statements val; + explicit tuple_literal(statements && val) : val(std::move(val)) { + for (const auto& item : this->val) chk_type(item); + } std::string type() const override { return "TupleLiteral"; } + value execute_impl(context & ctx) override { + auto arr = mk_val(); + for (const auto & item_stmt : val) { + arr->push_back(item_stmt->execute(ctx)); + } + return mk_val(std::move(arr->as_array())); + } }; struct object_literal : public expression { diff --git a/common/jinja/string.cpp b/common/jinja/string.cpp index 21ebde39e3..8087e15b35 100644 --- a/common/jinja/string.cpp +++ b/common/jinja/string.cpp @@ -61,6 +61,12 @@ size_t string::length() const { return len; } +void string::hash_update(hasher & hash) const noexcept { + for (const auto & part : parts) { + hash.update(part.val.data(), part.val.length()); + } +} + bool string::all_parts_are_input() const { for (const auto & part : parts) { if (!part.is_input) { diff --git a/common/jinja/string.h b/common/jinja/string.h index 78457f9e41..c4963000ad 100644 --- a/common/jinja/string.h +++ b/common/jinja/string.h @@ -4,6 +4,8 @@ #include #include +#include "utils.h" + namespace jinja { // allow differentiate between user input strings and template strings @@ -37,6 +39,7 @@ struct string { std::string str() const; size_t length() const; + void hash_update(hasher & hash) const noexcept; bool all_parts_are_input() const; bool is_uppercase() const; bool is_lowercase() const; diff --git a/common/jinja/utils.h b/common/jinja/utils.h index 1e9f2a12a1..de6947fc28 100644 --- a/common/jinja/utils.h +++ b/common/jinja/utils.h @@ -3,6 +3,8 @@ #include #include #include +#include +#include namespace jinja { @@ -46,4 +48,102 @@ static std::string fmt_error_with_source(const std::string & tag, const std::str return oss.str(); } +// Note: this is a simple hasher, not cryptographically secure, just for hash table usage +struct hasher { + static constexpr auto size_t_digits = sizeof(size_t) * 8; + static constexpr size_t prime = size_t_digits == 64 ? 0x100000001b3 : 0x01000193; + static constexpr size_t seed = size_t_digits == 64 ? 0xcbf29ce484222325 : 0x811c9dc5; + static constexpr auto block_size = sizeof(size_t); // in bytes; allowing the compiler to vectorize the computation + + static_assert(size_t_digits == 64 || size_t_digits == 32); + static_assert(block_size == 8 || block_size == 4); + + uint8_t buffer[block_size]; + size_t idx = 0; // current index in buffer + size_t state = seed; + + hasher() = default; + hasher(const std::type_info & type_inf) noexcept { + const auto type_hash = type_inf.hash_code(); + update(&type_hash, sizeof(type_hash)); + } + + // Properties: + // - update is not associative: update(a).update(b) != update(b).update(a) + // - update(a ~ b) == update(a).update(b) with ~ as concatenation operator --> useful for streaming + // - update("", 0) --> state unchanged with empty input + hasher& update(void const * bytes, size_t len) noexcept { + const uint8_t * c = static_cast(bytes); + if (len == 0) { + return *this; + } + size_t processed = 0; + + // first, fill the existing buffer if it's partial + if (idx > 0) { + size_t to_fill = block_size - idx; + if (to_fill > len) { + to_fill = len; + } + std::memcpy(buffer + idx, c, to_fill); + idx += to_fill; + processed += to_fill; + if (idx == block_size) { + update_block(buffer); + idx = 0; + } + } + + // process full blocks from the remaining input + for (; processed + block_size <= len; processed += block_size) { + update_block(c + processed); + } + + // buffer any remaining bytes + size_t remaining = len - processed; + if (remaining > 0) { + std::memcpy(buffer, c + processed, remaining); + idx = remaining; + } + return *this; + } + + // convenience function for testing only + hasher& update(const std::string & s) noexcept { + return update(s.data(), s.size()); + } + + // finalize and get the hash value + // note: after calling digest, the hasher state is modified, do not call update() again + size_t digest() noexcept { + // if there are remaining bytes in buffer, fill the rest with zeros and process + if (idx > 0) { + for (size_t i = idx; i < block_size; ++i) { + buffer[i] = 0; + } + update_block(buffer); + idx = 0; + } + + return state; + } + +private: + // IMPORTANT: block must have at least block_size bytes + void update_block(const uint8_t * block) noexcept { + size_t blk = static_cast(block[0]) + | (static_cast(block[1]) << 8) + | (static_cast(block[2]) << 16) + | (static_cast(block[3]) << 24); + if constexpr (block_size == 8) { + blk = blk | (static_cast(block[4]) << 32) + | (static_cast(block[5]) << 40) + | (static_cast(block[6]) << 48) + | (static_cast(block[7]) << 56); + } + state ^= blk; + state *= prime; + } +}; + } // namespace jinja diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp index d2ed824269..2d77068143 100644 --- a/common/jinja/value.cpp +++ b/common/jinja/value.cpp @@ -163,7 +163,7 @@ static value selectattr(const func_args & args) { args.ensure_vals(true, true, false, false); auto arr = args.get_pos(0)->as_array(); - auto attr_name = args.get_pos(1)->as_string().str(); + auto attribute = args.get_pos(1); auto out = mk_val(); value val_default = mk_val(); @@ -173,7 +173,7 @@ static value selectattr(const func_args & args) { if (!is_val(item)) { throw raised_exception("selectattr: item is not an object"); } - value attr_val = item->at(attr_name, val_default); + value attr_val = item->at(attribute, val_default); bool is_selected = attr_val->as_bool(); if constexpr (is_reject) is_selected = !is_selected; if (is_selected) out->push_back(item); @@ -217,7 +217,7 @@ static value selectattr(const func_args & args) { if (!is_val(item)) { throw raised_exception("selectattr: item is not an object"); } - value attr_val = item->at(attr_name, val_default); + value attr_val = item->at(attribute, val_default); func_args test_args(args.ctx); test_args.push_back(attr_val); // attribute value test_args.push_back(extra_arg); // extra argument @@ -741,6 +741,7 @@ const func_builtins & value_array_t::get_builtins() const { args.ensure_count(1, 4); args.ensure_vals(true, true, false, false); + auto val = args.get_pos(0); auto arg0 = args.get_pos(1); auto arg1 = args.get_pos(2, mk_val()); auto arg2 = args.get_pos(3, mk_val()); @@ -762,10 +763,8 @@ const func_builtins & value_array_t::get_builtins() const { if (step == 0) { throw raised_exception("slice step cannot be zero"); } - auto arr = slice(args.get_pos(0)->as_array(), start, stop, step); - auto res = mk_val(); - res->val_arr = std::move(arr); - return res; + auto arr = slice(val->as_array(), start, stop, step); + return is_val(val) ? mk_val(std::move(arr)) : mk_val(std::move(arr)); }}, {"selectattr", selectattr}, {"select", selectattr}, @@ -785,15 +784,14 @@ const func_builtins & value_array_t::get_builtins() const { } const int64_t attr_int = attr_is_int ? attribute->as_int() : 0; const std::string delim = val_delim->is_undefined() ? "" : val_delim->as_string().str(); - const std::string attr_name = attribute->is_undefined() ? "" : attribute->as_string().str(); std::string result; for (size_t i = 0; i < arr.size(); ++i) { value val_arr = arr[i]; if (!attribute->is_undefined()) { if (attr_is_int && is_val(val_arr)) { val_arr = val_arr->at(attr_int); - } else if (!attr_is_int && !attr_name.empty() && is_val(val_arr)) { - val_arr = val_arr->at(attr_name); + } else if (!attr_is_int && is_val(val_arr)) { + val_arr = val_arr->at(attribute); } } if (!is_val(val_arr) && !is_val(val_arr) && !is_val(val_arr)) { @@ -808,9 +806,7 @@ const func_builtins & value_array_t::get_builtins() const { }}, {"string", [](const func_args & args) -> value { args.ensure_vals(); - auto str = mk_val(); - gather_string_parts_recursive(args.get_pos(0), str); - return str; + return mk_val(args.get_pos(0)->as_string()); }}, {"tojson", tojson}, {"map", [](const func_args & args) -> value { @@ -821,26 +817,26 @@ const func_builtins & value_array_t::get_builtins() const { if (!is_val(args.get_args().at(1))) { throw not_implemented_exception("map: filter-mapping not implemented"); } + value val = args.get_pos(0); value attribute = args.get_kwarg_or_pos("attribute", 1); const bool attr_is_int = is_val(attribute); if (!is_val(attribute) && !attr_is_int) { throw raised_exception("map: attribute must be string or integer"); } const int64_t attr_int = attr_is_int ? attribute->as_int() : 0; - const std::string attr_name = attribute->as_string().str(); value default_val = args.get_kwarg("default", mk_val()); auto out = mk_val(); - auto arr = args.get_pos(0)->as_array(); + auto arr = val->as_array(); for (const auto & item : arr) { value attr_val; if (attr_is_int) { attr_val = is_val(item) ? item->at(attr_int, default_val) : default_val; } else { - attr_val = is_val(item) ? item->at(attr_name, default_val) : default_val; + attr_val = is_val(item) ? item->at(attribute, default_val) : default_val; } out->push_back(attr_val); } - return out; + return is_val(val) ? mk_val(std::move(out->as_array())) : out; }}, {"append", [](const func_args & args) -> value { args.ensure_count(2); @@ -867,6 +863,7 @@ const func_builtins & value_array_t::get_builtins() const { if (!is_val(args.get_pos(0))) { throw raised_exception("sort: first argument must be an array"); } + value val = args.get_pos(0); value val_reverse = args.get_kwarg_or_pos("reverse", 1); value val_case = args.get_kwarg_or_pos("case_sensitive", 2); value attribute = args.get_kwarg_or_pos("attribute", 3); @@ -875,8 +872,7 @@ const func_builtins & value_array_t::get_builtins() const { const bool reverse = val_reverse->as_bool(); // undefined == false const bool attr_is_int = is_val(attribute); const int64_t attr_int = attr_is_int ? attribute->as_int() : 0; - const std::string attr_name = attribute->is_undefined() ? "" : attribute->as_string().str(); - std::vector arr = cast_val(args.get_pos(0))->as_array(); // copy + std::vector arr = val->as_array(); // copy std::sort(arr.begin(), arr.end(),[&](const value & a, const value & b) { value val_a = a; value val_b = b; @@ -884,22 +880,23 @@ const func_builtins & value_array_t::get_builtins() const { if (attr_is_int && is_val(a) && is_val(b)) { val_a = a->at(attr_int); val_b = b->at(attr_int); - } else if (!attr_is_int && !attr_name.empty() && is_val(a) && is_val(b)) { - val_a = a->at(attr_name); - val_b = b->at(attr_name); + } else if (!attr_is_int && is_val(a) && is_val(b)) { + val_a = a->at(attribute); + val_b = b->at(attribute); } else { - throw raised_exception("sort: unsupported object attribute comparison"); + throw raised_exception("sort: unsupported object attribute comparison between " + a->type() + " and " + b->type()); } } return value_compare(val_a, val_b, reverse ? value_compare_op::gt : value_compare_op::lt); }); - return mk_val(arr); + return is_val(val) ? mk_val(std::move(arr)) : mk_val(std::move(arr)); }}, {"reverse", [](const func_args & args) -> value { args.ensure_vals(); - std::vector arr = cast_val(args.get_pos(0))->as_array(); // copy + value val = args.get_pos(0); + std::vector arr = val->as_array(); // copy std::reverse(arr.begin(), arr.end()); - return mk_val(arr); + return is_val(val) ? mk_val(std::move(arr)) : mk_val(std::move(arr)); }}, {"unique", [](const func_args &) -> value { throw not_implemented_exception("Array unique builtin not implemented"); @@ -930,7 +927,7 @@ const func_builtins & value_object_t::get_builtins() const { default_val = args.get_pos(2); } const value obj = args.get_pos(0); - std::string key = args.get_pos(1)->as_string().str(); + const value key = args.get_pos(1); return obj->at(key, default_val); }}, {"keys", [](const func_args & args) -> value { @@ -938,7 +935,7 @@ const func_builtins & value_object_t::get_builtins() const { const auto & obj = args.get_pos(0)->as_ordered_object(); auto result = mk_val(); for (const auto & pair : obj) { - result->push_back(mk_val(pair.first)); + result->push_back(pair.first); } return result; }}, @@ -956,15 +953,16 @@ const func_builtins & value_object_t::get_builtins() const { const auto & obj = args.get_pos(0)->as_ordered_object(); auto result = mk_val(); for (const auto & pair : obj) { - auto item = mk_val(); - item->push_back(mk_val(pair.first)); - item->push_back(pair.second); + auto item = mk_val(pair); result->push_back(std::move(item)); } return result; }}, {"tojson", tojson}, - {"string", tojson}, + {"string", [](const func_args & args) -> value { + args.ensure_vals(); + return mk_val(args.get_pos(0)->as_string()); + }}, {"length", [](const func_args & args) -> value { args.ensure_vals(); const auto & obj = args.get_pos(0)->as_ordered_object(); @@ -985,11 +983,11 @@ const func_builtins & value_object_t::get_builtins() const { const bool reverse = val_reverse->as_bool(); // undefined == false const bool by_value = is_val(val_by) && val_by->as_string().str() == "value" ? true : false; auto result = mk_val(val_input); // copy - std::sort(result->val_obj.ordered.begin(), result->val_obj.ordered.end(), [&](const auto & a, const auto & b) { + std::sort(result->val_obj.begin(), result->val_obj.end(), [&](const auto & a, const auto & b) { if (by_value) { return value_compare(a.second, b.second, reverse ? value_compare_op::gt : value_compare_op::lt); } else { - return reverse ? a.first > b.first : a.first < b.first; + return value_compare(a.first, b.first, reverse ? value_compare_op::gt : value_compare_op::lt); } }); return result; @@ -1134,6 +1132,8 @@ void global_from_json(context & ctx, const nlohmann::ordered_json & json_obj, bo } } +// recursively convert value to JSON string +// TODO: avoid circular references static void value_to_json_internal(std::ostringstream & oss, const value & val, int curr_lvl, int indent, const std::string_view item_sep, const std::string_view key_sep) { auto indent_str = [indent, curr_lvl]() -> std::string { return (indent > 0) ? std::string(curr_lvl * indent, ' ') : ""; @@ -1196,7 +1196,8 @@ static void value_to_json_internal(std::ostringstream & oss, const value & val, size_t i = 0; for (const auto & pair : obj) { oss << indent_str() << (indent > 0 ? std::string(indent, ' ') : ""); - oss << "\"" << pair.first << "\"" << key_sep; + value_to_json_internal(oss, mk_val(pair.first->as_string().str()), curr_lvl + 1, indent, item_sep, key_sep); + oss << key_sep; value_to_json_internal(oss, pair.second, curr_lvl + 1, indent, item_sep, key_sep); if (i < obj.size() - 1) { oss << item_sep; @@ -1219,4 +1220,19 @@ std::string value_to_json(const value & val, int indent, const std::string_view return oss.str(); } +// TODO: avoid circular references +std::string value_to_string_repr(const value & val) { + if (is_val(val)) { + const std::string val_str = val->as_string().str(); + + if (val_str.find('\'') != std::string::npos) { + return value_to_json(val); + } else { + return "'" + val_str + "'"; + } + } else { + return val->as_repr(); + } +} + } // namespace jinja diff --git a/common/jinja/value.h b/common/jinja/value.h index ccb05c6fd4..a2f92d2c69 100644 --- a/common/jinja/value.h +++ b/common/jinja/value.h @@ -1,8 +1,10 @@ #pragma once #include "string.h" +#include "utils.h" #include +#include #include #include #include @@ -93,7 +95,8 @@ void global_from_json(context & ctx, const T_JSON & json_obj, bool mark_input); struct func_args; // function argument values -using func_handler = std::function; +using func_hptr = value(const func_args &); +using func_handler = std::function; using func_builtins = std::map; enum value_compare_op { eq, ge, gt, lt, ne }; @@ -103,28 +106,9 @@ struct value_t { int64_t val_int; double val_flt; string val_str; - bool val_bool; std::vector val_arr; - - struct map { - // once set to true, all keys must be numeric - // caveat: we only allow either all numeric keys or all non-numeric keys - // for now, this only applied to for_statement in case of iterating over object keys/items - bool is_key_numeric = false; - std::map unordered; - std::vector> ordered; - void insert(const std::string & key, const value & val) { - if (unordered.find(key) != unordered.end()) { - // if key exists, remove from ordered list - ordered.erase(std::remove_if(ordered.begin(), ordered.end(), - [&](const std::pair & p) { return p.first == key; }), - ordered.end()); - } - unordered[key] = val; - ordered.push_back({key, val}); - } - } val_obj; + std::vector> val_obj; func_handler val_func; @@ -139,6 +123,7 @@ struct value_t { value_t(const value_t &) = default; virtual ~value_t() = default; + // Note: only for debugging and error reporting purposes virtual std::string type() const { return ""; } virtual int64_t as_int() const { throw std::runtime_error(type() + " is not an int value"); } @@ -146,7 +131,7 @@ struct value_t { virtual string as_string() const { throw std::runtime_error(type() + " is not a string value"); } virtual bool as_bool() const { throw std::runtime_error(type() + " is not a bool value"); } virtual const std::vector & as_array() const { throw std::runtime_error(type() + " is not an array value"); } - virtual const std::vector> & as_ordered_object() const { throw std::runtime_error(type() + " is not an object value"); } + virtual const std::vector> & as_ordered_object() const { throw std::runtime_error(type() + " is not an object value"); } virtual value invoke(const func_args &) const { throw std::runtime_error(type() + " is not a function value"); } virtual bool is_none() const { return false; } virtual bool is_undefined() const { return false; } @@ -154,43 +139,66 @@ struct value_t { throw std::runtime_error("No builtins available for type " + type()); } - virtual bool has_key(const std::string & key) { - return val_obj.unordered.find(key) != val_obj.unordered.end(); - } - virtual value & at(const std::string & key, value & default_val) { - auto it = val_obj.unordered.find(key); - if (it == val_obj.unordered.end()) { - return default_val; - } - return val_obj.unordered.at(key); - } - virtual value & at(const std::string & key) { - auto it = val_obj.unordered.find(key); - if (it == val_obj.unordered.end()) { - throw std::runtime_error("Key '" + key + "' not found in value of type " + type()); - } - return val_obj.unordered.at(key); - } - virtual value & at(int64_t index, value & default_val) { - if (index < 0) { - index += val_arr.size(); - } - if (index < 0 || static_cast(index) >= val_arr.size()) { - return default_val; - } - return val_arr[index]; - } - virtual value & at(int64_t index) { - if (index < 0) { - index += val_arr.size(); - } - if (index < 0 || static_cast(index) >= val_arr.size()) { - throw std::runtime_error("Index " + std::to_string(index) + " out of bounds for array of size " + std::to_string(val_arr.size())); - } - return val_arr[index]; - } + virtual bool has_key(const value &) { throw std::runtime_error(type() + " is not an object value"); } + virtual void insert(const value & /* key */, const value & /* val */) { throw std::runtime_error(type() + " is not an object value"); } + virtual value & at(const value & /* key */, value & /* default_val */) { throw std::runtime_error(type() + " is not an object value"); } + virtual value & at(const value & /* key */) { throw std::runtime_error(type() + " is not an object value"); } + virtual value & at(const std::string & /* key */, value & /* default_val */) { throw std::runtime_error(type() + " is not an object value"); } + virtual value & at(const std::string & /* key */) { throw std::runtime_error(type() + " is not an object value"); } + virtual value & at(int64_t /* idx */, value & /* default_val */) { throw std::runtime_error(type() + " is not an array value"); } + virtual value & at(int64_t /* idx */) { throw std::runtime_error(type() + " is not an array value"); } + virtual bool is_numeric() const { return false; } + virtual bool is_hashable() const { return false; } + virtual bool is_immutable() const { return true; } + virtual hasher unique_hash() const noexcept = 0; + // TODO: C++20 <=> operator + // NOTE: We are treating == as equivalent (for normal comparisons) and != as strict nonequal (for strict (is) comparisons) + virtual bool operator==(const value_t & other) const { return equivalent(other); } + virtual bool operator!=(const value_t & other) const { return nonequal(other); } + + // Note: only for debugging purposes virtual std::string as_repr() const { return as_string().str(); } + +protected: + virtual bool equivalent(const value_t &) const = 0; + virtual bool nonequal(const value_t & other) const { return !equivalent(other); } +}; + +// +// utils +// + +const func_builtins & global_builtins(); + +std::string value_to_json(const value & val, int indent = -1, const std::string_view item_sep = ", ", const std::string_view key_sep = ": "); + +// Note: only used for debugging purposes +std::string value_to_string_repr(const value & val); + +struct not_implemented_exception : public std::runtime_error { + not_implemented_exception(const std::string & msg) : std::runtime_error("NotImplemented: " + msg) {} +}; + +struct value_hasher { + size_t operator()(const value & val) const noexcept { + return val->unique_hash().digest(); + } +}; + +struct value_equivalence { + bool operator()(const value & lhs, const value & rhs) const { + return *lhs == *rhs; + } + bool operator()(const std::pair & lhs, const std::pair & rhs) const { + return *(lhs.first) == *(rhs.first) && *(lhs.second) == *(rhs.second); + } +}; + +struct value_equality { + bool operator()(const value & lhs, const value & rhs) const { + return !(*lhs != *rhs); + } }; // @@ -198,24 +206,49 @@ struct value_t { // struct value_int_t : public value_t { - value_int_t(int64_t v) { val_int = v; } + value_int_t(int64_t v) { + val_int = v; + val_flt = static_cast(v); + if (static_cast(val_flt) != v) { + val_flt = v < 0 ? -INFINITY : INFINITY; + } + } virtual std::string type() const override { return "Integer"; } virtual int64_t as_int() const override { return val_int; } - virtual double as_float() const override { return static_cast(val_int); } + virtual double as_float() const override { return val_flt; } virtual string as_string() const override { return std::to_string(val_int); } virtual bool as_bool() const override { return val_int != 0; } virtual const func_builtins & get_builtins() const override; + virtual bool is_numeric() const override { return true; } + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + return hasher(typeid(*this)) + .update(&val_int, sizeof(val_int)) + .update(&val_flt, sizeof(val_flt)); + } +protected: + virtual bool equivalent(const value_t & other) const override { + return other.is_numeric() && val_int == other.val_int && val_flt == other.val_flt; + } + virtual bool nonequal(const value_t & other) const override { + return !(typeid(*this) == typeid(other) && val_int == other.val_int); + } }; using value_int = std::shared_ptr; struct value_float_t : public value_t { - value_float_t(double v) { val_flt = v; } + value val; + value_float_t(double v) { + val_flt = v; + val_int = std::isfinite(v) ? static_cast(v) : 0; + val = mk_val(val_int); + } virtual std::string type() const override { return "Float"; } virtual double as_float() const override { return val_flt; } - virtual int64_t as_int() const override { return static_cast(val_flt); } + virtual int64_t as_int() const override { return val_int; } virtual string as_string() const override { std::string out = std::to_string(val_flt); out.erase(out.find_last_not_of('0') + 1, std::string::npos); // remove trailing zeros @@ -226,6 +259,24 @@ struct value_float_t : public value_t { return val_flt != 0.0; } virtual const func_builtins & get_builtins() const override; + virtual bool is_numeric() const override { return true; } + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + if (static_cast(val_int) == val_flt) { + return val->unique_hash(); + } else { + return hasher(typeid(*this)) + .update(&val_int, sizeof(val_int)) + .update(&val_flt, sizeof(val_flt)); + } + } +protected: + virtual bool equivalent(const value_t & other) const override { + return other.is_numeric() && val_int == other.val_int && val_flt == other.val_flt; + } + virtual bool nonequal(const value_t & other) const override { + return !(typeid(*this) == typeid(other) && val_flt == other.val_flt); + } }; using value_float = std::shared_ptr; @@ -247,19 +298,49 @@ struct value_string_t : public value_t { return val_str.length() > 0; } virtual const func_builtins & get_builtins() const override; + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + const auto type_hash = typeid(*this).hash_code(); + auto hash = hasher(); + hash.update(&type_hash, sizeof(type_hash)); + val_str.hash_update(hash); + return hash; + } void mark_input() { val_str.mark_input(); } +protected: + virtual bool equivalent(const value_t & other) const override { + return typeid(*this) == typeid(other) && val_str.str() == other.val_str.str(); + } }; using value_string = std::shared_ptr; struct value_bool_t : public value_t { - value_bool_t(bool v) { val_bool = v; } + value val; + value_bool_t(bool v) { + val_int = static_cast(v); + val_flt = static_cast(v); + val = mk_val(val_int); + } virtual std::string type() const override { return "Boolean"; } - virtual bool as_bool() const override { return val_bool; } - virtual string as_string() const override { return std::string(val_bool ? "True" : "False"); } + virtual int64_t as_int() const override { return val_int; } + virtual bool as_bool() const override { return val_int; } + virtual string as_string() const override { return std::string(val_int ? "True" : "False"); } virtual const func_builtins & get_builtins() const override; + virtual bool is_numeric() const override { return true; } + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + return val->unique_hash(); + } +protected: + virtual bool equivalent(const value_t & other) const override { + return other.is_numeric() && val_int == other.val_int && val_flt == other.val_flt; + } + virtual bool nonequal(const value_t & other) const override { + return !(typeid(*this) == typeid(other) && val_int == other.val_int); + } }; using value_bool = std::shared_ptr; @@ -269,13 +350,34 @@ struct value_array_t : public value_t { value_array_t(value & v) { val_arr = v->val_arr; } + value_array_t(std::vector && arr) { + val_arr = arr; + } value_array_t(const std::vector & arr) { val_arr = arr; } - void reverse() { std::reverse(val_arr.begin(), val_arr.end()); } - void push_back(const value & val) { val_arr.push_back(val); } - void push_back(value && val) { val_arr.push_back(std::move(val)); } + void reverse() { + if (is_immutable()) { + throw std::runtime_error("Attempting to modify immutable type"); + } + std::reverse(val_arr.begin(), val_arr.end()); + } + void push_back(const value & val) { + if (is_immutable()) { + throw std::runtime_error("Attempting to modify immutable type"); + } + val_arr.push_back(val); + } + void push_back(value && val) { + if (is_immutable()) { + throw std::runtime_error("Attempting to modify immutable type"); + } + val_arr.push_back(std::move(val)); + } value pop_at(int64_t index) { + if (is_immutable()) { + throw std::runtime_error("Attempting to modify immutable type"); + } if (index < 0) { index = static_cast(val_arr.size()) + index; } @@ -287,64 +389,225 @@ struct value_array_t : public value_t { return val; } virtual std::string type() const override { return "Array"; } + virtual bool is_immutable() const override { return false; } virtual const std::vector & as_array() const override { return val_arr; } virtual string as_string() const override { + const bool immutable = is_immutable(); std::ostringstream ss; - ss << "["; + ss << (immutable ? "(" : "["); for (size_t i = 0; i < val_arr.size(); i++) { if (i > 0) ss << ", "; - ss << val_arr.at(i)->as_repr(); + value val = val_arr.at(i); + ss << value_to_string_repr(val); } - ss << "]"; + if (immutable && val_arr.size() == 1) { + ss << ","; + } + ss << (immutable ? ")" : "]"); return ss.str(); } virtual bool as_bool() const override { return !val_arr.empty(); } + virtual value & at(int64_t index, value & default_val) override { + if (index < 0) { + index += val_arr.size(); + } + if (index < 0 || static_cast(index) >= val_arr.size()) { + return default_val; + } + return val_arr[index]; + } + virtual value & at(int64_t index) override { + if (index < 0) { + index += val_arr.size(); + } + if (index < 0 || static_cast(index) >= val_arr.size()) { + throw std::runtime_error("Index " + std::to_string(index) + " out of bounds for array of size " + std::to_string(val_arr.size())); + } + return val_arr[index]; + } virtual const func_builtins & get_builtins() const override; + virtual bool is_hashable() const override { + if (std::all_of(val_arr.begin(), val_arr.end(), [&](auto & val) -> bool { + return val->is_immutable() && val->is_hashable(); + })) { + return true; + } + return false; + } + virtual hasher unique_hash() const noexcept override { + auto hash = hasher(typeid(*this)); + for (const auto & val : val_arr) { + // must use digest to prevent problems from "concatenation" property of hasher + // for ex. hash of [ "ab", "c" ] should be different from [ "a", "bc" ] + const size_t val_hash = val->unique_hash().digest(); + hash.update(&val_hash, sizeof(size_t)); + } + return hash; + } +protected: + virtual bool equivalent(const value_t & other) const override { + return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_arr.begin(), val_arr.end(), other.val_arr.begin(), value_equivalence()); + } }; using value_array = std::shared_ptr; +struct value_tuple_t : public value_array_t { + value_tuple_t(value & v) { + val_arr = v->val_arr; + } + value_tuple_t(std::vector && arr) { + val_arr = arr; + } + value_tuple_t(const std::vector & arr) { + val_arr = arr; + } + value_tuple_t(const std::pair & pair) { + val_arr.push_back(pair.first); + val_arr.push_back(pair.second); + } + virtual std::string type() const override { return "Tuple"; } + virtual bool is_immutable() const override { return true; } +}; +using value_tuple = std::shared_ptr; + + struct value_object_t : public value_t { + std::unordered_map unordered; bool has_builtins = true; // context and loop objects do not have builtins value_object_t() = default; value_object_t(value & v) { val_obj = v->val_obj; - } - value_object_t(const std::map & obj) { - for (const auto & pair : obj) { - val_obj.insert(pair.first, pair.second); + for (const auto & pair : val_obj) { + unordered[pair.first] = pair.second; } } - value_object_t(const std::vector> & obj) { + value_object_t(const std::map & obj) { for (const auto & pair : obj) { - val_obj.insert(pair.first, pair.second); + insert(pair.first, pair.second); + } + } + value_object_t(const std::vector> & obj) { + for (const auto & pair : obj) { + insert(pair.first, pair.second); } } void insert(const std::string & key, const value & val) { - val_obj.insert(key, val); + insert(mk_val(key), val); } virtual std::string type() const override { return "Object"; } - virtual const std::vector> & as_ordered_object() const override { return val_obj.ordered; } + virtual bool is_immutable() const override { return false; } + virtual const std::vector> & as_ordered_object() const override { return val_obj; } + virtual string as_string() const override { + std::ostringstream ss; + ss << "{"; + for (size_t i = 0; i < val_obj.size(); i++) { + if (i > 0) ss << ", "; + auto & [key, val] = val_obj.at(i); + ss << value_to_string_repr(key) << ": " << value_to_string_repr(val); + } + ss << "}"; + return ss.str(); + } virtual bool as_bool() const override { - return !val_obj.unordered.empty(); + return !unordered.empty(); + } + virtual bool has_key(const value & key) override { + if (!key->is_immutable() || !key->is_hashable()) { + throw std::runtime_error("Object key of unhashable type: " + key->type()); + } + return unordered.find(key) != unordered.end(); + } + virtual void insert(const value & key, const value & val) override { + bool replaced = false; + if (is_immutable()) { + throw std::runtime_error("Attempting to modify immutable type"); + } + if (has_key(key)) { + // if key exists, replace value in ordered list instead of appending + for (auto & pair : val_obj) { + if (*(pair.first) == *key) { + pair.second = val; + replaced = true; + break; + } + } + } + unordered[key] = val; + if (!replaced) { + val_obj.push_back({key, val}); + } + } + virtual value & at(const value & key, value & default_val) override { + if (!has_key(key)) { + return default_val; + } + return unordered.at(key); + } + virtual value & at(const value & key) override { + if (!has_key(key)) { + throw std::runtime_error("Key '" + key->as_string().str() + "' not found in value of type " + type()); + } + return unordered.at(key); + } + virtual value & at(const std::string & key, value & default_val) override { + value key_val = mk_val(key); + return at(key_val, default_val); + } + virtual value & at(const std::string & key) override { + value key_val = mk_val(key); + return at(key_val); } virtual const func_builtins & get_builtins() const override; + virtual bool is_hashable() const override { + if (std::all_of(val_obj.begin(), val_obj.end(), [&](auto & pair) -> bool { + const auto & val = pair.second; + return val->is_immutable() && val->is_hashable(); + })) { + return true; + } + return false; + } + virtual hasher unique_hash() const noexcept override { + auto hash = hasher(typeid(*this)); + for (const auto & [key, val] : val_obj) { + // must use digest to prevent problems from "concatenation" property of hasher + // for ex. hash of key="ab", value="c" should be different from key="a", value="bc" + const size_t key_hash = key->unique_hash().digest(); + const size_t val_hash = val->unique_hash().digest(); + hash.update(&key_hash, sizeof(key_hash)); + hash.update(&val_hash, sizeof(val_hash)); + } + return hash; + } +protected: + virtual bool equivalent(const value_t & other) const override { + return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_obj.begin(), val_obj.end(), other.val_obj.begin(), value_equivalence()); + } }; using value_object = std::shared_ptr; // -// null and undefined types +// none and undefined types // struct value_none_t : public value_t { virtual std::string type() const override { return "None"; } virtual bool is_none() const override { return true; } virtual bool as_bool() const override { return false; } - virtual string as_string() const override { return string("None"); } + virtual string as_string() const override { return string(type()); } virtual std::string as_repr() const override { return type(); } virtual const func_builtins & get_builtins() const override; + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + return hasher(typeid(*this)); + } +protected: + virtual bool equivalent(const value_t & other) const override { + return typeid(*this) == typeid(other); + } }; using value_none = std::shared_ptr; @@ -356,6 +619,13 @@ struct value_undefined_t : public value_t { virtual bool as_bool() const override { return false; } virtual std::string as_repr() const override { return type(); } virtual const func_builtins & get_builtins() const override; + virtual hasher unique_hash() const noexcept override { + return hasher(typeid(*this)); + } +protected: + virtual bool equivalent(const value_t & other) const override { + return is_undefined() == other.is_undefined(); + } }; using value_undefined = std::shared_ptr; @@ -436,7 +706,23 @@ struct value_func_t : public value_t { return val_func(new_args); } virtual std::string type() const override { return "Function"; } - virtual std::string as_repr() const override { return type(); } + virtual std::string as_repr() const override { return type() + "<" + name + ">(" + (arg0 ? arg0->as_repr() : "") + ")"; } + virtual bool is_hashable() const override { return false; } + virtual hasher unique_hash() const noexcept override { + // Note: this is unused for now, we don't support function as object keys + // use function pointer as unique identifier + const auto target = val_func.target(); + return hasher(typeid(*this)).update(&target, sizeof(target)); + } +protected: + virtual bool equivalent(const value_t & other) const override { + // Note: this is unused for now, we don't support function as object keys + // compare function pointers + // (val_func == other.val_func does not work as std::function::operator== is only used for nullptr check) + const auto target_this = this->val_func.target(); + const auto target_other = other.val_func.target(); + return typeid(*this) == typeid(other) && target_this == target_other; + } }; using value_func = std::shared_ptr; @@ -447,18 +733,21 @@ struct value_kwarg_t : public value_t { value_kwarg_t(const std::string & k, const value & v) : key(k), val(v) {} virtual std::string type() const override { return "KwArg"; } virtual std::string as_repr() const override { return type(); } + virtual bool is_hashable() const override { return true; } + virtual hasher unique_hash() const noexcept override { + const auto type_hash = typeid(*this).hash_code(); + auto hash = val->unique_hash(); + hash.update(&type_hash, sizeof(type_hash)) + .update(key.data(), key.size()); + return hash; + } +protected: + virtual bool equivalent(const value_t & other) const override { + const value_kwarg_t & other_val = static_cast(other); + return typeid(*this) == typeid(other) && key == other_val.key && val == other_val.val; + } }; using value_kwarg = std::shared_ptr; -// utils - -const func_builtins & global_builtins(); -std::string value_to_json(const value & val, int indent = -1, const std::string_view item_sep = ", ", const std::string_view key_sep = ": "); - -struct not_implemented_exception : public std::runtime_error { - not_implemented_exception(const std::string & msg) : std::runtime_error("NotImplemented: " + msg) {} -}; - - } // namespace jinja diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index e142900723..d2a1437ca4 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -481,7 +481,7 @@ int main_automated_tests(void) { /* .name= */ "Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start)", /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", /* .expected_output= */ "[INST] You are a helpful assistant\n\nHello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[INST] Another question[/INST]", - /* .expected_output_jinja= */ "[INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[INST] You are a helpful assistant\n\nAnother question[/INST]", + /* .expected_output_jinja= */ "[INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[AVAILABLE_TOOLS] [[/AVAILABLE_TOOLS][INST] You are a helpful assistant\n\nAnother question[/INST]", /* .bos_token= */ "", /* .eos_token= */ "", }, @@ -489,7 +489,7 @@ int main_automated_tests(void) { /* .name= */ "Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start)", /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", /* .expected_output= */ "[INST]You are a helpful assistant\n\nHello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [INST]Another question[/INST]", - /* .expected_output_jinja= */ "[INST]Hello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [INST]You are a helpful assistant\n\nAnother question[/INST]", + /* .expected_output_jinja= */ "[INST]Hello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [AVAILABLE_TOOLS][[/AVAILABLE_TOOLS][INST]You are a helpful assistant\n\nAnother question[/INST]", /* .bos_token= */ "", /* .eos_token= */ "", }, diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index 54d3a0923b..7c6eeb311c 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -9,6 +9,7 @@ #include "jinja/runtime.h" #include "jinja/parser.h" #include "jinja/lexer.h" +#include "jinja/utils.h" #include "testing.h" @@ -30,6 +31,7 @@ static void test_tests(testing & t); static void test_string_methods(testing & t); static void test_array_methods(testing & t); static void test_object_methods(testing & t); +static void test_hasher(testing & t); static void test_fuzzing(testing & t); static bool g_python_mode = false; @@ -67,6 +69,7 @@ int main(int argc, char *argv[]) { t.test("array methods", test_array_methods); t.test("object methods", test_object_methods); if (!g_python_mode) { + t.test("hasher", test_hasher); t.test("fuzzing", test_fuzzing); } @@ -156,6 +159,18 @@ static void test_conditionals(testing & t) { "big" ); + test_template(t, "object comparison", + "{% if {0: 1, none: 2, 1.0: 3, '0': 4, true: 5} == {false: 1, none: 2, 1: 5, '0': 4} %}equal{% endif %}", + json::object(), + "equal" + ); + + test_template(t, "array comparison", + "{% if [0, 1.0, false] == [false, 1, 0.0] %}equal{% endif %}", + json::object(), + "equal" + ); + test_template(t, "logical and", "{% if a and b %}both{% endif %}", {{"a", true}, {"b", true}}, @@ -358,6 +373,30 @@ static void test_expressions(testing & t) { "b" ); + test_template(t, "array negative access", + "{{ items[-1] }}", + {{"items", json::array({"a", "b", "c"})}}, + "c" + ); + + test_template(t, "array slice", + "{{ items[1:-1]|string }}", + {{"items", json::array({"a", "b", "c"})}}, + "['b']" + ); + + test_template(t, "array slice step", + "{{ items[::2]|string }}", + {{"items", json::array({"a", "b", "c"})}}, + "['a', 'c']" + ); + + test_template(t, "tuple slice", + "{{ ('a', 'b', 'c')[::-1]|string }}", + json::object(), + "('c', 'b', 'a')" + ); + test_template(t, "arithmetic", "{{ (a + b) * c }}", {{"a", 2}, {"b", 3}, {"c", 4}}, @@ -401,6 +440,36 @@ static void test_set_statement(testing & t) { json::object(), "1" ); + + test_template(t, "set dict with mixed type keys", + "{% set d = {0: 1, none: 2, 1.0: 3, '0': 4, (0, 0): 5, false: 6, 1: 7} %}{{ d[(0, 0)] + d[0] + d[none] + d['0'] + d[false] + d[1.0] + d[1] }}", + json::object(), + "37" + ); + + test_template(t, "print dict with mixed type keys", + "{% set d = {0: 1, none: 2, 1.0: 3, '0': 4, (0, 0): 5, true: 6} %}{{ d|string }}", + json::object(), + "{0: 1, None: 2, 1.0: 6, '0': 4, (0, 0): 5}" + ); + + test_template(t, "print array with mixed types", + "{% set d = [0, none, 1.0, '0', true, (0, 0)] %}{{ d|string }}", + json::object(), + "[0, None, 1.0, '0', True, (0, 0)]" + ); + + test_template(t, "object member assignment with mixed key types", + "{% set d = namespace() %}{% set d.a = 123 %}{{ d['a'] == 123 }}", + json::object(), + "True" + ); + + test_template(t, "tuple unpacking", + "{% set t = (1, 2, 3) %}{% set a, b, c = t %}{{ a + b + c }}", + json::object(), + "6" + ); } static void test_filters(testing & t) { @@ -1312,6 +1381,154 @@ static void test_object_methods(testing & t) { {{"obj", {{"a", "b"}}}}, "True True" ); + + test_template(t, "expression as object key", + "{% set d = {'ab': 123} %}{{ d['a' + 'b'] == 123 }}", + json::object(), + "True" + ); + + test_template(t, "numeric as object key (template: Seed-OSS)", + "{% set d = {1: 'a', 2: 'b'} %}{{ d[1] == 'a' and d[2] == 'b' }}", + json::object(), + "True" + ); +} + +static void test_hasher(testing & t) { + static const std::vector> chunk_sizes = { + {1, 2}, + {1, 16}, + {8, 1}, + {1, 1024}, + {5, 512}, + {16, 256}, + {45, 122}, + {70, 634}, + }; + + static auto random_bytes = [](size_t length) -> std::string { + std::string data; + data.resize(length); + for (size_t i = 0; i < length; ++i) { + data[i] = static_cast(rand() % 256); + } + return data; + }; + + t.test("state unchanged with empty input", [](testing & t) { + jinja::hasher hasher; + hasher.update("some data"); + size_t initial_state = hasher.digest(); + hasher.update("", 0); + size_t final_state = hasher.digest(); + t.assert_true("Hasher state should remain unchanged", initial_state == final_state); + }); + + t.test("different inputs produce different hashes", [](testing & t) { + jinja::hasher hasher1; + hasher1.update("data one"); + size_t hash1 = hasher1.digest(); + + jinja::hasher hasher2; + hasher2.update("data two"); + size_t hash2 = hasher2.digest(); + + t.assert_true("Different inputs should produce different hashes", hash1 != hash2); + }); + + t.test("same inputs produce same hashes", [](testing & t) { + jinja::hasher hasher1; + hasher1.update("consistent data"); + size_t hash1 = hasher1.digest(); + + jinja::hasher hasher2; + hasher2.update("consistent data"); + size_t hash2 = hasher2.digest(); + + t.assert_true("Same inputs should produce same hashes", hash1 == hash2); + }); + + t.test("property: update(a ~ b) == update(a).update(b)", [](testing & t) { + for (const auto & [size1, size2] : chunk_sizes) { + std::string data1 = random_bytes(size1); + std::string data2 = random_bytes(size2); + + jinja::hasher hasher1; + hasher1.update(data1); + hasher1.update(data2); + size_t hash1 = hasher1.digest(); + + jinja::hasher hasher2; + hasher2.update(data1 + data2); + size_t hash2 = hasher2.digest(); + + t.assert_true( + "Hashing in multiple updates should match single update (" + std::to_string(size1) + ", " + std::to_string(size2) + ")", + hash1 == hash2); + } + }); + + t.test("property: update(a ~ b) == update(a).update(b) with more update passes", [](testing & t) { + static const std::vector sizes = {3, 732, 131, 13, 17, 256, 436, 99, 4}; + + jinja::hasher hasher1; + jinja::hasher hasher2; + + std::string combined_data; + for (size_t size : sizes) { + std::string data = random_bytes(size); + hasher1.update(data); + combined_data += data; + } + + hasher2.update(combined_data); + size_t hash1 = hasher1.digest(); + size_t hash2 = hasher2.digest(); + t.assert_true( + "Hashing in multiple updates should match single update with many chunks", + hash1 == hash2); + }); + + t.test("property: non associativity of update", [](testing & t) { + for (const auto & [size1, size2] : chunk_sizes) { + std::string data1 = random_bytes(size1); + std::string data2 = random_bytes(size2); + + jinja::hasher hasher1; + hasher1.update(data1); + hasher1.update(data2); + size_t hash1 = hasher1.digest(); + + jinja::hasher hasher2; + hasher2.update(data2); + hasher2.update(data1); + size_t hash2 = hasher2.digest(); + + t.assert_true( + "Hashing order should matter (" + std::to_string(size1) + ", " + std::to_string(size2) + ")", + hash1 != hash2); + } + }); + + t.test("property: different lengths produce different hashes (padding block size)", [](testing & t) { + std::string random_data = random_bytes(64); + + jinja::hasher hasher1; + hasher1.update(random_data); + size_t hash1 = hasher1.digest(); + + for (int i = 0; i < 16; ++i) { + random_data.push_back('A'); // change length + jinja::hasher hasher2; + hasher2.update(random_data); + size_t hash2 = hasher2.digest(); + + t.assert_true("Different lengths should produce different hashes (length " + std::to_string(random_data.size()) + ")", hash1 != hash2); + + hash1 = hash2; + } + }); } static void test_template_cpp(testing & t, const std::string & name, const std::string & tmpl, const json & vars, const std::string & expect) { From f2571df8b71ba78f9fb99f3fc951b514c2d2a4bb Mon Sep 17 00:00:00 2001 From: Vishal Singh Date: Wed, 28 Jan 2026 03:51:36 +0530 Subject: [PATCH 034/206] ggml-zendnn : update ZenDNN git tag to main branch (#19133) --- ggml/src/ggml-zendnn/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-zendnn/CMakeLists.txt b/ggml/src/ggml-zendnn/CMakeLists.txt index bdbfc74369..f5cf6eedd3 100644 --- a/ggml/src/ggml-zendnn/CMakeLists.txt +++ b/ggml/src/ggml-zendnn/CMakeLists.txt @@ -21,7 +21,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") ExternalProject_Add( zendnn GIT_REPOSITORY https://github.com/amd/ZenDNN.git - GIT_TAG zendnnl + GIT_TAG 21ce8f7879c86bf3637f707fae6f29e0951db5fe PREFIX ${ZENDNN_PREFIX} SOURCE_DIR ${ZENDNN_SOURCE_DIR} BINARY_DIR ${ZENDNN_BUILD_DIR} From 06961e2876c7ed5ebe43c7e7c9b284dedba567c9 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 27 Jan 2026 20:53:36 -0800 Subject: [PATCH 035/206] ggml webgpu: Split shared state (webgpu_context) into global state and per-thread state (#18976) * Squashed commit of the following: commit b3c6bf4b0450d8d452b934df27a0fb7cb53cd755 Author: Abhijit Ramesh Date: Mon Dec 1 18:29:00 2025 -0800 ggml webgpu: fix xielu parameter passing (#11) The XIELU operation was incorrectly using static_cast to convert float parameters to uint32_t, which converted numeric values instead of preserving IEEE 754 bit patterns. This caused incorrect values to be interpreted by the GPU shader. * Use reinterpret_cast to preserve float bit patterns when passing through uint32_t params buffer * Update WGSL shader parameter types from u32 to f32 * Re-enable XIELU support (was disabled due to numerical issues) Fixes NMSE test failures for XIELU operation on WebGPU backend. commit 5ca9b5e49ea7cddc9ab7c8b43a11a9c76a4dff4a Author: neha-ha <137219201+neha-ha@users.noreply.github.com> Date: Tue Nov 18 12:17:00 2025 -0800 Refactored pipelines and workgroup calculations (#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas Co-authored-by: Reese Levine Author: James Contini Date: Wed Oct 29 23:13:06 2025 -0700 formatted embed wgsl and ggml-webgpu.cpp commit e1f6baea31645e5d96ad53664acae856f74b96f4 Author: James Contini Date: Wed Oct 29 23:08:37 2025 -0700 implemented REPL_Template support and removed bug in unary operators kernel commit 8c70b8fece445cdc9a8c660dbddbf201e52da2bb Author: James Contini Date: Wed Oct 15 16:14:20 2025 -0700 responded and dealt with PR comments commit f9282c660c10dec4487d434549bdb707a9cd9f37 Author: James Contini Date: Sun Oct 12 13:41:41 2025 -0700 removed unnecesarry checking if node->src[1] exists for unary operators commit 4cf28d7dec41c29186d66152735b244c5699f9dc Author: James Contini Date: Sun Oct 12 13:32:45 2025 -0700 All operators (inlcluding xielu) working commit 74c6add1761a59d2c2ff60b60e8ad3c8300f6d3e Author: James Contini Date: Fri Oct 10 13:16:48 2025 -0700 fixed autoconfig commit 362749910be4f0120c8ffb21ceddeb7d2c088e51 Author: James Contini Date: Fri Oct 10 13:10:46 2025 -0700 removed vestigial files commit cb0858333785757804c5104e59c4981843207c16 Author: James Contini Date: Fri Oct 10 12:59:32 2025 -0700 abides by editor-config commit 5360e2852a4b51197d7d67d0a5d42e908b02d7ed Author: James Contini Date: Fri Oct 10 12:45:57 2025 -0700 rms_norm double declaration bug atoned commit 7b09baa4aa53711be5a126043670cc182c78bfcd Merge: 8a6ec843 74b8fc17 Author: James Contini Date: Fri Oct 10 11:50:03 2025 -0700 resolving merge conflicts commit 8a6ec843a50ab82f8cef59b4558eb63f318ba02d Author: James Contini Date: Wed Oct 8 18:06:47 2025 -0700 unary operators pass ggml tests commit c3ae38278a2db236adc5912c9140e4f0d63f2c19 Author: James Contini Date: Wed Oct 1 16:22:40 2025 -0700 neg passes backend test commit aa1c9b2f8877a405470ca56709c42a1fd43713de Author: James Contini Date: Tue Sep 30 23:55:27 2025 -0700 neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though Co-authored-by: James Contini Co-authored-by: Neha Abbas Co-authored-by: Abhijit Ramesh * Remove extra code and format * Add ops documentation (finally) * ggml webgpu: add SOFTPLUS unary operator Implements SOFTPLUS (log(1 + exp(x))) with f16/f32 support. Uses f32 precision for intermediate calculations to prevent f16 overflow. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * Follow Vulkan backend numerical stability pattern * ggml webgpu: add EXPM1 unary operator Implements EXPM1 (exp(x) - 1) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add FLOOR unary operator Implements FLOOR (rounds down to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add CEIL unary operator Implements CEIL (rounds up to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add ROUND unary operator Implements ROUND (rounds to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add TRUNC unary operator Implements TRUNC (truncates towards zero) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * docs : update WebGPU support for unary operators (FLOOR, CEIL, ROUND, TRUNC, EXPM1, SOFTPLUS) * Updates to webgpu get_memory * Move shared state (webgpu_context) and device creation out of registration context, device context, and buffer context, and move into backend context * Small cleanup * Move Instance, Device, Adapter, Device creation, and capabilities to global state while moving Queue, pipelines, and buffers to per-thread state. * Cleanups * More cleanup * Move staging_buf mutex to global context * Resolve merge * Resolve merge * Resolve merge * Clean up merge errors, delete forward declaration, and run clang-format * Rename device_init to backend_init * Move webgpu_context to backend_context * Move buffer context members into global context and refactor function calls * Run clang-format * Remove commends * Move parameter buffers to per-thread, add single memset_tensor param buf * Fix CI compilation issue * Fix builds for emscripten not supporting subgroups * cleanup * cleanup --------- Co-authored-by: Reese Levine --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 1246 ++++++++++++++------------ 1 file changed, 662 insertions(+), 584 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 584cea7698..22e2bfeb4c 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -47,7 +47,6 @@ double cpu_total_time_##id = \ std::chrono::duration(cpu_total_end_##id - cpu_total_start_##id).count(); \ (ctx)->cpu_time_ms[#id] += cpu_total_time_##id; - // fine-grained timing (not included in totals) # define WEBGPU_CPU_PROFILE_DETAIL_START(id) auto cpu_detail_start_##id = std::chrono::high_resolution_clock::now(); @@ -74,13 +73,13 @@ #define WEBGPU_MAX_WG_SIZE 288 #define WEBGPU_MUL_MAT_WG_SIZE 256 -#define WEBGPU_NUM_PARAM_BUFS 32u +#define WEBGPU_NUM_PARAM_BUFS 16u #define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 8u #define WEBGPU_WAIT_ANY_TIMEOUT_MS 0 // Maximum number of in-flight submissions per-thread, to avoid exhausting the parameter buffer pool #define WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD WEBGPU_NUM_PARAM_BUFS / WEBGPU_COMMAND_SUBMIT_BATCH_SIZE #define WEBGPU_PARAMS_BUF_SIZE_BYTES 128 // enough for 32 parameters -#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS 32 +#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS 16 #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 @@ -267,30 +266,67 @@ struct webgpu_command { #endif }; -// All the base objects needed to run operations on a WebGPU device -struct webgpu_context_struct { +struct webgpu_capabilities_base { + wgpu::Limits limits; + bool supports_subgroup_matrix = false; + + uint32_t sg_mat_m = 0; + uint32_t sg_mat_n = 0; + uint32_t sg_mat_k = 0; + + uint32_t subgroup_size = 0; + uint32_t max_subgroup_size = 0; + size_t memset_bytes_per_thread; +}; + +// Stores global webgpu members +struct webgpu_global_context_struct { wgpu::Instance instance; wgpu::Adapter adapter; wgpu::Device device; wgpu::Queue queue; - wgpu::Limits limits; - uint32_t max_subgroup_size; + webgpu_capabilities_base capabilities; + // Shared buffer to move data from device to host + wgpu::Buffer get_tensor_staging_buf; + // Global mutex for pipeline and staging buffer, will be refactored to exclude pipeline caches. + std::recursive_mutex mutex; - bool supports_subgroup_matrix = false; - uint32_t sg_mat_m; - uint32_t sg_mat_n; - uint32_t sg_mat_k; + webgpu_buf_pool memset_buf_pool; + std::map memset_pipelines; // variant or type index + std::atomic_uint inflight_threads = 0; - std::recursive_mutex mutex; - std::atomic_uint inflight_threads = 0; +#ifdef GGML_WEBGPU_CPU_PROFILE + // Profiling: labeled CPU time in ms (total) + std::unordered_map cpu_time_ms; + // Profiling: detailed CPU time in ms + std::unordered_map cpu_detail_ms; +#endif - webgpu_buf_pool param_buf_pool; - webgpu_buf_pool set_rows_error_buf_pool; +#ifdef GGML_WEBGPU_GPU_PROFILE + // Profiling: per-shader GPU time in ms + std::unordered_map shader_gpu_time_ms; + // Profiling: pool of timestamp query buffers (one per operation) + webgpu_gpu_profile_buf_pool timestamp_query_buf_pool; +#endif + +#ifdef GGML_WEBGPU_DEBUG + wgpu::Buffer debug_host_buf; + wgpu::Buffer debug_dev_buf; +#endif +}; + +typedef std::shared_ptr webgpu_global_context; + +// All the base objects needed to run operations on a WebGPU device +struct webgpu_context_struct { + // Points to global instances owned by ggml_backend_webgpu_reg_context + webgpu_global_context global_ctx; pre_wgsl::Preprocessor p; - std::map memset_pipelines; // variant or type index + webgpu_buf_pool param_buf_pool; + webgpu_buf_pool set_rows_error_buf_pool; std::map>> mul_mat_pipelines; // src0_type, src1_type, vectorized std::map>> @@ -326,57 +362,42 @@ struct webgpu_context_struct { size_t memset_bytes_per_thread; - // Staging buffer for reading data from the GPU - wgpu::Buffer get_tensor_staging_buf; - -#ifdef GGML_WEBGPU_DEBUG - wgpu::Buffer debug_host_buf; - wgpu::Buffer debug_dev_buf; -#endif - -#ifdef GGML_WEBGPU_CPU_PROFILE - // Profiling: labeled CPU time in ms (total) - std::unordered_map cpu_time_ms; - // Profiling: detailed CPU time in ms - std::unordered_map cpu_detail_ms; -#endif - -#ifdef GGML_WEBGPU_GPU_PROFILE - // Profiling: per-shader GPU time in ms - std::unordered_map shader_gpu_time_ms; - // Profiling: pool of timestamp query buffers (one per operation) - webgpu_gpu_profile_buf_pool timestamp_query_buf_pool; -#endif }; typedef std::shared_ptr webgpu_context; +// Metadata required for the ggml backend registration/discovery interface struct ggml_backend_webgpu_reg_context { - webgpu_context webgpu_ctx; - size_t device_count; - const char * name; + // Since the Instance is a global entrypoint into the WebGPU API, it lives here + webgpu_global_context webgpu_global_ctx; + size_t device_count; + const char * name; }; +// Per-device struct for the global logical device interface struct ggml_backend_webgpu_device_context { - webgpu_context webgpu_ctx; - std::string device_name; - std::string device_desc; + webgpu_global_context webgpu_global_ctx; + std::string device_name; + std::string device_desc; }; +// Per-thread data required to actually run WebGPU operations in a backend instance struct ggml_backend_webgpu_context { - webgpu_context webgpu_ctx; - std::string name; + webgpu_context webgpu_ctx; + std::once_flag init_once; + std::string name; }; +// Per-thread data related to buffers struct ggml_backend_webgpu_buffer_context { - webgpu_context webgpu_ctx; - wgpu::Buffer buffer; - std::string label; + wgpu::Buffer buffer; + std::string label; + webgpu_global_context global_ctx; - ggml_backend_webgpu_buffer_context(webgpu_context ctx, wgpu::Buffer buf, std::string lbl) : - webgpu_ctx(std::move(ctx)), + ggml_backend_webgpu_buffer_context(wgpu::Buffer buf, std::string lbl, webgpu_global_context global_ctx_) : buffer(std::move(buf)), - label(std::move(lbl)) {} + label(std::move(lbl)), + global_ctx(std::move(global_ctx_)) {} }; /* WebGPU object initializations */ @@ -444,7 +465,7 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device, /** WebGPU Actions */ // Wait for the queue to finish processing all submitted work -static void ggml_backend_webgpu_wait(webgpu_context & ctx, +static void ggml_backend_webgpu_wait(webgpu_global_context & ctx, std::vector & futures, bool block = true) { // If we have too many in-flight submissions, wait on the oldest one first. If there are many threads, @@ -476,11 +497,11 @@ static void ggml_backend_webgpu_wait(webgpu_context & ct } } -static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, - wgpu::Buffer & buffer, - wgpu::MapMode mode, - size_t offset, - size_t size) { +static void ggml_backend_webgpu_map_buffer(webgpu_global_context & ctx, + wgpu::Buffer & buffer, + wgpu::MapMode mode, + size_t offset, + size_t size) { ctx->instance.WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, [](wgpu::MapAsyncStatus status, wgpu::StringView message) { if (status != wgpu::MapAsyncStatus::Success) { @@ -495,7 +516,7 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, // 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) { +static void ggml_backend_webgpu_debug(webgpu_global_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(); @@ -507,7 +528,10 @@ static void ggml_backend_webgpu_debug(webgpu_context & ctx) { } #endif -static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_context ctx, std::vector commands) { +static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_context ctx, + std::vector commands, + webgpu_buf_pool & param_buf_pool, + webgpu_buf_pool * set_rows_error_buf_pool = nullptr) { std::vector command_buffers; std::vector params_bufs; std::vector set_rows_error_bufs; @@ -528,19 +552,19 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_context ctx, wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone( wgpu::CallbackMode::AllowSpontaneous, - [ctx, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { + [¶m_buf_pool, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { if (status != wgpu::QueueWorkDoneStatus::Success) { 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(params_bufs); + param_buf_pool.free_bufs(params_bufs); }); futures.push_back({ p_f }); for (const auto & bufs : set_rows_error_bufs) { wgpu::Future f = bufs.host_buf.MapAsync( wgpu::MapMode::Read, 0, bufs.host_buf.GetSize(), wgpu::CallbackMode::AllowSpontaneous, - [ctx, bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) { + [set_rows_error_buf_pool, bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) { if (status != wgpu::MapAsyncStatus::Success) { GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", std::string(message).c_str()); } else { @@ -549,7 +573,9 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_context ctx, 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({ bufs }); + if (set_rows_error_buf_pool) { + set_rows_error_buf_pool->free_bufs({ bufs }); + } } }); futures.push_back({ f }); @@ -581,7 +607,8 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_context ctx, } static webgpu_command ggml_backend_webgpu_build_multi( - webgpu_context & ctx, + webgpu_global_context & ctx, + webgpu_buf_pool & param_buf_pool, const std::vector & pipelines, const std::vector> & params_list, const std::vector> & bind_group_entries_list, @@ -595,7 +622,7 @@ static webgpu_command ggml_backend_webgpu_build_multi( std::vector bind_groups; for (size_t i = 0; i < pipelines.size(); i++) { - webgpu_pool_bufs params_bufs = ctx->param_buf_pool.alloc_bufs(); + webgpu_pool_bufs params_bufs = param_buf_pool.alloc_bufs(); ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, params_bufs.host_buf.GetSize()); @@ -672,34 +699,37 @@ static webgpu_command ggml_backend_webgpu_build_multi( return result; } -static webgpu_command ggml_backend_webgpu_build(webgpu_context & ctx, +static webgpu_command ggml_backend_webgpu_build(webgpu_global_context & ctx, + webgpu_buf_pool & param_buf_pool, webgpu_pipeline & pipeline, std::vector params, std::vector bind_group_entries, uint32_t wg_x, uint32_t wg_y = 1, std::optional set_rows_error_bufs = std::nullopt) { - return ggml_backend_webgpu_build_multi(ctx, + return ggml_backend_webgpu_build_multi(ctx, param_buf_pool, { pipeline }, { params }, { bind_group_entries }, { { wg_x, wg_y } }, set_rows_error_bufs); } -static void ggml_backend_webgpu_buffer_memset(webgpu_context & ctx, - wgpu::Buffer & buf, - uint32_t value, - size_t offset, - size_t size) { +static void ggml_backend_webgpu_buffer_memset(webgpu_global_context & ctx, + wgpu::Buffer & buf, + uint32_t value, + size_t offset, + size_t size) { std::vector params = { (uint32_t) offset, (uint32_t) size, value }; std::vector entries = { { .binding = 0, .buffer = buf, .offset = 0, .size = buf.GetSize() } }; - size_t bytes_per_wg = WEBGPU_MAX_WG_SIZE * ctx->memset_bytes_per_thread; + size_t bytes_per_wg = WEBGPU_MAX_WG_SIZE * ctx->capabilities.memset_bytes_per_thread; uint32_t wg_x = CEIL_DIV(size + 3, bytes_per_wg); - webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_pipelines[0], params, entries, wg_x); - std::vector futures = { ggml_backend_webgpu_submit(ctx, { command }) }; + webgpu_command command = + ggml_backend_webgpu_build(ctx, ctx->memset_buf_pool, ctx->memset_pipelines[0], params, entries, wg_x); + std::vector futures = { ggml_backend_webgpu_submit(ctx, { command }, + ctx->memset_buf_pool) }; ggml_backend_webgpu_wait(ctx, futures); } @@ -720,19 +750,19 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) { #ifdef GGML_WEBGPU_CPU_PROFILE std::cout << "\n[ggml_webgpu cpu profiling summary]\n"; double total_cpu = 0.0; - for (const auto & kv : ctx->webgpu_ctx->cpu_time_ms) { + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) { total_cpu += kv.second; } std::cout << "ggml_webgpu: total cpu time: " << total_cpu << " ms\n"; std::cout << "ggml_webgpu: cpu breakdown:\n"; - for (const auto & kv : ctx->webgpu_ctx->cpu_time_ms) { + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) { double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0; std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; } - if (ctx->webgpu_ctx->cpu_detail_ms.size() > 0) { + if (ctx->webgpu_ctx->global_ctx->cpu_detail_ms.size() > 0) { std::cout << "ggml_webgpu: cpu detailed breakdown:\n"; } - for (const auto & kv : ctx->webgpu_ctx->cpu_detail_ms) { + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_detail_ms) { double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0; std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; } @@ -741,12 +771,12 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) { #ifdef GGML_WEBGPU_GPU_PROFILE std::cout << "\n[ggml_webgpu gpu profiling summary]\n"; double total_gpu = 0.0; - for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) { + for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) { total_gpu += kv.second; } std::cout << "ggml_webgpu: total gpu time (all shaders): " << total_gpu << " ms\n"; std::cout << "\nggml_webgpu: gpu breakdown:\n"; - for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) { + for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) { double pct = (total_gpu > 0.0) ? (kv.second / total_gpu * 100.0) : 0.0; std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; } @@ -772,12 +802,12 @@ static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) { static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_tensor * t) { size_t offset = ggml_webgpu_tensor_offset(t); - return offset & (ctx->limits.minStorageBufferOffsetAlignment - 1); + return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); } static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) { size_t offset = ggml_webgpu_tensor_offset(t); - return offset & ~(ctx->limits.minStorageBufferOffsetAlignment - 1); + return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); } static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { @@ -818,28 +848,30 @@ static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, g }; uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE); - return ggml_backend_webgpu_build(ctx, ctx->cpy_pipelines[src->type][dst->type], params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->cpy_pipelines[src->type][dst->type], + params, entries, wg_x); } static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { const bool circular = ggml_get_op_params_i32(dst, 8) != 0; ggml_webgpu_pad_pipeline_key pipeline_key = { .circular = circular }; - ggml_webgpu_pad_shader_lib_context shader_lib_ctx = { .key = pipeline_key, - .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_pad_shader_lib_context shader_lib_ctx = { + .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->pad_pipelines.find(pipeline_key); if (it != ctx->pad_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->pad_pipelines.emplace(pipeline_key, pipeline); } @@ -891,7 +923,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g }; uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, @@ -907,21 +939,22 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, .vec4 = src->ne[0] % 4 == 0, .i64_idx = idx->type == GGML_TYPE_I64 }; - ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = { .key = key, - .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = { + .key = key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->set_rows_pipelines.find(key); if (it != ctx->set_rows_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->set_rows_pipelines.emplace(key, pipeline); } @@ -981,7 +1014,8 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3]; } uint32_t wg_x = CEIL_DIV(threads, decisions.wg_size); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, 1, error_bufs); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1, + error_bufs); } static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx, @@ -1023,7 +1057,7 @@ static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx, uint32_t vectorized = src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0; webgpu_pipeline pipeline = ctx->get_rows_pipelines[src->type][vectorized]; - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, @@ -1098,19 +1132,21 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, uint32_t batches = dst->ne[2] * dst->ne[3]; uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG); uint32_t total_wg = output_groups * batches; - wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; - wg_y = CEIL_DIV(total_wg, ctx->limits.maxComputeWorkgroupsPerDimension); + wg_x = total_wg % ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); } else { pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized]; uint32_t wg_m; uint32_t wg_n; #ifndef __EMSCRIPTEN__ - if (ctx->supports_subgroup_matrix) { + if (ctx->global_ctx->capabilities.supports_subgroup_matrix) { // The total number of subgroups/workgroups needed per matrix. - uint32_t wg_m_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M * ctx->sg_mat_m; + uint32_t wg_m_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M * + ctx->global_ctx->capabilities.sg_mat_m; wg_m = CEIL_DIV(dst->ne[0], wg_m_sg_tile); - uint32_t wg_n_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N * ctx->sg_mat_n; - wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile); + uint32_t wg_n_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N * + ctx->global_ctx->capabilities.sg_mat_n; + wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile); } else { #endif uint32_t tile_m_s = WEBGPU_MUL_MAT_TILE_M * WEBGPU_MUL_MAT_WG_SIZE_M; @@ -1124,9 +1160,10 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, wg_x = wg_m * wg_n * dst->ne[2] * dst->ne[3]; } } - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, wg_y); } +#ifndef __EMSCRIPTEN__ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, ggml_tensor * Q, ggml_tensor * K, @@ -1210,8 +1247,8 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, .offset = ggml_webgpu_tensor_align_offset(ctx, dst), .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); - bool kv_direct = - (K->type == GGML_TYPE_F16) && (Q->ne[0] % ctx->sg_mat_k == 0) && (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0); + bool kv_direct = (K->type == GGML_TYPE_F16) && (Q->ne[0] % ctx->global_ctx->capabilities.sg_mat_k == 0) && + (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0); ggml_webgpu_flash_attn_pipeline_key key = { .kv_type = K->type, @@ -1223,25 +1260,27 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, .uses_logit_softcap = logit_softcap != 0.0f, }; - webgpu_pipeline pipeline; + webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->flash_attn_pipelines.find(key); if (it != ctx->flash_attn_pipelines.end()) { - pipeline = it->second; + pipeline = it->second; } else { - ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { .key = key, - .sg_mat_m = ctx->sg_mat_m, - .sg_mat_n = ctx->sg_mat_n, - .sg_mat_k = ctx->sg_mat_k, - .wg_mem_limit_bytes = - ctx->limits.maxComputeWorkgroupStorageSize, - .max_subgroup_size = ctx->max_subgroup_size }; + ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { + .key = key, + .sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m, + .sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n, + .sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k, + .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size + }; ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->flash_attn_pipelines.emplace(key, pipeline); } @@ -1250,11 +1289,11 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, ggml_webgpu_flash_attn_shader_decisions decisions = *static_cast(pipeline.context); - uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions.q_tile); uint32_t wg_x = wg_per_head * Q->ne[2] * Q->ne[3]; // wg per head * number of heads * number of batches - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } +#endif static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { bool is_unary = dst->op == GGML_OP_UNARY; @@ -1264,21 +1303,22 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s ggml_webgpu_unary_pipeline_key pipeline_key = { .type = dst->type, .op = op, .is_unary = is_unary, .inplace = inplace }; - ggml_webgpu_unary_shader_lib_context shader_lib_ctx = { .key = pipeline_key, - .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_unary_shader_lib_context shader_lib_ctx = { + .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->unary_pipelines.find(pipeline_key); if (it != ctx->unary_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->unary_pipelines.emplace(pipeline_key, pipeline); } @@ -1346,7 +1386,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s } uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, @@ -1391,7 +1431,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, } uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { @@ -1426,7 +1466,8 @@ static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * s .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->rms_norm_pipelines[inplace], params, entries, ggml_nrows(src)); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->rms_norm_pipelines[inplace], params, + entries, ggml_nrows(src)); } static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, @@ -1513,7 +1554,7 @@ static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, webgpu_pipeline pipeline = ctx->rope_pipelines[dst->type][has_freq_factor][inplace]; uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_glu(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) { @@ -1565,7 +1606,7 @@ static webgpu_command ggml_webgpu_glu(webgpu_context & ctx, ggml_tensor * src0, webgpu_pipeline pipeline = ctx->glu_pipelines[ggml_get_glu_op(dst)][dst->type][split]; uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { @@ -1602,7 +1643,8 @@ static webgpu_command ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, } uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); - return ggml_backend_webgpu_build(ctx, ctx->scale_pipelines[inplace], params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, ctx->scale_pipelines[inplace], params, + entries, wg_x); } static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, @@ -1674,7 +1716,8 @@ static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->soft_max_pipelines[mask_type][has_sink][inplace], params, entries, + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, + ctx->soft_max_pipelines[mask_type][has_sink][inplace], params, entries, ggml_nrows(dst)); } @@ -1696,25 +1739,26 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = src->ne[0] % 4 == 0, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->argmax_pipelines.find(shader_lib_ctx.vec4); if (it != ctx->argmax_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline); } } uint32_t wg_x = ggml_nelements(dst); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { @@ -1722,13 +1766,13 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr // ascending order is 0, descending order is 1 const int32_t order = is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(dst, 0); - ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = { .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup, - .wg_mem_limit_bytes = - ctx->limits.maxComputeWorkgroupStorageSize, - .order = order }; + ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = { + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, + .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .order = order + }; - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); webgpu_pipeline argsort_pipeline; auto it = ctx->argsort_pipelines.find(order); if (it != ctx->argsort_pipelines.end()) { @@ -1736,7 +1780,8 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_argsort_shader(ctx->p, wgsl_argsort, shader_lib_ctx); - argsort_pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + argsort_pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); argsort_pipeline.context = processed.decisions; ctx->argsort_pipelines.emplace(order, argsort_pipeline); } @@ -1751,7 +1796,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_argsort_merge_shader(ctx->p, wgsl_argsort_merge, shader_lib_ctx); argsort_merge_pipeline = - ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); argsort_merge_pipeline.context = processed.decisions; ctx->argsort_merge_pipelines.emplace(order, argsort_merge_pipeline); } @@ -1780,9 +1825,10 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr const bool start_in_tmp = (merge_passes % 2) == 1; - const size_t dst_offset = ggml_webgpu_tensor_offset(dst); - const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); - const size_t tmp_offset = ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->limits.minStorageBufferOffsetAlignment); + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); + const size_t tmp_offset = + ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); const size_t tmp_binding_size = ROUNDUP_POW2(idx_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT); const size_t dst_binding_size = ROUNDUP_POW2(idx_nbytes + ggml_webgpu_tensor_misalignment(ctx, dst), WEBGPU_STORAGE_BUF_BINDING_MULT); @@ -1813,10 +1859,10 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr }; const uint32_t total_wg_init = npr * nrows; - const uint32_t max_wg = ctx->limits.maxComputeWorkgroupsPerDimension; - const uint32_t wg_x_init = std::min(total_wg_init, max_wg); - const uint32_t wg_y_init = CEIL_DIV(total_wg_init, wg_x_init); - std::vector init_entries = { + const uint32_t max_wg = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + const uint32_t wg_x_init = std::min(total_wg_init, max_wg); + const uint32_t wg_y_init = CEIL_DIV(total_wg_init, wg_x_init); + std::vector init_entries = { { .binding = 0, .buffer = ggml_webgpu_tensor_buf(src), .offset = ggml_webgpu_tensor_align_offset(ctx, src), @@ -1830,7 +1876,8 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr workgroups_list.push_back({ wg_x_init, wg_y_init }); if (merge_passes == 0) { - return ggml_backend_webgpu_build_multi(ctx, pipelines, params_list, entries_list, workgroups_list); + return ggml_backend_webgpu_build_multi(ctx->global_ctx, ctx->param_buf_pool, pipelines, params_list, + entries_list, workgroups_list); } bool in_is_tmp = start_in_tmp; @@ -1891,7 +1938,8 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr in_is_tmp = !in_is_tmp; } - return ggml_backend_webgpu_build_multi(ctx, pipelines, params_list, entries_list, workgroups_list); + return ggml_backend_webgpu_build_multi(ctx->global_ctx, ctx->param_buf_pool, pipelines, params_list, entries_list, + workgroups_list); } static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { @@ -1912,24 +1960,25 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = false, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->cumsum_pipelines.find(1); if (it != ctx->cumsum_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->cumsum_pipelines.emplace(1, pipeline); } } uint32_t wg_x = ggml_nrows(dst); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { @@ -1956,25 +2005,26 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = false, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->sum_rows_pipelines.find(1); if (it != ctx->sum_rows_pipelines.end()) { pipeline = it->second; } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->sum_rows_pipelines.emplace(1, pipeline); } } uint32_t wg_x = total_sum ? 1 : ggml_nrows(dst); - return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } // Returns the encoded command, or std::nullopt if the operation is a no-op @@ -2009,7 +2059,11 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_MUL_MAT: return ggml_webgpu_mul_mat(ctx, src0, src1, node); case GGML_OP_FLASH_ATTN_EXT: +#ifndef __EMSCRIPTEN__ return ggml_webgpu_flash_attn(ctx, src0, src1, src2, node->src[3], node->src[4], node); +#else + return std::nullopt; +#endif case GGML_OP_ADD: { int inplace = ggml_webgpu_tensor_equal(src0, node); @@ -2070,12 +2124,12 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)"); - ggml_backend_webgpu_context * backend_ctx = static_cast(backend->context); + ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context; webgpu_context ctx = backend_ctx->webgpu_ctx; WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute); - ctx->inflight_threads++; + ctx->global_ctx->inflight_threads++; std::vector commands; std::vector futures; @@ -2084,25 +2138,27 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str commands.push_back(*cmd); } // compute the batch size based on the number of inflight threads - uint32_t inflight_threads = ctx->inflight_threads; + uint32_t inflight_threads = ctx->global_ctx->inflight_threads; uint32_t batch_size = std::min(std::max(1u, WEBGPU_NUM_PARAM_BUFS / std::max(inflight_threads, 1u)), WEBGPU_COMMAND_SUBMIT_BATCH_SIZE); if (commands.size() >= batch_size) { - futures.push_back(ggml_backend_webgpu_submit(ctx, commands)); + futures.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool, + &ctx->set_rows_error_buf_pool)); // Process events and check for completed submissions - ctx->instance.ProcessEvents(); - ggml_backend_webgpu_wait(ctx, futures, false); + ctx->global_ctx->instance.ProcessEvents(); + ggml_backend_webgpu_wait(ctx->global_ctx, futures, false); commands.clear(); } } if (!commands.empty()) { - webgpu_submission_futures new_futures = ggml_backend_webgpu_submit(ctx, commands); + webgpu_submission_futures new_futures = + ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool, &ctx->set_rows_error_buf_pool); futures.push_back(new_futures); } - ggml_backend_webgpu_wait(ctx, futures); - ctx->inflight_threads--; - WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx); + ggml_backend_webgpu_wait(ctx->global_ctx, futures); + ctx->global_ctx->inflight_threads--; + WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx); return GGML_STATUS_SUCCESS; } @@ -2159,8 +2215,8 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe // This is a trick to set all bytes of a u32 to the same 1 byte value. uint32_t val32 = (uint32_t) value * 0x01010101; - ggml_backend_webgpu_buffer_memset(buf_ctx->webgpu_ctx, buf_ctx->buffer, val32, total_offset, size); - WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->webgpu_ctx); + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, total_offset, size); + WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->global_ctx); } static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, @@ -2169,15 +2225,14 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t offset, size_t size) { WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor); - ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx; + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; - webgpu_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4); + buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4); if (size % 4 != 0) { // If size is not a multiple of 4, we need to memset the remaining bytes @@ -2190,21 +2245,21 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, ((uint8_t *) &val32)[i] = ((const uint8_t *) data)[size - remaining_size + i]; } // memset the remaining bytes - ggml_backend_webgpu_buffer_memset(webgpu_ctx, buf_ctx->buffer, val32, total_offset + (size - remaining_size), - remaining_size); + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, + total_offset + (size - remaining_size), remaining_size); } else { // wait for WriteBuffer to complete - webgpu_ctx->instance.WaitAny( - webgpu_ctx->queue.OnSubmittedWorkDone(wgpu::CallbackMode::AllowSpontaneous, + buf_ctx->global_ctx->instance.WaitAny(buf_ctx->global_ctx->queue.OnSubmittedWorkDone( + 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", std::string(message).c_str()); } }), - UINT64_MAX); + UINT64_MAX); } - WEBGPU_CPU_PROFILE_TOTAL_END(set_tensor, webgpu_ctx); + WEBGPU_CPU_PROFILE_TOTAL_END(set_tensor, buf_ctx->global_ctx); } static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, @@ -2216,8 +2271,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); - webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx; - wgpu::Device device = webgpu_ctx->device; + wgpu::Device device = buf_ctx->global_ctx->device; size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; @@ -2227,42 +2281,45 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, final_size = size + (4 - (size % 4)); } - std::lock_guard lock(webgpu_ctx->mutex); + std::lock_guard lock(buf_ctx->global_ctx->mutex); - if (webgpu_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->get_tensor_staging_buf.GetSize() < final_size) { + if (buf_ctx->global_ctx->get_tensor_staging_buf == nullptr || + buf_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { // Create a new staging buffer if it doesn't exist or is too small - if (webgpu_ctx->get_tensor_staging_buf) { - webgpu_ctx->get_tensor_staging_buf.Destroy(); + if (buf_ctx->global_ctx->get_tensor_staging_buf) { + buf_ctx->global_ctx->get_tensor_staging_buf.Destroy(); } - ggml_webgpu_create_buffer(device, webgpu_ctx->get_tensor_staging_buf, final_size, + ggml_webgpu_create_buffer(device, buf_ctx->global_ctx->get_tensor_staging_buf, final_size, wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "get_tensor_staging_buf"); } // Copy the data from the buffer to the staging buffer wgpu::CommandEncoder encoder = device.CreateCommandEncoder(); - encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->get_tensor_staging_buf, 0, final_size); + encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, buf_ctx->global_ctx->get_tensor_staging_buf, 0, + final_size); wgpu::CommandBuffer commands = encoder.Finish(); // Submit the command buffer to the queue - webgpu_ctx->queue.Submit(1, &commands); + buf_ctx->global_ctx->queue.Submit(1, &commands); // Map the staging buffer to read the data - ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size); + ggml_backend_webgpu_map_buffer(buf_ctx->global_ctx, buf_ctx->global_ctx->get_tensor_staging_buf, + wgpu::MapMode::Read, 0, final_size); // Must specify size here since the staging buffer might be larger than the tensor size - const void * mapped_range = webgpu_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); + const void * mapped_range = buf_ctx->global_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); // Copy the data from the mapped range to the output buffer std::memcpy(data, mapped_range, size); - webgpu_ctx->get_tensor_staging_buf.Unmap(); - WEBGPU_CPU_PROFILE_TOTAL_END(get_tensor, webgpu_ctx); + buf_ctx->global_ctx->get_tensor_staging_buf.Unmap(); + WEBGPU_CPU_PROFILE_TOTAL_END(get_tensor, buf_ctx->global_ctx); } static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")"); WEBGPU_CPU_PROFILE_TOTAL_START(clear); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - ggml_backend_webgpu_buffer_memset(buf_ctx->webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); - WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->webgpu_ctx); + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, value, 0, buffer->size); + WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->global_ctx); } static ggml_backend_buffer_i ggml_backend_webgpu_buffer_interface = { @@ -2292,28 +2349,30 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b int buffer_id = buffer_count++; std::string buf_name = "tensor_buf" + std::to_string(buffer_id); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes"); - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); - wgpu::Buffer buf; - ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), + ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + wgpu::Buffer buf; + ggml_webgpu_create_buffer(ctx->webgpu_global_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, buf_name.c_str()); ggml_backend_webgpu_buffer_context * buf_ctx = - new ggml_backend_webgpu_buffer_context(ctx->webgpu_ctx, buf, buf_name); + new ggml_backend_webgpu_buffer_context(buf, buf_name, ctx->webgpu_global_ctx); return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size); } static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); - return ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment; + ggml_backend_webgpu_device_context * dev_ctx = + static_cast(buft->device->context); + return dev_ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; } // maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding. static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); - return ctx->webgpu_ctx->limits.maxStorageBufferBindingSize; + ggml_backend_webgpu_device_context * dev_ctx = + static_cast(buft->device->context); + return dev_ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize; } static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, @@ -2322,7 +2381,7 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer size_t res = ggml_nbytes(tensor); switch (tensor->op) { case GGML_OP_ARGSORT: - res = ROUNDUP_POW2(res * 2 + ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment, + res = ROUNDUP_POW2(res * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment, WEBGPU_STORAGE_BUF_BINDING_MULT); break; case GGML_OP_TOP_K: @@ -2330,8 +2389,9 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer const ggml_tensor * src0 = tensor->src[0]; if (src0) { const size_t full = sizeof(int32_t) * ggml_nelements(src0); - res = ROUNDUP_POW2(full * 2 + ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment, - WEBGPU_STORAGE_BUF_BINDING_MULT); + res = ROUNDUP_POW2( + full * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); } } break; @@ -2359,7 +2419,7 @@ static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); // TODO: for now, return maxBufferSize as both free and total memory // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates. - uint64_t max_buffer_size = ctx->webgpu_ctx->limits.maxBufferSize; + uint64_t max_buffer_size = ctx->webgpu_global_ctx->capabilities.limits.maxBufferSize; // If we're on a 32-bit system, clamp to UINTPTR_MAX #if UINTPTR_MAX < UINT64_MAX uint64_t max_ptr_size = static_cast(UINTPTR_MAX); @@ -2402,66 +2462,67 @@ static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_si return constants; } -static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) { +static void ggml_webgpu_init_memset_pipeline(webgpu_global_context & ctx) { // we use the maximum workgroup size for the memset pipeline - size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; + size_t max_threads = WEBGPU_MAX_WG_SIZE * ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; // Size the bytes_per_thread so that the largest buffer size can be handled - webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->limits.maxStorageBufferBindingSize, max_threads); + ctx->capabilities.memset_bytes_per_thread = + CEIL_DIV(ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads); std::vector constants(2); - constants[0].key = "wg_size"; - constants[0].value = WEBGPU_MAX_WG_SIZE; - constants[1].key = "bytes_per_thread"; - constants[1].value = webgpu_ctx->memset_bytes_per_thread; - webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_memset, "memset", constants); + constants[0].key = "wg_size"; + constants[0].value = WEBGPU_MAX_WG_SIZE; + constants[1].key = "bytes_per_thread"; + constants[1].value = ctx->capabilities.memset_bytes_per_thread; + ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(ctx->device, wgsl_memset, "memset", constants); } static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { // Q4/Q5/Q8 classic quantizations webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_1][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_1][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q8_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); // K-quantizations webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q2_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q3_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q6_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); // IQ quantizations (2-, 3-, 4-bit variants) webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); // 1-bit and 4-bit IQ variants webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_M][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_NL][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_XS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); std::string proc_mul_mat_f32_f32; std::string proc_mul_mat_f32_f32_vec; @@ -2474,18 +2535,18 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { std::vector mul_mat_constants; #ifndef __EMSCRIPTEN__ - if (webgpu_ctx->supports_subgroup_matrix) { + if (webgpu_ctx->global_ctx->capabilities.supports_subgroup_matrix) { std::map sg_matrix_repls; - sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = std::to_string(webgpu_ctx->max_subgroup_size); + sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = + std::to_string(webgpu_ctx->global_ctx->capabilities.max_subgroup_size); sg_matrix_repls["WEBGPU_TILE_K"] = std::to_string(WEBGPU_MUL_MAT_TILE_K); sg_matrix_repls["WEBGPU_SUBGROUP_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M); sg_matrix_repls["WEBGPU_SUBGROUP_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N); - sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = std::to_string(webgpu_ctx->sg_mat_m); - sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = std::to_string(webgpu_ctx->sg_mat_n); - sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = std::to_string(webgpu_ctx->sg_mat_k); - + sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_m); + sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_n); + sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.sg_mat_k); proc_mul_mat_f32_f32 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32, sg_matrix_repls); proc_mul_mat_f32_f32_vec = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32_vec, sg_matrix_repls); @@ -2522,21 +2583,21 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { #endif webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); std::vector mul_mat_vec_constants(3); mul_mat_vec_constants[0].key = "WORKGROUP_SIZE"; @@ -2547,171 +2608,171 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); } static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_I32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q8_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q2_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q3_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_M][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_NL][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_XS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); } static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F32] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_I32] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_i32, "cpy_f32_i32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_i32, "cpy_f32_i32", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F16] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F32] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F16] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); } static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32, "add_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16, "add_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); } static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32, "sub_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16, "sub_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); } static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32, "mul_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16, "mul_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); } static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32, "div_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16, "div_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); } static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); webgpu_ctx->rms_norm_pipelines[0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm, "rms_norm", constants); - webgpu_ctx->rms_norm_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm, "rms_norm", constants); + webgpu_ctx->rms_norm_pipelines[1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); } static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32, "rope_f32", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32, "rope_f32", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16, "rope_f16", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16, "rope_f16", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); } static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { @@ -2719,68 +2780,68 @@ static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { // REGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); // GEGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); // SWIGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); // SWIGLU_OAI webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); // GEGLU_ERF webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); // GEGLU_QUICK webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->scale_pipelines[0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32, "scale_f32", constants); - webgpu_ctx->scale_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32, "scale_f32", constants); + webgpu_ctx->scale_pipelines[1] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32_inplace, + "scale_f32_inplace", constants); } static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { @@ -2788,56 +2849,243 @@ static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { // f32 (no mask) webgpu_ctx->soft_max_pipelines[2][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); - webgpu_ctx->soft_max_pipelines[2][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); - webgpu_ctx->soft_max_pipelines[2][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); + webgpu_ctx->soft_max_pipelines[2][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[2][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); // f32 mask (mask_type = 0) - webgpu_ctx->soft_max_pipelines[0][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); + webgpu_ctx->soft_max_pipelines[0][0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); - webgpu_ctx->soft_max_pipelines[0][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[0][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, + "soft_max_f32_mask_f32_sink_inplace", constants); // f16 mask (mask_type = 1) - webgpu_ctx->soft_max_pipelines[1][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); + webgpu_ctx->soft_max_pipelines[1][0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); - webgpu_ctx->soft_max_pipelines[1][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); + webgpu_ctx->soft_max_pipelines[1][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, + "soft_max_f32_mask_f16_sink_inplace", constants); } -// TODO: move most initialization logic here -static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) { +static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { + wgpu::RequestAdapterOptions options = {}; + +#ifndef __EMSCRIPTEN__ + // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215 + const char * const adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" }; + wgpu::DawnTogglesDescriptor adapterTogglesDesc; + adapterTogglesDesc.enabledToggles = adapterEnabledToggles; + adapterTogglesDesc.enabledToggleCount = 2; + options.nextInChain = &adapterTogglesDesc; +#endif + + ctx->webgpu_global_ctx->instance.WaitAny( + ctx->webgpu_global_ctx->instance.RequestAdapter( + &options, wgpu::CallbackMode::AllowSpontaneous, + [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { + if (status != wgpu::RequestAdapterStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); + return; + } + ctx->webgpu_global_ctx->adapter = std::move(adapter); + }), + UINT64_MAX); + GGML_ASSERT(ctx->webgpu_global_ctx->adapter != nullptr); + + ctx->webgpu_global_ctx->adapter.GetLimits(&ctx->webgpu_global_ctx->capabilities.limits); + + wgpu::AdapterInfo info{}; +#ifndef __EMSCRIPTEN__ + wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; + if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + info.nextInChain = &subgroup_matrix_configs; + } +#endif + ctx->webgpu_global_ctx->adapter.GetInfo(&info); + wgpu::SupportedFeatures features; + ctx->webgpu_global_ctx->adapter.GetFeatures(&features); + // we require f16 support + GGML_ASSERT(ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16)); + +#ifndef __EMSCRIPTEN__ + // Only support square f16 matrices of size 8 or 16 for now + bool valid_subgroup_matrix_config = false; + if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { + const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; + if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && + config.componentType == wgpu::SubgroupMatrixComponentType::F16 && + config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { + ctx->webgpu_global_ctx->capabilities.sg_mat_m = config.M; + ctx->webgpu_global_ctx->capabilities.sg_mat_n = config.N; + ctx->webgpu_global_ctx->capabilities.sg_mat_k = config.K; + valid_subgroup_matrix_config = true; + break; + } + } + } + ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config; +#endif + + // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. + // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. + ctx->webgpu_global_ctx->capabilities.max_subgroup_size = info.subgroupMaxSize; + // Initialize device + std::vector required_features = { wgpu::FeatureName::ShaderF16 }; + +#ifndef __EMSCRIPTEN__ + required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); + if (ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) { + required_features.push_back(wgpu::FeatureName::Subgroups); + required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); + } +#endif + +#ifdef GGML_WEBGPU_GPU_PROFILE + required_features.push_back(wgpu::FeatureName::TimestampQuery); +#endif + + wgpu::DeviceDescriptor dev_desc; + dev_desc.requiredLimits = &ctx->webgpu_global_ctx->capabilities.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_UNUSED(reason); + GGML_UNUSED(message); + //TODO: uncomment once proper free logic is in place + //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason), + //std::string(message).c_str()); + }); + dev_desc.SetUncapturedErrorCallback( + [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) { + GGML_UNUSED(device); + GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast(reason), + std::string(message).c_str()); + }); + +#ifndef __EMSCRIPTEN__ + // Enable Dawn-specific toggles to increase native performance + // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these, + // only for native performance? + const char * const deviceEnabledToggles[] = { "skip_validation", "disable_robustness", "disable_workgroup_init", + "disable_polyfills_on_integer_div_and_mod" }; + const char * const deviceDisabledToggles[] = { "timestamp_quantization" }; + wgpu::DawnTogglesDescriptor deviceTogglesDesc; + deviceTogglesDesc.enabledToggles = deviceEnabledToggles; + deviceTogglesDesc.enabledToggleCount = 4; + deviceTogglesDesc.disabledToggles = deviceDisabledToggles; + deviceTogglesDesc.disabledToggleCount = 1; + + dev_desc.nextInChain = &deviceTogglesDesc; +#endif + + ctx->webgpu_global_ctx->instance.WaitAny( + ctx->webgpu_global_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->webgpu_global_ctx->device = std::move(device); + }), + UINT64_MAX); + GGML_ASSERT(ctx->webgpu_global_ctx->device != nullptr); + + ggml_webgpu_init_memset_pipeline(ctx->webgpu_global_ctx); + ctx->webgpu_global_ctx->memset_buf_pool.init(ctx->webgpu_global_ctx->device, 1, WEBGPU_PARAMS_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); + ctx->webgpu_global_ctx->queue = ctx->webgpu_global_ctx->device.GetQueue(); + +#ifdef GGML_WEBGPU_GPU_PROFILE + // Initialize buffer pool for timestamp queries, used for profiling + ctx->webgpu_global_ctx->timestamp_query_buf_pool.init(ctx->webgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, + WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, + wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); +#endif + + 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, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, + std::string(info.device).c_str(), std::string(info.description).c_str()); + return true; +} + +static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) { + ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) dev->context; + webgpu_context webgpu_ctx = std::make_shared(); + webgpu_ctx->global_ctx = dev_ctx->webgpu_global_ctx; + webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_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->global_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_mul_mat_pipeline(webgpu_ctx); + ggml_webgpu_init_get_rows_pipeline(webgpu_ctx); + ggml_webgpu_init_cpy_pipeline(webgpu_ctx); + ggml_webgpu_init_add_pipeline(webgpu_ctx); + ggml_webgpu_init_sub_pipeline(webgpu_ctx); + ggml_webgpu_init_mul_pipeline(webgpu_ctx); + ggml_webgpu_init_div_pipeline(webgpu_ctx); + ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx); + ggml_webgpu_init_rope_pipeline(webgpu_ctx); + ggml_webgpu_init_glu_pipeline(webgpu_ctx); + ggml_webgpu_init_scale_pipeline(webgpu_ctx); + ggml_webgpu_init_soft_max_pipeline(webgpu_ctx); +#ifdef GGML_WEBGPU_DEBUG + // Initialize debug buffers + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->global_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->global_ctx->device, webgpu_ctx->global_ctx->debug_dev_buf, + WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); +#endif + return webgpu_ctx; +} + +static ggml_backend_t ggml_backend_webgpu_backend_init(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); - WEBGPU_LOG_DEBUG("ggml_backend_webgpu_device_init()"); + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_backend_init()"); - ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); - webgpu_context webgpu_ctx = dev_ctx->webgpu_ctx; + ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); - 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; + auto * backend_ctx = new ggml_backend_webgpu_context(); + backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; + backend_ctx->webgpu_ctx = initialize_webgpu_context(dev); // See GGML Backend Interface section - static ggml_backend backend = { + auto * backend = new ggml_backend(); + *backend = { /* .guid = */ ggml_backend_webgpu_guid(), /* .interface = */ ggml_backend_webgpu_i, /* .device = */ dev, - /* .context = */ &backend_ctx, + /* .context = */ backend_ctx, }; - return &backend; + return backend; } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { @@ -2854,7 +3102,8 @@ static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggm }, /* .device = */ dev, - /* .context = */ NULL, + /* .context = */ + NULL }; return &ggml_backend_webgpu_buffer_type; @@ -2895,16 +3144,16 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) { static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); - webgpu_context webgpu_ctx = ctx->webgpu_ctx; - ggml_tensor * src0 = op->src[0]; ggml_tensor * src1 = op->src[1]; ggml_tensor * src2 = op->src[2]; // on smaller devices (or CI), tensors may be larger than the max storage buffer size - if (ggml_nbytes(op) > webgpu_ctx->limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->limits.maxStorageBufferBindingSize)) { + if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && + ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { return false; } @@ -2984,17 +3233,19 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const } case GGML_OP_FLASH_ATTN_EXT: { - if (!webgpu_ctx->supports_subgroup_matrix) { +#ifndef __EMSCRIPTEN__ + if (!ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) { break; } // Head dimensions must fit in workgroup memory with minimum tile sizes - size_t limit_bytes = webgpu_ctx->limits.maxComputeWorkgroupStorageSize; + size_t limit_bytes = ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; const bool has_mask = op->src[3] != nullptr; - const bool kv_direct = src1->type == GGML_TYPE_F16 && (src0->ne[0] % webgpu_ctx->sg_mat_k) == 0 && + const bool kv_direct = src1->type == GGML_TYPE_F16 && + (src0->ne[0] % ctx->webgpu_global_ctx->capabilities.sg_mat_k) == 0 && (src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD) == 0; const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes( - webgpu_ctx->sg_mat_m, webgpu_ctx->sg_mat_n, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], - has_mask, kv_direct); + ctx->webgpu_global_ctx->capabilities.sg_mat_m, ctx->webgpu_global_ctx->capabilities.sg_mat_n, + (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, kv_direct); if (min_bytes > limit_bytes) { break; } @@ -3003,6 +3254,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_Q4_0 || src1->type == GGML_TYPE_Q8_0) && src2->type == src1->type && op->type == GGML_TYPE_F32; +#endif break; } case GGML_OP_RMS_NORM: @@ -3099,10 +3351,13 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const default: break; } - if (ggml_nbytes(op) > webgpu_ctx->limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src2 != nullptr && ggml_nbytes(src2) > webgpu_ctx->limits.maxStorageBufferBindingSize)) { + if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && + ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src2 != nullptr && + ggml_nbytes(src2) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { supports_op = false; WEBGPU_LOG_DEBUG("ggml_webgpu op not supported due to size: "); } @@ -3127,7 +3382,7 @@ static struct ggml_backend_device_i ggml_backend_webgpu_device_i = { /* .get_memory = */ ggml_backend_webgpu_device_get_memory, /* .get_type = */ ggml_backend_webgpu_device_get_type, /* .get_props = */ ggml_backend_webgpu_device_get_props, - /* .init_backend = */ ggml_backend_webgpu_device_init, + /* .init_backend = */ ggml_backend_webgpu_backend_init, /* .get_buffer_type = */ ggml_backend_webgpu_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, /* .buffer_from_host_ptr = */ NULL, @@ -3156,6 +3411,7 @@ static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) { // TODO: Does this need to be thread safe? Is it only called once? // TODO: move most logic to device_init function so backend can be freed/initialized properly // Only one device is supported for now + static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { GGML_ASSERT(index == 0); WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()"); @@ -3164,189 +3420,12 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_backend_webgpu_reg_context * reg_ctx = static_cast(reg->context); - webgpu_context ctx = reg_ctx->webgpu_ctx; - - wgpu::RequestAdapterOptions options = {}; - -#ifndef __EMSCRIPTEN__ - // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215 - const char * const adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" }; - wgpu::DawnTogglesDescriptor adapterTogglesDesc; - adapterTogglesDesc.enabledToggles = adapterEnabledToggles; - adapterTogglesDesc.enabledToggleCount = 2; - options.nextInChain = &adapterTogglesDesc; -#endif - - ctx->instance.WaitAny(ctx->instance.RequestAdapter( - &options, wgpu::CallbackMode::AllowSpontaneous, - [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { - if (status != wgpu::RequestAdapterStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); - return; - } - ctx->adapter = std::move(adapter); - }), - UINT64_MAX); - GGML_ASSERT(ctx->adapter != nullptr); - - ctx->adapter.GetLimits(&ctx->limits); - - wgpu::AdapterInfo info{}; -#ifndef __EMSCRIPTEN__ - wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { - info.nextInChain = &subgroup_matrix_configs; - } -#endif - ctx->adapter.GetInfo(&info); - - wgpu::SupportedFeatures features; - ctx->adapter.GetFeatures(&features); - // we require f16 support - GGML_ASSERT(ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16)); - -#ifndef __EMSCRIPTEN__ - // Only support square f16 matrices of size 8 or 16 for now - bool valid_subgroup_matrix_config = false; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { - for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { - const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; - if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && - config.componentType == wgpu::SubgroupMatrixComponentType::F16 && - config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { - ctx->sg_mat_m = config.M; - ctx->sg_mat_n = config.N; - ctx->sg_mat_k = config.K; - valid_subgroup_matrix_config = true; - break; - } - } - } - - ctx->supports_subgroup_matrix = valid_subgroup_matrix_config; -#endif - // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. - // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. - ctx->max_subgroup_size = info.subgroupMaxSize; - - // Initialize device - std::vector required_features = { wgpu::FeatureName::ShaderF16 }; - -#ifndef __EMSCRIPTEN__ - required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); - if (ctx->supports_subgroup_matrix) { - required_features.push_back(wgpu::FeatureName::Subgroups); - required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); - } -#endif - -#ifdef GGML_WEBGPU_GPU_PROFILE - required_features.push_back(wgpu::FeatureName::TimestampQuery); -#endif - - 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_UNUSED(reason); - GGML_UNUSED(message); - //TODO: uncomment once proper free logic is in place - //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason), - //std::string(message).c_str()); - }); - dev_desc.SetUncapturedErrorCallback( - [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) { - GGML_UNUSED(device); - GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast(reason), - std::string(message).c_str()); - }); - -#ifndef __EMSCRIPTEN__ - // Enable Dawn-specific toggles to increase native performance - // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these, - // only for native performance? - const char * const deviceEnabledToggles[] = { "skip_validation", "disable_robustness", "disable_workgroup_init", - "disable_polyfills_on_integer_div_and_mod" }; - const char * const deviceDisabledToggles[] = { "timestamp_quantization" }; - wgpu::DawnTogglesDescriptor deviceTogglesDesc; - deviceTogglesDesc.enabledToggles = deviceEnabledToggles; - deviceTogglesDesc.enabledToggleCount = 4; - deviceTogglesDesc.disabledToggles = deviceDisabledToggles; - deviceTogglesDesc.disabledToggleCount = 1; - - dev_desc.nextInChain = &deviceTogglesDesc; -#endif - - 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); - -#ifdef GGML_WEBGPU_GPU_PROFILE - // Initialize buffer pool for timestamp queries (profiling) - ctx->timestamp_query_buf_pool.init(ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, - WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, - wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, - wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); -#endif - - 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_get_rows_pipeline(ctx); - ggml_webgpu_init_cpy_pipeline(ctx); - ggml_webgpu_init_add_pipeline(ctx); - ggml_webgpu_init_sub_pipeline(ctx); - ggml_webgpu_init_mul_pipeline(ctx); - ggml_webgpu_init_div_pipeline(ctx); - ggml_webgpu_init_rms_norm_pipeline(ctx); - ggml_webgpu_init_rope_pipeline(ctx); - ggml_webgpu_init_glu_pipeline(ctx); - ggml_webgpu_init_scale_pipeline(ctx); - ggml_webgpu_init_soft_max_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 + create_webgpu_device(reg_ctx); static ggml_backend_webgpu_device_context device_ctx; - device_ctx.webgpu_ctx = ctx; - device_ctx.device_name = GGML_WEBGPU_NAME; - 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, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, - std::string(info.device).c_str(), std::string(info.description).c_str()); - + device_ctx.device_name = GGML_WEBGPU_NAME; + device_ctx.device_desc = GGML_WEBGPU_NAME; + device_ctx.webgpu_global_ctx = reg_ctx->webgpu_global_ctx; // See GGML Backend Device Interface section static ggml_backend_device device = { /* .iface = */ ggml_backend_webgpu_device_i, @@ -3354,7 +3433,7 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t /* .context = */ &device_ctx, }; - WEBGPU_CPU_PROFILE_TOTAL_END(reg_get_device, ctx); + WEBGPU_CPU_PROFILE_TOTAL_END(reg_get_device, reg_ctx->webgpu_global_ctx); return &device; } @@ -3370,10 +3449,7 @@ static const struct ggml_backend_reg_i ggml_backend_webgpu_reg_i = { ggml_backend_reg_t ggml_backend_webgpu_reg() { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_reg()"); - webgpu_context webgpu_ctx = std::make_shared(); - static ggml_backend_webgpu_reg_context ctx; - ctx.webgpu_ctx = webgpu_ctx; ctx.name = GGML_WEBGPU_NAME; ctx.device_count = 1; @@ -3390,15 +3466,17 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { instance_descriptor.nextInChain = &instanceTogglesDesc; #endif - webgpu_ctx->instance = wgpu::CreateInstance(&instance_descriptor); + wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); + ctx.webgpu_global_ctx = webgpu_global_context(new webgpu_global_context_struct()); + ctx.webgpu_global_ctx->instance = std::move(inst); #ifdef __EMSCRIPTEN__ - if (webgpu_ctx->instance == nullptr) { + if (ctx.webgpu_global_ctx->instance == nullptr) { GGML_LOG_ERROR("ggml_webgpu: Failed to create WebGPU instance. Make sure either -sASYNCIFY or -sJSPI is set\n"); return nullptr; } #endif - GGML_ASSERT(webgpu_ctx->instance != nullptr); + GGML_ASSERT(ctx.webgpu_global_ctx->instance != nullptr); static ggml_backend_reg reg = { /* .api_version = */ GGML_BACKEND_API_VERSION, @@ -3411,7 +3489,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { ggml_backend_t ggml_backend_webgpu_init(void) { ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_webgpu_reg(), 0); - return ggml_backend_webgpu_device_init(dev, nullptr); + return ggml_backend_webgpu_backend_init(dev, nullptr); } GGML_BACKEND_DL_IMPL(ggml_backend_webgpu_reg) From eef375ce167cf36d8ed2cf347be95a7a7a90fb5e Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 28 Jan 2026 05:59:30 +0100 Subject: [PATCH 036/206] sampling : remove sampling branching in output_reserve (#18811) * sampling : remove sampling branching in output_reserve This commit updates output_reserve in llama-context.cpp to always allocate sampling buffers regardless of whether sampling is needed for the current batch. The motivation for this is to avoid reallocations and branching based on the sampling requirements of the batch. --- src/llama-context.cpp | 88 ++++++++++++++++--------------------------- src/llama-context.h | 2 +- 2 files changed, 33 insertions(+), 57 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 0b2b05c419..10b306a853 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -253,11 +253,7 @@ llama_context::llama_context( // graph outputs buffer { - // resized during inference when a batch uses more outputs - // Create a dummy batch for initialization. - llama_batch dummy_batch = {}; - dummy_batch.n_tokens = 0; - if (output_reserve(params.n_seq_max, dummy_batch) < params.n_seq_max) { + if (output_reserve(params.n_seq_max) < params.n_seq_max) { throw std::runtime_error("failed to reserve initial output buffer"); } @@ -1225,7 +1221,7 @@ int llama_context::encode(const llama_batch & batch_inp) { n_queued_tokens += n_tokens; // reserve output buffer - if (output_reserve(n_tokens, batch_inp) < n_tokens) { + if (output_reserve(n_tokens) < n_tokens) { LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens); return -2; }; @@ -1456,6 +1452,23 @@ static void copy_tensor_async_candidates( } } +static bool needs_raw_logits(const llama_ubatch & ubatch, const std::map & samplers) { + for (uint32_t i = 0; i < ubatch.n_tokens; i++) { + if (!ubatch.output[i]) { + continue; + } + + // Check if the output token has at least one sequence without a backend sampler. + for (int32_t j = 0; j < ubatch.n_seq_id[i]; ++j) { + llama_seq_id seq_id = ubatch.seq_id[i][j]; + if (samplers.find(seq_id) == samplers.end()) { + return true; + } + } + } + return false; // all sequences use backend sampling +} + int llama_context::decode(const llama_batch & batch_inp) { GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT @@ -1588,7 +1601,7 @@ int llama_context::decode(const llama_batch & batch_inp) { } // reserve output buffer - if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) { + if (output_reserve(n_outputs_all) < n_outputs_all) { LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all); return -2; }; @@ -1661,10 +1674,7 @@ int llama_context::decode(const llama_batch & batch_inp) { } // extract logits - // For multi-sequence batches that mix backend samplers and CPU sampler - // this is currently inefficient as we copy all logits even for the - // backend sampled tokens. - if (logits && t_logits && n_outputs > 0) { + if (logits && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) { ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits); GGML_ASSERT(backend_res != nullptr); GGML_ASSERT(logits != nullptr); @@ -1734,11 +1744,8 @@ int llama_context::decode(const llama_batch & batch_inp) { } } - // This flag indicates whether a backend sampler has actually sampled a specific - // token, or if it has produced probabilites. If true, we can skip the normal copying of logits and embeddings. - const bool has_sampled = !res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty(); - - if (has_samplers && has_sampled) { + // Copy backend sampling output if this ubatch produced any sampling tensors. + if (has_samplers && (!res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty())) { const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev); const auto stride = n_vocab; @@ -1813,7 +1820,8 @@ int llama_context::decode(const llama_batch & batch_inp) { // output // -uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & batch) { +uint32_t llama_context::output_reserve(int32_t n_outputs) { + const auto & hparams = model.hparams; const auto & vocab = model.vocab; @@ -1832,45 +1840,16 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba has_embd = true; } - // Check which sampling modes are needed for the current batch. - // TODO: avoid this branching by working with the worst-case - bool has_sampling = false; - bool cpu_logits = false; - - if (batch.logits) { - for (int32_t i = 0; i < batch.n_tokens; i++) { - if (!batch.logits[i]) { - continue; - } - for (int32_t j = 0; j < batch.n_seq_id[i]; j++) { - llama_seq_id seq_id = batch.seq_id[i][j]; - if (sampling.samplers.find(seq_id) != sampling.samplers.end()) { - has_sampling = true; - } else { - cpu_logits = true; - } - } - } - } else { - // When batch.logits is nullptr (when loading state with a dummy batch), - // allocate CPU logits. - cpu_logits = true; - } size_t backend_float_count = 0; size_t backend_token_count = 0; - // Allocate CPU logits buffer only if needed by sequences in this batch - logits_size = (has_logits && cpu_logits) ? n_vocab*n_outputs_max : 0; + logits_size = has_logits ? n_vocab*n_outputs_max : 0; embd_size = has_embd ? n_embd_out*n_outputs_max : 0; - // TODO: avoid this branching by working with the worst-case - if (!has_sampling) { - sampling.logits_size = 0; - sampling.probs_size = 0; - sampling.sampled_size = 0; - sampling.candidates_size = 0; - } else { + // Allocate backend sampling output buffers if there are backend samplers configured. + const bool has_sampling = !sampling.samplers.empty(); + if (has_sampling) { sampling.logits_size = n_vocab*n_outputs_max; sampling.probs_size = n_vocab*n_outputs_max; sampling.sampled_size = n_outputs_max; @@ -1928,7 +1907,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs, const llama_batch & ba size_t offset = 0; uint8_t * base = (uint8_t *) output_base; - logits = (has_logits && cpu_logits) ? output_base : nullptr; + logits = has_logits ? output_base : nullptr; offset += logits_size * sizeof(float); embd = has_embd ? (float *) (base + offset) : nullptr; @@ -2614,10 +2593,7 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { auto n_outputs = this->n_outputs; io.read_to(&n_outputs, sizeof(n_outputs)); - // Create a dummy batch for state loading. - llama_batch dummy_batch = {}; - dummy_batch.n_tokens = 0; - if (n_outputs > output_reserve(n_outputs, dummy_batch)) { + if (n_outputs > output_reserve(n_outputs)) { throw std::runtime_error("could not reserve outputs"); } @@ -2862,7 +2838,7 @@ void llama_context::opt_epoch_iter( } // reserve output buffer - if (output_reserve(n_outputs_all, balloc->get_batch()) < n_outputs_all) { + if (output_reserve(n_outputs_all) < n_outputs_all) { LLAMA_LOG_ERROR("%s: could not reserve space for batch with %d outputs\n", __func__, n_outputs_all); GGML_ABORT("TODO: handle this error"); }; diff --git a/src/llama-context.h b/src/llama-context.h index 86decc05fb..8e71cdd1dc 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -212,7 +212,7 @@ private: // Make sure enough space is available for outputs. // Returns max number of outputs for which space was reserved. - uint32_t output_reserve(int32_t n_outputs, const llama_batch & batch); + uint32_t output_reserve(int32_t n_outputs); void output_reorder(); From c5c64f72ac452b4be8335631ac9269f10355b597 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Jan 2026 09:11:13 +0200 Subject: [PATCH 037/206] llama : disable Direct IO by default (#19109) * llama : disable Direct IO by default * cont : override mmap if supported --- common/arg.cpp | 7 ++----- common/common.h | 2 +- include/llama.h | 2 +- src/llama-model-loader.cpp | 8 ++++---- src/llama-model.cpp | 2 +- src/llama-quant.cpp | 2 +- 6 files changed, 10 insertions(+), 13 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 04fd375d56..2f68bdc1c0 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2198,18 +2198,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex add_opt(common_arg( {"--mmap"}, {"--no-mmap"}, - string_format("whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + string_format("whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), [](common_params & params, bool value) { params.use_mmap = value; - if (value) { - params.use_direct_io = false; // disable direct io when mmap is explicitly enabled - } } ).set_env("LLAMA_ARG_MMAP")); add_opt(common_arg( {"-dio", "--direct-io"}, {"-ndio", "--no-direct-io"}, - string_format("use DirectIO if available. Takes precedence over --mmap (default: %s)", params.use_direct_io ? "enabled" : "disabled"), + string_format("use DirectIO if available. (default: %s)", params.use_direct_io ? "enabled" : "disabled"), [](common_params & params, bool value) { params.use_direct_io = value; } diff --git a/common/common.h b/common/common.h index 96c990c05d..21c11f457d 100644 --- a/common/common.h +++ b/common/common.h @@ -438,7 +438,7 @@ struct common_params { bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool use_mmap = true; // enable mmap to use filesystem cache - bool use_direct_io = true; // read from disk without buffering for faster model loading + bool use_direct_io = false; // read from disk without buffering bool use_mlock = false; // use mlock to keep model in memory bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation diff --git a/include/llama.h b/include/llama.h index c3360ae57c..bf4e28a8be 100644 --- a/include/llama.h +++ b/include/llama.h @@ -309,7 +309,7 @@ extern "C" { // Keep the booleans together to avoid misalignment during copy-by-value. bool vocab_only; // only load the vocabulary, no weights bool use_mmap; // use mmap if possible - bool use_direct_io; // use direct io, takes precedence over use_mmap + bool use_direct_io; // use direct io, takes precedence over use_mmap when supported bool use_mlock; // force system to keep model in RAM bool check_tensors; // validate model tensor data bool use_extra_bufts; // use extra buffer types (used for weight repacking) diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 383b8dc761..1501e392ca 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -541,15 +541,15 @@ llama_model_loader::llama_model_loader( if (use_mmap && use_direct_io) { if (files.back()->has_direct_io()) { - // Disable mmap, as DirectIO is available - use_mmap = false; LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__); + use_mmap = false; } else { - // Disable DirectIO and reopen file using std::fopen for mmap + LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); use_direct_io = false; + + // reopen file using std::fopen for mmap files.pop_back(); files.emplace_back(new llama_file(fname.c_str(), "rb", false)); - LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); } } diff --git a/src/llama-model.cpp b/src/llama-model.cpp index cc784e1cb0..72490a89b5 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -8125,7 +8125,7 @@ llama_model_params llama_model_default_params() { /*.kv_overrides =*/ nullptr, /*.vocab_only =*/ false, /*.use_mmap =*/ true, - /*.use_direct_io =*/ true, + /*.use_direct_io =*/ false, /*.use_mlock =*/ false, /*.check_tensors =*/ false, /*.use_extra_bufts =*/ true, diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index a2b8d4e56c..776222cb6f 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -545,7 +545,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } std::vector splits = {}; - llama_model_loader ml(fname_inp, splits, use_mmap, /*use_direct_io*/ true, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); + llama_model_loader ml(fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching llama_model model(llama_model_default_params()); From b931f81b5a3bc3e16bd74cebc8fee8cbd69f8d4d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Jan 2026 09:11:40 +0200 Subject: [PATCH 038/206] server : adjust spec tests to generate up to 16 tokens (#19093) --- tools/server/tests/unit/test_speculative.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py index d2f3fba5fe..eebd3cc8fa 100644 --- a/tools/server/tests/unit/test_speculative.py +++ b/tools/server/tests/unit/test_speculative.py @@ -30,6 +30,7 @@ def test_with_and_without_draft(): "prompt": "I believe the meaning of life is", "temperature": 0.0, "top_k": 1, + "n_predict": 16, }) assert res.status_code == 200 content_no_draft = res.body["content"] @@ -42,6 +43,7 @@ def test_with_and_without_draft(): "prompt": "I believe the meaning of life is", "temperature": 0.0, "top_k": 1, + "n_predict": 16, }) assert res.status_code == 200 content_draft = res.body["content"] @@ -68,6 +70,7 @@ def test_different_draft_min_draft_max(): "prompt": "I believe the meaning of life is", "temperature": 0.0, "top_k": 1, + "n_predict": 16, }) assert res.status_code == 200 if last_content is not None: From 2eee6c866c89bcb101693c8b33fa6e1a7f98932c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Jan 2026 09:15:11 +0200 Subject: [PATCH 039/206] CUDA: tune GLM 4.7 Flash FA kernel selection logic (DGX Spark) (#19142) --- ggml/src/ggml-cuda/common.cuh | 1 + ggml/src/ggml-cuda/fattn.cu | 8 ++++++++ 2 files changed, 9 insertions(+) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 09a491a836..3335f443ae 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -53,6 +53,7 @@ // While BW spans CC 1000, 1100 & 1200, we are integrating Tensor Core instructions available to 1200 family, see // https://docs.nvidia.com/cutlass/media/docs/cpp/blackwell_functionality.html#blackwell-sm120-gemms #define GGML_CUDA_CC_BLACKWELL 1200 +#define GGML_CUDA_CC_DGX_SPARK 1210 #define GGML_CUDA_CC_RUBIN 1300 #define GGML_CUDA_CC_OFFSET_AMD 0x1000000 #define GGML_CUDA_CC_OFFSET_MTHREADS 0x0100000 diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index b061fdf9a2..fe18ff6c7d 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -147,6 +147,14 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); const int gqa_ratio = Q->ne[2] / K->ne[2]; if (gqa_ratio == 20) { // GLM 4.7 Flash + if (cc >= GGML_CUDA_CC_DGX_SPARK) { + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); + break; + } + ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst); + break; + } if (cc >= GGML_CUDA_CC_BLACKWELL) { if (Q->ne[1] <= 4 && K->ne[1] >= 65536) { ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst); From 631cbfcc7a21869e2f3f6b78ed88e9863cc5a862 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 28 Jan 2026 09:15:27 +0200 Subject: [PATCH 040/206] cuda : fix "V is K view" check for non-unified KV cache (#19145) --- common/arg.cpp | 5 +++-- ggml/src/ggml-cuda/fattn-common.cuh | 2 +- ggml/src/ggml-cuda/fattn.cu | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 2f68bdc1c0..cd3a1b6397 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1295,9 +1295,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-kvu", "--kv-unified"}, + {"-no-kvu", "--no-kv-unified"}, "use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)", - [](common_params & params) { - params.kv_unified = true; + [](common_params & params, bool value) { + params.kv_unified = value; } ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED})); add_opt(common_arg( diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 3d7daccfdf..b6a7460da8 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -789,7 +789,7 @@ void launch_fattn( const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; - const bool V_is_K_view = V->view_src && V->view_offs == 0 && (V->view_src == K || V->view_src == K->view_src); + const bool V_is_K_view = V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs)); const ggml_tensor * mask = dst->src[3]; const ggml_tensor * sinks = dst->src[4]; diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index fe18ff6c7d..195904ee20 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -310,7 +310,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const } } - const bool V_is_K_view = V->view_src && V->view_offs == 0 && (V->view_src == K || V->view_src == K->view_src); + const bool V_is_K_view = V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs)); const int cc = ggml_cuda_info().devices[device].cc; From 6ad70c5a779f770a37b91c51d88daf30313fca08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= <1478977+Alcpz@users.noreply.github.com> Date: Wed, 28 Jan 2026 07:15:56 +0000 Subject: [PATCH 041/206] ggml-cpu: arm64: Q4_K scale unroll and vectorization (#19108) --- ggml/src/ggml-cpu/arch/arm/repack.cpp | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index f40226494c..99bb70274c 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -3148,16 +3148,17 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, // Scales[i] corresponds to column i const int scale_offset = cp * 2; - for (int blk = 0; blk < 2; blk++) { - const int32x4_t block_scale = { - (int32_t) q4sb_scales[blk][scale_offset], - (int32_t) q4sb_scales[blk][scale_offset], - (int32_t) q4sb_scales[blk][scale_offset + 1], - (int32_t) q4sb_scales[blk][scale_offset + 1], - }; - acc[cp] = vmlaq_s32(acc[cp], sb_acc[blk], block_scale); - acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc[blk + 2], block_scale); - } + const int32_t scale_00 = q4sb_scales[0][scale_offset]; + const int32_t scale_01 = q4sb_scales[0][scale_offset + 1]; + const int32_t scale_10 = q4sb_scales[1][scale_offset]; + const int32_t scale_11 = q4sb_scales[1][scale_offset + 1]; + const int32x4_t block_scale_0 = vcombine_s32(vdup_n_s32(scale_00), vdup_n_s32(scale_01)); + const int32x4_t block_scale_1 = vcombine_s32(vdup_n_s32(scale_10), vdup_n_s32(scale_11)); + + acc[cp] = vmlaq_s32(acc[cp], sb_acc[0], block_scale_0); + acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc[2], block_scale_0); + acc[cp] = vmlaq_s32(acc[cp], sb_acc[1], block_scale_1); + acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc[3], block_scale_1); } // Multiply Acc bsum + mins From b7feacf7f39d79518e6ca48a9fcf697c5244b585 Mon Sep 17 00:00:00 2001 From: Kevin Pouget Date: Wed, 28 Jan 2026 10:49:40 +0100 Subject: [PATCH 042/206] ggml: new backend for Virglrenderer API Remoting acceleration (v2) (#18718) --- CODEOWNERS | 1 + ggml/CMakeLists.txt | 3 + ggml/include/ggml-virtgpu.h | 16 + ggml/src/CMakeLists.txt | 1 + ggml/src/ggml-backend-reg.cpp | 14 + ggml/src/ggml-virtgpu/CMakeLists.txt | 70 +++ .../ggml-virtgpu/apir_cs_ggml-rpc-front.cpp | 87 +++ ggml/src/ggml-virtgpu/backend/CMakeLists.txt | 21 + .../backend/apir_cs_ggml-rpc-back.cpp | 115 ++++ .../ggml-virtgpu/backend/backend-convert.h | 13 + .../backend/backend-dispatched-backend.cpp | 65 +++ .../backend-dispatched-buffer-type.cpp | 89 ++++ .../backend/backend-dispatched-buffer.cpp | 131 +++++ .../backend/backend-dispatched-device.cpp | 148 ++++++ .../backend/backend-dispatched.cpp | 46 ++ .../backend/backend-dispatched.gen.h | 130 +++++ .../ggml-virtgpu/backend/backend-dispatched.h | 23 + .../ggml-virtgpu/backend/backend-virgl-apir.h | 32 ++ ggml/src/ggml-virtgpu/backend/backend.cpp | 148 ++++++ .../backend/shared/api_remoting.h | 90 ++++ .../backend/shared/apir_backend.gen.h | 36 ++ .../backend/shared/apir_backend.h | 46 ++ .../src/ggml-virtgpu/backend/shared/apir_cs.h | 383 ++++++++++++++ .../backend/shared/apir_cs_ggml.h | 211 ++++++++ .../ggml-virtgpu/backend/shared/apir_cs_rpc.h | 54 ++ .../ggml-virtgpu/ggml-backend-buffer-type.cpp | 98 ++++ ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp | 119 +++++ ggml/src/ggml-virtgpu/ggml-backend-device.cpp | 144 +++++ ggml/src/ggml-virtgpu/ggml-backend-reg.cpp | 137 +++++ ggml/src/ggml-virtgpu/ggml-backend.cpp | 69 +++ ggml/src/ggml-virtgpu/ggml-remoting.h | 68 +++ .../ggml-virtgpu/ggmlremoting_functions.yaml | 168 ++++++ ggml/src/ggml-virtgpu/include/apir_hw.h | 9 + ggml/src/ggml-virtgpu/regenerate_remoting.py | 322 +++++++++++ ggml/src/ggml-virtgpu/virtgpu-apir.h | 15 + .../ggml-virtgpu/virtgpu-forward-backend.cpp | 50 ++ .../virtgpu-forward-buffer-type.cpp | 125 +++++ .../ggml-virtgpu/virtgpu-forward-buffer.cpp | 157 ++++++ .../ggml-virtgpu/virtgpu-forward-device.cpp | 200 +++++++ ggml/src/ggml-virtgpu/virtgpu-forward-impl.h | 29 + ggml/src/ggml-virtgpu/virtgpu-forward.gen.h | 51 ++ ggml/src/ggml-virtgpu/virtgpu-shm.cpp | 99 ++++ ggml/src/ggml-virtgpu/virtgpu-shm.h | 23 + ggml/src/ggml-virtgpu/virtgpu-utils.cpp | 179 +++++++ ggml/src/ggml-virtgpu/virtgpu-utils.h | 86 +++ ggml/src/ggml-virtgpu/virtgpu.cpp | 498 ++++++++++++++++++ ggml/src/ggml-virtgpu/virtgpu.h | 92 ++++ 47 files changed, 4711 insertions(+) create mode 100644 ggml/include/ggml-virtgpu.h create mode 100644 ggml/src/ggml-virtgpu/CMakeLists.txt create mode 100644 ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/CMakeLists.txt create mode 100644 ggml/src/ggml-virtgpu/backend/apir_cs_ggml-rpc-back.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-convert.h create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h create mode 100644 ggml/src/ggml-virtgpu/backend/backend-dispatched.h create mode 100644 ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h create mode 100644 ggml/src/ggml-virtgpu/backend/backend.cpp create mode 100644 ggml/src/ggml-virtgpu/backend/shared/api_remoting.h create mode 100644 ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h create mode 100644 ggml/src/ggml-virtgpu/backend/shared/apir_backend.h create mode 100644 ggml/src/ggml-virtgpu/backend/shared/apir_cs.h create mode 100644 ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h create mode 100644 ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h create mode 100644 ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp create mode 100644 ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp create mode 100644 ggml/src/ggml-virtgpu/ggml-backend-device.cpp create mode 100644 ggml/src/ggml-virtgpu/ggml-backend-reg.cpp create mode 100644 ggml/src/ggml-virtgpu/ggml-backend.cpp create mode 100644 ggml/src/ggml-virtgpu/ggml-remoting.h create mode 100644 ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml create mode 100644 ggml/src/ggml-virtgpu/include/apir_hw.h create mode 100755 ggml/src/ggml-virtgpu/regenerate_remoting.py create mode 100644 ggml/src/ggml-virtgpu/virtgpu-apir.h create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward-impl.h create mode 100644 ggml/src/ggml-virtgpu/virtgpu-forward.gen.h create mode 100644 ggml/src/ggml-virtgpu/virtgpu-shm.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-shm.h create mode 100644 ggml/src/ggml-virtgpu/virtgpu-utils.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu-utils.h create mode 100644 ggml/src/ggml-virtgpu/virtgpu.cpp create mode 100644 ggml/src/ggml-virtgpu/virtgpu.h diff --git a/CODEOWNERS b/CODEOWNERS index 55f5011dfa..6086abb564 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -67,6 +67,7 @@ /ggml/src/ggml-rpc/ @rgerganov /ggml/src/ggml-threading.* @ggerganov /ggml/src/ggml-vulkan/ @0cc4m +/ggml/src/ggml-virtgpu/ @kpouget /ggml/src/ggml-webgpu/ @reeselevine /ggml/src/ggml-zdnn/ @taronaeo @Andreas-Krebbel @AlekseiNikiforovIBM /ggml/src/ggml.c @ggerganov diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 0176ca1ce9..b0b8e57898 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -228,6 +228,8 @@ option(GGML_WEBGPU_CPU_PROFILE "ggml: enable WebGPU profiling (CPU) option(GGML_WEBGPU_GPU_PROFILE "ggml: enable WebGPU profiling (GPU)" OFF) option(GGML_WEBGPU_JSPI "ggml: use JSPI for WebGPU" ON) option(GGML_ZDNN "ggml: use zDNN" OFF) +option(GGML_VIRTGPU "ggml: use the VirtGPU/Virglrenderer API Remoting frontend" OFF) +option(GGML_VIRTGPU_BACKEND "ggml: build the VirtGPU/Virglrenderer API Remoting backend" OFF) option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT}) option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF) option(GGML_METAL_SHADER_DEBUG "ggml: compile Metal with -fno-fast-math" OFF) @@ -320,6 +322,7 @@ set(GGML_PUBLIC_HEADERS include/ggml-opt.h include/ggml-metal.h include/ggml-rpc.h + include/ggml-virtgpu.h include/ggml-sycl.h include/ggml-vulkan.h include/ggml-webgpu.h diff --git a/ggml/include/ggml-virtgpu.h b/ggml/include/ggml-virtgpu.h new file mode 100644 index 0000000000..1cb4bd7a03 --- /dev/null +++ b/ggml/include/ggml-virtgpu.h @@ -0,0 +1,16 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_REMOTING_FRONTEND_NAME "RemotingFrontend" + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_virtgpu_reg(); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 6192a87046..260ad48f0e 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -451,6 +451,7 @@ ggml_add_backend(HIP) ggml_add_backend(METAL) ggml_add_backend(MUSA) ggml_add_backend(RPC) +ggml_add_backend(VirtGPU) ggml_add_backend(SYCL) ggml_add_backend(Vulkan) ggml_add_backend(WebGPU) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index 6bee1bc4b4..dd991f262e 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -69,6 +69,10 @@ #include "ggml-rpc.h" #endif +#ifdef GGML_USE_VIRTGPU_FRONTEND +#include "ggml-virtgpu.h" +#endif + #ifdef GGML_USE_CANN #include "ggml-cann.h" #endif @@ -180,7 +184,12 @@ struct ggml_backend_registry { register_backend(ggml_backend_sycl_reg()); #endif #ifdef GGML_USE_VULKAN + // Add runtime disable check + if (getenv("GGML_DISABLE_VULKAN") == nullptr) { register_backend(ggml_backend_vk_reg()); + } else { + GGML_LOG_DEBUG("Vulkan backend disabled by GGML_DISABLE_VULKAN environment variable\n"); + } #endif #ifdef GGML_USE_WEBGPU register_backend(ggml_backend_webgpu_reg()); @@ -188,6 +197,10 @@ struct ggml_backend_registry { #ifdef GGML_USE_ZDNN register_backend(ggml_backend_zdnn_reg()); #endif +#ifdef GGML_USE_VIRTGPU_FRONTEND + register_backend(ggml_backend_virtgpu_reg()); +#endif + #ifdef GGML_USE_OPENCL register_backend(ggml_backend_opencl_reg()); #endif @@ -604,6 +617,7 @@ void ggml_backend_load_all_from_path(const char * dir_path) { ggml_backend_load_best("rpc", silent, dir_path); ggml_backend_load_best("sycl", silent, dir_path); ggml_backend_load_best("vulkan", silent, dir_path); + ggml_backend_load_best("virtgpu", silent, dir_path); ggml_backend_load_best("opencl", silent, dir_path); ggml_backend_load_best("hexagon", silent, dir_path); ggml_backend_load_best("musa", silent, dir_path); diff --git a/ggml/src/ggml-virtgpu/CMakeLists.txt b/ggml/src/ggml-virtgpu/CMakeLists.txt new file mode 100644 index 0000000000..e6b020beb5 --- /dev/null +++ b/ggml/src/ggml-virtgpu/CMakeLists.txt @@ -0,0 +1,70 @@ +cmake_minimum_required(VERSION 3.19) +cmake_policy(SET CMP0114 NEW) + +include(ExternalProject) + +message(STATUS "Including the VirtGPU/Virglrenderer API Remoting") + +# Download venus_hw.h from virglrenderer repository +ExternalProject_Add( + venus_hw_header + URL https://gitlab.freedesktop.org/virgl/virglrenderer/-/raw/virglrenderer-1.2.0/src/venus_hw.h + DOWNLOAD_NO_EXTRACT YES + DOWNLOAD_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include + DOWNLOAD_NAME venus_hw.h + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "" + LOG_DOWNLOAD ON +) + +if (NOT GGML_VIRTGPU_BACKEND STREQUAL "ONLY") + message(STATUS "Enable the VirtGPU/Virglrenderer API Remoting frontend library") + + find_package(PkgConfig REQUIRED) + pkg_check_modules(DRM REQUIRED libdrm) + if (NOT GGML_BACKEND_DL) + # cannot simply use USE_VIRTGPU, as in the 'else()' case the + # frontend isn't compiled + target_compile_definitions(ggml PUBLIC "GGML_USE_VIRTGPU_FRONTEND") + endif() + + ggml_add_backend_library(ggml-virtgpu + ggml-backend-buffer.cpp + ggml-backend.cpp + ggml-backend-device.cpp + ggml-backend-reg.cpp + ggml-backend-buffer-type.cpp + virtgpu-apir.h + virtgpu-forward.gen.h + virtgpu.cpp + virtgpu-shm.cpp + virtgpu-utils.cpp + virtgpu-forward-device.cpp + virtgpu-forward-buffer-type.cpp + virtgpu-forward-buffer.cpp + virtgpu-forward-backend.cpp + virtgpu-forward-impl.h + apir_cs_ggml-rpc-front.cpp + ../../include/ggml-virtgpu.h) + + target_include_directories(ggml-virtgpu PUBLIC /usr/include/libdrm/) + + target_link_libraries(ggml-virtgpu PUBLIC ${DRM_LIBRARIES}) + target_include_directories(ggml-virtgpu PUBLIC ${DRM_INCLUDE_DIRS}) + target_compile_options(ggml-virtgpu PUBLIC ${DRM_CFLAGS_OTHER}) + + target_include_directories(ggml-virtgpu PUBLIC ./include) + target_include_directories(ggml-virtgpu PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) + + # Ensure venus_hw.h is downloaded before building ggml-virtgpu + add_dependencies(ggml-virtgpu venus_hw_header) + + target_compile_options(ggml-virtgpu PRIVATE -std=c++20) +else() + message(STATUS "Not building the VirtGPU/Virglrenderer API Remoting frontend library") +endif() + +if (NOT GGML_VIRTGPU_BACKEND STREQUAL "OFF") + add_subdirectory("backend") +endif() diff --git a/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp b/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp new file mode 100644 index 0000000000..f60ae3556c --- /dev/null +++ b/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp @@ -0,0 +1,87 @@ +#include "backend/shared/apir_cs_rpc.h" +#include "ggml-backend-impl.h" +#include "ggml-impl.h" +#include "ggml-remoting.h" + +#include +#include +#include +#include + +apir_rpc_tensor apir_serialize_tensor(const ggml_tensor * tensor) { + apir_rpc_tensor result; + result.id = reinterpret_cast(tensor); + result.type = tensor->type; + if (tensor->buffer) { + ggml_backend_buffer_t buffer = tensor->buffer; + + result.buffer = BUFFER_TO_HOST_HANDLE(buffer); + } else { + result.buffer = 0; + } + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result.ne[i] = tensor->ne[i]; + result.nb[i] = tensor->nb[i]; + } + result.op = tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result.op_params[i] = tensor->op_params[i]; + } + result.flags = tensor->flags; + for (uint32_t i = 0; i < GGML_MAX_SRC; i++) { + result.src[i] = reinterpret_cast(tensor->src[i]); + } + result.view_src = reinterpret_cast(tensor->view_src); + result.view_offs = tensor->view_offs; + result.data = reinterpret_cast(tensor->data); + if (tensor->data) { + if (!tensor->buffer) { + GGML_ABORT("tensor has data but not buffer"); + } + // tensor->data is serialized as an offset to the buffer base address + result.data -= reinterpret_cast(BUFFER_TO_GGML_CONTEXT(tensor->buffer)->base); + } + snprintf(result.name, GGML_MAX_NAME, "%s", tensor->name); + return result; +} + +void apir_add_tensor(ggml_tensor * tensor, + std::vector & tensors, + std::unordered_set & visited) { + if (tensor == nullptr) { + return; + } + if (visited.find(tensor) != visited.end()) { + return; + } + visited.insert(tensor); + for (int i = 0; i < GGML_MAX_SRC; i++) { + apir_add_tensor(tensor->src[i], tensors, visited); + } + apir_add_tensor(tensor->view_src, tensors, visited); + tensors.push_back(apir_serialize_tensor(tensor)); +} + +void apir_serialize_graph(const ggml_cgraph * cgraph, std::vector & output) { + uint32_t n_nodes = cgraph->n_nodes; + std::vector tensors; + std::unordered_set visited; + for (uint32_t i = 0; i < n_nodes; i++) { + apir_add_tensor(cgraph->nodes[i], tensors, visited); + } + // serialization format: + // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(apir_rpc_tensor)) | + uint32_t n_tensors = tensors.size(); + int output_size = + sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(apir_rpc_tensor); + output.resize(output_size, 0); + memcpy(output.data(), &n_nodes, sizeof(n_nodes)); + for (uint32_t i = 0; i < n_nodes; i++) { + memcpy(output.data() + sizeof(n_nodes) + i * sizeof(uint64_t), &cgraph->nodes[i], sizeof(uint64_t)); + } + uint32_t * out_ntensors = (uint32_t *) (output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t)); + *out_ntensors = n_tensors; + apir_rpc_tensor * out_tensors = + (apir_rpc_tensor *) (output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t)); + memcpy(out_tensors, tensors.data(), n_tensors * sizeof(apir_rpc_tensor)); +} diff --git a/ggml/src/ggml-virtgpu/backend/CMakeLists.txt b/ggml/src/ggml-virtgpu/backend/CMakeLists.txt new file mode 100644 index 0000000000..0b49c403b9 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/CMakeLists.txt @@ -0,0 +1,21 @@ +cmake_minimum_required(VERSION 3.19) +cmake_policy(SET CMP0114 NEW) + +message(STATUS "Enable the VirtGPU/Virglrenderer backend library") + +ggml_add_backend_library(ggml-virtgpu-backend + backend.cpp + backend-dispatched.cpp + backend-dispatched-backend.cpp + backend-dispatched-device.cpp + backend-dispatched-buffer.cpp + backend-dispatched-buffer-type.cpp + shared/api_remoting.h + shared/apir_backend.h + shared/apir_cs.h + apir_cs_ggml-rpc-back.cpp) + +target_compile_options(ggml-virtgpu-backend PRIVATE -std=c++20) + +# Add include directory for ggml-backend-impl.h and other core headers +target_include_directories(ggml-virtgpu-backend PRIVATE ../..) diff --git a/ggml/src/ggml-virtgpu/backend/apir_cs_ggml-rpc-back.cpp b/ggml/src/ggml-virtgpu/backend/apir_cs_ggml-rpc-back.cpp new file mode 100644 index 0000000000..60a8a93bfb --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/apir_cs_ggml-rpc-back.cpp @@ -0,0 +1,115 @@ +#include "ggml-backend-impl.h" +#include "ggml-impl.h" +#include "shared/apir_cs_rpc.h" + +#include +#include +#include +#include + +std::unordered_set backend_buffers; + +void apir_track_backend_buffer(ggml_backend_buffer_t buffer) { + backend_buffers.insert(buffer); +} + +bool apir_untrack_backend_buffer(ggml_backend_buffer_t buffer) { + auto it = backend_buffers.find(buffer); + if (it == backend_buffers.end()) { + return false; + } + + backend_buffers.erase(it); + return true; +} + +std::unordered_set apir_get_track_backend_buffers() { + return backend_buffers; +} + +ggml_tensor * apir_deserialize_tensor(ggml_context * ctx, const apir_rpc_tensor * tensor) { + ggml_tensor * result = + ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = tensor->nb[i]; + } + result->buffer = reinterpret_cast(tensor->buffer); + if (result->buffer && backend_buffers.find(result->buffer) == backend_buffers.end()) { + printf("WARNING: HOST BUFFER NOT FOUND | %p\n", (void *) result->buffer); + result->buffer = nullptr; + } + + uint64_t tensor_data = tensor->data; + if (result->buffer) { + // require that the tensor data does not go beyond the buffer end + uint64_t tensor_size = (uint64_t) ggml_nbytes(result); + uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer); + uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer); + + // tensor->data is serialized as an offset to the buffer base address + tensor_data += buffer_start; + + GGML_ASSERT(tensor_data + tensor_size >= tensor_data); // check for overflow + GGML_ASSERT(tensor_data >= buffer_start && tensor_data + tensor_size <= buffer_start + buffer_size); + } + + result->op = (ggml_op) tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result->op_params[i] = tensor->op_params[i]; + } + result->flags = tensor->flags; + result->data = reinterpret_cast(tensor_data); + ggml_set_name(result, tensor->name); + return result; +} + +ggml_tensor * apir_create_node(uint64_t id, + ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map) { + if (id == 0) { + return nullptr; + } + if (tensor_map.find(id) != tensor_map.end()) { + return tensor_map[id]; + } + const apir_rpc_tensor * tensor = tensor_ptrs.at(id); + ggml_tensor * result = apir_deserialize_tensor(ctx, tensor); + if (result == nullptr) { + return nullptr; + } + tensor_map[id] = result; + for (int i = 0; i < GGML_MAX_SRC; i++) { + result->src[i] = apir_create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); + } + result->view_src = apir_create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); + result->view_offs = tensor->view_offs; + return result; +} + +ggml_cgraph * apir_deserialize_graph(uint32_t n_nodes, + uint32_t n_tensors, + const apir_rpc_tensor * tensors, + const uint64_t * nodes) { + size_t buf_size = ggml_tensor_overhead() * (n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); + ggml_init_params params = { + /*.mem_size =*/buf_size, + /*.mem_buffer =*/NULL, + /*.no_alloc =*/true, + }; + ggml_context * ctx = ggml_init(params); + ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false); + graph->n_nodes = n_nodes; + std::unordered_map tensor_ptrs; + for (uint32_t i = 0; i < n_tensors; i++) { + tensor_ptrs[tensors[i].id] = &tensors[i]; + } + std::unordered_map tensor_map; + for (uint32_t i = 0; i < n_nodes; i++) { + int64_t id; + memcpy(&id, &nodes[i], sizeof(id)); + graph->nodes[i] = apir_create_node(id, ctx, tensor_ptrs, tensor_map); + } + + return graph; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-convert.h b/ggml/src/ggml-virtgpu/backend/backend-convert.h new file mode 100644 index 0000000000..1978d21f7e --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-convert.h @@ -0,0 +1,13 @@ +#include "shared/apir_backend.h" + +#define BUFFER_TO_HOST_HANDLE(name) ggml_buffer_to_apir_handle(name) + +static inline apir_buffer_host_handle_t ggml_buffer_to_apir_handle(ggml_backend_buffer_t buffer) { + // in the backend, the buffer handle is the buffer pointer + return (apir_buffer_host_handle_t) buffer; +} + +static inline apir_buffer_type_host_handle_t ggml_buffer_type_to_apir_handle(ggml_backend_buffer_type_t buft) { + // in the backend, the buffer handle is the buffer pointer + return (apir_buffer_type_host_handle_t) buft; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp new file mode 100644 index 0000000000..77b4ee71e1 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp @@ -0,0 +1,65 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "shared/apir_backend.h" + +#include + +uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(enc); + + static bool async_backend_initialized = false; + static bool async_backend; + + if (!async_backend_initialized) { + ggml_backend_dev_props props; + + dev->iface.get_props(dev, &props); + async_backend = props.caps.async; + async_backend_initialized = true; + } + + uint32_t shmem_res_id; + apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); + + const void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); + if (!shmem_data) { + GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n"); + apir_decoder_set_fatal(dec); + return 1; + } + size_t cgraph_size; + apir_decode_size_t(dec, &cgraph_size); + + apir_decoder secondary_dec = apir_new_decoder((const char *) shmem_data, cgraph_size); + + ggml_cgraph * cgraph = apir_decode_ggml_cgraph(&secondary_dec, cgraph_size); + + ggml_status status; +#if APIR_BACKEND_CHECK_SUPPORTS_OP == 1 + for (int idx = 0; idx < cgraph->n_nodes; idx++) { + ggml_tensor * op = ggml_graph_node(cgraph, idx); + if (dev->iface.supports_op(dev, op)) { + continue; + } + GGML_LOG_ERROR("Graph node %d (%s) not supported by the backend\n", idx, ggml_op_desc(op)); + + status = GGML_STATUS_ABORTED; + apir_encode_ggml_status(enc, &status); + + return 0; + } +#endif + status = bck->iface.graph_compute(bck, cgraph); + + if (async_backend) { + bck->iface.synchronize(bck); + } + + apir_encode_ggml_status(enc, &status); + + return 0; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp new file mode 100644 index 0000000000..8ea1bb4fb4 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp @@ -0,0 +1,89 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include + +uint32_t backend_buffer_type_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + const char * string = buft->iface.get_name(buft); + + const size_t string_size = strlen(string) + 1; + apir_encode_array_size(enc, string_size); + apir_encode_char_array(enc, string, string_size); + + return 0; +} + +uint32_t backend_buffer_type_get_alignment(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + size_t value = buft->iface.get_alignment(buft); + apir_encode_size_t(enc, &value); + + return 0; +} + +uint32_t backend_buffer_type_get_max_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + size_t value = buft->iface.get_max_size(buft); + apir_encode_size_t(enc, &value); + + return 0; +} + +uint32_t backend_buffer_type_is_host(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + bool is_host = buft->iface.is_host(buft); + apir_encode_bool_t(enc, &is_host); + + return 0; +} + +uint32_t backend_buffer_type_alloc_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + size_t size; + apir_decode_size_t(dec, &size); + + ggml_backend_buffer_t buffer; + + buffer = buft->iface.alloc_buffer(buft, size); + + apir_encode_ggml_buffer(enc, buffer); + + if (buffer) { + apir_track_backend_buffer(buffer); + } + + return 0; +} + +uint32_t backend_buffer_type_get_alloc_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_type_t buft; + buft = apir_decode_ggml_buffer_type(dec); + + const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec); + + size_t value = buft->iface.get_alloc_size(buft, op); + + apir_encode_size_t(enc, &value); + + return 0; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp new file mode 100644 index 0000000000..cf81888e98 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp @@ -0,0 +1,131 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include + +uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + uintptr_t base = (uintptr_t) buffer->iface.get_base(buffer); + apir_encode_uintptr_t(enc, &base); + + return 0; +} + +uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(enc); + + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + ggml_tensor * tensor; + // safe to remove the const qualifier here + tensor = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); + + uint32_t shmem_res_id; + apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); + + size_t offset; + apir_decode_size_t(dec, &offset); + + size_t size; + apir_decode_size_t(dec, &size); + + void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); + + if (!shmem_data) { + GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n"); + return 1; + } + + buffer->iface.set_tensor(buffer, tensor, shmem_data, offset, size); + + return 0; +} + +uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(enc); + + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + const ggml_tensor * tensor; + // safe to remove the const qualifier here + tensor = apir_decode_ggml_tensor(dec); + + uint32_t shmem_res_id; + apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); + + size_t offset; + apir_decode_size_t(dec, &offset); + + size_t size; + apir_decode_size_t(dec, &size); + + void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); + if (!shmem_data) { + GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n"); + return 1; + } + + buffer->iface.get_tensor(buffer, tensor, shmem_data, offset, size); + + return 0; +} + +uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + const ggml_tensor * src; + // safe to remove the const qualifier here + src = apir_decode_ggml_tensor(dec); + ggml_tensor * dst = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); + + bool ret = buffer->iface.cpy_tensor(buffer, src, (ggml_tensor *) dst); + + apir_encode_bool_t(enc, &ret); + + return 0; +} + +uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(enc); + + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + uint8_t value; + apir_decode_uint8_t(dec, &value); + + buffer->iface.clear(buffer, value); + + return 0; +} + +uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(enc); + + ggml_backend_buffer_t buffer; + buffer = apir_decode_ggml_buffer(dec); + + if (!apir_untrack_backend_buffer(buffer)) { + GGML_LOG_WARN("%s: unknown buffer %p\n", __func__, (void *) buffer); + return 1; + } + + buffer->iface.free_buffer(buffer); + + return 0; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp new file mode 100644 index 0000000000..497f737a88 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp @@ -0,0 +1,148 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include + +uint32_t backend_device_get_device_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + int32_t dev_count = reg->iface.get_device_count(reg); + apir_encode_int32_t(enc, &dev_count); + + return 0; +} + +uint32_t backend_device_get_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + int32_t dev_count = reg->iface.get_device_count(reg); + apir_encode_int32_t(enc, &dev_count); + + return 0; +} + +uint32_t backend_device_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + const char * string = dev->iface.get_name(dev); + + const size_t string_size = strlen(string) + 1; + apir_encode_array_size(enc, string_size); + apir_encode_char_array(enc, string, string_size); + + return 0; +} + +uint32_t backend_device_get_description(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + const char * string = dev->iface.get_description(dev); + + const size_t string_size = strlen(string) + 1; + apir_encode_array_size(enc, string_size); + apir_encode_char_array(enc, string, string_size); + + return 0; +} + +uint32_t backend_device_get_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + uint32_t type = dev->iface.get_type(dev); + apir_encode_uint32_t(enc, &type); + + return 0; +} + +uint32_t backend_device_get_memory(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + size_t free, total; + dev->iface.get_memory(dev, &free, &total); + + apir_encode_size_t(enc, &free); + apir_encode_size_t(enc, &total); + + return 0; +} + +uint32_t backend_device_supports_op(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + + const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec); + + bool supports_op = dev->iface.supports_op(dev, op); + + apir_encode_bool_t(enc, &supports_op); + + return 0; +} + +uint32_t backend_device_get_buffer_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + ggml_backend_buffer_type_t bufft = dev->iface.get_buffer_type(dev); + + apir_encode_ggml_buffer_type(enc, bufft); + + return 0; +} + +uint32_t backend_device_get_props(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + ggml_backend_dev_props props; + dev->iface.get_props(dev, &props); + + apir_encode_bool_t(enc, &props.caps.async); + apir_encode_bool_t(enc, &props.caps.host_buffer); + apir_encode_bool_t(enc, &props.caps.buffer_from_host_ptr); + apir_encode_bool_t(enc, &props.caps.events); + + return 0; +} + +uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { + GGML_UNUSED(ctx); + GGML_UNUSED(dec); + + uint32_t shmem_res_id; + apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); + + void * shmem_ptr = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); + if (!shmem_ptr) { + GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n"); + apir_decoder_set_fatal(dec); + return 1; + } + + size_t size; + apir_decode_size_t(dec, &size); + size_t max_tensor_size; + apir_decode_size_t(dec, &max_tensor_size); + + ggml_backend_buffer_t buffer; + buffer = dev->iface.buffer_from_host_ptr(dev, shmem_ptr, size, max_tensor_size); + + apir_encode_ggml_buffer(enc, buffer); + apir_encode_ggml_buffer_type(enc, buffer->buft); + + if (buffer) { + apir_track_backend_buffer(buffer); + } + + return 0; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp new file mode 100644 index 0000000000..51d445725f --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp @@ -0,0 +1,46 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include + +ggml_backend_reg_t reg = NULL; +ggml_backend_dev_t dev = NULL; +ggml_backend_t bck = NULL; + +uint64_t timer_start = 0; +uint64_t timer_total = 0; +uint64_t timer_count = 0; + +uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) { + if (reg != NULL) { + GGML_LOG_WARN("%s: already initialized\n", __func__); + return APIR_BACKEND_INITIALIZE_ALREADY_INITED; + } + ggml_backend_reg_t (*ggml_backend_reg_fct)(void) = (ggml_backend_reg_t (*)()) ggml_backend_reg_fct_p; + + reg = ggml_backend_reg_fct(); + if (reg == NULL) { + GGML_LOG_ERROR("%s: backend registration failed\n", __func__); + return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED; + } + + if (!reg->iface.get_device_count(reg)) { + GGML_LOG_ERROR("%s: backend initialization failed: no device found\n", __func__); + return APIR_BACKEND_INITIALIZE_NO_DEVICE; + } + + dev = reg->iface.get_device(reg, 0); + + if (!dev) { + GGML_LOG_ERROR("%s: backend initialization failed: no device received\n", __func__); + return APIR_BACKEND_INITIALIZE_NO_DEVICE; + } + + bck = dev->iface.init_backend(dev, NULL); + + return APIR_BACKEND_INITIALIZE_SUCCESS; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h new file mode 100644 index 0000000000..b81fd5039b --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h @@ -0,0 +1,130 @@ +#pragma once + +/* device */ +uint32_t backend_device_get_device_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_description(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_memory(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_supports_op(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_buffer_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_get_props(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); + +/* buffer-type */ +uint32_t backend_buffer_type_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_type_get_alignment(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_type_get_max_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_type_is_host(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_type_alloc_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_type_get_alloc_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); + +/* buffer */ +uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); +uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); + +/* backend */ +uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); + +static inline const char * backend_dispatch_command_name(ApirBackendCommandType type) { + switch (type) { + /* device */ + case APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT: + return "backend_device_get_device_count"; + case APIR_COMMAND_TYPE_DEVICE_GET_COUNT: + return "backend_device_get_count"; + case APIR_COMMAND_TYPE_DEVICE_GET_NAME: + return "backend_device_get_name"; + case APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION: + return "backend_device_get_description"; + case APIR_COMMAND_TYPE_DEVICE_GET_TYPE: + return "backend_device_get_type"; + case APIR_COMMAND_TYPE_DEVICE_GET_MEMORY: + return "backend_device_get_memory"; + case APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP: + return "backend_device_supports_op"; + case APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE: + return "backend_device_get_buffer_type"; + case APIR_COMMAND_TYPE_DEVICE_GET_PROPS: + return "backend_device_get_props"; + case APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR: + return "backend_device_buffer_from_ptr"; + /* buffer-type */ + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME: + return "backend_buffer_type_get_name"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT: + return "backend_buffer_type_get_alignment"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE: + return "backend_buffer_type_get_max_size"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST: + return "backend_buffer_type_is_host"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER: + return "backend_buffer_type_alloc_buffer"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE: + return "backend_buffer_type_get_alloc_size"; + /* buffer */ + case APIR_COMMAND_TYPE_BUFFER_GET_BASE: + return "backend_buffer_get_base"; + case APIR_COMMAND_TYPE_BUFFER_SET_TENSOR: + return "backend_buffer_set_tensor"; + case APIR_COMMAND_TYPE_BUFFER_GET_TENSOR: + return "backend_buffer_get_tensor"; + case APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR: + return "backend_buffer_cpy_tensor"; + case APIR_COMMAND_TYPE_BUFFER_CLEAR: + return "backend_buffer_clear"; + case APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER: + return "backend_buffer_free_buffer"; + /* backend */ + case APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE: + return "backend_backend_graph_compute"; + + default: + return "unknown"; + } +} + +extern "C" { +static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = { + + /* device */ + + /* APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT = */ backend_device_get_device_count, + /* APIR_COMMAND_TYPE_DEVICE_GET_COUNT = */ backend_device_get_count, + /* APIR_COMMAND_TYPE_DEVICE_GET_NAME = */ backend_device_get_name, + /* APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION = */ backend_device_get_description, + /* APIR_COMMAND_TYPE_DEVICE_GET_TYPE = */ backend_device_get_type, + /* APIR_COMMAND_TYPE_DEVICE_GET_MEMORY = */ backend_device_get_memory, + /* APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP = */ backend_device_supports_op, + /* APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE = */ backend_device_get_buffer_type, + /* APIR_COMMAND_TYPE_DEVICE_GET_PROPS = */ backend_device_get_props, + /* APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR = */ backend_device_buffer_from_ptr, + + /* buffer-type */ + + /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME = */ backend_buffer_type_get_name, + /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT = */ backend_buffer_type_get_alignment, + /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE = */ backend_buffer_type_get_max_size, + /* APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST = */ backend_buffer_type_is_host, + /* APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER = */ backend_buffer_type_alloc_buffer, + /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE = */ backend_buffer_type_get_alloc_size, + + /* buffer */ + + /* APIR_COMMAND_TYPE_BUFFER_GET_BASE = */ backend_buffer_get_base, + /* APIR_COMMAND_TYPE_BUFFER_SET_TENSOR = */ backend_buffer_set_tensor, + /* APIR_COMMAND_TYPE_BUFFER_GET_TENSOR = */ backend_buffer_get_tensor, + /* APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR = */ backend_buffer_cpy_tensor, + /* APIR_COMMAND_TYPE_BUFFER_CLEAR = */ backend_buffer_clear, + /* APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER = */ backend_buffer_free_buffer, + + /* backend */ + + /* APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE = */ backend_backend_graph_compute, +}; +} diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h new file mode 100644 index 0000000000..6ccbecf078 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h @@ -0,0 +1,23 @@ +#pragma once + +#include +#include + +#include + +#include "backend-convert.h" +#include "backend-virgl-apir.h" +#include "shared/apir_backend.h" +#include "shared/apir_cs.h" +#include "shared/apir_cs_ggml.h" + +struct virgl_apir_context { + uint32_t ctx_id; + virgl_apir_callbacks * iface; +}; + +typedef uint32_t (*backend_dispatch_t)(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); + +#include "backend-dispatched.gen.h" + +uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p); diff --git a/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h b/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h new file mode 100644 index 0000000000..44b347f853 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h @@ -0,0 +1,32 @@ +#pragma once + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "shared/api_remoting.h" + +#include +#include +#include + +extern ggml_backend_reg_t reg; +extern ggml_backend_dev_t dev; +extern ggml_backend_t bck; + +struct virgl_apir_callbacks { + const char * (*get_config)(uint32_t virgl_ctx_id, const char * key); + void * (*get_shmem_ptr)(uint32_t virgl_ctx_id, uint32_t res_id); +}; + +extern "C" { +ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs); +void apir_backend_deinit(uint32_t virgl_ctx_id); +uint32_t apir_backend_dispatcher(uint32_t virgl_ctx_id, + virgl_apir_callbacks * virgl_cbs, + uint32_t cmd_type, + char * dec_cur, + const char * dec_end, + char * enc_cur, + const char * enc_end, + char ** enc_cur_after); +} diff --git a/ggml/src/ggml-virtgpu/backend/backend.cpp b/ggml/src/ggml-virtgpu/backend/backend.cpp new file mode 100644 index 0000000000..95d602ed60 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/backend.cpp @@ -0,0 +1,148 @@ +#include "backend-dispatched.h" +#include "backend-virgl-apir.h" + +#include "shared/api_remoting.h" +#include "shared/apir_backend.h" +#include "shared/apir_cs.h" + +#include +#include + +#include + +#define APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV "APIR_LLAMA_CPP_GGML_LIBRARY_PATH" +#define APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV "APIR_LLAMA_CPP_GGML_LIBRARY_REG" +#define APIR_LLAMA_CPP_LOG_TO_FILE_ENV "APIR_LLAMA_CPP_LOG_TO_FILE" + +#define GGML_DEFAULT_BACKEND_REG "ggml_backend_init" + +static void * backend_library_handle = NULL; +static FILE * apir_logfile = NULL; + +static void log_to_file_callback(enum ggml_log_level level, const char * text, void * user_data) { + FILE * logfile = (FILE *)user_data; + fprintf(logfile, "[%d] %s", level, text); + fflush(logfile); +} + +extern "C" { +void apir_backend_deinit(uint32_t virgl_ctx_id) { + GGML_UNUSED(virgl_ctx_id); + + auto buffers = apir_get_track_backend_buffers(); + for (const auto & buffer : buffers) { + apir_untrack_backend_buffer(buffer); + buffer->iface.free_buffer(buffer); + } + + if (dev) { + size_t free, total; + dev->iface.get_memory(dev, &free, &total); + GGML_LOG_INFO("%s: free memory: %ld MB\n", __func__, (size_t) free / 1024 / 1024); + } + + if (backend_library_handle) { + GGML_LOG_INFO("%s: The GGML backend library was loaded. Unloading it.\n", __func__); + dlclose(backend_library_handle); + backend_library_handle = NULL; + } + + if (apir_logfile) { + fclose(apir_logfile); + apir_logfile = NULL; + } +} + +#define APIR_GGML_LIBRARY_PATH_KEY "ggml.library.path" +#define APIR_GGML_LIBRARY_REG_KEY "ggml.library.reg" + +ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs) { + const char * dlsym_error; + + const char * apir_log_to_file = getenv(APIR_LLAMA_CPP_LOG_TO_FILE_ENV); + if (apir_log_to_file) { + apir_logfile = fopen(apir_log_to_file, "w"); + if (apir_logfile) { + ggml_log_set(log_to_file_callback, apir_logfile); + } else { + GGML_LOG_INFO("Could not open the log file at '%s'\n", apir_log_to_file); + } + } + + const char * library_name = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_PATH_KEY); + const char * virgl_library_reg = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_REG_KEY); + const char * library_reg = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG; + + if (!library_name) { + GGML_LOG_ERROR("cannot open the GGML library: env var '%s' not defined\n", APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV); + + return APIR_LOAD_LIBRARY_ENV_VAR_MISSING; + } + + backend_library_handle = dlopen(library_name, RTLD_LAZY); + + if (!backend_library_handle) { + GGML_LOG_ERROR("cannot open the GGML library: %s\n", dlerror()); + + return APIR_LOAD_LIBRARY_CANNOT_OPEN; + } + + if (!library_reg) { + GGML_LOG_ERROR("cannot register the GGML library: env var '%s' not defined\n", APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV); + + return APIR_LOAD_LIBRARY_ENV_VAR_MISSING; + } + + void * ggml_backend_reg_fct = dlsym(backend_library_handle, library_reg); + dlsym_error = dlerror(); + if (dlsym_error) { + GGML_LOG_ERROR("cannot find the GGML backend registration symbol '%s' (from %s): %s\n", library_reg, + APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV, dlsym_error); + + return APIR_LOAD_LIBRARY_SYMBOL_MISSING; + } + + uint32_t ret = backend_dispatch_initialize(ggml_backend_reg_fct); + + return (ApirLoadLibraryReturnCode) (APIR_LOAD_LIBRARY_INIT_BASE_INDEX + ret); +} + +uint32_t apir_backend_dispatcher(uint32_t virgl_ctx_id, + virgl_apir_callbacks * virgl_cbs, + uint32_t cmd_type, + char * dec_cur, + const char * dec_end, + char * enc_cur, + const char * enc_end, + char ** enc_cur_after) { + apir_encoder enc = { + .cur = enc_cur, + .start = enc_cur, + .end = enc_end, + .fatal = false, + }; + + apir_decoder dec = { + .cur = dec_cur, + .end = dec_end, + .fatal = false, + }; + + virgl_apir_context ctx = { + .ctx_id = virgl_ctx_id, + .iface = virgl_cbs, + }; + + if (cmd_type >= APIR_BACKEND_DISPATCH_TABLE_COUNT) { + GGML_LOG_ERROR("Received an invalid dispatch index (%d >= %d)\n", cmd_type, APIR_BACKEND_DISPATCH_TABLE_COUNT); + return APIR_BACKEND_FORWARD_INDEX_INVALID; + } + + backend_dispatch_t forward_fct = apir_backend_dispatch_table[cmd_type]; + uint32_t ret = forward_fct(&enc, &dec, &ctx); + + *enc_cur_after = enc.cur; + + return ret; +} +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h b/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h new file mode 100644 index 0000000000..f19a5d12d1 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h @@ -0,0 +1,90 @@ +#pragma once + +/* the rest of this file must match virglrenderer/src/apir-protocol.h */ + +#include + +#include + +#define APIR_PROTOCOL_MAJOR 0 +#define APIR_PROTOCOL_MINOR 1 + +#define APIR_HANDSHAKE_MAGIC 0xab1e + +enum ApirCommandType { + APIR_COMMAND_TYPE_HANDSHAKE = 0, + APIR_COMMAND_TYPE_LOADLIBRARY = 1, + APIR_COMMAND_TYPE_FORWARD = 2, + + APIR_COMMAND_TYPE_LENGTH = 3, +}; + +typedef uint64_t ApirCommandFlags; + +enum ApirLoadLibraryReturnCode { + APIR_LOAD_LIBRARY_SUCCESS = 0, + APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR = 1, + APIR_LOAD_LIBRARY_ALREADY_LOADED = 2, + APIR_LOAD_LIBRARY_ENV_VAR_MISSING = 3, + APIR_LOAD_LIBRARY_CANNOT_OPEN = 4, + APIR_LOAD_LIBRARY_SYMBOL_MISSING = 5, + APIR_LOAD_LIBRARY_INIT_BASE_INDEX = 6, // anything above this is a APIR backend library initialization return code +}; + +enum ApirForwardReturnCode { + APIR_FORWARD_SUCCESS = 0, + APIR_FORWARD_NO_DISPATCH_FCT = 1, + APIR_FORWARD_TIMEOUT = 2, + + APIR_FORWARD_BASE_INDEX = 3, // anything above this is a APIR backend library forward return code +} ; + +__attribute__((unused)) static inline const char * apir_command_name(ApirCommandType type) { + switch (type) { + case APIR_COMMAND_TYPE_HANDSHAKE: + return "HandShake"; + case APIR_COMMAND_TYPE_LOADLIBRARY: + return "LoadLibrary"; + case APIR_COMMAND_TYPE_FORWARD: + return "Forward"; + default: + return "unknown"; + } +} + +__attribute__((unused)) static const char * apir_load_library_error(ApirLoadLibraryReturnCode code) { +#define APIR_LOAD_LIBRARY_ERROR(code_name) \ + do { \ + if (code == code_name) \ + return #code_name; \ + } while (0) + + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_SUCCESS); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_ALREADY_LOADED); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_ENV_VAR_MISSING); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_CANNOT_OPEN); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_SYMBOL_MISSING); + APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_INIT_BASE_INDEX); + + return "Unknown APIR_COMMAND_TYPE_LoadLibrary error"; + +#undef APIR_LOAD_LIBRARY_ERROR +} + +__attribute__((unused)) static const char * apir_forward_error(ApirForwardReturnCode code) { +#define APIR_FORWARD_ERROR(code_name) \ + do { \ + if (code == code_name) \ + return #code_name; \ + } while (0) + + APIR_FORWARD_ERROR(APIR_FORWARD_SUCCESS); + APIR_FORWARD_ERROR(APIR_FORWARD_NO_DISPATCH_FCT); + APIR_FORWARD_ERROR(APIR_FORWARD_TIMEOUT); + APIR_FORWARD_ERROR(APIR_FORWARD_BASE_INDEX); + + return "Unknown APIR_COMMAND_TYPE_FORWARD error"; + +#undef APIR_FORWARD_ERROR +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h new file mode 100644 index 0000000000..d214b6f2a9 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h @@ -0,0 +1,36 @@ +typedef enum ApirBackendCommandType { + + /* device */ + APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT = 0, + APIR_COMMAND_TYPE_DEVICE_GET_COUNT = 1, + APIR_COMMAND_TYPE_DEVICE_GET_NAME = 2, + APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION = 3, + APIR_COMMAND_TYPE_DEVICE_GET_TYPE = 4, + APIR_COMMAND_TYPE_DEVICE_GET_MEMORY = 5, + APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP = 6, + APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE = 7, + APIR_COMMAND_TYPE_DEVICE_GET_PROPS = 8, + APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR = 9, + + /* buffer-type */ + APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME = 10, + APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT = 11, + APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE = 12, + APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST = 13, + APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER = 14, + APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE = 15, + + /* buffer */ + APIR_COMMAND_TYPE_BUFFER_GET_BASE = 16, + APIR_COMMAND_TYPE_BUFFER_SET_TENSOR = 17, + APIR_COMMAND_TYPE_BUFFER_GET_TENSOR = 18, + APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR = 19, + APIR_COMMAND_TYPE_BUFFER_CLEAR = 20, + APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER = 21, + + /* backend */ + APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE = 22, + + // last command_type index + 1 + APIR_BACKEND_DISPATCH_TABLE_COUNT = 23, +} ApirBackendCommandType; diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h new file mode 100644 index 0000000000..f3efa52c72 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h @@ -0,0 +1,46 @@ +#pragma once + +#include "apir_backend.gen.h" + +#include // for uintptr_t +#include // for timespec, clock_gettime + +#define APIR_BACKEND_INITIALIZE_SUCCESS 0 +#define APIR_BACKEND_INITIALIZE_CANNOT_OPEN_BACKEND_LIBRARY 1 +#define APIR_BACKEND_INITIALIZE_CANNOT_OPEN_GGML_LIBRARY 2 +#define APIR_BACKEND_INITIALIZE_MISSING_BACKEND_SYMBOLS 3 +#define APIR_BACKEND_INITIALIZE_MISSING_GGML_SYMBOLS 4 +#define APIR_BACKEND_INITIALIZE_BACKEND_FAILED 5 +#define APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED 6 +#define APIR_BACKEND_INITIALIZE_ALREADY_INITED 7 +#define APIR_BACKEND_INITIALIZE_NO_DEVICE 8 + + +// new entries here need to be added to the apir_backend_initialize_error function below + +#define APIR_BACKEND_FORWARD_INDEX_INVALID 6 + +// 0 is fast, 1 avoids the backend to crash if an unsupported tensor is received +#define APIR_BACKEND_CHECK_SUPPORTS_OP 0 + +typedef uintptr_t apir_buffer_type_host_handle_t; +typedef uintptr_t apir_buffer_host_handle_t; + +static const char * apir_backend_initialize_error(int code) { +#define APIR_BACKEND_INITIALIZE_ERROR(code_name) \ + do { \ + if (code == code_name) \ + return #code_name; \ + } while (0) + + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_SUCCESS); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_CANNOT_OPEN_BACKEND_LIBRARY); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_CANNOT_OPEN_GGML_LIBRARY); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_BACKEND_SYMBOLS); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_GGML_SYMBOLS); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_FAILED); + + return "Unknown APIR_BACKEND_INITIALIZE error:/"; + +#undef APIR_BACKEND_INITIALIZE_ERROR +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h new file mode 100644 index 0000000000..27a61091ff --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h @@ -0,0 +1,383 @@ +#pragma once + +#include "ggml-impl.h" + +#include +#include + +#define likely(x) __builtin_expect(!!(x), 1) +#define unlikely(x) __builtin_expect(!!(x), 0) + +struct apir_encoder { + char * cur; + const char * start; + const char * end; + bool fatal; + +}; + +struct apir_decoder { + const char * cur; + const char * end; + bool fatal; +}; + +/* + * new encoder and decoder + */ + +static apir_decoder apir_new_decoder(const char * ptr, size_t size) { + apir_decoder dec = { + .cur = ptr, + .end = ptr + size, + .fatal = false, + }; + + return dec; +} + +static apir_encoder apir_new_encoder(char * ptr, size_t size) { + apir_encoder enc = { + .cur = ptr, + .start = ptr, + .end = ptr + size, + .fatal = false, + }; + + return enc; +} + +/* + * fatal flag handling + */ + +static inline void apir_encoder_reset_fatal(apir_encoder * enc) { + enc->fatal = false; +} + +static inline void apir_encoder_set_fatal(apir_encoder * enc) { + enc->fatal = true; +} + +static inline bool apir_encoder_get_fatal(const apir_encoder * enc) { + return enc->fatal; +} + +static inline void apir_decoder_reset_fatal(apir_decoder * dec) { + dec->fatal = false; +} + +static inline void apir_decoder_set_fatal(apir_decoder * dec) { + dec->fatal = true; +} + +static inline bool apir_decoder_get_fatal(const apir_decoder * dec) { + return dec->fatal; +} + +/* + * encode peek + */ + +static inline bool apir_decoder_peek_internal(apir_decoder * dec, + size_t size, + void * val, + size_t val_size) { + assert(val_size <= size); + + if (unlikely(size > (size_t) (dec->end - dec->cur))) { + GGML_LOG_ERROR("reading too much from the decoder ...\n"); + apir_decoder_set_fatal(dec); + memset(val, 0, val_size); + return false; + } + + /* we should not rely on the compiler to optimize away memcpy... */ + memcpy(val, dec->cur, val_size); + return true; +} + +static inline void apir_decoder_peek(apir_decoder * dec, size_t size, void * val, size_t val_size) { + apir_decoder_peek_internal(dec, size, val, val_size); +} + +static inline const void * apir_decoder_use_inplace(apir_decoder * dec, size_t size) { + if (unlikely(size > (size_t) (dec->end - dec->cur))) { + GGML_LOG_ERROR("reading too much from the decoder ...\n"); + apir_decoder_set_fatal(dec); + return NULL; + } + const void * addr = dec->cur; + dec->cur += size; + + return addr; +} + +/* + * read/write + */ + +static inline void apir_decoder_read(apir_decoder * dec, size_t size, void * val, size_t val_size) { + if (apir_decoder_peek_internal(dec, size, val, val_size)) { + dec->cur += size; + } +} + +static inline char * apir_encoder_write(apir_encoder * enc, size_t size, const void * val, size_t val_size) { + assert(val_size <= size); + assert(size <= ((size_t) (enc->end - enc->cur))); + + char * write_addr = enc->cur; + /* we should not rely on the compiler to optimize away memcpy... */ + memcpy(write_addr, val, val_size); + enc->cur += size; + + return write_addr; +} + +/* + * encode/decode + */ + +static inline void apir_decode(apir_decoder * dec, size_t size, void * data, size_t data_size) { + assert(size % 4 == 0); + apir_decoder_read(dec, size, data, data_size); +} + +static inline void apir_encode(apir_encoder * enc, size_t size, const void * data, size_t data_size) { + assert(size % 4 == 0); + apir_encoder_write(enc, size, data, data_size); +} + +/* + * typed encode/decode + */ + +/* uint8_t */ + +static inline void apir_encode_uint8_t(apir_encoder * enc, const uint8_t * val) { + apir_encode(enc, sizeof(int), val, sizeof(*val)); +} + +static inline void apir_decode_uint8_t(apir_decoder * dec, uint8_t * val) { + apir_decode(dec, sizeof(int), val, sizeof(*val)); +} + +/* uint64_t */ + +static inline void apir_encode_uint64_t(apir_encoder * enc, const uint64_t * val) { + apir_encode(enc, 8, val, sizeof(*val)); +} + +static inline void apir_decode_uint64_t(apir_decoder * dec, uint64_t * val) { + apir_decode(dec, 8, val, sizeof(*val)); +} + +static inline void apir_encode_uint64_t_array(apir_encoder * enc, const uint64_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_encode(enc, size, val, size); +} + +static inline void apir_decode_uint64_t_array(apir_decoder * dec, uint64_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_decode(dec, size, val, size); +} + +static inline const uint64_t * apir_decode_uint64_t_array_inplace(apir_decoder * dec, uint32_t count) { + return (uint64_t *) (uintptr_t) apir_decoder_use_inplace(dec, count * sizeof(uint64_t)); +} + +/* int32_t */ + +static inline void apir_encode_int32_t(apir_encoder * enc, const int32_t * val) { + apir_encode(enc, 4, val, sizeof(*val)); +} + +static inline void apir_decode_int32_t(apir_decoder * dec, int32_t * val) { + apir_decode(dec, 4, val, sizeof(*val)); +} + +static inline void apir_encode_int32_t_array(apir_encoder * enc, const int32_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_encode(enc, size, val, size); +} + +static inline void apir_decode_int32_t_array(apir_decoder * dec, int32_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_decode(dec, size, val, size); +} + +/* array size (uint64_t) */ + +static inline void apir_encode_array_size(apir_encoder * enc, uint64_t size) { + apir_encode_uint64_t(enc, &size); +} + +static inline uint64_t apir_decode_array_size(apir_decoder * dec, uint64_t expected_size) { + uint64_t size; + apir_decode_uint64_t(dec, &size); + if (size != expected_size) { + GGML_LOG_ERROR("Couldn't decode array from the decoder\n"); + apir_decoder_set_fatal(dec); + size = 0; + } + return size; +} + +static inline uint64_t apir_decode_array_size_unchecked(apir_decoder * dec) { + uint64_t size; + apir_decode_uint64_t(dec, &size); + return size; +} + +/* non-array pointer */ + +static inline bool apir_encode_simple_pointer(apir_encoder * enc, const void * val) { + apir_encode_array_size(enc, val ? 1 : 0); + return val; +} + +static inline bool apir_decode_simple_pointer(apir_decoder * dec) { + return apir_decode_array_size_unchecked(dec); +} + +/* uint32_t */ + +static inline void apir_encode_uint32_t(apir_encoder * enc, const uint32_t * val) { + apir_encode(enc, 4, val, sizeof(*val)); +} + +static inline void apir_decode_uint32_t(apir_decoder * dec, uint32_t * val) { + apir_decode(dec, 4, val, sizeof(*val)); +} + +static inline void apir_encode_uint32_t_array(apir_encoder * enc, const uint32_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_encode(enc, size, val, size); +} + +static inline void apir_decode_uint32_t_array(apir_decoder * dec, uint32_t * val, uint32_t count) { + const size_t size = sizeof(*val) * count; + assert(size >= count); + apir_decode(dec, size, val, size); +} + +/* size_t */ + +static inline void apir_encode_size_t(apir_encoder * enc, const size_t * val) { + const uint64_t tmp = *val; + apir_encode_uint64_t(enc, &tmp); +} + +static inline void apir_decode_size_t(apir_decoder * dec, size_t * val) { + uint64_t tmp; + apir_decode_uint64_t(dec, &tmp); + *val = tmp; +} + +static inline void apir_encode_size_t_array(apir_encoder * enc, const size_t * val, uint32_t count) { + if (sizeof(size_t) == sizeof(uint64_t)) { + apir_encode_uint64_t_array(enc, (const uint64_t *) val, count); + } else { + for (uint32_t i = 0; i < count; i++) { + apir_encode_size_t(enc, &val[i]); + } + } +} + +static inline void apir_decode_size_t_array(apir_decoder * dec, size_t * val, uint32_t count) { + if (sizeof(size_t) == sizeof(uint64_t)) { + apir_decode_uint64_t_array(dec, (uint64_t *) val, count); + } else { + for (uint32_t i = 0; i < count; i++) { + apir_decode_size_t(dec, &val[i]); + } + } +} + +/* opaque blob */ + +static inline void apir_encode_blob_array(apir_encoder * enc, const void * val, size_t size) { + apir_encode(enc, (size + 3) & ~3, val, size); +} + +static inline void apir_decode_blob_array(apir_decoder * dec, void * val, size_t size) { + apir_decode(dec, (size + 3) & ~3, val, size); +} + +/* string */ + +static inline void apir_encode_char_array(apir_encoder * enc, const char * val, size_t size) { + assert(size && strlen(val) < size); + apir_encode_blob_array(enc, val, size); +} + +static inline void apir_decode_char_array(apir_decoder * dec, char * val, size_t size) { + apir_decode_blob_array(dec, val, size); + if (size) { + val[size - 1] = '\0'; + } else { + GGML_LOG_ERROR("Couldn't decode the blog array\n"); + apir_decoder_set_fatal(dec); + } +} + +/* (temp) buffer allocation */ + +static inline void * apir_decoder_alloc_array(size_t size, size_t count) { + size_t alloc_size; + if (unlikely(__builtin_mul_overflow(size, count, &alloc_size))) { + GGML_LOG_ERROR("overflow in array allocation of %zu * %zu bytes\n", size, count); + return NULL; + } + + return malloc(alloc_size); +} + +/* bool */ + +static inline void apir_encode_bool_t(apir_encoder * enc, const bool * val) { + apir_encode(enc, sizeof(int), val, sizeof(bool)); +} + +static inline void apir_decode_bool_t(apir_decoder * dec, bool * val) { + apir_decode(dec, sizeof(int), val, sizeof(bool)); +} + +/* apir_buffer_type_host_handle_t */ + +static inline void apir_encode_apir_buffer_type_host_handle_t(apir_encoder * enc, + const apir_buffer_type_host_handle_t * val) { + apir_encode(enc, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t)); +} + +static inline void apir_decode_apir_buffer_type_host_handle_t(apir_decoder * dec, + apir_buffer_type_host_handle_t * val) { + apir_decode(dec, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t)); +} + +/* apir_buffer_host_handle_t */ + +static inline void apir_encode_apir_buffer_host_handle_t(apir_encoder * enc, + const apir_buffer_host_handle_t * val) { + apir_encode(enc, sizeof(apir_buffer_host_handle_t), val, sizeof(apir_buffer_host_handle_t)); +} + +static inline void apir_decode_apir_buffer_host_handle_t(apir_decoder * dec, apir_buffer_host_handle_t * val) { + apir_decode(dec, sizeof(apir_buffer_host_handle_t), val, sizeof(apir_buffer_host_handle_t)); +} + +/* uintptr_t */ + +static inline void apir_encode_uintptr_t(apir_encoder * enc, const uintptr_t * val) { + apir_encode(enc, sizeof(*val), val, sizeof(*val)); +} + +static inline void apir_decode_uintptr_t(apir_decoder * dec, uintptr_t * val) { + apir_decode(dec, sizeof(*val), val, sizeof(*val)); +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h new file mode 100644 index 0000000000..070c3b25fb --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h @@ -0,0 +1,211 @@ +#include "ggml-impl.h" +#include "apir_cs.h" +#include "apir_cs_rpc.h" + +// ggml_buffer_to_apir_host_handle(ggml_backend_buffer_t buffer); + +static inline void apir_encode_ggml_buffer_host_handle(apir_encoder * enc, + const apir_buffer_host_handle_t * handle); + +static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec); + +/* apir_rpc_tensor */ + +static inline void apir_encode_rcp_tensor(apir_encoder * enc, const apir_rpc_tensor * apir_rpc_tensor) { + size_t apir_rpc_tensor_size = sizeof(*apir_rpc_tensor); + apir_encode(enc, apir_rpc_tensor_size, apir_rpc_tensor, apir_rpc_tensor_size); +} + +static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_inplace(apir_decoder * dec) { + size_t apir_rpc_tensor_size = sizeof(apir_rpc_tensor); + + return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size); +} + +static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_array_inplace(apir_decoder * dec, + uint32_t n_tensors) { + size_t apir_rpc_tensor_size = sizeof(apir_rpc_tensor) * n_tensors; + + return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size); +} + +/* ggml_tensor */ + +static inline void apir_encode_ggml_tensor(apir_encoder * enc, const ggml_tensor * tensor) { + apir_rpc_tensor serialized = apir_serialize_tensor(tensor); + + apir_encode_rcp_tensor(enc, &serialized); +} + +static inline const ggml_tensor * apir_decode_ggml_tensor(apir_decoder * dec) { + const apir_rpc_tensor * apir_rpc_tensor = apir_decode_apir_rpc_tensor_inplace(dec); + ggml_init_params params{ + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + + const ggml_tensor * tensor = apir_deserialize_tensor(ctx, apir_rpc_tensor); + + return tensor; +} + +/* *** ggml_backend_buffer_type_t *** */ + +// ggml_backend_buffer_type_t is a POINTER (to a struct). +// Only the host pointer is shared between the host and guest. +// The guest stores it in `buft->context`. +// The host simply writes the pointer address in the buffer variable. + +static inline void apir_encode_ggml_buffer_type(apir_encoder * enc, ggml_backend_buffer_type_t buft) { + apir_buffer_type_host_handle_t handle = ggml_buffer_type_to_apir_handle(buft); + apir_encoder_write(enc, sizeof(handle), &handle, sizeof(handle)); +} + +static inline ggml_backend_buffer_type_t apir_decode_ggml_buffer_type(apir_decoder * dec) { + apir_buffer_type_host_handle_t handle; + + apir_decoder_read(dec, sizeof(handle), &handle, sizeof(handle)); + + return (ggml_backend_buffer_type_t) handle; +} + +static inline apir_buffer_type_host_handle_t apir_decode_apir_buffer_type_host_handle(apir_decoder * dec) { + apir_buffer_type_host_handle_t handle; + + apir_decoder_read(dec, sizeof(handle), &handle, sizeof(handle)); + + return handle; +} + +/* *** ggml_backend_type_t *** */ + +// ggml_backend_buffer_t is a POINTER. +// same logic as for ggml_backend_buffer_type_t + +static inline void apir_encode_ggml_buffer(apir_encoder * enc, const ggml_backend_buffer_t buffer) { + apir_buffer_host_handle_t handle = BUFFER_TO_HOST_HANDLE(buffer); + apir_encoder_write(enc, sizeof(handle), &handle, sizeof(handle)); +} + +static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec) { + ggml_backend_buffer_t buffer; + size_t buffer_ptr_size = sizeof(buffer); + + apir_decoder_read(dec, buffer_ptr_size, &buffer, buffer_ptr_size); + + return buffer; +} + +/* enum ggml_status */ + +static inline void apir_encode_ggml_status(apir_encoder * enc, const ggml_status * status) { + apir_encoder_write(enc, sizeof(*status), status, sizeof(*status)); +} + +static inline void apir_decode_ggml_status(apir_decoder * dec, ggml_status * status) { + apir_decoder_read(dec, sizeof(*status), status, sizeof(*status)); +} + +/* virtgpu_shmem */ + +static inline void apir_encode_virtgpu_shmem_res_id(apir_encoder * enc, uint32_t shmem_res_id) { + apir_encode_uint32_t(enc, &shmem_res_id); +} + +static inline void apir_decode_virtgpu_shmem_res_id(apir_decoder * dec, uint32_t * shmem_res_id) { + apir_decode_uint32_t(dec, shmem_res_id); +} + +/* ggml_cgraph */ + +static inline size_t apir_serialize_ggml_cgraph(ggml_cgraph * cgraph, std::vector & cgraph_data) { + apir_serialize_graph(cgraph, cgraph_data); + + return cgraph_data.size(); +} + +static inline void apir_encode_cgraph_data(apir_encoder * enc, std::vector & cgraph_data) { + size_t cgraph_size = cgraph_data.size(); + + apir_encode(enc, cgraph_size, cgraph_data.data(), cgraph_size); +} + +static inline ggml_cgraph * apir_decode_ggml_cgraph(apir_decoder * dec, size_t cgraph_size) { + GGML_UNUSED(cgraph_size); + + uint32_t n_nodes; + apir_decode_uint32_t(dec, &n_nodes); + const uint64_t * nodes = apir_decode_uint64_t_array_inplace(dec, n_nodes); + + uint32_t n_tensors; + apir_decode_uint32_t(dec, &n_tensors); + const apir_rpc_tensor * tensors = apir_decode_apir_rpc_tensor_array_inplace(dec, n_tensors); + + return apir_deserialize_graph(n_nodes, n_tensors, tensors, nodes); +} + +static inline void apir_encode_ggml_buffer_handle(apir_encoder * enc, const apir_buffer_host_handle_t * handle) { + apir_encoder_write(enc, sizeof(*handle), &handle, sizeof(*handle)); +} + +static inline void apir_encode_ggml_tensor_inline(apir_encoder * enc, const ggml_tensor * tensor) { + size_t tensor_size = sizeof(*tensor); + + if (tensor->extra) { + GGML_ABORT("Cannot pass tensors with extra"); + } + + if (tensor->src[0] && tensor->buffer) { + static int first = 1; + if (first) { + GGML_LOG_WARN("Cannot pass tensors with src and buffer\n"); + first = 0; + } + } + + apir_encoder_write(enc, tensor_size, tensor, tensor_size); + + // tensor->data is a pointer inside the device buffer. No need to touch it + // tensor->buffer is a pointer to a buffer. Encoding the buffer handle in sequence. + // (could also make a copy of the tensor, and update locally.) + + if (tensor->buffer) { + apir_buffer_host_handle_t buffer_handle = ggml_buffer_to_apir_handle(tensor->buffer); + apir_encode_ggml_buffer_handle(enc, &buffer_handle); + } + + if (tensor->view_src) { + apir_encoder_write(enc, tensor_size, tensor->view_src, tensor_size); + } + + for (int i = 0; tensor->src[i]; i++) { + const ggml_tensor * tensor_src = tensor->src[i]; + apir_encoder_write(enc, tensor_size, tensor_src, tensor_size); + } +} + +static inline const ggml_tensor * apir_decode_ggml_tensor_inplace(apir_decoder * dec) { + // it safe to remove the `const` qualifier here, we *do* want to + // modify the shared memory data to fix the `src` pointers. + ggml_tensor * tensor = (ggml_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, sizeof(ggml_tensor)); + + // tensor->data is a pointer inside the device buffer. No need to touch it + // tensor->buffer is a pointer to a buffer. Decode the buffer handle encoded in sequence. + if (tensor->buffer) { + tensor->buffer = apir_decode_ggml_buffer(dec); + } + + if (tensor->view_src) { + ggml_tensor * tensor_view_src = (ggml_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, sizeof(ggml_tensor)); + tensor->view_src = tensor_view_src; + } + + for (int i = 0; tensor->src[i]; i++) { + ggml_tensor * tensor_src = (ggml_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, sizeof(ggml_tensor)); + tensor->src[i] = tensor_src; // overwrite op->src[i] pointer with the actual location of the src tensor + } + + return tensor; +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h new file mode 100644 index 0000000000..f681798952 --- /dev/null +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h @@ -0,0 +1,54 @@ +#include "ggml.h" +#include "ggml-backend-impl.h" + +#include +#include +#include +#include + +// ggml_tensor is serialized into apir_rpc_tensor +struct apir_rpc_tensor { + uint64_t id; + uint32_t type; + uint64_t buffer; + uint32_t ne[GGML_MAX_DIMS]; + uint32_t nb[GGML_MAX_DIMS]; + uint32_t op; + int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; + int32_t flags; + uint64_t src[GGML_MAX_SRC]; + uint64_t view_src; + uint64_t view_offs; + uint64_t data; + char name[GGML_MAX_NAME]; + + char padding[4]; +}; + +/* frontend */ + +apir_rpc_tensor apir_serialize_tensor(const ggml_tensor * tensor); + +void apir_serialize_graph(const ggml_cgraph * cgraph, std::vector & output); + +/* backend */ + +void apir_track_backend_buffer(ggml_backend_buffer_t buffer); +bool apir_untrack_backend_buffer(ggml_backend_buffer_t buffer); +std::unordered_set apir_get_track_backend_buffers(); + +void apir_add_tensor(ggml_tensor * tensor, + std::vector & tensors, + std::unordered_set & visited); + +ggml_tensor * apir_deserialize_tensor(ggml_context * ctx, const apir_rpc_tensor * tensor); + +ggml_tensor * apir_create_node(uint64_t id, + ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map); + +ggml_cgraph * apir_deserialize_graph(uint32_t n_nodes, + uint32_t n_tensors, + const apir_rpc_tensor * tensors, + const uint64_t * nodes); diff --git a/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp new file mode 100644 index 0000000000..7f650659b8 --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp @@ -0,0 +1,98 @@ +#include "ggml-remoting.h" + +static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) malloc(sizeof(*context)); + if (!context) { + GGML_ABORT("Couldn't allocate the buffer context ..."); + } + + context->gpu = gpu; + + bool async__unused, host_buffer__unused, events__unused; + bool buffer_from_host_ptr; + apir_device_get_props(gpu, &async__unused, &host_buffer__unused, &buffer_from_host_ptr, &events__unused); + + if (buffer_from_host_ptr) { + context->apir_context = apir_device_buffer_from_ptr(gpu, size, size); + context->base = context->apir_context.shmem.mmap_ptr; + context->is_from_ptr = true; + } else { + context->apir_context = apir_buffer_type_alloc_buffer(gpu, buft, size); + context->is_from_ptr = false; + context->base = NULL; + } + + ggml_backend_buffer_t buffer = + ggml_backend_buffer_init(buft, ggml_backend_remoting_buffer_interface, (void *) context, size); + + return buffer; +} + +static const char * ggml_backend_remoting_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + return apir_buffer_type_get_name(gpu, buft); +} + +static size_t ggml_backend_remoting_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + static size_t align = 0; + + if (align == 0) { + align = apir_buffer_type_get_alignment(gpu, buft); + } + + return align; +} + +static size_t ggml_backend_remoting_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + static size_t max_size = 0; + if (max_size == 0) { + max_size = apir_buffer_type_get_max_size(gpu, buft); + } + + return max_size; +} + +static bool ggml_backend_remoting_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + return apir_buffer_type_is_host(gpu, buft); +} + +static size_t ggml_backend_remoting_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, + const ggml_tensor * tensor) { + virtgpu * gpu = BUFT_TO_GPU(buft); + + if (tensor->buffer == NULL + || !tensor->buffer->context + || !buft->device->iface.supports_buft(buft->device, tensor->buffer->buft)) { + return ggml_nbytes(tensor); + } + + return apir_buffer_type_get_alloc_size(gpu, buft, tensor); +} + +const ggml_backend_buffer_type_i ggml_backend_remoting_buffer_type_interface = { + /* .get_name = */ ggml_backend_remoting_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_remoting_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_remoting_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_remoting_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_remoting_buffer_type_get_alloc_size, + /* .is_host = */ NULL, +}; + +const ggml_backend_buffer_type_i ggml_backend_remoting_buffer_from_ptr_type_interface = { + /* .get_name = */ ggml_backend_remoting_buffer_type_get_name, + /* .alloc_buffer = */ NULL, + /* .get_alignment = */ ggml_backend_remoting_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_remoting_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_remoting_buffer_type_get_alloc_size, + /* .is_host = */ NULL, +}; diff --git a/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp b/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp new file mode 100644 index 0000000000..6b95362dd8 --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp @@ -0,0 +1,119 @@ +#include "ggml-remoting.h" + +#define BUFFER_TO_GPU(name) ((ggml_backend_remoting_buffer_context *) (name)->context)->gpu + +static void * ggml_backend_remoting_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) buffer->context; + if (context->base) { + return context->base; + } + + context->base = apir_buffer_get_base(BUFFER_TO_GPU(buffer), BUFFER_TO_APIR_CONTEXT(buffer)); + + return context->base; +} + +static void ggml_backend_remoting_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + virtgpu * gpu = BUFFER_TO_GPU(buffer); + + ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); + if (context->is_from_ptr) { + memcpy((char *) tensor->data + offset, data, size); + } else { + apir_buffer_set_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size); + } + + return; +} + +static void ggml_backend_remoting_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + virtgpu * gpu = BUFFER_TO_GPU(buffer); + ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); + if (context->is_from_ptr) { + memcpy(data, (const char *) tensor->data + offset, size); + } else { + apir_buffer_get_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size); + } +} + +static void ggml_backend_remoting_buffer_set_tensor_from_ptr(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + UNUSED(buffer); + + memcpy((char *) tensor->data + offset, data, size); + + return; +} + +static void ggml_backend_remoting_buffer_get_tensor_from_ptr(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + UNUSED(buffer); + + memcpy(data, (const char *) tensor->data + offset, size); +} + +static bool ggml_backend_remoting_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * src, + ggml_tensor * dst) { + virtgpu * gpu = BUFFER_TO_GPU(buffer); + + bool ret = apir_buffer_cpy_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), src, dst); + + return ret; +} + +static void ggml_backend_remoting_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + virtgpu * gpu = BUFFER_TO_GPU(buffer); + + apir_buffer_clear(gpu, BUFFER_TO_APIR_CONTEXT(buffer), value); + + return; +} + +static void ggml_backend_remoting_buffer_free_buffer(ggml_backend_buffer_t buffer) { + virtgpu * gpu = BUFFER_TO_GPU(buffer); + + apir_buffer_free_buffer(gpu, BUFFER_TO_APIR_CONTEXT(buffer)); + + ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); + free(context); + buffer->context = NULL; +} + +const ggml_backend_buffer_i ggml_backend_remoting_buffer_interface = { + /* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer, + /* .get_base = */ ggml_backend_remoting_buffer_get_base, + /* .init_tensor = */ NULL, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor, + /* .clear = */ ggml_backend_remoting_buffer_clear, + /* .reset = */ NULL, +}; + +const ggml_backend_buffer_i ggml_backend_remoting_buffer_from_ptr_interface = { + /* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer, + /* .get_base = */ ggml_backend_remoting_buffer_get_base, + /* .init_tensor = */ NULL, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor_from_ptr, + /* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor_from_ptr, + /* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor, + /* .clear = */ ggml_backend_remoting_buffer_clear, + /* .reset = */ NULL, +}; diff --git a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp new file mode 100644 index 0000000000..579eb99078 --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp @@ -0,0 +1,144 @@ +#include "ggml-remoting.h" + +static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) { + virtgpu * gpu = DEV_TO_GPU(dev); + + return apir_device_get_name(gpu); +} + +static const char * ggml_backend_remoting_device_get_description(ggml_backend_dev_t dev) { + virtgpu * gpu = DEV_TO_GPU(dev); + + return apir_device_get_description(gpu); +} + +static enum ggml_backend_dev_type ggml_backend_remoting_device_get_type(ggml_backend_dev_t dev) { + virtgpu * gpu = DEV_TO_GPU(dev); + + static enum ggml_backend_dev_type type; + static bool has_type = false; + if (!has_type) { + has_type = true; + type = (enum ggml_backend_dev_type) apir_device_get_type(gpu); + } + + return type; +} + +static void ggml_backend_remoting_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + virtgpu * gpu = DEV_TO_GPU(dev); + + return apir_device_get_memory(gpu, free, total); +} + +static bool ggml_backend_remoting_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { +#if USE_ALWAYS_TRUE_SUPPORTS_OP == 1 + /* ggml-rpc cheats it like this */ + /* with the current implementation of serialize_tensor, the src/view aren't properly passed */ + UNUSED(dev); + UNUSED(op); + + return true; +#else + virtgpu * gpu = DEV_TO_GPU(dev); + + return apir_device_supports_op(gpu, op); +#endif +} + +static bool ggml_backend_remoting_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + bool supported = buft->device == dev; + + return supported; +} + +static bool ggml_backend_remoting_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + UNUSED(dev); + UNUSED(op); + + return false; +} + +static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_remoting_device_get_name(dev); + props->description = ggml_backend_remoting_device_get_description(dev); + props->type = ggml_backend_remoting_device_get_type(dev); + ggml_backend_remoting_device_get_memory(dev, &props->memory_free, &props->memory_total); + + virtgpu * gpu = DEV_TO_GPU(dev); + apir_device_get_props(gpu, &props->caps.async, &props->caps.host_buffer, &props->caps.buffer_from_host_ptr, + &props->caps.events); + + props->caps.buffer_from_host_ptr = false; + props->caps.async = false; + props->caps.events = false; +} + +ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev) { + virtgpu * gpu = DEV_TO_GPU(dev); + + apir_buffer_type_host_handle_t ctx = apir_device_get_buffer_type(gpu); + + static ggml_backend_buffer_type buft{ + /* .iface = */ ggml_backend_remoting_buffer_type_interface, + /* .device = */ dev, + /* .context = */ (void *) ctx, + }; + + return &buft; +} + +static ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_from_ptr_type(ggml_backend_dev_t dev) { + virtgpu * gpu = DEV_TO_GPU(dev); + + apir_buffer_type_host_handle_t ctx = apir_device_get_buffer_type(gpu); + + static ggml_backend_buffer_type buft{ + /* .iface = */ ggml_backend_remoting_buffer_from_ptr_type_interface, + /* .device = */ dev, + /* .context = */ (void *) ctx, + }; + + return &buft; +} + +static ggml_backend_buffer_t ggml_backend_remoting_device_buffer_from_ptr(ggml_backend_dev_t dev, + void * ptr, + size_t size, + size_t max_tensor_size) { + virtgpu * gpu = DEV_TO_GPU(dev); + + ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) malloc(sizeof(*context)); + if (!context) { + GGML_ABORT("Couldn't allocate the buffer context ..."); + } + + context->gpu = gpu; + context->apir_context = apir_device_buffer_from_ptr(gpu, size, max_tensor_size); + context->base = ptr; + context->is_from_ptr = true; + + ggml_backend_buffer_t buffer = + ggml_backend_buffer_init(ggml_backend_remoting_device_get_buffer_from_ptr_type(dev), + ggml_backend_remoting_buffer_from_ptr_interface, (void *) context, size); + + return buffer; +} + +const ggml_backend_device_i ggml_backend_remoting_device_interface = { + /* .get_name = */ ggml_backend_remoting_device_get_name, + /* .get_description = */ ggml_backend_remoting_device_get_description, + /* .get_memory = */ ggml_backend_remoting_device_get_memory, + /* .get_type = */ ggml_backend_remoting_device_get_type, + /* .get_props = */ ggml_backend_remoting_device_get_props, + /* .init_backend = */ ggml_backend_remoting_device_init, + /* .get_buffer_type = */ ggml_backend_remoting_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ ggml_backend_remoting_device_buffer_from_ptr, + /* .supports_op = */ ggml_backend_remoting_device_supports_op, + /* .supports_buft = */ ggml_backend_remoting_device_supports_buft, + /* .offload_op = */ ggml_backend_remoting_device_offload_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; diff --git a/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp new file mode 100644 index 0000000000..c46cf51c02 --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp @@ -0,0 +1,137 @@ +#include "ggml-remoting.h" +#include "ggml-virtgpu.h" + +#include +#include + +static virtgpu * apir_initialize() { + static virtgpu * apir_gpu_instance = NULL; + static bool apir_initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + + if (apir_initialized) { + return apir_gpu_instance; + } + + apir_gpu_instance = create_virtgpu(); + if (!apir_gpu_instance) { + GGML_ABORT("failed to initialize the virtgpu"); + } + + apir_initialized = true; + } + + return apir_gpu_instance; +} + +static int ggml_backend_remoting_get_device_count() { + virtgpu * gpu = apir_initialize(); + if (!gpu) { + GGML_LOG_WARN("apir_initialize failed\n"); + return 0; + } + + return apir_device_get_count(gpu); +} + +static size_t ggml_backend_remoting_reg_get_device_count(ggml_backend_reg_t reg) { + UNUSED(reg); + + return ggml_backend_remoting_get_device_count(); +} + +static std::vector devices; + +ggml_backend_dev_t ggml_backend_remoting_get_device(size_t device) { + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static void ggml_backend_remoting_reg_init_devices(ggml_backend_reg_t reg) { + if (devices.size() > 0) { + GGML_LOG_INFO("%s: already initialized\n", __func__); + return; + } + + virtgpu * gpu = apir_initialize(); + if (!gpu) { + GGML_LOG_ERROR("apir_initialize failed\n"); + return; + } + + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + for (int i = 0; i < ggml_backend_remoting_get_device_count(); i++) { + ggml_backend_remoting_device_context * ctx = new ggml_backend_remoting_device_context; + char desc[256] = "API Remoting device"; + + ctx->device = i; + ctx->name = GGML_REMOTING_FRONTEND_NAME + std::to_string(i); + ctx->description = desc; + ctx->gpu = gpu; + + ggml_backend_dev_t dev = new ggml_backend_device{ + /* .iface = */ ggml_backend_remoting_device_interface, + /* .reg = */ reg, + /* .context = */ ctx, + }; + devices.push_back(dev); + } + initialized = true; + } + } +} + +static ggml_backend_dev_t ggml_backend_remoting_reg_get_device(ggml_backend_reg_t reg, size_t device) { + UNUSED(reg); + + return ggml_backend_remoting_get_device(device); +} + +static const char * ggml_backend_remoting_reg_get_name(ggml_backend_reg_t reg) { + UNUSED(reg); + + return GGML_REMOTING_FRONTEND_NAME; +} + +static const ggml_backend_reg_i ggml_backend_remoting_reg_i = { + /* .get_name = */ ggml_backend_remoting_reg_get_name, + /* .get_device_count = */ ggml_backend_remoting_reg_get_device_count, + /* .get_device = */ ggml_backend_remoting_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_virtgpu_reg() { + virtgpu * gpu = apir_initialize(); + if (!gpu) { + GGML_LOG_ERROR("virtgpu_apir_initialize failed\n"); + return NULL; + } + + static ggml_backend_reg reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_remoting_reg_i, + /* .context = */ gpu, + }; + + static bool initialized = false; + if (initialized) { + return ® + } + initialized = true; + + ggml_backend_remoting_reg_init_devices(®); + + GGML_LOG_INFO("%s: initialized\n", __func__); + + return ® +} + +GGML_BACKEND_DL_IMPL(ggml_backend_virtgpu_reg) diff --git a/ggml/src/ggml-virtgpu/ggml-backend.cpp b/ggml/src/ggml-virtgpu/ggml-backend.cpp new file mode 100644 index 0000000000..5cd6c0c060 --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-backend.cpp @@ -0,0 +1,69 @@ +#include "ggml-remoting.h" +#include "../../include/ggml-virtgpu.h" + +static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) { + UNUSED(backend); + + return "API Remoting backend"; +} + +static void ggml_backend_remoting_free(ggml_backend_t backend) { + delete backend; +} + +static ggml_status ggml_backend_remoting_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + virtgpu * gpu = DEV_TO_GPU(backend->device); + + return apir_backend_graph_compute(gpu, cgraph); +} + +static void ggml_backend_remoting_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) { + virtgpu * gpu = DEV_TO_GPU(backend->device); +#if true + UNUSED(gpu); + UNUSED(cgraph); +#else + // not working yet + + apir_backend_graph_optimize(gpu, cgraph); +#endif +} + +static ggml_backend_i ggml_backend_remoting_interface = { + /* .get_name = */ ggml_backend_remoting_get_name, + /* .free = */ ggml_backend_remoting_free, + /* .set_tensor_async = */ NULL, // ggml_backend_remoting_set_tensor_async, + /* .get_tensor_async = */ NULL, // ggml_backend_remoting_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_remoting_cpy_tensor_async, + /* .synchronize = */ NULL, // ggml_backend_remoting_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_remoting_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .graph_optimize = */ ggml_backend_remoting_graph_optimize, +}; + +static ggml_guid_t ggml_backend_remoting_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x14, 0x03, 0x86, 0x02, + 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + + return &guid; +} + +ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params) { + UNUSED(params); + + ggml_backend_remoting_device_context * ctx = (ggml_backend_remoting_device_context *) dev->context; + + ggml_backend_t remoting_backend = new ggml_backend{ + /* .guid = */ ggml_backend_remoting_guid(), + /* .interface = */ ggml_backend_remoting_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_virtgpu_reg(), ctx->device), + /* .context = */ ctx, + }; + + return remoting_backend; +} diff --git a/ggml/src/ggml-virtgpu/ggml-remoting.h b/ggml/src/ggml-virtgpu/ggml-remoting.h new file mode 100644 index 0000000000..36fc6b2a7b --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggml-remoting.h @@ -0,0 +1,68 @@ +#pragma once + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "virtgpu.h" + +#include +#include + +// USE_ALWAYS_TRUE_SUPPORTS_OP: 1 is fast, 0 avoid micro-benchmark crashes + +#define USE_ALWAYS_TRUE_SUPPORTS_OP 1 +#define USE_METAL_GUEST_SUPPORTS_OP 0 + +#define DEV_TO_GPU(name) ((ggml_backend_remoting_device_context *) (name)->context)->gpu + +#define BUFFER_TO_GGML_CONTEXT(name) ((ggml_backend_remoting_buffer_context *) (name)->context) + +#define BUFFER_TO_APIR_CONTEXT(name) &((ggml_backend_remoting_buffer_context *) (name)->context)->apir_context + +#define BUFFER_TO_HOST_HANDLE(name) ((ggml_backend_remoting_buffer_context *) (name)->context)->apir_context.host_handle + +#define GET_DEVICE_CONTEXT() (ggml_backend_remoting_device_context *) ggml_backend_remoting_get_device(0)->context + +#define BUFT_TO_GPU(name) ((ggml_backend_remoting_device_context *) (name)->device->context)->gpu + +struct ggml_backend_remoting_device_context { + size_t device; + std::string name; + std::string description; + + std::vector> shared_memory; + + virtgpu * gpu; +}; + +struct ggml_backend_remoting_buffer_context { + apir_buffer_context_t apir_context; + + virtgpu * gpu; + + void * base; + + bool is_from_ptr; +}; + +extern const ggml_backend_buffer_type_i ggml_backend_remoting_buffer_type_interface; +extern const ggml_backend_device_i ggml_backend_remoting_device_interface; +extern const ggml_backend_buffer_i ggml_backend_remoting_buffer_interface; +extern const ggml_backend_buffer_type_i ggml_backend_remoting_buffer_from_ptr_type_interface; +extern const ggml_backend_buffer_i ggml_backend_remoting_buffer_from_ptr_interface; + +ggml_backend_dev_t ggml_backend_remoting_get_device(size_t device); +ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params); +ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev); + +static inline apir_buffer_type_host_handle_t ggml_buffer_type_to_apir_handle(ggml_backend_buffer_type_t buft) { + // in the backend, the buffer handle is the buffer pointer + return (apir_buffer_type_host_handle_t) buft->context; +} + +static inline apir_buffer_host_handle_t ggml_buffer_to_apir_handle(ggml_backend_buffer_t buffer) { + if (!buffer->context) { + GGML_ABORT("%s: no context available :/", __func__); + } + return BUFFER_TO_HOST_HANDLE(buffer); +} diff --git a/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml b/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml new file mode 100644 index 0000000000..0b7cccfe9c --- /dev/null +++ b/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml @@ -0,0 +1,168 @@ +# YAML schema for GGML remoting API functions +# This defines the structure for generating the remoting layer code + +# Configuration for the generated files +config: + # Base path for the generated files + base_path: "ggml/src" + + # Header files to update + files: + apir_backend_header: "ggml-virtgpu-apir/backend/shared/apir_backend.gen.h" + backend_dispatched_header: "ggml-virtgpu-apir/backend/backend-dispatched.gen.h" + virtgpu_forward_header: "ggml-virtgpu-apir/virtgpu-forward.gen.h" + +# Simplified function definitions with grouping and metadata combined +functions: + device: + group_description: "device" + functions: + get_device_count: + # No specific metadata - uses default void return and base params + + get_count: + frontend_return: "int" + + get_name: + frontend_return: "const char *" + + get_description: + frontend_return: "const char *" + + get_type: + frontend_return: "uint32_t" + + get_memory: + frontend_return: "void" + frontend_extra_params: + - "size_t *free" + - "size_t *total" + + supports_op: + frontend_return: "bool" + frontend_extra_params: + - "const ggml_tensor *op" + + get_buffer_type: + frontend_return: "apir_buffer_type_host_handle_t" + + get_props: + frontend_return: "void" + frontend_extra_params: + - "bool *async" + - "bool *host_buffer" + - "bool *buffer_from_host_ptr" + - "bool *events" + + buffer_from_ptr: + frontend_return: "apir_buffer_context_t" + frontend_extra_params: + - "size_t size" + - "size_t max_tensor_size" + + buffer_type: + group_description: "buffer-type" + functions: + get_name: + frontend_return: "const char *" + frontend_extra_params: + - "ggml_backend_buffer_type_t buft" + + get_alignment: + frontend_return: "size_t" + frontend_extra_params: + - "ggml_backend_buffer_type_t buft" + + get_max_size: + frontend_return: "size_t" + frontend_extra_params: + - "ggml_backend_buffer_type_t buft" + + is_host: + frontend_return: "bool" + frontend_extra_params: + - "ggml_backend_buffer_type_t buft" + + alloc_buffer: + frontend_return: "apir_buffer_context_t" + frontend_extra_params: + - "ggml_backend_buffer_type_t buffer_buft" + - "size_t size" + + get_alloc_size: + frontend_return: "size_t" + frontend_extra_params: + - "ggml_backend_buffer_type_t buft" + - "const ggml_tensor *op" + + buffer: + group_description: "buffer" + functions: + get_base: + frontend_return: "void *" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + + set_tensor: + frontend_return: "void" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + - "ggml_tensor *tensor" + - "const void *data" + - "size_t offset" + - "size_t size" + + get_tensor: + frontend_return: "void" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + - "const ggml_tensor *tensor" + - "void *data" + - "size_t offset" + - "size_t size" + + cpy_tensor: + frontend_return: "bool" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + - "const ggml_tensor *src" + - "const ggml_tensor *dst" + + clear: + frontend_return: "void" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + - "uint8_t value" + + free_buffer: + frontend_return: "void" + frontend_extra_params: + - "apir_buffer_context_t *buffer_context" + + backend: + group_description: "backend" + functions: + graph_compute: + frontend_return: "ggml_status" + frontend_extra_params: + - "ggml_cgraph *cgraph" + + graph_optimize: + frontend_return: "ggml_cgraph *" + frontend_extra_params: + - "ggml_cgraph *cgraph" + enabled: false + +# Naming patterns used for code generation +naming_patterns: + # How to generate enum names + enum_prefix: "APIR_COMMAND_TYPE_" + + # How to generate backend function names + backend_function_prefix: "backend_" + + # How to generate frontend function names + frontend_function_prefix: "apir_" + + # Standard frontend first parameter + frontend_base_param: "struct virtgpu *gpu" diff --git a/ggml/src/ggml-virtgpu/include/apir_hw.h b/ggml/src/ggml-virtgpu/include/apir_hw.h new file mode 100644 index 0000000000..33af045ca2 --- /dev/null +++ b/ggml/src/ggml-virtgpu/include/apir_hw.h @@ -0,0 +1,9 @@ +#pragma once + +#include + +struct virgl_renderer_capset_apir { + uint32_t apir_version; + uint32_t supports_blob_resources; + uint32_t reserved[4]; // For future expansion +}; diff --git a/ggml/src/ggml-virtgpu/regenerate_remoting.py b/ggml/src/ggml-virtgpu/regenerate_remoting.py new file mode 100755 index 0000000000..4174a24327 --- /dev/null +++ b/ggml/src/ggml-virtgpu/regenerate_remoting.py @@ -0,0 +1,322 @@ +#!/usr/bin/env python3 +""" +# Generated by Claude AI + +Script to completely regenerate the GGML remoting codebase from YAML configuration. + +This script reads api_functions.yaml and regenerates all the header files and +implementation templates for the GGML remoting layer. + +Usage: + python regenerate_remoting.py + +The script will: +1. Read ggmlremoting_functions.yaml configuration +2. Generate updated header files +3. Generate implementation templates in dedicated files +4. Show a summary of what was generated +""" + +import yaml +from typing import Dict, List, Any +from pathlib import Path +import os +import subprocess +import shutil +import logging + +NL = '\n' # can't have f"{'\n'}" in f-strings + + +class RemotingCodebaseGenerator: + def __init__(self, yaml_path: str = "ggmlremoting_functions.yaml"): + """Initialize the generator with the YAML configuration.""" + self.yaml_path = yaml_path + + if not Path(yaml_path).exists(): + raise FileNotFoundError(f"Configuration file {yaml_path} not found") + + with open(yaml_path, 'r') as f: + self.config = yaml.safe_load(f) + + self.functions = self.config['functions'] + self.naming_patterns = self.config['naming_patterns'] + self.config_data = self.config['config'] + + # Check if clang-format is available + self.clang_format_available = self._check_clang_format_available() + + def _check_clang_format_available(self) -> bool: + """Check if clang-format is available in the system PATH.""" + return shutil.which("clang-format") is not None + + def _format_file_with_clang_format(self, file_path: Path) -> bool: + """Format a file with clang-format -i. Returns True if successful, False otherwise.""" + if not self.clang_format_available: + return False + + try: + subprocess.run( + ["clang-format", "-i", str(file_path)], + check=True, + capture_output=True, + text=True + ) + return True + except subprocess.CalledProcessError: + logging.exception(f" ⚠️ clang-format failed for {file_path}") + return False + except Exception as e: + logging.exception(f" ⚠️ Unexpected error formatting {file_path}: {e}") + return False + + def generate_enum_name(self, group_name: str, function_name: str) -> str: + """Generate the APIR_COMMAND_TYPE enum name for a function.""" + prefix = self.naming_patterns['enum_prefix'] + return f"{prefix}{group_name.upper()}_{function_name.upper()}" + + def generate_backend_function_name(self, group_name: str, function_name: str) -> str: + """Generate the backend function name.""" + function_key = f"{group_name}_{function_name}" + overrides = self.naming_patterns.get('backend_function_overrides', {}) + + if function_key in overrides: + return overrides[function_key] + + prefix = self.naming_patterns['backend_function_prefix'] + return f"{prefix}{group_name}_{function_name}" + + def generate_frontend_function_name(self, group_name: str, function_name: str) -> str: + """Generate the frontend function name.""" + prefix = self.naming_patterns['frontend_function_prefix'] + return f"{prefix}{group_name}_{function_name}" + + def get_enabled_functions(self) -> List[Dict[str, Any]]: + """Get all enabled functions with their metadata.""" + functions = [] + enum_value = 0 + + for group_name, group_data in self.functions.items(): + group_description = group_data['group_description'] + + for function_name, func_metadata in group_data['functions'].items(): + # Handle case where func_metadata is None or empty (functions with only comments) + if func_metadata is None: + func_metadata = {} + + # Functions are enabled by default unless explicitly disabled + if func_metadata.get('enabled', True): + functions.append({ + 'group_name': group_name, + 'function_name': function_name, + 'enum_name': self.generate_enum_name(group_name, function_name), + 'enum_value': enum_value, + 'backend_function': self.generate_backend_function_name(group_name, function_name), + 'frontend_function': self.generate_frontend_function_name(group_name, function_name), + 'frontend_return': func_metadata.get('frontend_return', 'void'), + 'frontend_extra_params': func_metadata.get('frontend_extra_params', []), + 'group_description': group_description, + 'newly_added': func_metadata.get('newly_added', False) + }) + enum_value += 1 + + return functions + + def generate_apir_backend_header(self) -> str: + """Generate the complete apir_backend.h file.""" + functions = self.get_enabled_functions() + + # Generate the enum section + enum_lines = ["typedef enum ApirBackendCommandType {"] + current_group = None + + for func in functions: + # Add comment for new group + if func['group_name'] != current_group: + enum_lines.append("") + enum_lines.append(f" /* {func['group_description']} */") + current_group = func['group_name'] + + enum_lines.append(f" {func['enum_name']} = {func['enum_value']},") + + # Add the count + total_count = len(functions) + enum_lines.append("\n // last command_type index + 1") + enum_lines.append(f" APIR_BACKEND_DISPATCH_TABLE_COUNT = {total_count},") + enum_lines.append("} ApirBackendCommandType;") + + # Full header template + header_content = NL.join(enum_lines) + "\n" + + return header_content + + def generate_backend_dispatched_header(self) -> str: + """Generate the complete backend-dispatched.h file.""" + functions = self.get_enabled_functions() + + # Function declarations + decl_lines = [] + current_group = None + + for func in functions: + if func['group_name'] != current_group: + decl_lines.append(f"\n/* {func['group_description']} */") + current_group = func['group_name'] + + signature = "uint32_t" + params = "apir_encoder *enc, apir_decoder *dec, virgl_apir_context *ctx" + decl_lines.append(f"{signature} {func['backend_function']}({params});") + + # Switch cases + switch_lines = [] + current_group = None + + for func in functions: + if func['group_name'] != current_group: + switch_lines.append(f" /* {func['group_description']} */") + current_group = func['group_name'] + + switch_lines.append(f" case {func['enum_name']}: return \"{func['backend_function']}\";") + + # Dispatch table + table_lines = [] + current_group = None + + for func in functions: + if func['group_name'] != current_group: + table_lines.append(f"\n /* {func['group_description']} */") + table_lines.append("") + current_group = func['group_name'] + + table_lines.append(f" /* {func['enum_name']} = */ {func['backend_function']},") + + header_content = f'''\ +#pragma once + +{NL.join(decl_lines)} + +static inline const char *backend_dispatch_command_name(ApirBackendCommandType type) +{{ + switch (type) {{ +{NL.join(switch_lines)} + + default: return "unknown"; + }} +}} + +extern "C" {{ +static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = {{ + {NL.join(table_lines)} +}}; +}} +''' + return header_content + + def generate_virtgpu_forward_header(self) -> str: + """Generate the complete virtgpu-forward.gen.h file.""" + functions = self.get_enabled_functions() + + decl_lines = [] + current_group = None + + for func in functions: + if func['group_name'] != current_group: + decl_lines.append("") + decl_lines.append(f"/* {func['group_description']} */") + current_group = func['group_name'] + + # Build parameter list + params = [self.naming_patterns['frontend_base_param']] + params.extend(func['frontend_extra_params']) + param_str = ', '.join(params) + + decl_lines.append(f"{func['frontend_return']} {func['frontend_function']}({param_str});") + + header_content = f'''\ +#pragma once +{NL.join(decl_lines)} +''' + return header_content + + def regenerate_codebase(self) -> None: + """Regenerate the entire remoting codebase.""" + logging.info("🔄 Regenerating GGML Remoting Codebase...") + logging.info("=" * 50) + + # Detect if we're running from frontend directory + current_dir = os.getcwd() + is_frontend_dir = current_dir.endswith('ggml-virtgpu') + + if is_frontend_dir: + # Running from ggml/src/ggml-virtgpu-apir + logging.info("📍 Detected frontend directory execution") + frontend_base = Path(".") + else: + # Running from project root (fallback to original behavior) + logging.info("📍 Detected project root execution") + base_path = self.config_data.get('base_path', 'ggml/src') + frontend_base = Path(base_path) / "ggml-virtgpu" + + # Compute final file paths + backend_base = frontend_base / "backend" + apir_backend_path = backend_base / "shared" / "apir_backend.gen.h" + backend_dispatched_path = backend_base / "backend-dispatched.gen.h" + virtgpu_forward_path = frontend_base / "virtgpu-forward.gen.h" + + # Create output directories for each file + apir_backend_path.parent.mkdir(parents=True, exist_ok=True) + backend_dispatched_path.parent.mkdir(parents=True, exist_ok=True) + virtgpu_forward_path.parent.mkdir(parents=True, exist_ok=True) + + # Generate header files + logging.info("📁 Generating header files...") + + apir_backend_content = self.generate_apir_backend_header() + apir_backend_path.write_text(apir_backend_content) + logging.info(f" ✅ {apir_backend_path.resolve()}") + + backend_dispatched_content = self.generate_backend_dispatched_header() + backend_dispatched_path.write_text(backend_dispatched_content) + logging.info(f" ✅ {backend_dispatched_path.resolve()}") + + virtgpu_forward_content = self.generate_virtgpu_forward_header() + virtgpu_forward_path.write_text(virtgpu_forward_content) + logging.info(f" ✅ {virtgpu_forward_path.resolve()}") + + # Format generated files with clang-format + generated_files = [apir_backend_path, backend_dispatched_path, virtgpu_forward_path] + + if not self.clang_format_available: + logging.warning("\n⚠️clang-format not found in PATH. Generated files will not be formatted." + " Install clang-format to enable automatic code formatting.") + else: + logging.info("\n🎨 Formatting files with clang-format...") + for file_path in generated_files: + if self._format_file_with_clang_format(file_path): + logging.info(f" ✅ Formatted {file_path.name}") + else: + logging.warning(f" ❌ Failed to format {file_path.name}") + + # Generate summary + functions = self.get_enabled_functions() + total_functions = len(functions) + + logging.info("\n📊 Generation Summary:") + logging.info("=" * 50) + logging.info(f" Total functions: {total_functions}") + logging.info(f" Function groups: {len(self.functions)}") + logging.info(" Header files: 3") + logging.info(f" Working directory: {current_dir}") + + +def main(): + try: + generator = RemotingCodebaseGenerator() + generator.regenerate_codebase() + except Exception as e: + logging.exception(f"❌ Error: {e}") + exit(1) + + +if __name__ == "__main__": + main() diff --git a/ggml/src/ggml-virtgpu/virtgpu-apir.h b/ggml/src/ggml-virtgpu/virtgpu-apir.h new file mode 100644 index 0000000000..238f960acd --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-apir.h @@ -0,0 +1,15 @@ +#include "backend/shared/apir_backend.h" +#include "ggml-alloc.h" +#include "ggml-impl.h" +#include "ggml.h" +#include "virtgpu-shm.h" +#include "virtgpu-utils.h" + +struct apir_buffer_context_t { + apir_buffer_host_handle_t host_handle; + + struct virtgpu_shmem shmem; + apir_buffer_type_host_handle_t buft_host_handle; +}; + +#include "virtgpu-forward.gen.h" diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp new file mode 100644 index 0000000000..bf3c41011a --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp @@ -0,0 +1,50 @@ +#include "virtgpu-forward-impl.h" + +static long long current_time_ms() { + timespec ts; + clock_gettime(CLOCK_REALTIME, &ts); // Use CLOCK_MONOTONIC for elapsed time + return (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; +} + +ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE); + + std::vector cgraph_data; + size_t cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + + if (cgraph_size <= gpu->data_shmem.mmap_size) { + // prefer the init-time allocated page, if large enough + shmem = &gpu->data_shmem; + } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) { + GGML_ABORT("Couldn't allocate the guest-host shared buffer"); + } + + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + + apir_encode_size_t(encoder, &cgraph_size); + + char * shmem_data = (char *) shmem->mmap_ptr; + apir_encoder secondary_enc = apir_new_encoder(shmem_data, cgraph_size); + + apir_encode_cgraph_data(&secondary_enc, cgraph_data); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + ggml_status status = GGML_STATUS_ABORTED; + apir_decode_ggml_status(decoder, &status); + + remote_call_finish(gpu, encoder, decoder); + + if (shmem != &gpu->data_shmem) { + virtgpu_shmem_destroy(gpu, shmem); + } + + return status; +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp new file mode 100644 index 0000000000..03cb09e064 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp @@ -0,0 +1,125 @@ +#include "virtgpu-forward-impl.h" + +const char * apir_buffer_type_get_name(virtgpu * gpu, ggml_backend_buffer_type_t buft) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME); + + apir_encode_ggml_buffer_type(encoder, buft); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR("%s: Could not allocate the device name buffer\n", __func__); + apir_decoder_set_fatal(decoder); + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +size_t apir_buffer_type_get_alignment(virtgpu * gpu, ggml_backend_buffer_type_t buft) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT); + + apir_encode_ggml_buffer_type(encoder, buft); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t alignment; + apir_decode_size_t(decoder, &alignment); + + remote_call_finish(gpu, encoder, decoder); + + return alignment; +} + +size_t apir_buffer_type_get_max_size(virtgpu * gpu, ggml_backend_buffer_type_t buft) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE); + + apir_encode_ggml_buffer_type(encoder, buft); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t max_size; + apir_decode_size_t(decoder, &max_size); + + remote_call_finish(gpu, encoder, decoder); + + return max_size; +} + +bool apir_buffer_type_is_host(virtgpu * gpu, ggml_backend_buffer_type_t buft) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST); + + apir_encode_ggml_buffer_type(encoder, buft); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + bool is_host; + apir_decode_bool_t(decoder, &is_host); + + remote_call_finish(gpu, encoder, decoder); + + return is_host; +} + +apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, ggml_backend_buffer_type_t buft, size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + apir_buffer_context_t buffer_context; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER); + + apir_encode_ggml_buffer_type(encoder, buft); + + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle); + + remote_call_finish(gpu, encoder, decoder); + + return buffer_context; +} + +size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, ggml_backend_buffer_type_t buft, const ggml_tensor * op) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE); + + apir_encode_ggml_buffer_type(encoder, buft); + + apir_encode_ggml_tensor_inline(encoder, op); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t alloc_size; + apir_decode_size_t(decoder, &alloc_size); + + remote_call_finish(gpu, encoder, decoder); + + return alloc_size; +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp new file mode 100644 index 0000000000..3181e39440 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp @@ -0,0 +1,157 @@ +#include "virtgpu-forward-impl.h" + +void * apir_buffer_get_base(virtgpu * gpu, apir_buffer_context_t * buffer_context) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_GET_BASE); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + uintptr_t base; + apir_decode_uintptr_t(decoder, &base); + + remote_call_finish(gpu, encoder, decoder); + + return (void *) base; +} + +void apir_buffer_set_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_SET_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, tensor); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + + if (size <= gpu->data_shmem.mmap_size) { + // prefer the init-time allocated page, if large enough + shmem = &gpu->data_shmem; + + } else if (virtgpu_shmem_create(gpu, size, shmem)) { + GGML_ABORT("Couldn't allocate the guest-host shared buffer"); + } + + memcpy(shmem->mmap_ptr, data, size); + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + + apir_encode_size_t(encoder, &offset); + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); + + if (shmem != &gpu->data_shmem) { + virtgpu_shmem_destroy(gpu, shmem); + } + + return; +} + +void apir_buffer_get_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_GET_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, tensor); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + + if (size <= gpu->data_shmem.mmap_size) { + // prefer the init-time allocated page, if large enough + shmem = &gpu->data_shmem; + + } else if (virtgpu_shmem_create(gpu, size, shmem)) { + GGML_ABORT("Couldn't allocate the guest-host shared buffer"); + } + + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + apir_encode_size_t(encoder, &offset); + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + memcpy(data, shmem->mmap_ptr, size); + + remote_call_finish(gpu, encoder, decoder); + + if (shmem != &gpu->data_shmem) { + virtgpu_shmem_destroy(gpu, shmem); + } +} + +bool apir_buffer_cpy_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * src, + const ggml_tensor * dst) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, src); + apir_encode_ggml_tensor(encoder, dst); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + bool ret_val; + apir_decode_bool_t(decoder, &ret_val); + + remote_call_finish(gpu, encoder, decoder); + + return ret_val; +} + +void apir_buffer_clear(virtgpu * gpu, apir_buffer_context_t * buffer_context, uint8_t value) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_CLEAR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_uint8_t(encoder, &value); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); +} + +void apir_buffer_free_buffer(virtgpu * gpu, apir_buffer_context_t * buffer_context) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp new file mode 100644 index 0000000000..3e45e55bdc --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp @@ -0,0 +1,200 @@ +#include "virtgpu-forward-impl.h" +#include "virtgpu-shm.h" + +int apir_device_get_count(virtgpu * gpu) { + static int32_t dev_count = -1; + if (dev_count != -1) { + return dev_count; + } + + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_COUNT); + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_int32_t(decoder, &dev_count); + + remote_call_finish(gpu, encoder, decoder); + + return dev_count; +} + +const char * apir_device_get_name(virtgpu * gpu) { + static char * string = nullptr; + if (string) { + return string; + } + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_NAME); + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR("%s: Could not allocate the device name buffer\n", __func__); + return NULL; + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +const char * apir_device_get_description(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR("%s: Could not allocate the device description buffer\n", __func__); + + return NULL; + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +uint32_t apir_device_get_type(virtgpu * gpu) { + static uint32_t dev_type = 255; + if (dev_type != 255) { + return dev_type; + } + + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_TYPE); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_uint32_t(decoder, &dev_type); + + remote_call_finish(gpu, encoder, decoder); + + return dev_type; +} + +void apir_device_get_memory(virtgpu * gpu, size_t * free, size_t * total) { + static size_t dev_free = 0; + static size_t dev_total = 0; + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_MEMORY); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_size_t(decoder, &dev_free); + apir_decode_size_t(decoder, &dev_total); + + *free = dev_free; + *total = dev_total; + + remote_call_finish(gpu, encoder, decoder); + + return; +} + +bool apir_device_supports_op(virtgpu * gpu, const ggml_tensor * op) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP); + + apir_encode_ggml_tensor_inline(encoder, op); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + bool supports_op; + apir_decode_bool_t(decoder, &supports_op); + + remote_call_finish(gpu, encoder, decoder); + + return supports_op; +} + +apir_buffer_type_host_handle_t apir_device_get_buffer_type(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_buffer_type_host_handle_t buft_handle; + apir_decode_apir_buffer_type_host_handle_t(decoder, &buft_handle); + + remote_call_finish(gpu, encoder, decoder); + + return buft_handle; +} + +void apir_device_get_props(virtgpu * gpu, + bool * async, + bool * host_buffer, + bool * buffer_from_host_ptr, + bool * events) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_PROPS); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_bool_t(decoder, async); + apir_decode_bool_t(decoder, host_buffer); + apir_decode_bool_t(decoder, buffer_from_host_ptr); + apir_decode_bool_t(decoder, events); + + remote_call_finish(gpu, encoder, decoder); + + return; +} + +apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, size_t max_tensor_size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + apir_buffer_context_t buffer_context; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR); + + if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) { + GGML_ABORT("Couldn't allocate the guest-host shared buffer"); + } + + apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id); + + apir_encode_size_t(encoder, &size); + apir_encode_size_t(encoder, &max_tensor_size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle); + buffer_context.buft_host_handle = apir_decode_apir_buffer_type_host_handle(decoder); + + remote_call_finish(gpu, encoder, decoder); + + return buffer_context; +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h new file mode 100644 index 0000000000..eea3e7e5a9 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h @@ -0,0 +1,29 @@ +#include "virtgpu.h" + +#include "ggml-remoting.h" +#include "backend/shared/apir_backend.h" +#include "backend/shared/apir_cs_ggml.h" + +#include "ggml-backend-impl.h" + +#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__) \ + do { \ + int32_t forward_flag = (int32_t) apir_command_type__; \ + encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, forward_flag); \ + if (!encoder_name) { \ + GGML_ABORT("%s: failed to prepare the remote call encoder", __func__); \ + } \ + } while (0) + +#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name) \ + do { \ + ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \ + if (!decoder_name) { \ + GGML_ABORT("%s: failed to kick the remote call", __func__); \ + } \ + if (ret_name < APIR_FORWARD_BASE_INDEX) { \ + GGML_ABORT("%s: failed to forward the API call: %s: code %d", __func__, \ + apir_forward_error(ret_name), ret_name); \ + } \ + ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX); \ + } while (0) diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h new file mode 100644 index 0000000000..c27c07f086 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h @@ -0,0 +1,51 @@ +#pragma once + +/* device */ +void apir_device_get_device_count(struct virtgpu * gpu); +int apir_device_get_count(struct virtgpu * gpu); +const char * apir_device_get_name(struct virtgpu * gpu); +const char * apir_device_get_description(struct virtgpu * gpu); +uint32_t apir_device_get_type(struct virtgpu * gpu); +void apir_device_get_memory(struct virtgpu * gpu, size_t * free, size_t * total); +bool apir_device_supports_op(struct virtgpu * gpu, const ggml_tensor * op); +apir_buffer_type_host_handle_t apir_device_get_buffer_type(struct virtgpu * gpu); +void apir_device_get_props(struct virtgpu * gpu, + bool * async, + bool * host_buffer, + bool * buffer_from_host_ptr, + bool * events); +apir_buffer_context_t apir_device_buffer_from_ptr(struct virtgpu * gpu, size_t size, size_t max_tensor_size); + +/* buffer-type */ +const char * apir_buffer_type_get_name(struct virtgpu * gpu, ggml_backend_buffer_type_t buft); +size_t apir_buffer_type_get_alignment(struct virtgpu * gpu, ggml_backend_buffer_type_t buft); +size_t apir_buffer_type_get_max_size(struct virtgpu * gpu, ggml_backend_buffer_type_t buft); +bool apir_buffer_type_is_host(struct virtgpu * gpu, ggml_backend_buffer_type_t buft); +apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu * gpu, + ggml_backend_buffer_type_t buffer_buft, + size_t size); +size_t apir_buffer_type_get_alloc_size(struct virtgpu * gpu, ggml_backend_buffer_type_t buft, const ggml_tensor * op); + +/* buffer */ +void * apir_buffer_get_base(struct virtgpu * gpu, apir_buffer_context_t * buffer_context); +void apir_buffer_set_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size); +void apir_buffer_get_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size); +bool apir_buffer_cpy_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * src, + const ggml_tensor * dst); +void apir_buffer_clear(struct virtgpu * gpu, apir_buffer_context_t * buffer_context, uint8_t value); +void apir_buffer_free_buffer(struct virtgpu * gpu, apir_buffer_context_t * buffer_context); + +/* backend */ +ggml_status apir_backend_graph_compute(struct virtgpu * gpu, ggml_cgraph * cgraph); diff --git a/ggml/src/ggml-virtgpu/virtgpu-shm.cpp b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp new file mode 100644 index 0000000000..4def405a62 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp @@ -0,0 +1,99 @@ +#include "virtgpu-shm.h" + +#include "virtgpu.h" + +#include + +static uint32_t virtgpu_ioctl_resource_create_blob(virtgpu * gpu, + uint32_t blob_mem, + uint32_t blob_flags, + size_t blob_size, + uint64_t blob_id, + uint32_t * res_id) { +#ifdef SIMULATE_BO_SIZE_FIX + blob_size = align64(blob_size, 4096); +#endif + + drm_virtgpu_resource_create_blob args = { + .blob_mem = blob_mem, + .blob_flags = blob_flags, + .bo_handle = 0, + .res_handle = 0, + .size = blob_size, + .pad = 0, + .cmd_size = 0, + .cmd = 0, + .blob_id = blob_id, + }; + + if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_RESOURCE_CREATE_BLOB, &args)) { + return 0; + } + + *res_id = args.res_handle; + return args.bo_handle; +} + +static void virtgpu_ioctl_gem_close(virtgpu * gpu, uint32_t gem_handle) { + drm_gem_close args = { + .handle = gem_handle, + .pad = 0, + }; + + const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_GEM_CLOSE, &args); + assert(!ret); +#ifdef NDEBUG + UNUSED(ret); +#endif +} + +static void * virtgpu_ioctl_map(virtgpu * gpu, uint32_t gem_handle, size_t size) { + drm_virtgpu_map args = { + .offset = 0, + .handle = gem_handle, + .pad = 0, + }; + + if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_MAP, &args)) { + return NULL; + } + + void * ptr = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_SHARED, gpu->fd, args.offset); + if (ptr == MAP_FAILED) { + return NULL; + } + + return ptr; +} + +void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem) { + munmap(shmem->mmap_ptr, shmem->mmap_size); + virtgpu_ioctl_gem_close(gpu, shmem->gem_handle); +} + +int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem) { + size = align64(size, 16384); + + uint32_t res_id; + uint32_t gem_handle = virtgpu_ioctl_resource_create_blob(gpu, VIRTGPU_BLOB_MEM_HOST3D, + VIRTGPU_BLOB_FLAG_USE_MAPPABLE, size, 0, &res_id); + + if (!gem_handle) { + return 1; + } + + void * ptr = virtgpu_ioctl_map(gpu, gem_handle, size); + if (!ptr) { + virtgpu_ioctl_gem_close(gpu, gem_handle); + GGML_LOG_ERROR("virtgpu_ioctl_map FAILED\n"); + exit(1); + return 1; + } + + shmem->res_id = res_id; + shmem->mmap_size = size; + shmem->mmap_ptr = ptr; + shmem->gem_handle = gem_handle; + + return 0; +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-shm.h b/ggml/src/ggml-virtgpu/virtgpu-shm.h new file mode 100644 index 0000000000..606860a094 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-shm.h @@ -0,0 +1,23 @@ +#pragma once + +#include "virtgpu-utils.h" + +#include + +#include +#include +#include +#include + +struct virtgpu; + +struct virtgpu_shmem { + uint32_t res_id; + size_t mmap_size; + void * mmap_ptr; + + uint32_t gem_handle; +}; + +int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem); +void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem); diff --git a/ggml/src/ggml-virtgpu/virtgpu-utils.cpp b/ggml/src/ggml-virtgpu/virtgpu-utils.cpp new file mode 100644 index 0000000000..8a2805e990 --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-utils.cpp @@ -0,0 +1,179 @@ +#include "virtgpu-utils.h" + +#include +#include + +#include + +#define NODE_ALLOC_ALIGN 64 +#define NODE_PTR_MASK (~((uintptr_t) NODE_ALLOC_ALIGN - 1)) +#define NODE_LEVEL_MASK ((uintptr_t) NODE_ALLOC_ALIGN - 1) +#define NULL_NODE 0 + +#define os_malloc_aligned(_size, _align) _aligned_malloc(_size, _align) +#define os_free_aligned(_ptr) free(_ptr) +#define p_atomic_cmpxchg(v, old, _new) __sync_val_compare_and_swap((v), (old), (_new)) + +static inline uint64_t util_logbase2_64(uint64_t n) { +#if defined(HAVE___BUILTIN_CLZLL) + return ((sizeof(uint64_t) * 8 - 1) - __builtin_clzll(n | 1)); +#else + uint64_t pos = 0ull; + if (n >= 1ull << 32) { + n >>= 32; + pos += 32; + } + if (n >= 1ull << 16) { + n >>= 16; + pos += 16; + } + if (n >= 1ull << 8) { + n >>= 8; + pos += 8; + } + if (n >= 1ull << 4) { + n >>= 4; + pos += 4; + } + if (n >= 1ull << 2) { + n >>= 2; + pos += 2; + } + if (n >= 1ull << 1) { + pos += 1; + } + return pos; +#endif +} + +void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size) { + memset(arr, 0, sizeof(*arr)); + arr->elem_size = elem_size; + arr->node_size_log2 = util_logbase2_64(node_size); + assert(node_size >= 2 && node_size == (1ull << arr->node_size_log2)); +} + +static inline void * os_malloc_aligned(size_t size, size_t alignment) { + void * ptr; + alignment = (alignment + sizeof(void *) - 1) & ~(sizeof(void *) - 1); + if (posix_memalign(&ptr, alignment, size) != 0) { + return NULL; + } + return ptr; +} + +static inline void * _util_sparse_array_node_data(uintptr_t handle) { + return (void *) (handle & NODE_PTR_MASK); +} + +static inline unsigned _util_sparse_array_node_level(uintptr_t handle) { + return handle & NODE_LEVEL_MASK; +} + +static inline void _util_sparse_array_node_finish(util_sparse_array * arr, uintptr_t node) { + if (_util_sparse_array_node_level(node) > 0) { + uintptr_t * children = (uintptr_t *) _util_sparse_array_node_data(node); + size_t node_size = 1ull << arr->node_size_log2; + for (size_t i = 0; i < node_size; i++) { + if (children[i]) { + _util_sparse_array_node_finish(arr, children[i]); + } + } + } + + os_free_aligned(_util_sparse_array_node_data(node)); +} + +static inline uintptr_t _util_sparse_array_node(void * data, unsigned level) { + assert(data != NULL); + assert(((uintptr_t) data & NODE_LEVEL_MASK) == 0); + assert((level & NODE_PTR_MASK) == 0); + return (uintptr_t) data | level; +} + +inline uintptr_t _util_sparse_array_node_alloc(util_sparse_array * arr, unsigned level) { + size_t size; + if (level == 0) { + size = arr->elem_size << arr->node_size_log2; + } else { + size = sizeof(uintptr_t) << arr->node_size_log2; + } + + void * data = os_malloc_aligned(size, NODE_ALLOC_ALIGN); + memset(data, 0, size); + + return _util_sparse_array_node(data, level); +} + +static inline uintptr_t _util_sparse_array_set_or_free_node(uintptr_t * node_ptr, uintptr_t cmp_node, uintptr_t node) { + uintptr_t prev_node = p_atomic_cmpxchg(node_ptr, cmp_node, node); + + if (prev_node != cmp_node) { + /* We lost the race. Free this one and return the one that was already + * allocated. + */ + os_free_aligned(_util_sparse_array_node_data(node)); + return prev_node; + } else { + return node; + } +} + +void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx) { + const unsigned node_size_log2 = arr->node_size_log2; + uintptr_t root = p_atomic_read(&arr->root); + if (unlikely(!root)) { + unsigned root_level = 0; + uint64_t idx_iter = idx >> node_size_log2; + while (idx_iter) { + idx_iter >>= node_size_log2; + root_level++; + } + uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level); + root = _util_sparse_array_set_or_free_node(&arr->root, NULL_NODE, new_root); + } + + while (1) { + unsigned root_level = _util_sparse_array_node_level(root); + uint64_t root_idx = idx >> (root_level * node_size_log2); + if (likely(root_idx < (1ull << node_size_log2))) { + break; + } + + /* In this case, we have a root but its level is low enough that the + * requested index is out-of-bounds. + */ + uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level + 1); + + uintptr_t * new_root_children = (uintptr_t *) _util_sparse_array_node_data(new_root); + new_root_children[0] = root; + + /* We only add one at a time instead of the whole tree because it's + * easier to ensure correctness of both the tree building and the + * clean-up path. Because we're only adding one node we never have to + * worry about trying to free multiple things without freeing the old + * things. + */ + root = _util_sparse_array_set_or_free_node(&arr->root, root, new_root); + } + + void * node_data = _util_sparse_array_node_data(root); + unsigned node_level = _util_sparse_array_node_level(root); + while (node_level > 0) { + uint64_t child_idx = (idx >> (node_level * node_size_log2)) & ((1ull << node_size_log2) - 1); + + uintptr_t * children = (uintptr_t *) node_data; + uintptr_t child = p_atomic_read(&children[child_idx]); + + if (unlikely(!child)) { + child = _util_sparse_array_node_alloc(arr, node_level - 1); + child = _util_sparse_array_set_or_free_node(&children[child_idx], NULL_NODE, child); + } + + node_data = _util_sparse_array_node_data(child); + node_level = _util_sparse_array_node_level(child); + } + + uint64_t elem_idx = idx & ((1ull << node_size_log2) - 1); + return (void *) ((char *) node_data + (elem_idx * arr->elem_size)); +} diff --git a/ggml/src/ggml-virtgpu/virtgpu-utils.h b/ggml/src/ggml-virtgpu/virtgpu-utils.h new file mode 100644 index 0000000000..a0036b4e2b --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu-utils.h @@ -0,0 +1,86 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define unlikely(x) __builtin_expect(!!(x), 0) +#define likely(x) __builtin_expect(!!(x), 1) + +#ifndef UNUSED +# define UNUSED(x) (void) (x) +#endif + +/** Checks is a value is a power of two. Does not handle zero. */ +#define IS_POT(v) (((v) & ((v) - 1)) == 0) + +/** Checks is a value is a power of two. Zero handled. */ +#define IS_POT_NONZERO(v) ((v) != 0 && IS_POT(v)) + +/** Align a value to a power of two */ +#define ALIGN_POT(x, pot_align) (((x) + (pot_align) - 1) & ~((pot_align) - 1)) + +#define p_atomic_read(_v) __atomic_load_n((_v), __ATOMIC_ACQUIRE) + +static inline bool util_is_power_of_two_nonzero64(uint64_t v) { + return IS_POT_NONZERO(v); +} + +static inline uint64_t align64(uint64_t value, uint64_t alignment) { + assert(util_is_power_of_two_nonzero64(alignment)); + return ALIGN_POT(value, alignment); +} + +struct list_head { + list_head * prev; + list_head * next; +}; + +struct util_sparse_array { + size_t elem_size; + unsigned node_size_log2; + + uintptr_t root; +}; + +void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx); +void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size); + +inline void os_time_sleep(int64_t usecs) { + timespec time; + time.tv_sec = usecs / 1000000; + time.tv_nsec = (usecs % 1000000) * 1000; + while (clock_nanosleep(CLOCK_MONOTONIC, 0, &time, &time) == EINTR) + ; +} + +struct timer_data { + long long start; + long long total; + long long count; +}; + +static inline void start_timer(timer_data * timer) { + timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + timer->start = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; +} + +// returns the duration in ns +static inline long long stop_timer(timer_data * timer) { + timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + long long timer_end = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; + + long long duration = (timer_end - timer->start); + timer->total += duration; + timer->count += 1; + + return duration; +} diff --git a/ggml/src/ggml-virtgpu/virtgpu.cpp b/ggml/src/ggml-virtgpu/virtgpu.cpp new file mode 100644 index 0000000000..005c8e21db --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu.cpp @@ -0,0 +1,498 @@ +#include "virtgpu.h" + +#include +#include + +#include +#include +#include + +static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr dev); +static virt_gpu_result_t virtgpu_open(virtgpu * gpu); + +static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu); +static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu); + +static int virtgpu_ioctl_context_init(virtgpu * gpu, virgl_renderer_capset capset_id); +static int virtgpu_ioctl_get_caps(virtgpu * gpu, + virgl_renderer_capset id, + uint32_t version, + void * capset, + size_t capset_size); +static uint64_t virtgpu_ioctl_getparam(virtgpu * gpu, uint64_t param); +static void virtgpu_init_renderer_info(virtgpu * gpu); + +static void log_call_duration(long long call_duration_ns, const char * name); + +const uint64_t APIR_HANDSHAKE_MAX_WAIT_MS = 2 * 1000; // 2s +const uint64_t APIR_LOADLIBRARY_MAX_WAIT_MS = 60 * 1000; // 60s + +static int virtgpu_handshake(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + + encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_HANDSHAKE, 0); + if (!encoder) { + GGML_ABORT("%s: failed to prepare the remote call encoder", __func__); + return 1; + } + + /* write handshake props */ + + uint32_t guest_major = APIR_PROTOCOL_MAJOR; + uint32_t guest_minor = APIR_PROTOCOL_MINOR; + apir_encode_uint32_t(encoder, &guest_major); + apir_encode_uint32_t(encoder, &guest_minor); + + /* *** */ + + uint32_t ret_magic; + long long call_duration_ns; + ret_magic = remote_call(gpu, encoder, &decoder, APIR_HANDSHAKE_MAX_WAIT_MS, &call_duration_ns); + log_call_duration(call_duration_ns, "API Remoting handshake"); + + if (!decoder) { + GGML_ABORT( + "%s: failed to initiate the communication with the virglrenderer library. " + "Most likely, the wrong virglrenderer library was loaded in the hypervisor.", + __func__); + return 1; + } + + /* read handshake return values */ + + uint32_t host_major; + uint32_t host_minor; + + if (ret_magic != APIR_HANDSHAKE_MAGIC) { + GGML_ABORT("%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic, + apir_backend_initialize_error(ret_magic)); + } else { + apir_decode_uint32_t(decoder, &host_major); + apir_decode_uint32_t(decoder, &host_minor); + } + + remote_call_finish(gpu, encoder, decoder); + + if (ret_magic != APIR_HANDSHAKE_MAGIC) { + return 1; + } + + GGML_LOG_INFO("%s: Guest is running with %u.%u\n", __func__, guest_major, guest_minor); + GGML_LOG_INFO("%s: Host is running with %u.%u\n", __func__, host_major, host_minor); + + if (guest_major != host_major) { + GGML_LOG_ERROR("Host major (%d) and guest major (%d) version differ\n", host_major, guest_major); + } else if (guest_minor != host_minor) { + GGML_LOG_WARN("Host minor (%d) and guest minor (%d) version differ\n", host_minor, guest_minor); + } + + return 0; +} + +static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirLoadLibraryReturnCode ret; + + encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_LOADLIBRARY, 0); + if (!encoder) { + GGML_ABORT("%s: hypercall error: failed to prepare the remote call encoder", __func__); + return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR; + } + + long long call_duration_ns; + + ret = (ApirLoadLibraryReturnCode) remote_call(gpu, encoder, &decoder, APIR_LOADLIBRARY_MAX_WAIT_MS, + &call_duration_ns); + log_call_duration(call_duration_ns, "API Remoting LoadLibrary"); + + if (!decoder) { + GGML_ABORT("%s: hypercall error: failed to kick the API remoting hypercall.\n", __func__); + return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR; + } + + remote_call_finish(gpu, encoder, decoder); + + if (ret == APIR_LOAD_LIBRARY_SUCCESS) { + GGML_LOG_INFO("%s: The API Remoting backend was successfully loaded and initialized\n", __func__); + + return ret; + } + + // something wrong happened, find out what. + + if (ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) { + GGML_ABORT("%s: virglrenderer could not load the API Remoting backend library: %s (code %d)", __func__, + apir_load_library_error(ret), ret); + return ret; + } + + GGML_LOG_INFO("%s: virglrenderer successfully loaded the API Remoting backend library", __func__); + + ApirLoadLibraryReturnCode apir_ret = (ApirLoadLibraryReturnCode) (ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX); + + if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) { + GGML_ABORT("%s: the API Remoting backend library couldn't load the backend library: apir code=%d | %s)", + __func__, apir_ret, apir_load_library_error(apir_ret)); + } else { + uint32_t lib_ret = apir_ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX; + GGML_ABORT("%s: the API Remoting backend library initialize its backend library: apir code=%d)", __func__, + lib_ret); + } + return ret; +} + +virtgpu * create_virtgpu() { + virtgpu * gpu = new virtgpu(); + + gpu->use_apir_capset = getenv("GGML_REMOTING_USE_APIR_CAPSET") != nullptr; + util_sparse_array_init(&gpu->shmem_array, sizeof(virtgpu_shmem), 1024); + + if (virtgpu_open(gpu) != APIR_SUCCESS) { + GGML_ABORT("%s: failed to open the virtgpu device", __func__); + return NULL; + } + + if (virtgpu_init_capset(gpu) != APIR_SUCCESS) { + GGML_ABORT("%s: failed to initialize the GPU capset", __func__); + return NULL; + } + + if (virtgpu_init_context(gpu) != APIR_SUCCESS) { + GGML_ABORT("%s: failed to initialize the GPU context", __func__); + return NULL; + } + + if (virtgpu_shmem_create(gpu, SHMEM_REPLY_SIZE, &gpu->reply_shmem)) { + GGML_ABORT("%s: failed to create the shared reply memory pages", __func__); + return NULL; + } + + if (virtgpu_shmem_create(gpu, SHMEM_DATA_SIZE, &gpu->data_shmem)) { + GGML_ABORT("%s: failed to create the shared data memory pages", __func__); + return NULL; + } + + if (virtgpu_handshake(gpu)) { + GGML_ABORT("%s: failed to handshake with the virglrenderer library", __func__); + return NULL; + } + + if (virtgpu_load_library(gpu) != APIR_LOAD_LIBRARY_SUCCESS) { + GGML_ABORT("%s: failed to load the backend library", __func__); + return NULL; + } + + return gpu; +} + +static virt_gpu_result_t virtgpu_open(virtgpu * gpu) { + drmDevicePtr devs[8]; + int count = drmGetDevices2(0, devs, ARRAY_SIZE(devs)); + if (count < 0) { + GGML_LOG_ERROR("%s: failed to enumerate DRM devices\n", __func__); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + virt_gpu_result_t result = APIR_ERROR_INITIALIZATION_FAILED; + for (int i = 0; i < count; i++) { + result = virtgpu_open_device(gpu, devs[i]); + if (result == APIR_SUCCESS) { + break; + } + } + + drmFreeDevices(devs, count); + + return result; +} + +static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr dev) { + const char * node_path = dev->nodes[DRM_NODE_RENDER]; + + int fd = open(node_path, O_RDWR | O_CLOEXEC); + if (fd < 0) { + GGML_ABORT("failed to open %s", node_path); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + drmVersionPtr version = drmGetVersion(fd); + if (!version || strcmp(version->name, "virtio_gpu") || version->version_major != 0) { + if (version) { + GGML_ABORT("unknown DRM driver %s version %d", version->name, version->version_major); + } else { + GGML_ABORT("failed to get DRM driver version"); + } + + if (version) { + drmFreeVersion(version); + } + close(fd); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + gpu->fd = fd; + + drmFreeVersion(version); + + GGML_LOG_INFO("using DRM device %s\n", node_path); + + return APIR_SUCCESS; +} + +static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu) { + assert(!gpu->capset.version); + const int ret = virtgpu_ioctl_context_init(gpu, gpu->capset.id); + if (ret) { + GGML_LOG_INFO("failed to initialize context: %s\n", strerror(errno)); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + return APIR_SUCCESS; +} + +static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) { + if (gpu->use_apir_capset) { + GGML_LOG_INFO("Using the APIR capset\n"); + gpu->capset.id = VIRTGPU_DRM_CAPSET_APIR; + } else { + GGML_LOG_INFO("Using the Venus capset\n"); + gpu->capset.id = VIRTGPU_DRM_CAPSET_VENUS; + } + gpu->capset.version = 0; + + int ret = + virtgpu_ioctl_get_caps(gpu, gpu->capset.id, gpu->capset.version, &gpu->capset.data, sizeof(gpu->capset.data)); + + if (ret) { + GGML_LOG_INFO("failed to get APIR v%d capset: %s\n", gpu->capset.version, strerror(errno)); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + assert(gpu->capset.data.supports_blob_resources); + + return APIR_SUCCESS; +} + +static int virtgpu_ioctl_context_init(virtgpu * gpu, virgl_renderer_capset capset_id) { + drm_virtgpu_context_set_param ctx_set_params[3] = { + { + .param = VIRTGPU_CONTEXT_PARAM_CAPSET_ID, + .value = capset_id, + }, + { + .param = VIRTGPU_CONTEXT_PARAM_NUM_RINGS, + .value = 1, + }, + { + .param = VIRTGPU_CONTEXT_PARAM_POLL_RINGS_MASK, + .value = 0, /* don't generate drm_events on fence signaling */ + }, + }; + + drm_virtgpu_context_init args = { + .num_params = ARRAY_SIZE(ctx_set_params), + .pad = 0, + .ctx_set_params = (uintptr_t) &ctx_set_params, + }; + + return virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_CONTEXT_INIT, &args); +} + +static int virtgpu_ioctl_get_caps(virtgpu * gpu, + virgl_renderer_capset id, + uint32_t version, + void * capset, + size_t capset_size) { + drm_virtgpu_get_caps args = { + .cap_set_id = id, + .cap_set_ver = version, + .addr = (uintptr_t) capset, + .size = (__u32) capset_size, + .pad = 0, + }; + + return virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_GET_CAPS, &args); +} + +static uint64_t virtgpu_ioctl_getparam(virtgpu * gpu, uint64_t param) { + /* val must be zeroed because kernel only writes the lower 32 bits */ + uint64_t val = 0; + drm_virtgpu_getparam args = { + .param = param, + .value = (uintptr_t) &val, + }; + + const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_GETPARAM, &args); + return ret ? 0 : val; +} + +apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags) { + /* + * Prepare the command encoder and its buffer + */ + + static char encoder_buffer[4096]; + + static apir_encoder enc; + enc = { + .cur = encoder_buffer, + .start = encoder_buffer, + .end = encoder_buffer + sizeof(encoder_buffer), + .fatal = false, + }; + + /* + * Fill the command encoder with the common args: + * - cmd_type (int32_t) + * - cmd_flags (int32_t) + * - reply res id (uint32_t) + */ + + int32_t cmd_type = apir_cmd_type; + + // for testing during the hypervisor transition + if (!gpu->use_apir_capset) { + cmd_type += VENUS_COMMAND_TYPE_LENGTH; + } + apir_encode_int32_t(&enc, &cmd_type); + apir_encode_int32_t(&enc, &cmd_flags); + + uint32_t reply_res_id = gpu->reply_shmem.res_id; + apir_encode_uint32_t(&enc, &reply_res_id); + + return &enc; +} + +void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec) { + UNUSED(gpu); + + if (!enc) { + GGML_LOG_ERROR("Invalid (null) encoder\n"); + } + + if (!dec) { + GGML_LOG_ERROR("Invalid (null) decoder\n"); + } + + if (apir_encoder_get_fatal(enc)) { + GGML_LOG_ERROR("Failed to encode the output parameters.\n"); + } + + if (apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR("Failed to decode the input parameters.\n"); + } +} + +uint32_t remote_call(virtgpu * gpu, + apir_encoder * encoder, + apir_decoder ** decoder, + float max_wait_ms, + long long * call_duration_ns) { + /* + * Prepare the reply notification pointer + */ + + volatile std::atomic_uint * atomic_reply_notif = (volatile std::atomic_uint *) gpu->reply_shmem.mmap_ptr; + *atomic_reply_notif = 0; + + /* + * Trigger the execbuf ioctl + */ + + drm_virtgpu_execbuffer args = { + .flags = VIRTGPU_EXECBUF_RING_IDX, + .size = (uint32_t) (encoder->cur - encoder->start), + .command = (uintptr_t) encoder->start, + + .bo_handles = 0, + .num_bo_handles = 0, + + .fence_fd = 0, + .ring_idx = 0, + .syncobj_stride = 0, + .num_in_syncobjs = 0, + .num_out_syncobjs = 0, + .in_syncobjs = 0, + .out_syncobjs = 0, + }; + + *decoder = NULL; + + int ret = drmIoctl(gpu->fd, DRM_IOCTL_VIRTGPU_EXECBUFFER, &args); + + if (ret != 0) { + GGML_ABORT("%s: the virtgpu EXECBUFFER ioctl failed (%d)", __func__, ret); + } + + /* + * Wait for the response notification + */ + timer_data wait_host_reply_timer = { 0, 0, 0 }; + + start_timer(&wait_host_reply_timer); + + timespec ts_start, ts_end; + clock_gettime(CLOCK_MONOTONIC, &ts_start); + long long start_time = (long long) ts_start.tv_sec * 1000000000LL + ts_start.tv_nsec; + + bool timedout = false; + uint32_t notif_value = 0; + while (true) { + notif_value = std::atomic_load_explicit(atomic_reply_notif, std::memory_order_acquire); + + if (notif_value != 0) { + break; + } + + int64_t base_sleep_us = 15; + + os_time_sleep(base_sleep_us); + + if (max_wait_ms) { + clock_gettime(CLOCK_MONOTONIC, &ts_end); + long long end_time = (long long) ts_end.tv_sec * 1000000000LL + ts_end.tv_nsec; + float duration_ms = (end_time - start_time) / 1000000; + + if (duration_ms > max_wait_ms) { + timedout = true; + break; + } + } + } + + if (call_duration_ns) { + *call_duration_ns = stop_timer(&wait_host_reply_timer); + } + + if (max_wait_ms && timedout) { + GGML_LOG_ERROR("timed out waiting for the host answer...\n"); + return APIR_FORWARD_TIMEOUT; + } + + /* + * Prepare the decoder + */ + static apir_decoder response_dec; + response_dec.cur = (char *) gpu->reply_shmem.mmap_ptr + sizeof(*atomic_reply_notif); + response_dec.end = (char *) gpu->reply_shmem.mmap_ptr + gpu->reply_shmem.mmap_size; + *decoder = &response_dec; + + // extract the actual return value from the notif flag + uint32_t returned_value = notif_value - 1; + return returned_value; +} + +static void log_call_duration(long long call_duration_ns, const char * name) { + double call_duration_ms = (double) call_duration_ns / 1e6; // 1 millisecond = 1e6 nanoseconds + double call_duration_s = (double) call_duration_ns / 1e9; // 1 second = 1e9 nanoseconds + + if (call_duration_s > 1) { + GGML_LOG_INFO("%s: waited %.2fs for the %s host reply...\n", __func__, call_duration_s, name); + } else if (call_duration_ms > 1) { + GGML_LOG_INFO("%s: waited %.2fms for the %s host reply...\n", __func__, call_duration_ms, name); + } else { + GGML_LOG_INFO("%s: waited %lldns for the %s host reply...\n", __func__, call_duration_ns, name); + } +} diff --git a/ggml/src/ggml-virtgpu/virtgpu.h b/ggml/src/ggml-virtgpu/virtgpu.h new file mode 100644 index 0000000000..d4bb42e20b --- /dev/null +++ b/ggml/src/ggml-virtgpu/virtgpu.h @@ -0,0 +1,92 @@ +#pragma once + +#include "virtgpu-utils.h" +#include "virtgpu-shm.h" +#include "virtgpu-apir.h" + +#include "backend/shared/api_remoting.h" +#include "backend/shared/apir_cs.h" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#define VIRGL_RENDERER_UNSTABLE_APIS 1 +#include "apir_hw.h" +#include +#include "venus_hw.h" + +#ifndef VIRTGPU_DRM_CAPSET_APIR +// Will be defined include/drm/virtgpu_drm.h when +// https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs +// is merged +#define VIRTGPU_DRM_CAPSET_APIR 10 +#endif + +// Mesa/Virlgrenderer Venus internal. Only necessary during the +// Venus->APIR transition in Virglrenderer +#define VENUS_COMMAND_TYPE_LENGTH 331 + +#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16 +#define VIRTGPU_DRM_CAPSET_VENUS 4 +#endif + +typedef uint32_t virgl_renderer_capset; + +/* from src/virtio/vulkan/vn_renderer_virtgpu.c */ +#define VIRTGPU_PCI_VENDOR_ID 0x1af4 +#define VIRTGPU_PCI_DEVICE_ID 0x1050 +#define VIRTGPU_BLOB_MEM_GUEST_VRAM 0x0004 +#define VIRTGPU_PARAM_GUEST_VRAM 9 + +#define SHMEM_DATA_SIZE 0x1830000 // 24MiB +#define SHMEM_REPLY_SIZE 0x4000 + +#define ARRAY_SIZE(x) (sizeof(x) / sizeof((x)[0])) + +enum virt_gpu_result_t { + APIR_SUCCESS = 0, + APIR_ERROR_INITIALIZATION_FAILED = -1, +}; + +#define PRINTFLIKE(f, a) __attribute__((format(__printf__, f, a))) + +struct virtgpu { + bool use_apir_capset; + + int fd; + + struct { + virgl_renderer_capset id; + uint32_t version; + virgl_renderer_capset_apir data; + } capset; + + util_sparse_array shmem_array; + + /* APIR communication pages */ + virtgpu_shmem reply_shmem; + virtgpu_shmem data_shmem; +}; + +static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) { + return drmIoctl(gpu->fd, request, args); +} + +virtgpu * create_virtgpu(); + +apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags); + +uint32_t remote_call(virtgpu * gpu, + apir_encoder * enc, + apir_decoder ** dec, + float max_wait_ms, + long long * call_duration_ns); + +void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec); From 0a95026da9ff86ce48e7c48b802281320cbbd3bf Mon Sep 17 00:00:00 2001 From: Ben Chen Date: Wed, 28 Jan 2026 19:30:16 +0800 Subject: [PATCH 043/206] doc: add build instruction to use Vulkan backend on macos (#19029) --- docs/build.md | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/docs/build.md b/docs/build.md index e088c519a7..3a43f2a45a 100644 --- a/docs/build.md +++ b/docs/build.md @@ -495,6 +495,37 @@ Finally, after finishing your build, you should be able to do something like thi # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` +### For Mac users: + +Generally, follow LunarG's [Getting Started with the MacOS Vulkan SDK](https://vulkan.lunarg.com/doc/sdk/latest/mac/getting_started.html) guide for installation and setup of the Vulkan SDK. There are two options of Vulkan drivers on macOS, both of which implement translation layers to map Vulkan to Metal. They can be hot-swapped by setting the `VK_ICD_FILENAMES` environment variable to point to the respective ICD JSON file. + +Check the box for "KosmicKrisp" during the LunarG Vulkan SDK installation. + +Set environment variable for the LunarG Vulkan SDK after installation (and optionally add to your shell profile for persistence): +```bash +source /path/to/vulkan-sdk/setup-env.sh +``` + +#### Using MoltenVK + +MoltenVK is the default Vulkan driver installed with the LunarG Vulkan SDK on macOS, so you can use the above environment variable settings as is. + +#### Using KosmicKrisp + +Override the environment variable for KosmicKrisp: +```bash +export VK_ICD_FILENAMES=$VULKAN_SDK/share/vulkan/icd.d/libkosmickrisp_icd.json +export VK_DRIVER_FILES=$VULKAN_SDK/share/vulkan/icd.d/libkosmickrisp_icd.json +``` + +#### Build + +This is the only step different from [above](#common-steps) instructions. +```bash +cmake -B build -DGGML_VULKAN=1 -DGGML_METAL=OFF +cmake --build build --config Release +``` + ## CANN This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU. From 88d23ad515e60c64db8df22b3de23cba00d044ab Mon Sep 17 00:00:00 2001 From: Oleksandr Kuvshynov <661042+okuvshynov@users.noreply.github.com> Date: Wed, 28 Jan 2026 06:35:54 -0500 Subject: [PATCH 044/206] vulkan: handle device dedup on MacOS + Vega II Duo cards (#19058) Deduplication here relied on the fact that vulkan would return unique UUID for different physical GPUs. It is at the moment not always the case. On Mac Pro 2019 running Mac OS, with 2 Vega II Duo cards (so, 4 GPU total), MotlenVK would assign same UUID to pairs of GPUs, unless they are connected with Infinity Fabric. See more details here: KhronosGroup/MoltenVK#2683. The right way is to fix that in MoltenVK, but until it is fixed, llama.cpp would only recognize 2 of 4 GPUs in such configuration. The deduplication logic here is changed to only filter GPUs if UUID is same but driver is different. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index b5e5dba95f..514f290d09 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -5522,22 +5522,32 @@ static void ggml_vk_instance_init() { if ((new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) && ggml_vk_device_is_supported(devices[i])) { // Check if there are two physical devices corresponding to the same GPU + // This handles the case where the same GPU appears with different drivers (e.g., RADV + AMDVLK on Linux), + // see https://github.com/ggml-org/llama.cpp/pull/7582 for original deduplication. + // However, for MoltenVK on macOS, multiple GPUs on the same card may report the same UUID, + // see https://github.com/KhronosGroup/MoltenVK/issues/2683. Until this is fixed, we'll only deduplicate + // when drivers differ (same driver + same UUID = likely different GPUs) auto old_device = std::find_if( vk_instance.device_indices.begin(), vk_instance.device_indices.end(), - [&devices, &new_id](const size_t k){ + [&devices, &new_id, &new_driver](const size_t k){ vk::PhysicalDeviceProperties2 old_props; + vk::PhysicalDeviceDriverProperties old_driver; vk::PhysicalDeviceIDProperties old_id; - old_props.pNext = &old_id; + old_props.pNext = &old_driver; + old_driver.pNext = &old_id; devices[k].getProperties2(&old_props); - bool equals = std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID)); - equals = equals || ( + bool same_uuid = std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID)); + same_uuid = same_uuid || ( old_id.deviceLUIDValid && new_id.deviceLUIDValid && std::equal(std::begin(old_id.deviceLUID), std::end(old_id.deviceLUID), std::begin(new_id.deviceLUID)) ); - return equals; + // Only deduplicate if same UUID AND different drivers + // (same driver + same UUID on MoltenVK = likely different GPUs on multi-GPU card) + bool different_driver = (old_driver.driverID != new_driver.driverID); + return same_uuid && different_driver; } ); if (old_device == vk_instance.device_indices.end()) { From 60368e1d73b2238c0b5a2897fdaed22172a56160 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Wed, 28 Jan 2026 14:40:29 +0100 Subject: [PATCH 045/206] jinja : undefined should be treated as sequence/iterable (return string/array) by filters/tests (#19147) * undefined is treated as iterable (string/array) by filters `tojson` is not a supported `undefined` filter * add tests * add sequence and iterable tests keep it DRY and fix some types --- common/jinja/value.cpp | 61 +++++++++++++-- tests/test-jinja.cpp | 174 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 228 insertions(+), 7 deletions(-) diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp index 2d77068143..17d43826c0 100644 --- a/common/jinja/value.cpp +++ b/common/jinja/value.cpp @@ -114,6 +114,18 @@ static T slice(const T & array, int64_t start, int64_t stop, int64_t step = 1) { return result; } +template +static value empty_value_fn(const func_args &) { + if constexpr (std::is_same_v) { + return mk_val(0); + } else if constexpr (std::is_same_v) { + return mk_val(0.0); + } else if constexpr (std::is_same_v) { + return mk_val(false); + } else { + return mk_val(); + } +} template static value test_type_fn(const func_args & args) { args.ensure_count(1); @@ -128,6 +140,13 @@ static value test_type_fn(const func_args & args) { JJ_DEBUG("test_type_fn: type=%s or %s result=%d", typeid(T).name(), typeid(U).name(), is_type ? 1 : 0); return mk_val(is_type); } +template +static value test_type_fn(const func_args & args) { + args.ensure_count(1); + bool is_type = is_val(args.get_pos(0)) || is_val(args.get_pos(0)) || is_val(args.get_pos(0)); + JJ_DEBUG("test_type_fn: type=%s, %s or %s result=%d", typeid(T).name(), typeid(U).name(), typeid(V).name(), is_type ? 1 : 0); + return mk_val(is_type); +} template static value test_compare_fn(const func_args & args) { args.ensure_count(2, 2); @@ -347,8 +366,8 @@ const func_builtins & global_builtins() { {"test_is_integer", test_type_fn}, {"test_is_float", test_type_fn}, {"test_is_number", test_type_fn}, - {"test_is_iterable", test_type_fn}, - {"test_is_sequence", test_type_fn}, + {"test_is_iterable", test_type_fn}, + {"test_is_sequence", test_type_fn}, {"test_is_mapping", test_type_fn}, {"test_is_lower", [](const func_args & args) -> value { args.ensure_vals(); @@ -1003,7 +1022,12 @@ const func_builtins & value_none_t::get_builtins() const { static const func_builtins builtins = { {"default", default_value}, {"tojson", tojson}, - {"string", [](const func_args &) -> value { return mk_val("None"); }} + {"string", [](const func_args &) -> value { + return mk_val("None"); + }}, + {"safe", [](const func_args &) -> value { + return mk_val("None"); + }}, }; return builtins; } @@ -1012,10 +1036,33 @@ const func_builtins & value_none_t::get_builtins() const { const func_builtins & value_undefined_t::get_builtins() const { static const func_builtins builtins = { {"default", default_value}, - {"tojson", [](const func_args & args) -> value { - args.ensure_vals(); - return mk_val("null"); - }}, + {"capitalize", empty_value_fn}, + {"first", empty_value_fn}, + {"items", empty_value_fn}, + {"join", empty_value_fn}, + {"last", empty_value_fn}, + {"length", empty_value_fn}, + {"list", empty_value_fn}, + {"lower", empty_value_fn}, + {"map", empty_value_fn}, + {"max", empty_value_fn}, + {"min", empty_value_fn}, + {"reject", empty_value_fn}, + {"rejectattr", empty_value_fn}, + {"replace", empty_value_fn}, + {"reverse", empty_value_fn}, + {"safe", empty_value_fn}, + {"select", empty_value_fn}, + {"selectattr", empty_value_fn}, + {"sort", empty_value_fn}, + {"string", empty_value_fn}, + {"strip", empty_value_fn}, + {"sum", empty_value_fn}, + {"title", empty_value_fn}, + {"truncate", empty_value_fn}, + {"unique", empty_value_fn}, + {"upper", empty_value_fn}, + {"wordcount", empty_value_fn}, }; return builtins; } diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index 7c6eeb311c..f6114f1e2f 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -329,6 +329,12 @@ static void test_loops(testing & t) { "empty" ); + test_template(t, "for undefined empty", + "{% for i in items %}{{ i }}{% else %}empty{% endfor %}", + json::object(), + "empty" + ); + test_template(t, "nested for", "{% for i in a %}{% for j in b %}{{ i }}{{ j }}{% endfor %}{% endfor %}", {{"a", json::array({1, 2})}, {"b", json::array({"x", "y"})}}, @@ -1018,6 +1024,18 @@ static void test_tests(testing & t) { {{"x", {{"a", 1}}}}, "yes" ); + + test_template(t, "undefined is sequence", + "{{ 'yes' if x is sequence }}", + json::object(), + "yes" + ); + + test_template(t, "undefined is iterable", + "{{ 'yes' if x is iterable }}", + json::object(), + "yes" + ); } static void test_string_methods(testing & t) { @@ -1122,6 +1140,54 @@ static void test_string_methods(testing & t) { {{"s", "banana"}}, "bXnXna" ); + + test_template(t, "undefined|capitalize", + "{{ arr|capitalize }}", + json::object(), + "" + ); + + test_template(t, "undefined|title", + "{{ arr|title }}", + json::object(), + "" + ); + + test_template(t, "undefined|truncate", + "{{ arr|truncate(9) }}", + json::object(), + "" + ); + + test_template(t, "undefined|upper", + "{{ arr|upper }}", + json::object(), + "" + ); + + test_template(t, "undefined|lower", + "{{ arr|lower }}", + json::object(), + "" + ); + + test_template(t, "undefined|replace", + "{{ arr|replace('a', 'b') }}", + json::object(), + "" + ); + + test_template(t, "undefined|trim", + "{{ arr|trim }}", + json::object(), + "" + ); + + test_template(t, "undefined|wordcount", + "{{ arr|wordcount }}", + json::object(), + "0" + ); } static void test_array_methods(testing & t) { @@ -1289,6 +1355,108 @@ static void test_array_methods(testing & t) { // {{"arr", json::array({"a", "b", "c"})}}, // "a,x,b,c" // ); + + test_template(t, "undefined|select", + "{% for item in items|select('odd') %}{{ item.name }} {% endfor %}", + json::object(), + "" + ); + + test_template(t, "undefined|selectattr", + "{% for item in items|selectattr('active') %}{{ item.name }} {% endfor %}", + json::object(), + "" + ); + + test_template(t, "undefined|reject", + "{% for item in items|reject('even') %}{{ item.name }} {% endfor %}", + json::object(), + "" + ); + + test_template(t, "undefined|rejectattr", + "{% for item in items|rejectattr('active') %}{{ item.name }} {% endfor %}", + json::object(), + "" + ); + + test_template(t, "undefined|list", + "{{ arr|list|string }}", + json::object(), + "[]" + ); + + test_template(t, "undefined|string", + "{{ arr|string }}", + json::object(), + "" + ); + + test_template(t, "undefined|first", + "{{ arr|first }}", + json::object(), + "" + ); + + test_template(t, "undefined|last", + "{{ arr|last }}", + json::object(), + "" + ); + + test_template(t, "undefined|length", + "{{ arr|length }}", + json::object(), + "0" + ); + + test_template(t, "undefined|join", + "{{ arr|join }}", + json::object(), + "" + ); + + test_template(t, "undefined|sort", + "{{ arr|sort|string }}", + json::object(), + "[]" + ); + + test_template(t, "undefined|reverse", + "{{ arr|reverse|join }}", + json::object(), + "" + ); + + test_template(t, "undefined|map", + "{% for v in arr|map(attribute='age') %}{{ v }} {% endfor %}", + json::object(), + "" + ); + + test_template(t, "undefined|min", + "{{ arr|min }}", + json::object(), + "" + ); + + test_template(t, "undefined|max", + "{{ arr|max }}", + json::object(), + "" + ); + + test_template(t, "undefined|unique", + "{{ arr|unique|join }}", + json::object(), + "" + ); + + test_template(t, "undefined|sum", + "{{ arr|sum }}", + json::object(), + "0" + ); } static void test_object_methods(testing & t) { @@ -1393,6 +1561,12 @@ static void test_object_methods(testing & t) { json::object(), "True" ); + + test_template(t, "undefined|items", + "{{ arr|items|join }}", + json::object(), + "" + ); } static void test_hasher(testing & t) { From 0cd7032ca4f1f2ac0c9527a62a76ed4dc6ad26fe Mon Sep 17 00:00:00 2001 From: Patryk Kaminski Date: Wed, 28 Jan 2026 16:33:54 +0100 Subject: [PATCH 046/206] ggml-sycl: remove unused syclcompat header (#19140) The syclcompat/math.hpp is not used anymore. The change that intrduced it was successfuly reverted (https://github.com/ggml-org/llama.cpp/pull/17826). This include path will become obsolete and dropped in oneAPI 2026.0 effectively breaking ggml-sycl builds. --- ggml/src/ggml-sycl/dpct/helper.hpp | 1 - 1 file changed, 1 deletion(-) diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index 30ec1e8daf..8ae8098717 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -15,7 +15,6 @@ #include #include -#include #include #ifdef GGML_SYCL_USE_INTEL_ONEMKL From ebf57258702b23098d7bdcbd46008a95b2401075 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 28 Jan 2026 16:49:36 +0100 Subject: [PATCH 047/206] convert : yield Mamba2Model/GraniteMoeModel modify_tensors (#19157) * convert : yield Mamba2Model/GraniteMoeModel modify_tensors This commit updates the `GraniteHybridModel` class' modify_tensors function to properly delegate to `Mamba2Model.modify_tensors` and `GraniteMoeModel.modify_tensors` using 'yield from' instead of 'return'. The motivation for this is that modify_tensors is a generator function (it uses 'yield from'), but the two calls above use return statements but don't yield anything which means that the the caller of this function will not receive any yielded values from it. And this causes layer tensors to be silently dropped during conversion. --- convert_hf_to_gguf.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 6e6e618989..a391717e32 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -8912,13 +8912,16 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel): name.endswith("block_sparse_moe.input_linear.weight") or "shared_mlp" in name ): - return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + return # Determine whether this is a mamba layer or an attention layer if bid in self._ssm_layers: - return Mamba2Model.modify_tensors(self, data_torch, name, bid) + yield from Mamba2Model.modify_tensors(self, data_torch, name, bid) + return elif bid in self._attn_layers: - return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid) + return yield from ModelBase.modify_tensors(self, data_torch, name, bid) def set_gguf_parameters(self): From 72d3b1898a9c81152710cc37dd1dfd26764055d9 Mon Sep 17 00:00:00 2001 From: Sascha Rogmann <59577610+srogmann@users.noreply.github.com> Date: Wed, 28 Jan 2026 18:42:42 +0100 Subject: [PATCH 048/206] =?UTF-8?q?spec=20:=20add=20self=E2=80=91speculati?= =?UTF-8?q?ve=20decoding=20(no=20draft=20model=20required)=20+=20refactor?= =?UTF-8?q?=20(#18471)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * server: introduce self-speculative decoding * server: moved self-call into speculative.cpp * can_speculate() includes self-speculation Co-authored-by: Georgi Gerganov * server: can_speculate() tests self-spec * server: replace can_speculate() with slot.can_speculate() Co-authored-by: Sigbjørn Skjæret * common: use %zu format specifier for size_t in logging Co-authored-by: Sigbjørn Skjæret * server: can_speculate() requires a task instance * common: ngram map, config self-speculative decoding * common: add enum common_speculative_type * common: add vector of speculative states * common: add option --spec-draftless * server: cleanup (remove slot.batch_spec, rename) * common: moved self-spec impl to ngram-map * common: cleanup (use common_speculative_state_draft) * spec : refactor * cont : naming * spec: remove --spec-config * doc: (draftless) speculative decoding * common: print performance in spec decoding * minor : cleanup * common : better names * minor : cleanup + fix build * minor: comments * CODEOWNERS: add common/ngram-map.* (#18471) * common : rename speculative.draftless_type -> speculative.type * ngram-map : fix uninitialized values * ngram-map : take into account the input can become shorter * ngram-map : revert len check for now * arg : change `--spec-draftless` -> `--spec-type` * spec : add common_speculative_state::accept() * spec : refactor + add common_speculative_begin() * spec : fix begin() call with mtmd * spec : additional refactor + remove common_speculative_params --------- Co-authored-by: Georgi Gerganov Co-authored-by: Sigbjørn Skjæret --- CODEOWNERS | 1 + common/CMakeLists.txt | 2 + common/arg.cpp | 87 +- common/common.cpp | 9 +- common/common.h | 64 +- common/ngram-cache.cpp | 7 +- common/ngram-cache.h | 4 +- common/ngram-map.cpp | 367 ++++++ common/ngram-map.h | 105 ++ common/speculative.cpp | 1012 +++++++++++++---- common/speculative.h | 44 +- docs/speculative.md | 120 ++ examples/lookup/lookup-create.cpp | 4 +- examples/lookup/lookup-stats.cpp | 10 +- examples/lookup/lookup.cpp | 12 +- .../speculative-simple/speculative-simple.cpp | 72 +- examples/speculative/speculative.cpp | 4 +- tools/server/server-context.cpp | 146 +-- tools/server/server-task.cpp | 23 + 19 files changed, 1649 insertions(+), 444 deletions(-) create mode 100644 common/ngram-map.cpp create mode 100644 common/ngram-map.h create mode 100644 docs/speculative.md diff --git a/CODEOWNERS b/CODEOWNERS index 6086abb564..e573a3d2e6 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -18,6 +18,7 @@ /common/jinja/ @ngxson @CISC @aldehir /common/llguidance.* @ggerganov /common/log.* @ggerganov +/common/ngram-map.* @srogmann /common/peg-parser.* @aldehir /common/sampling.* @ggerganov /common/speculative.* @ggerganov diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index ae02c0bd77..3bc7bc6210 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -73,6 +73,8 @@ add_library(${TARGET} STATIC log.h ngram-cache.cpp ngram-cache.h + ngram-map.cpp + ngram-map.h peg-parser.cpp peg-parser.h preset.cpp diff --git a/common/arg.cpp b/common/arg.cpp index cd3a1b6397..a685c418bf 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -6,6 +6,7 @@ #include "json-schema-to-grammar.h" #include "log.h" #include "sampling.h" +#include "speculative.h" #include "preset.h" // fix problem with std::min and std::max @@ -579,14 +580,14 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.mmproj = res.mmproj; } // only download mmproj if the current example is using it - for (auto & ex : mmproj_examples) { + for (const auto & ex : mmproj_examples) { if (ctx_arg.ex == ex) { common_params_handle_model(params.mmproj, params.hf_token, params.offline); break; } } - common_params_handle_model(params.speculative.model, params.hf_token, params.offline); - common_params_handle_model(params.vocoder.model, params.hf_token, params.offline); + common_params_handle_model(params.speculative.mparams_dft, params.hf_token, params.offline); + common_params_handle_model(params.vocoder.model, params.hf_token, params.offline); } // model is required (except for server) @@ -1216,16 +1217,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-lcs", "--lookup-cache-static"}, "FNAME", "path to static lookup cache to use for lookup decoding (not updated by generation)", [](common_params & params, const std::string & value) { - params.lookup_cache_static = value; + params.speculative.lookup_cache_static = value; } - ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-lcd", "--lookup-cache-dynamic"}, "FNAME", "path to dynamic lookup cache to use for lookup decoding (updated by generation)", [](common_params & params, const std::string & value) { - params.lookup_cache_dynamic = value; + params.speculative.lookup_cache_dynamic = value; } - ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-c", "--ctx-size"}, "N", string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), @@ -2563,7 +2564,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-hfd", "-hfrd", "--hf-repo-draft"}, "/[:quant]", "Same as --hf-repo, but for the draft model (default: unused)", [](common_params & params, const std::string & value) { - params.speculative.model.hf_repo = value; + params.speculative.mparams_dft.hf_repo = value; } ).set_env("LLAMA_ARG_HFD_REPO")); add_opt(common_arg( @@ -3384,7 +3385,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-md", "--model-draft"}, "FNAME", "draft model for speculative decoding (default: unused)", [](common_params & params, const std::string & value) { - params.speculative.model.path = value; + params.speculative.mparams_dft.path = value; } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT")); add_opt(common_arg( @@ -3394,6 +3395,66 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.replacements.push_back({ tgt, dft }); } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v]", + string_format("type of speculative decoding to use when no draft model is provided (default: %s)\n", + common_speculative_type_to_str(params.speculative.type).c_str()), + [](common_params & params, const std::string & value) { + if (value == "none") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NONE; + } else if (value == "ngram-cache") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_CACHE; + } else if (value == "ngram-simple") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE; + } else if (value == "ngram-map-k") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K; + } else if (value == "ngram-map-k4v") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V; + } else { + throw std::invalid_argument("unknown speculative decoding type without draft model"); + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-size-n"}, "N", + string_format("ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_size_n), + [](common_params & params, int value) { + if (value < 1 || value > 1024) { + throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive"); + } + params.speculative.ngram_size_n = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-size-m"}, "N", + string_format("ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_size_m), + [](common_params & params, int value) { + if (value < 1 || value > 1024) { + throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive"); + } + params.speculative.ngram_size_m = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-check-rate"}, "N", + string_format("ngram check rate for ngram-simple/ngram-map speculative decoding (default: %d)", params.speculative.ngram_check_rate), + [](common_params & params, int value) { + if (value < 1) { + throw std::invalid_argument("ngram check rate must be at least 1"); + } + params.speculative.ngram_check_rate = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-min-hits"}, "N", + string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits), + [](common_params & params, int value) { + if (value < 1) { + throw std::invalid_argument("ngram min hits must be at least 1"); + } + params.speculative.ngram_min_hits = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-ctkd", "--cache-type-k-draft"}, "TYPE", string_format( @@ -3620,8 +3681,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { 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.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.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; params.port = 8012; params.n_ubatch = 1024; params.n_batch = 1024; @@ -3636,8 +3697,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.model.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF"; 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.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; params.port = 8012; params.n_ubatch = 1024; params.n_batch = 1024; diff --git a/common/common.cpp b/common/common.cpp index 26250abb6c..3aa396127c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1097,7 +1097,10 @@ common_init_result::common_init_result(common_params & params) : if (params.fit_params) { LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__); llama_params_fit(params.model.path.c_str(), &mparams, &cparams, - params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx, + params.tensor_split, + params.tensor_buft_overrides.data(), + params.fit_params_target.data(), + params.fit_params_min_ctx, params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); } @@ -1208,10 +1211,6 @@ std::vector & common_init_result::lora() { return pimpl->lora; } -void common_init_result::free_context() { - pimpl->context.reset(); -} - common_init_result_ptr common_init_from_params(common_params & params) { common_init_result_ptr res(new common_init_result(params)); diff --git a/common/common.h b/common/common.h index 21c11f457d..fd3ab8cd18 100644 --- a/common/common.h +++ b/common/common.h @@ -164,6 +164,16 @@ enum common_params_sampling_config : uint64_t { COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA = 1 << 11, }; +enum common_speculative_type { + COMMON_SPECULATIVE_TYPE_NONE, // no speculative decoding + COMMON_SPECULATIVE_TYPE_DRAFT, // draft model + COMMON_SPECULATIVE_TYPE_EAGLE3, // eagle draft model + COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, // simple self-speculative decoding + COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, // self-speculative decoding with n-gram keys only + COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values + COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, // self-speculative decoding with 3-level n-gram cache + COMMON_SPECULATIVE_TYPE_COUNT // number of types, unknown type +}; // sampling parameters struct common_params_sampling { @@ -243,16 +253,35 @@ struct common_params_model { }; struct common_params_speculative { - std::vector devices; // devices to use for offloading + common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding - int32_t n_ctx = 0; // draft context size - int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding - int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding - int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - float p_split = 0.1f; // speculative decoding split probability - float p_min = 0.75f; // minimum speculative decoding probability (greedy) - std::vector> replacements; // main to speculative model replacements - std::vector tensor_buft_overrides; + // general-purpose speculative decoding parameters + + int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding + int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding + float p_split = 0.1f; // speculative decoding split probability + float p_min = 0.75f; // minimum speculative decoding probability (greedy) + + // ngram-based speculative decoding + + uint16_t ngram_size_n = 12; // ngram size for lookup + uint16_t ngram_size_m = 48; // mgram size for speculative tokens + uint16_t ngram_check_rate = 1; // check rate for ngram lookup + uint16_t ngram_min_hits = 1; // minimum hits at ngram/mgram lookup for mgram to be proposed + + std::string lookup_cache_static; // path of static ngram cache file for lookup decoding // NOLINT + std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding // NOLINT + + // draft-model speculative decoding + + struct common_params_model mparams_dft; + + llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts + + llama_context_params cparams_dft; // these are the parameters for the draft llama_context + + int32_t n_ctx = 0; // draft context size + int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) 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 @@ -260,7 +289,14 @@ struct common_params_speculative { struct cpu_params cpuparams; struct cpu_params cpuparams_batch; - struct common_params_model model; + std::vector devices; // devices to use for offloading + + std::vector> replacements; // main to speculative model replacements + std::vector tensor_buft_overrides; + + bool has_dft() const { + return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty(); + } }; struct common_params_vocoder { @@ -378,8 +414,6 @@ struct common_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT std::string input_prefix = ""; // string to prefix user inputs with // NOLINT std::string input_suffix = ""; // string to suffix user inputs with // NOLINT - std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT - std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT std::string logits_file = ""; // file for saving *all* logits // NOLINT // llama-debug specific options @@ -575,10 +609,6 @@ struct common_params { // return false from callback to abort model loading or true to continue llama_progress_callback load_progress_callback = NULL; void * load_progress_callback_user_data = NULL; - - bool has_speculative() const { - return !speculative.model.path.empty() || !speculative.model.hf_repo.empty(); - } }; // call once at the start of a program if it uses libcommon @@ -714,8 +744,6 @@ struct common_init_result { std::vector & lora(); - void free_context(); - private: struct impl; std::unique_ptr pimpl; diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp index d1a4d84c40..dce54b3647 100644 --- a/common/ngram-cache.cpp +++ b/common/ngram-cache.cpp @@ -192,12 +192,12 @@ void common_ngram_cache_draft( break; } - LOG(" - draft candidate: token=%d\n", drafted_token); + LOG_DBG(" - draft candidate: token=%d\n", drafted_token); draft.push_back(drafted_token); } } -void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) { +void common_ngram_cache_save(common_ngram_cache & ngram_cache, const std::string & filename) { std::ofstream file_out(filename, std::ios::binary); for (std::pair item : ngram_cache) { const common_ngram ngram = item.first; @@ -217,10 +217,9 @@ void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & fil file_out.write(reinterpret_cast(&count), sizeof(int32_t)); } } - } -common_ngram_cache common_ngram_cache_load(std::string & filename) { +common_ngram_cache common_ngram_cache_load(const std::string & filename) { std::ifstream hashmap_file(filename, std::ios::binary); if (!hashmap_file) { throw std::ifstream::failure("Unable to open file " + filename); diff --git a/common/ngram-cache.h b/common/ngram-cache.h index dfe012abe4..6e7cfea966 100644 --- a/common/ngram-cache.h +++ b/common/ngram-cache.h @@ -88,12 +88,12 @@ void common_ngram_cache_draft( // Save an ngram cache to a file. // ngram_cache: the ngram cache to save. // filename: the path under which to save the ngram cache. -void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename); +void common_ngram_cache_save(common_ngram_cache & ngram_cache, const std::string & filename); // Load an ngram cache saved with common_ngram_cache_save. // filename: the path from which to load the ngram cache. // returns: an ngram cache containing the information saved to filename. -common_ngram_cache common_ngram_cache_load(std::string & filename); +common_ngram_cache common_ngram_cache_load(const std::string & filename); // Merge two ngram caches. // ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add. diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp new file mode 100644 index 0000000000..930e7a3c10 --- /dev/null +++ b/common/ngram-map.cpp @@ -0,0 +1,367 @@ +#include "common.h" +#include "log.h" +#include "ngram-map.h" + +#include +#include +#include +#include + +// n-gram simple +// + +/** + * Perform speculative generation using the model's own token history. + * Searches for a matching pattern in the token history and returns draft tokens. + * + * @param state Current state of this implementation + * @param tokens Token history to search in + * @param sampled Last sampled token + * @return Vector of draft tokens, empty if no matching pattern is found + */ +llama_tokens common_ngram_simple_draft( + common_ngram_simple_state & state, + const llama_tokens & tokens, llama_token sampled) { + + // Simple implementation of self-speculative decoding without a draft model. + // + const size_t cur_len = tokens.size(); + // Only check every check_rate tokens to save compute + // i.e., perform check if (cur_len - idx_last_check) >= check_rate + if (state.idx_last_check + state.config.check_rate > cur_len) { + llama_tokens draft_tokens; + return draft_tokens; + } + + size_t n_draft_min = state.config.size_ngram; // size of n-gram to lookup in token history + size_t n_draft_max = state.config.size_mgram; // the m-gram following the found n-gram is used for draft + + // vector for tokens we want to verify. + // return empty vector if there is no match. + llama_tokens draft_tokens; + + // We need at least n_draft_min + n_draft_max + 1 tokens. + if (cur_len <= static_cast(n_draft_min + n_draft_max + 1)) { + return draft_tokens; + } + + // pattern search + llama_tokens pattern; + pattern.reserve(n_draft_min); + for (size_t j = cur_len - n_draft_min + 1; j < cur_len; ++j) { + pattern.push_back(tokens[j]); + } + pattern.push_back(sampled); // add the last token to the pattern + + // We do a search in the token history. + state.idx_last_check = cur_len; + + size_t match_pos = 0; // we ignore position 0, position 0 == no match + // search backwards, but skip the current match (we are currently there) + for (size_t j = cur_len - n_draft_min - 1; j > 0; --j) { + bool match = true; + for (size_t k = 0; k < pattern.size(); ++k) { + if (tokens[j + k] != pattern[k]) { + match = false; + break; + } + } + if (match) { + match_pos = j; + break; + } + } + if (match_pos == 0) { + return draft_tokens; + } + + const size_t copy_max = std::min( + n_draft_max, + cur_len - (match_pos + n_draft_min) + ); + if (copy_max < n_draft_min) { + return draft_tokens; + } + LOG_DBG("%s: #tokens = %zu: found matching pattern at pos %zu, length %zu, draft length %zu\n", + __func__, cur_len, + match_pos, pattern.size(), copy_max); + + draft_tokens.reserve(copy_max); + for (size_t j = 0; j < copy_max; ++j) { + draft_tokens.push_back(tokens[match_pos + n_draft_min + j]); + } + return draft_tokens; +} + + +// n-gram map +// + +// maximum number of counted values of a ngram map value. +#define COMMON_NGRAM_MAX_VALUE_COUNT 16380 + +static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length); + +void common_ngram_map_draft(common_ngram_map & map, + const llama_tokens & inp, llama_token sampled, + llama_tokens & draft) { + // reset last key and value. + map.last_draft_created = false; + map.last_draft_key_idx = 0; + map.last_draft_value_idx = 0; + + const size_t cur_len = inp.size(); + const uint16_t n = map.size_key; + const uint16_t m = map.size_value; + if (cur_len < static_cast(2 * n + m)) { + return; + } + + // Only check every check_rate tokens to save compute + // i.e., perform check if (cur_len - idx_last_check) >= check_rate + if (map.idx_last_check + map.check_rate > cur_len) { + return; + } + map.idx_last_check = cur_len; + + // search pattern, the key n-gram + std::vector key_tokens; + key_tokens.reserve(n); + for (size_t j = cur_len - n + 1; j < cur_len; ++j) { + key_tokens.push_back(inp[j]); + } + key_tokens.push_back(sampled); + + // search for the key in the map + size_t match_pos = 0; + for (size_t j = cur_len - n - m - 1; j > 0; --j) { + bool match = true; + for (size_t k = 0; k < n; ++k) { + if (inp[j + k] != key_tokens[k]) { + match = false; + break; + } + } + if (match) { + match_pos = j; + break; + } + } + if (match_pos > 0) { + LOG_INF("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__, + cur_len, n, m, key_tokens.size(), sampled, match_pos); + } + + if (match_pos == 0) { + return; + } + + // We have a match, now we look for the statistics of the key. + size_t key_offset = map.keys.size(); // offset in the map + // We iterate through the std::vector map->keys. + for (size_t i = 0; i < map.keys.size(); ++i) { + bool match = true; + for (size_t j = 0; j < n; ++j) { + if (inp[map.keys[i].key_idx + j] != key_tokens[j]) { + match = false; + break; + } + } + if (match) { + key_offset = i; + break; + } + } + if (key_offset == map.keys.size()) { + // We create a new key-entry, it will get offset key_offset. + common_ngram_map_key new_key; + new_key.key_idx = match_pos; + new_key.stat_idx = 0; + new_key.key_num = 0; + for (int i = 0; i < COMMON_NGRAM_MAX_VALUES; ++i) { + new_key.values[i].value_num = 0; + new_key.values[i].n_accepted = m; + } + map.keys.push_back(new_key); + } + + // our key n-gram: + common_ngram_map_key & curr_key = map.keys[key_offset]; + + // update number of key hits + curr_key.key_num = (uint16_t) std::min((int) map.keys[key_offset].key_num + 1, + (int) COMMON_NGRAM_MAX_VALUE_COUNT); + + if (map.key_only) { + // simple mode: + // Fill in the draft with the m tokens following the key. + // We work with value values[0] only. + int n_draft_tokens = std::min((int) m, (int) curr_key.values[0].n_accepted); + + for (int i = 0; i < n_draft_tokens; ++i) { + draft.push_back(inp[match_pos + n + i]); + } + + LOG_INF("%s: key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__, + key_offset, curr_key.key_num, draft.size()); + + map.last_draft_created = false; + map.last_draft_key_idx = key_offset; + map.last_draft_value_idx = 0; // value 0 is used for simple mode + return; + } + + if (curr_key.key_num < map.min_hits) { + // not enough hits to consider this a good draft + LOG_DBG("%s: key_offset = %zu, key_num = %d, min_hits = %d, no draft\n", __func__, + key_offset, curr_key.key_num, map.min_hits); + return; + } + + // complex mode: examine the different m-grams after this key n-gram. + // + + // determine all (max COMMON_NGRAM_MAX_VALUES) m-grams after the key n-gram. + for (size_t i = curr_key.stat_idx; i <= match_pos; ++i) { + // begins the key n-gram at index i? + bool match_key = true; + for (size_t k = 0; k < n; ++k) { + if (inp[i + k] != key_tokens[k]) { + match_key = false; + break; + } + } + if (!match_key) { + continue; + } + + // Do we haven a existing value m-gram or a new one after the key at index i? + size_t idx_begin_value_key = i + n; + int idx_value = -1; + for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) { + size_t idx_begin_value_v = curr_key.values[v].value_idx; + if (idx_begin_value_v == 0) { + // We found an empty value slot => we found a new value m-gram after the key n-gram. + curr_key.values[v].value_idx = idx_begin_value_key; + curr_key.values[v].value_num = 0; + curr_key.values[v].n_accepted = m; + idx_value = v; + break; + } + bool match = true; + for (size_t j = 0; j < m; ++j) { + if (inp[idx_begin_value_key + j] != inp[idx_begin_value_v + j]) { + match = false; + break; + } + } + if (match) { + // We found an existing value m-gram after the key n-gram. + idx_value = v; + break; + } + } + if (idx_value >= 0) { + // We found a value m-gram of the key n-gram. + curr_key.values[idx_value].value_num = (uint16_t) std::min((int) curr_key.values[idx_value].value_num + 1, + (int) COMMON_NGRAM_MAX_VALUE_COUNT); + } + } + // the statistics are updated up to match_pos. + curr_key.stat_idx = match_pos; + + // Do we have a value we could use for the draft? + uint16_t max_occur = 0; + int slot_max = 0; + for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) { + uint16_t curr_occur = curr_key.values[v].value_num; + if (curr_occur > max_occur) { + max_occur = curr_occur; + slot_max = v; + } + } + // What is sum of the other occurences? + uint32_t sum_occur = 0; + for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) { + if (v == slot_max) { + continue; + } + uint16_t curr_occur = curr_key.values[v].value_num; + sum_occur += curr_occur; + } + + LOG_INF("%s: key_offset = %zu, max_occur = %d, sum_occur = %d, slot_max = %d [%zu/%d, %zu/%d, %zu/%d, %zu/%d]\n", __func__, + key_offset, + max_occur, sum_occur, slot_max, + curr_key.values[0].value_idx, curr_key.values[0].value_num, + curr_key.values[1].value_idx, curr_key.values[1].value_num, + curr_key.values[2].value_idx, curr_key.values[2].value_num, + curr_key.values[3].value_idx, curr_key.values[3].value_num + ); + // Print the tokens of the four values (if idx != 0), use LOG_INF + for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) { + if (curr_key.values[v].value_idx != 0) { + LOG_INF("%s: value[%d] = %s\n", __func__, v, common_tokens_to_str(inp, curr_key.values[v].value_idx, m).c_str()); + } + } + + if (sum_occur > 0 && max_occur < 3 * sum_occur) { + // The most frequent value is not much more frequent than the other values. + // We do not use the draft. + return; + } + + // We use the most frequent value values[slot_max] for the draft. + // Fill in the draft with the m tokens following the key. + int n_draft_tokens = std::min((int) m, (int) curr_key.values[slot_max].n_accepted); + + for (int i = 0; i < n_draft_tokens; ++i) { + draft.push_back(inp[match_pos + n + i]); + } + + LOG_INF("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__, + key_offset, slot_max, + curr_key.key_num, draft.size()); + + map.last_draft_created = true; + map.last_draft_key_idx = key_offset; + map.last_draft_value_idx = slot_max; // value used for draft generation. +} + +void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted) { + if (!map.last_draft_created) { + return; + } + + // find the key and its chosen value. + const size_t key_idx = map.last_draft_key_idx; + const size_t val_idx = map.last_draft_value_idx; + + // find key corresponding to key_idx. + common_ngram_map_key & curr_key = map.keys[key_idx]; + // find value corresponding to val_idx. + struct common_ngram_map_value & curr_value = curr_key.values[val_idx]; // value used for draft generation. + + // update the value statistics + LOG_INF("common_ngram_map_send_accepted: n_accepted = %d, prev value_num = %d\n", + n_accepted, curr_value.n_accepted); + curr_value.n_accepted = n_accepted; +} + +// Helper functions. +// + +// Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...]. +std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) { + std::ostringstream oss; + oss << '['; + for (size_t i = 0; i < length; ++i) { + if (i > 0) { + oss << ", "; + } + oss << inp[start + i]; + } + oss << ']'; + return oss.str(); +} + diff --git a/common/ngram-map.h b/common/ngram-map.h new file mode 100644 index 0000000000..bf91883f0c --- /dev/null +++ b/common/ngram-map.h @@ -0,0 +1,105 @@ +#pragma once +// +// common/ngram-map.h: structures used to manage a map from n-grams to a list of m-grams +// +// These structures are used to do a lookup of n-grams followed by m-grams in token history. +// +// There are two algorithms implemented: +// 1. ngram_simple: lookup of n-grams followed by m-grams in token history. +// 2. ngram_map: lookup of n-grams followed by m-grams in token history using a map. +// The map is a vector of key n-grams, and for each key n-gram there is a list of value m-grams. +// + +#include "llama.h" + +#include + +// n-gram simple +// + +// config of n-gram simple. +struct common_ngram_simple_config { + uint16_t size_ngram; // size of n-grams to lookup in self-mode + uint16_t size_mgram; // size of m-grams to draft in self-mode + uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token +}; + +// current state (and config) of n-gram simple. +struct common_ngram_simple_state { + common_ngram_simple_config config; + + size_t idx_last_check = 0; // index of last check in context history (mutable) + + common_ngram_simple_state(const common_ngram_simple_config & config) + : config(config) {} +}; + +// Searches for a n-gram in the history and checks whether a draft sequence should be generated. +// state: the ngram simple state to search in. +// inp: the tokens generated so far. +// sampled: the token that was just sampled. +// draft: vector to store the draft tokens, initially empty. +llama_tokens common_ngram_simple_draft( + common_ngram_simple_state & state, + const llama_tokens & tokens, llama_token sampled); + + +// n-gram map +// + +// maximum number of m-gram values stored for each key n-gram. +#define COMMON_NGRAM_MAX_VALUES 4 + +// statistics of a m-gram after a known n-gram +struct common_ngram_map_value { + size_t value_idx = 0; // index of value m-gram in token-history (0 if unused) + uint16_t value_num = 0; // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot) + int16_t n_accepted = -1; // number of accepted tokens at last draft (-1 if unused) +}; + +// statistics of a n-gram +struct common_ngram_map_key { + size_t key_idx; // index of key n-gram in token-history + size_t stat_idx; // index of last token of stastistics computation (key_num, values) + + uint16_t key_num; // number of occurences of this key n-gram in token-history + common_ngram_map_value values[COMMON_NGRAM_MAX_VALUES]; // some known values after the key +}; + +// map from n-grams to following m-grams in token-history +struct common_ngram_map { + uint16_t size_key; // size of key n-grams + uint16_t size_value; // size of value m-grams + + bool key_only; // true if only key n-grams are used, no values. + + // first draft: vector only, no map. + std::vector keys; // key n-grams which occur several times in token-history + uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token + uint16_t min_hits; // minimum number of key hits to consider a draft + + common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys, + uint16_t check_rate, uint16_t min_hits) + : size_key(sz_key), size_value(sz_value), key_only(only_keys), + check_rate(check_rate), min_hits(min_hits) {} + + bool last_draft_created = false; // true if a draft was created at last call. + size_t last_draft_key_idx = 0; // index of last key used for draft generation. + uint16_t last_draft_value_idx = 0; // index of last value used for draft generation. + + size_t idx_last_check = 0; // index of last check in context history +}; + + +// Searches for the n-gram in the history and checks whether a draft sequence should be generated. +// map: the ngram map to search in. +// inp: the tokens generated so far. +// sampled: the token that was just sampled. +// draft: vector to store the draft tokens, initially empty. +void common_ngram_map_draft( + common_ngram_map & map, + const llama_tokens & inp, llama_token sampled, + llama_tokens & draft); + +// Update the statistics of a value after a draft was processed. +void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted); diff --git a/common/speculative.cpp b/common/speculative.cpp index 3e83b0964c..3f314b5d57 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -1,99 +1,54 @@ #include "speculative.h" +#include "common.h" #include "ggml.h" #include "llama.h" #include "log.h" -#include "common.h" +#include "ngram-cache.h" +#include "ngram-map.h" #include "sampling.h" -#include #include +#include +#include #include #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 -struct common_speculative { - struct llama_context * ctx_tgt; // only used for retokenizing from ctx_dft - struct llama_context * ctx_dft; - struct common_sampler * smpl; - - llama_batch batch; - llama_tokens prompt_dft; - bool vocab_dft_compatible = true; // whether retokenization is needed - std::map tgt_dft_replacements = {}; +const std::vector common_speculative_types = { + COMMON_SPECULATIVE_TYPE_NONE, + COMMON_SPECULATIVE_TYPE_DRAFT, + COMMON_SPECULATIVE_TYPE_EAGLE3, + COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, + COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, + COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, + COMMON_SPECULATIVE_TYPE_NGRAM_CACHE }; -struct common_speculative * common_speculative_init( - struct llama_context * ctx_tgt, - struct llama_context * ctx_dft) { - auto * result = new common_speculative { - /* .ctx_tgt = */ ctx_tgt, - /* .ctx_dft = */ ctx_dft, - /* .smpl = */ nullptr, - /* .batch = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1), - /* .prompt_dft = */ {}, - /* .vocab_dft_compatible = */ false, - }; +const std::map common_speculative_type_from_name_map = { + {"none", COMMON_SPECULATIVE_TYPE_NONE}, + {"draft", COMMON_SPECULATIVE_TYPE_DRAFT}, + {"eagle3", COMMON_SPECULATIVE_TYPE_EAGLE3}, + {"ngram_simple", COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE}, + {"ngram_map_k", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K}, + {"ngram_map_k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V}, + {"ngram_cache", COMMON_SPECULATIVE_TYPE_NGRAM_CACHE} +}; - // TODO: optimize or pass from outside? -#if 0 - { - common_params_sampling params; - params.no_perf = false; +struct common_speculative_config { + common_speculative_type type; + common_params_speculative params; - params.top_k = 40; - params.top_p = 0.9; + common_speculative_config(common_speculative_type t, + const common_params_speculative & p = common_params_speculative{}) : type(t), params(p) {} +}; - params.samplers = { - COMMON_SAMPLER_TYPE_TOP_K, - COMMON_SAMPLER_TYPE_TOP_P, - COMMON_SAMPLER_TYPE_INFILL, - }; - - result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); - } -#else - { - common_params_sampling params; - params.no_perf = false; - - params.top_k = 10; - - params.samplers = { - COMMON_SAMPLER_TYPE_TOP_K, - }; - - result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); - } -#endif - - result->vocab_dft_compatible = common_speculative_are_compatible(ctx_tgt, ctx_dft); - LOG_DBG("vocab_dft_compatible = %d\n", result->vocab_dft_compatible); - - return result; -} - -void common_speculative_free(struct common_speculative * spec) { - if (spec == nullptr) { - return; - } - - common_sampler_free(spec->smpl); - - llama_batch_free(spec->batch); - - delete spec; -} - -bool common_speculative_are_compatible( - const struct llama_context * ctx_tgt, - const struct llama_context * ctx_dft) { - const struct llama_model * model_tgt = llama_get_model(ctx_tgt); - const struct llama_model * model_dft = llama_get_model(ctx_dft); - - const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); - const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); +static bool common_speculative_are_compatible( + const llama_model * model_tgt, + const llama_model * model_dft) { + const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); + const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); const bool vocab_type_tgt = llama_vocab_type(vocab_tgt); LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt); @@ -134,11 +89,12 @@ bool common_speculative_are_compatible( for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) { const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i); const char * token_text_dft = llama_vocab_get_text(vocab_dft, i); + if (std::strcmp(token_text_tgt, token_text_dft) != 0) { LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__); LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i, - common_token_to_piece(ctx_tgt, i).c_str(), - common_token_to_piece(ctx_dft, i).c_str()); + common_token_to_piece(vocab_tgt, i).c_str(), + common_token_to_piece(vocab_dft, i).c_str()); return false; } } @@ -147,215 +103,779 @@ bool common_speculative_are_compatible( return true; } -void common_speculative_add_replacement_tgt_dft( - struct common_speculative * spec, - const char *source, const char *dest) { - spec->tgt_dft_replacements[source] = dest; -} +// state of an implementation of speculative decoding +// +// each implementation has a unique type and a state that is implementation-specific +// in a subclass of common_speculative_state +struct common_speculative_state { + const enum common_speculative_type type; -static std::string replace_to_dft( - struct common_speculative * spec, - const std::string& input) { - std::string result = input; - for (const auto & pair : spec->tgt_dft_replacements) { - size_t pos = result.find(pair.first); - while (pos != std::string::npos) { - result.replace(pos, pair.first.length(), pair.second); - pos = result.find(pair.first, pos + pair.second.length()); - } - } - return result; -} + size_t drafts_call_count = 0; // number of times this implementation was called. + size_t drafts_generated_count = 0; // number of times a draft or part was generated by this implementation. + size_t drafts_accepted_count = 0; // number of times a draft or part was accepted by the target model. + size_t drafts_generated_tokens = 0; // number of tokens generated by this implementation. + size_t drafts_accepted_tokens = 0; // number of tokens accepted by the target model. -static std::string replace_to_tgt( - struct common_speculative * spec, - const std::string& input) { - std::string result = input; - for (const auto& pair : spec->tgt_dft_replacements) { - size_t pos = result.find(pair.second); - while (pos != std::string::npos) { - result.replace(pos, pair.second.length(), pair.first); - pos = result.find(pair.second, pos + pair.first.length()); - } - } - return result; -} + // TODO: track performance of most recent calls + const bool gen_perf = true; // whether to generate performance stats. + int64_t gen_duration_us = 0; // total time spent in this implementation in microseconds. -llama_tokens common_speculative_gen_draft( - struct common_speculative * spec, - struct common_speculative_params params, - const llama_tokens & prompt_tgt_main_model, // specified in target model vocab - llama_token id_last) { - auto & batch = spec->batch; - auto & ctx_tgt = spec->ctx_tgt; - auto & ctx_dft = spec->ctx_dft; - auto & smpl = spec->smpl; - auto & prompt_dft = spec->prompt_dft; + common_speculative_state(enum common_speculative_type type) : type(type) {} - auto * mem_dft = llama_get_memory(ctx_dft); + virtual ~common_speculative_state() = default; - int reuse_i = 0; - int reuse_n = 0; + virtual void begin(const llama_tokens & prompt) = 0; - const int n_ctx = llama_n_ctx(ctx_dft) - params.n_draft; + virtual void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) = 0; - llama_tokens prompt_tgt_draft_model; - if (!spec->vocab_dft_compatible) { - std::string text; - text = common_detokenize(ctx_tgt, prompt_tgt_main_model, true); - text = replace_to_dft(spec, text); - LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str()); - prompt_tgt_draft_model = common_tokenize(ctx_dft, text, false, true); + virtual void accept(uint16_t n_accepted) = 0; +}; - // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation - const auto * model_tgt = llama_get_model(ctx_tgt); - const auto * vocab_tgt = llama_model_get_vocab(model_tgt); +struct common_speculative_state_draft : public common_speculative_state { + llama_context * ctx_tgt; // only used for retokenizing from ctx_dft + llama_context * ctx_dft; - int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false); - GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last"); - text.resize(-n_chars); - llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false); - text = replace_to_dft(spec, text); + common_sampler * smpl; - LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str()); - id_last = common_tokenize(ctx_dft, text, false, true)[0]; - } - // prompt_tgt's tokens will always be compatible with ctx_dft - const llama_tokens &prompt_tgt = - spec->vocab_dft_compatible ? prompt_tgt_main_model : prompt_tgt_draft_model; + llama_batch batch; + llama_tokens prompt_dft; - const int i_start = std::max(0, (int) prompt_tgt.size() - n_ctx); + bool vocab_cmpt = true; // whether retokenization is needed + std::unordered_map vocab_map; - // reuse as much as possible from the old draft context - // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt - for (int i = 0; i < (int) prompt_dft.size(); ++i) { - int cur = 0; - while (i_start + cur < (int) prompt_tgt.size() && - i + cur < (int) prompt_dft.size() && - prompt_tgt[i_start + cur] == prompt_dft[i + cur]) { - cur++; + common_speculative_state_draft( + enum common_speculative_type type, + llama_context * ctx_tgt, + llama_context * ctx_dft, + const std::vector> & replacements) + : common_speculative_state(type) + , ctx_tgt(ctx_tgt) + , ctx_dft(ctx_dft) + { + batch = llama_batch_init(llama_n_batch(ctx_dft), 0, 1); + smpl = nullptr; + + // TODO: optimize or pass from outside? + // { + // common_params_sampling params; + // params.no_perf = false; + // + // params.top_k = 40; + // params.top_p = 0.9; + // + // params.samplers = { + // COMMON_SAMPLER_TYPE_TOP_K, + // COMMON_SAMPLER_TYPE_TOP_P, + // COMMON_SAMPLER_TYPE_INFILL, + // }; + // + // result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); + // } + { + common_params_sampling params; + params.no_perf = false; + params.top_k = 10; + params.samplers = { + COMMON_SAMPLER_TYPE_TOP_K, + }; + + smpl = common_sampler_init(llama_get_model(ctx_dft), params); } - if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) { - reuse_i = i; - reuse_n = cur; + vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft)); + LOG_DBG("vocab_cmpt = %d\n", vocab_cmpt); + + if (!vocab_cmpt) { + LOG_WRN("the target and draft vocabs are not compatible - tokens will be translated between the two\n"); + + for (const auto & pair : replacements) { + vocab_map[pair.first] = pair.second; + } } } - LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size()); + ~common_speculative_state_draft() override { + llama_perf_context_print(ctx_dft); - llama_tokens result; - result.reserve(params.n_draft); + llama_free(ctx_dft); - if (reuse_n == 0) { - llama_memory_clear(mem_dft, false); - prompt_dft.clear(); - } else { - // this happens when a previous draft has been discarded (for example, due to being too small), but the - // target model agreed with it. in this case, we simply pass back the previous results to save compute - if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) { - for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) { - result.push_back(prompt_dft[i]); + common_sampler_free(smpl); - if (params.n_draft <= (int) result.size()) { - break; - } + llama_batch_free(batch); + } + + void begin(const llama_tokens & prompt) override { + GGML_UNUSED(prompt); + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) override { + auto * spec = this; + + auto & batch = spec->batch; + auto & ctx_tgt = spec->ctx_tgt; + auto & ctx_dft = spec->ctx_dft; + auto & smpl = spec->smpl; + auto & prompt_dft = spec->prompt_dft; + + auto * mem_dft = llama_get_memory(ctx_dft); + + int reuse_i = 0; + int reuse_n = 0; + + const int n_ctx = llama_n_ctx(ctx_dft) - params.n_max; + + llama_tokens prompt_cnv; + if (!spec->vocab_cmpt) { + std::string text; + + text = common_detokenize(ctx_tgt, prompt_tgt, true); + text = replace_to_dft(text); + + LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str()); + + prompt_cnv = common_tokenize(ctx_dft, text, false, true); + + // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation + const auto * model_tgt = llama_get_model(ctx_tgt); + const auto * vocab_tgt = llama_model_get_vocab(model_tgt); + + int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false); + GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last"); + + text.resize(-n_chars); + llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false); + text = replace_to_dft(text); + + LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str()); + id_last = common_tokenize(ctx_dft, text, false, true)[0]; + } + + const llama_tokens & prompt_cur = spec->vocab_cmpt ? prompt_tgt : prompt_cnv; + + const int i_start = std::max(0, (int) prompt_cur.size() - n_ctx); + + // reuse as much as possible from the old draft context + // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt + for (int i = 0; i < (int) prompt_dft.size(); ++i) { + int cur = 0; + while (i_start + cur < (int) prompt_cur.size() && + i + cur < (int) prompt_dft.size() && + prompt_cur[i_start + cur] == prompt_dft[i + cur]) { + cur++; } - return result; + if ((cur >= 256 || n_ctx >= (int) prompt_cur.size()) && cur > reuse_n) { + reuse_i = i; + reuse_n = cur; + } } - if (reuse_i > 0) { - llama_memory_seq_rm (mem_dft, 0, 0, reuse_i); - llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i); + LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size()); - prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i); + result.clear(); + result.reserve(params.n_max); + + if (reuse_n == 0) { + llama_memory_clear(mem_dft, false); + prompt_dft.clear(); + } else { + // this happens when a previous draft has been discarded (for example, due to being too small), but the + // target model agreed with it. in this case, we simply pass back the previous results to save compute + if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) { + for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) { + result.push_back(prompt_dft[i]); + + if (params.n_max <= (int) result.size()) { + break; + } + } + + return; + } + + if (reuse_i > 0) { + llama_memory_seq_rm (mem_dft, 0, 0, reuse_i); + llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i); + + prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i); + } + + if (reuse_n < (int) prompt_dft.size()) { + llama_memory_seq_rm (mem_dft, 0, reuse_n, -1); + prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end()); + } } - if (reuse_n < (int) prompt_dft.size()) { - llama_memory_seq_rm (mem_dft, 0, reuse_n, -1); - prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end()); - } - } - - // prepare a batch to evaluate any new tokens in the prompt - common_batch_clear(batch); - - for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) { - //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]); - common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false); - - prompt_dft.push_back(prompt_tgt[i]); - } - - // we should rarely end-up here during normal decoding - if (batch.n_tokens > 0) { - //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str()); - - llama_decode(ctx_dft, batch); - } - - const llama_pos n_past = prompt_dft.size(); - - LOG_DBG("%s: n_past = %d\n", __func__, n_past); - - common_batch_clear(batch); - common_batch_add (batch, id_last, n_past, { 0 }, true); - - prompt_dft.push_back(id_last); - - LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str()); - - llama_decode(ctx_dft, batch); - - common_sampler_reset(smpl); - - // sample n_draft tokens from the draft model - for (int i = 0; i < params.n_draft; ++i) { + // prepare a batch to evaluate any new tokens in the prompt common_batch_clear(batch); - common_sampler_sample(smpl, ctx_dft, 0, true); + for (size_t i = i_start + reuse_n; i < prompt_cur.size(); ++i) { + //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_cur[i]); + common_batch_add(batch, prompt_cur[i], i - i_start, { 0 }, false); - 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", - k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); + prompt_dft.push_back(prompt_cur[i]); } - // add drafted token for each sequence - const llama_token id = cur_p->data[0].id; + // we should rarely end-up here during normal decoding + if (batch.n_tokens > 0) { + //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str()); - common_sampler_accept(smpl, id, true); - - result.push_back(id); - - if (params.n_draft <= (int) result.size()) { - break; + llama_decode(ctx_dft, batch); } - // only collect very high-confidence draft tokens - if (cur_p->data[0].p < params.p_min) { - break; - } + const llama_pos n_past = prompt_dft.size(); - common_batch_add(batch, id, n_past + i + 1, { 0 }, true); + LOG_DBG("%s: n_past = %d\n", __func__, n_past); + + common_batch_clear(batch); + common_batch_add (batch, id_last, n_past, { 0 }, true); + + prompt_dft.push_back(id_last); + + LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str()); - // evaluate the drafted tokens on the draft model llama_decode(ctx_dft, batch); - prompt_dft.push_back(id); + common_sampler_reset(smpl); + + // sample n_draft tokens from the draft model + for (int i = 0; i < params.n_max; ++i) { + common_batch_clear(batch); + + common_sampler_sample(smpl, ctx_dft, 0, true); + + 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", + k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); + } + + // add drafted token for each sequence + const llama_token id = cur_p->data[0].id; + + common_sampler_accept(smpl, id, true); + + result.push_back(id); + + if (params.n_max <= (int) result.size()) { + break; + } + + // only collect very high-confidence draft tokens + if (cur_p->data[0].p < params.p_min) { + break; + } + + common_batch_add(batch, id, n_past + i + 1, { 0 }, true); + + // evaluate the drafted tokens on the draft model + llama_decode(ctx_dft, batch); + + prompt_dft.push_back(id); + } + + if (!spec->vocab_cmpt) { + std::string detokenized = common_detokenize(ctx_dft, result, true); + detokenized = replace_to_tgt(detokenized); + LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str()); + result = common_tokenize(ctx_tgt, detokenized, false, true); + if (result.size() > (size_t)params.n_max) { + result.resize(params.n_max); + } + } } - if (!spec->vocab_dft_compatible) { - std::string detokenized = common_detokenize(ctx_dft, result, true); - detokenized = replace_to_tgt(spec, detokenized); - LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str()); - result = common_tokenize(ctx_tgt, detokenized, false, true); - if (result.size() > (size_t)params.n_draft) { - result.resize(params.n_draft); + void accept(uint16_t n_accepted) override { + // noop + GGML_UNUSED(n_accepted); + } + + std::string replace_to_dft(const std::string & input) const { + std::string result = input; + + for (const auto & pair : this->vocab_map) { + size_t pos = result.find(pair.first); + while (pos != std::string::npos) { + result.replace(pos, pair.first.length(), pair.second); + pos = result.find(pair.first, pos + pair.second.length()); + } } + + return result; + } + + std::string replace_to_tgt(const std::string & input) const { + std::string result = input; + + for (const auto & pair : this->vocab_map) { + size_t pos = result.find(pair.second); + while (pos != std::string::npos) { + result.replace(pos, pair.second.length(), pair.first); + pos = result.find(pair.second, pos + pair.first.length()); + } + } + + return result; + } +}; + +struct common_speculative_state_eagle3 : public common_speculative_state { + common_speculative_state_eagle3(enum common_speculative_type type) : common_speculative_state(type) {} + + void begin(const llama_tokens & prompt) override { + GGML_UNUSED(prompt); + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & draft_tokens) override { + // TODO: implement + GGML_UNUSED(params); + GGML_UNUSED(prompt_tgt); + GGML_UNUSED(id_last); + GGML_UNUSED(draft_tokens); + } + + void accept(uint16_t n_accepted) override { + // noop + GGML_UNUSED(n_accepted); + } +}; + +// state of self-speculation (simple implementation, not ngram-map) +struct common_speculative_state_ngram_simple : public common_speculative_state { + common_ngram_simple_state state; + + common_speculative_state_ngram_simple( + enum common_speculative_type type, + common_ngram_simple_state state) + : common_speculative_state(type), state(state) {} + + void begin(const llama_tokens & prompt) override { + GGML_UNUSED(prompt); + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) override { + result = common_ngram_simple_draft(state, prompt_tgt, id_last); + GGML_UNUSED(params); + } + + void accept(uint16_t n_accepted) override { + // noop + GGML_UNUSED(n_accepted); + } +}; + +struct common_speculative_state_ngram_map_k : public common_speculative_state { + // draft ngram map for speculative decoding without draft model + common_ngram_map map; + + common_speculative_state_ngram_map_k( + enum common_speculative_type type, + common_ngram_map map) + : common_speculative_state(type), map(std::move(map)) {} + + void begin(const llama_tokens & prompt) override { + GGML_UNUSED(prompt); + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) override { + common_ngram_map_draft(map, prompt_tgt, id_last, result); + GGML_UNUSED(params); + } + + void accept(uint16_t n_accepted) override { + common_ngram_map_accept(map, n_accepted); + } +}; + +struct common_speculative_state_ngram_cache : public common_speculative_state { + uint16_t n_draft; + bool save_dynamic; + bool save_static; + + common_ngram_cache ngram_cache_context; + common_ngram_cache ngram_cache_dynamic; + common_ngram_cache ngram_cache_static; + + size_t cache_size = 0; // number of tokens in n-gram cache + + common_speculative_state_ngram_cache( + const enum common_speculative_type type, + const std::string & path_static, + const std::string & path_dynamic, + uint16_t n_draft, + bool save_dynamic, + bool save_static) + : common_speculative_state(type) + , n_draft(n_draft) + , save_dynamic(save_dynamic) + , save_static(save_static) + { + if (!path_static.empty()) { + try { + ngram_cache_static = common_ngram_cache_load(path_static); + } catch (...) { + LOG_ERR("failed to open static lookup cache: %s", path_static.c_str()); + GGML_ABORT("Couldn't read static lookup cache"); + } + } + + if (!path_dynamic.empty()) { + try { + ngram_cache_dynamic = common_ngram_cache_load(path_dynamic); + } catch (...) { + LOG_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str()); + GGML_ABORT("Couldn't read dynamic lookup cache"); + } + } + } + + void begin(const llama_tokens & prompt) override { + GGML_UNUSED(prompt); + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) override { + GGML_UNUSED(params); + + if (cache_size < prompt_tgt.size() + 1) { + llama_tokens tokens_new; + tokens_new.reserve(prompt_tgt.size() + 1 - cache_size); + for (size_t j = cache_size; j < prompt_tgt.size(); ++j) { + tokens_new.push_back(prompt_tgt[j]); + } + tokens_new.push_back(id_last); // add the last token + + // Update context ngram cache with new prompt_tgt: + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, + tokens_new, tokens_new.size(), false); + cache_size = prompt_tgt.size() + 1; + } + + llama_tokens inp; + inp.reserve(prompt_tgt.size() + 1); + for (size_t j = 0; j < prompt_tgt.size(); ++j) { + inp.push_back(prompt_tgt[j]); + } + inp.push_back(id_last); + + result.push_back(id_last); + + common_ngram_cache_draft(inp, result, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, + ngram_cache_context, + ngram_cache_dynamic, + ngram_cache_static); + + if (result.size() > 0) { + // delete first token in result (which is the id_last token) + result.erase(result.begin()); + } + } + + void accept(uint16_t n_accepted) override { + // TODO: noop + GGML_UNUSED(n_accepted); + } +}; + +struct common_speculative { + std::vector> impls; // list of implementations to use and their states + common_speculative_state * curr_impl = nullptr; // current implementation in use (for stats) +}; + +static common_ngram_map get_common_ngram_map(const common_speculative_config & config) { + uint16_t size_key = config.params.ngram_size_n; + uint16_t size_value = config.params.ngram_size_m; + bool key_only = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K); + uint16_t check_rate = config.params.ngram_check_rate; + uint16_t min_hits = config.params.ngram_min_hits; + + return common_ngram_map(size_key, size_value, key_only, check_rate, min_hits); +} + +static common_speculative_state_ngram_cache create_state_ngram_cache( + const std::string & path_static, const std::string & path_dynamic, + const common_speculative_config & config) { + uint16_t n_draft = 8; // TODO get from config? + + // TODO bool param in common/common.h to set save_static/save_dynamic? + bool save_static = false; + bool save_dynamic = false; + + common_speculative_state_ngram_cache state(config.type, path_static, path_dynamic, n_draft, save_static, save_dynamic); + + return state; +} + +std::string common_speculative_type_name_str() { + std::string result; + for (size_t i = 0; i < common_speculative_types.size(); i++) { + if (i > 0) { + result += ", "; + } + result += common_speculative_type_to_str(common_speculative_types[i]); } return result; } + +std::string common_speculative_type_to_str(enum common_speculative_type type) { + switch (type) { + case COMMON_SPECULATIVE_TYPE_NONE: return "none"; + case COMMON_SPECULATIVE_TYPE_DRAFT: return "draft"; + case COMMON_SPECULATIVE_TYPE_EAGLE3: return "eagle3"; + case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: return "ngram_simple"; + case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: return "ngram_map_k"; + case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram_map_k4v"; + case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: return "ngram_cache"; + default: return "unknown"; + } +} + +enum common_speculative_type common_speculative_type_from_name(const std::string & name) { + const auto it = common_speculative_type_from_name_map.find(name); + if (it == common_speculative_type_from_name_map.end()) { + return COMMON_SPECULATIVE_TYPE_COUNT; + } + return it->second; +} + +// initialization of the speculative decoding system +// +common_speculative * common_speculative_init( + const common_params_speculative & params, + llama_context * ctx_tgt) { + llama_context * ctx_dft = nullptr; + if (params.model_dft) { + ctx_dft = llama_init_from_model(params.model_dft, params.cparams_dft); + if (ctx_dft == nullptr) { + LOG_ERR("%s", "failed to create draft context\n"); + return nullptr; + } + } + + // Compute the implementations to use based on the config and their order of preference + std::vector configs = {}; // list of speculative configs to try + { + bool has_draft = !params.mparams_dft.path.empty(); + bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3 + + bool has_ngram_cache = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_CACHE); + bool has_ngram_simple = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE); + bool has_ngram_map_k = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K); + bool has_ngram_map_k4v = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V); + + // In a more complex implementation we could use the same implementation but with different parameters. + // This was initially used in PR-18471 but removed to simplify the code. + if (has_ngram_simple) { + // This implementation can guess a lot of tokens without any draft model. + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params)); + } + if (has_ngram_map_k) { + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, params)); + } + if (has_ngram_map_k4v) { + // This implementation can guess tokens with high acceptance rate but is more expensive. + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params)); + } + if (has_ngram_cache) { + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params)); + } + if (has_draft) { + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT, params)); + } + if (has_draft_eagle3) { + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_EAGLE3, params)); + } + } + + std::vector> impls = {}; + + for (const common_speculative_config & config : configs) { + LOG_DBG("%s: adding implementation %s\n", __func__, common_speculative_type_to_str(config.type).c_str()); + switch (config.type) { + case COMMON_SPECULATIVE_TYPE_NONE: + break; + case COMMON_SPECULATIVE_TYPE_DRAFT: { + impls.push_back(std::make_unique(config.type, + /* .ctx_tgt = */ ctx_tgt, + /* .ctx_dft = */ ctx_dft, + /* .replacements = */ params.replacements + )); + break; + } + case COMMON_SPECULATIVE_TYPE_EAGLE3: { + impls.push_back(std::make_unique(config.type)); + break; + } + case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: { + common_ngram_map ngram_map = get_common_ngram_map(config); + + uint16_t ngram_size_key = ngram_map.size_key; + uint16_t mgram_size_value = ngram_map.size_value; + uint16_t check_rate = ngram_map.check_rate; + + auto config_simple = common_ngram_simple_config{ + /* .size_ngram = */ ngram_size_key, + /* .size_mgram = */ mgram_size_value, + /* .check_rate = */ check_rate + }; + auto state = std::make_unique( + /* .type = */ config.type, + /* .state = */ common_ngram_simple_state(config_simple) + ); + impls.push_back(std::move(state)); + break; + } + case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: + case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: { + impls.push_back(std::make_unique( + (config.type), + get_common_ngram_map(config) + )); + break; + } + case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: { + auto state = create_state_ngram_cache( + params.lookup_cache_static, params.lookup_cache_dynamic, config); + impls.push_back(std::make_unique(state)); + break; + } + default: + break; + } + } + + if (impls.empty()) { + LOG_WRN("%s", "no implementations specified for speculative decoding\n"); + return nullptr; + } + + auto * result = new common_speculative { + /* .impls = */ std::move(impls) + }; + + return result; +} + +void common_speculative_free(common_speculative * spec) { + if (spec == nullptr) { + return; + } + + delete spec; +} + +void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt) { + if (spec == nullptr) { + return; + } + + for (auto & impl : spec->impls) { + impl->begin(prompt); + } +} + +llama_tokens common_speculative_draft( + common_speculative * spec, + const common_params_speculative & params, + const llama_tokens & prompt_tgt, // specified in target model vocab + llama_token id_last) { + llama_tokens result; + + spec->curr_impl = nullptr; // reset current implementation + + for (auto & impl : spec->impls) { + { + const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; + + impl->draft(params, prompt_tgt, id_last, result); + + const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; + + impl->drafts_call_count++; + impl->gen_duration_us += t_now_us - t_start_us; // accumulate duration for this implementation + } + + if (!result.empty()) { + LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__, + common_speculative_type_to_str(impl.get()->type).c_str(), + prompt_tgt.size(), + impl.get()->drafts_call_count, result.size()); + + spec->curr_impl = impl.get(); // set current implementation for stats + impl->drafts_generated_count++; + impl->drafts_generated_tokens += result.size(); + + break; // We have a draft, so break out of the loop and return it. + } + } + + return result; +} + +void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) { + if (n_accepted == 0) { + return; + } + + common_speculative_state * impl = spec->curr_impl; + + GGML_ASSERT(impl); + + if (n_accepted > 0) { + impl->drafts_accepted_count++; + impl->drafts_accepted_tokens += n_accepted; + } + + impl->accept(n_accepted); +} + +void common_speculative_print_stats(const common_speculative * spec) { + if (spec == nullptr) { + return; + } + + for (const auto & impl : spec->impls) { + std::string str_perf; + if (impl->gen_perf) { + std::ostringstream oss; + oss << std::fixed << std::setprecision(3) << impl->gen_duration_us / 1000.0; + str_perf = ", dur = " + oss.str() + " ms"; + } else { + str_perf = ""; + } + + LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n", + common_speculative_type_to_str(impl->type).c_str(), + impl->drafts_call_count, + impl->drafts_generated_count, + impl->drafts_accepted_count, + impl->drafts_generated_tokens, + impl->drafts_accepted_tokens, + str_perf.c_str()); + } +} diff --git a/common/speculative.h b/common/speculative.h index e69d7aaa1e..9e1888e4be 100644 --- a/common/speculative.h +++ b/common/speculative.h @@ -5,31 +5,33 @@ struct common_speculative; -struct common_speculative_params { - int n_draft = 16; // max drafted tokens - int n_reuse = 256; +// comma separated list of all types +std::string common_speculative_type_name_str(); - float p_min = 0.75f; // min probability required to accept a token in the draft -}; +// convert string to type +enum common_speculative_type common_speculative_type_from_name(const std::string & name); -struct common_speculative * common_speculative_init( - struct llama_context * ctx_tgt, - struct llama_context * ctx_dft -); +// convert type to string +std::string common_speculative_type_to_str(enum common_speculative_type type); -void common_speculative_free(struct common_speculative * spec); +common_speculative * common_speculative_init( + const common_params_speculative & params, + llama_context * ctx_tgt); -bool common_speculative_are_compatible( - const struct llama_context * ctx_tgt, - const struct llama_context * ctx_dft); +void common_speculative_free(common_speculative * spec); -void common_speculative_add_replacement_tgt_dft( - struct common_speculative * spec, - const char *source, const char *dest); +// optionally call once at the beginning of a new generation +void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt); // sample up to n_draft tokens and add them to the batch using the draft model -llama_tokens common_speculative_gen_draft( - struct common_speculative * spec, - struct common_speculative_params params, - const llama_tokens & prompt, - llama_token id_last); +llama_tokens common_speculative_draft( + common_speculative * spec, + const common_params_speculative & params, + const llama_tokens & prompt, + llama_token id_last); + +// informs the speculative decoder that n_accepted tokens were accepted by the target model +void common_speculative_accept(common_speculative * spec, uint16_t n_accepted); + +// print statistics about the speculative decoding +void common_speculative_print_stats(const common_speculative * spec); diff --git a/docs/speculative.md b/docs/speculative.md new file mode 100644 index 0000000000..8281eaa2d3 --- /dev/null +++ b/docs/speculative.md @@ -0,0 +1,120 @@ +# Speculative Decoding + +llama.cpp supports speculative decoding, a technique that can significantly accelerate token generation by predicting multiple tokens ahead of the main model. + +[Speculative decoding](https://en.wikipedia.org/wiki/Transformer_(deep_learning)#Speculative_decoding) leverages the fact that computing n tokens in a batch (as in prompt processing) is more efficient than computing n sequentially (as in response generation). By generating draft tokens quickly and then verifying them with the target model in a single batch, this approach can achieve substantial speedups when the draft predictions are frequently correct. + +## Implementations + +The `llama-server` application supports several implementations of speculative decoding: + +### Draft Model (`draft`) + +A much smaller model (called the _draft model_) generates drafts. +A draft model is the most used approach in speculative decoding. + +### n-gram Cache (`ngram-cache`) + +An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences. +A draft is computed using probabilities derived from these statistics. External statistics can also be loaded from files for improved accuracy. + +See: + +- #5479, #6828, #6848 + +### n-gram Map (`ngram-simple`, `ngram-map-*`) + +These implementations search the token history for patterns and use matching sequences as draft candidates. +They require no additional model but rely on patterns that have already appeared in the generated text. +An example to use this approach can be the rewriting of source code by a LLM. + +#### n-gram Map (`ngram-simple`) + +This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead. + +#### n-gram Map Key (`ngram-map-k`) + +This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`) before generating drafts. + +The number of accepted tokens is stored for each used n-gram. + +#### n-gram Map Key-4-Values (`ngram-map-k4v`) + +This experimental implementation looks for the current n-gram of size n (called the _key_) in the token history. For each key, up to four _values_ (n-grams of size m, called _mgrams_) are tracked. An internal statistic counts the occurrences of each mgram after the key n-gram. If one mgram is significantly more frequent than the others, it is used as the draft. + +The number of accepted tokens is stored for each used n-gram. + +**Example:** Server options to be used if there are a lot of longer repetitions. +```bash +llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 +``` + + +## Command-Line Options + +If a draft model is combined with a draftless decoding the draftless decoding has higher precedence. + +``` +--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v] + type of speculative decoding to use when no draft model is provided + (default: none) +--spec-ngram-size-n N ngram size N for ngram-simple/ngram-map speculative decoding, length + of lookup n-gram (default: 12) +--spec-ngram-size-m N ngram size M for ngram-simple/ngram-map speculative decoding, length + of draft m-gram (default: 48) +--spec-ngram-check-rate N ngram check rate for ngram-simple/ngram-map speculative decoding + (default: 1) +--spec-ngram-min-hits N minimum hits for ngram-map speculative decoding (default: 1) +``` + +### `--spec-type TYPE` + +Specifies a type of speculative decoding without draft model. + +| Type | Description | +|------|-------------| +| `none` | No speculative decoding (default) | +| `ngram-cache` | Use n-gram cache lookup | +| `ngram-simple` | Use simple n-gram pattern matching | +| `ngram-map-k` | Use n-gram pattern matching with n-gram-keys | +| `ngram-map-k4v` | Use n-gram pattern matching with n-gram-keys and up to four m-gram values (experimental) | + +**Example:** Server-instance used to refactor source code. +```bash +./llama-server [...] --spec-type ngram-simple +``` + +### `--spec-ngram-size-n N` + +Sets the size N of the lookup n-gram for n-gram map based speculative decoding. +The n-gram size N determines how many tokens in a row to look back when searching for matching patterns. + +### `--spec-ngram-size-m M` + +Sets the size M of the draft m-gram for n-gram map based speculative decoding. +The m-gram size determines how many tokens to draft when a match is found. +Larger values can provide more speedup but may reduce acceptance rate. + +### `--spec-ngram-check-rate R` + +This option aims at performance if the n-gram lookup in history is to costly. A lookup will be executed at every R tokens (default is 1, every token). + +### `--spec-ngram-min-hits H` + +This option defines how often a key has to appear in the token history to be used as a draft (default is 1). + +## Statistics +Each speculative decoding implementation prints statistics. + +``` +draft acceptance rate = 0.57576 ( 171 accepted / 297 generated) +statistics ngram_simple: #calls = 15, #gen drafts = 5, #acc drafts = 5, #gen tokens = 187, #acc tokens = 73 +statistics draft: #calls = 10, #gen drafts = 10, #acc drafts = 10, #gen tokens = 110, #acc tokens = 98 +``` + +- `#calls`: number of calls of this implementations +- `#gen drafts`: number of drafts generated by this implementation +- `#acc drafts`: number of drafts accepted (partially) by the main model +- `#gen tokens`: number of tokens generated by this implementation (including rejected tokens) +- `#acc tokens`: number of tokens accepted by the main model + diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index bb94a8fe06..f7b6ea1b19 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -32,9 +32,9 @@ int main(int argc, char ** argv){ common_ngram_cache ngram_cache; common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); - fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str()); + fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.lookup_cache_static.c_str()); - common_ngram_cache_save(ngram_cache, params.lookup_cache_static); + common_ngram_cache_save(ngram_cache, params.speculative.lookup_cache_static); return 0; } diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 135f6fcab9..ae28b2e6e8 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -46,18 +46,18 @@ int main(int argc, char ** argv){ { const int64_t t_start_draft_us = ggml_time_us(); - if (!params.lookup_cache_static.empty()) { + if (!params.speculative.lookup_cache_static.empty()) { try { - ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static); + ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static); } catch (std::ifstream::failure const &) { - LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str()); exit(1); } } - if (!params.lookup_cache_dynamic.empty()) { + if (!params.speculative.lookup_cache_dynamic.empty()) { try { - ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic); + ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic); } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program } diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 27f159940a..8e73138a5f 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -51,18 +51,18 @@ int main(int argc, char ** argv){ const int64_t t_start_draft_us = ggml_time_us(); common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false); - if (!params.lookup_cache_static.empty()) { + if (!params.speculative.lookup_cache_static.empty()) { try { - ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static); + ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static); } catch (std::ifstream::failure const &) { - LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str()); exit(1); } } - if (!params.lookup_cache_dynamic.empty()) { + if (!params.speculative.lookup_cache_dynamic.empty()) { try { - ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic); + ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic); } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program } @@ -210,7 +210,7 @@ int main(int argc, char ** argv){ // Update dynamic ngram cache with context ngram cache and save it to disk: common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context); - common_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic); + common_ngram_cache_save(ngram_cache_dynamic, params.speculative.lookup_cache_dynamic); LOG("\n\n"); diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp index 8141052a22..d8b1f5a480 100644 --- a/examples/speculative-simple/speculative-simple.cpp +++ b/examples/speculative-simple/speculative-simple.cpp @@ -24,7 +24,7 @@ int main(int argc, char ** argv) { common_init(); - if (params.speculative.model.path.empty()) { + if (params.speculative.mparams_dft.path.empty()) { LOG_ERR("%s: --model-draft is required\n", __func__); return 1; } @@ -34,10 +34,8 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); llama_model * model_tgt = NULL; - //llama_model * model_dft = NULL; llama_context * ctx_tgt = NULL; - llama_context * ctx_dft = NULL; // load the target model auto llama_init_tgt = common_init_from_params(params); @@ -48,26 +46,38 @@ int main(int argc, char ** argv) { const llama_vocab * vocab = llama_model_get_vocab(model_tgt); // load the draft model - params.devices = params.speculative.devices; - params.model = params.speculative.model; - params.n_ctx = params.speculative.n_ctx; - params.n_batch = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch; - params.n_gpu_layers = params.speculative.n_gpu_layers; + llama_model_ptr model_dft; - if (params.speculative.cpuparams.n_threads > 0) { - params.cpuparams.n_threads = params.speculative.cpuparams.n_threads; - } + // TODO: simplify this logic + { + const auto & params_spec = params.speculative; - params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; - params.tensor_buft_overrides = params.speculative.tensor_buft_overrides; + auto params_dft = params; - auto llama_init_dft = common_init_from_params(params); + params_dft.n_parallel = 1; + params_dft.n_ctx = params_spec.n_ctx; + params_dft.n_batch = llama_n_ctx_seq(ctx_tgt); + params_dft.devices = params_spec.devices; + params_dft.model = params_spec.mparams_dft; + params_dft.n_gpu_layers = params_spec.n_gpu_layers; - //model_dft = llama_init_dft->model(); - ctx_dft = llama_init_dft->context(); + if (params_spec.cpuparams.n_threads > 0) { + params_dft.cpuparams.n_threads = params.speculative.cpuparams.n_threads; + params_dft.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; + } - if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) { - LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str()); + params_dft.tensor_buft_overrides = params.speculative.tensor_buft_overrides; + + auto mparams_dft = common_model_params_to_llama(params_dft); + + model_dft.reset(llama_model_load_from_file(params_dft.model.path.c_str(), mparams_dft)); + if (model_dft == nullptr) { + LOG_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str()); + return 1; + } + + params.speculative.model_dft = model_dft.get(); + params.speculative.cparams_dft = common_context_params_to_llama(params_dft); } // Tokenize the prompt @@ -92,12 +102,6 @@ int main(int argc, char ** argv) { LOG("%s", common_token_to_piece(ctx_tgt, id).c_str()); } - // how many tokens to draft each time - int n_draft = params.speculative.n_max; - int n_draft_min = params.speculative.n_min; - - float p_min = params.speculative.p_min; - int n_predict = 0; int n_drafted = 0; int n_accept = 0; @@ -127,15 +131,11 @@ int main(int argc, char ** argv) { int n_past = inp.size() - 1; // init the speculator - struct common_speculative_params params_spec; - params_spec.n_draft = n_draft; - params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft; - params_spec.p_min = p_min; + const auto & params_spec = params.speculative; - struct common_speculative * spec = common_speculative_init(ctx_tgt, ctx_dft); - for (auto &pair : params.speculative.replacements) { - common_speculative_add_replacement_tgt_dft(spec, pair.first.c_str(), pair.second.c_str()); - } + struct common_speculative * spec = common_speculative_init(params.speculative, ctx_tgt); + + common_speculative_begin(spec, prompt_tgt); llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1); @@ -151,7 +151,7 @@ int main(int argc, char ** argv) { // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens // from a cache or lookup tables. // - llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last); + llama_tokens draft = common_speculative_draft(spec, params_spec, prompt_tgt, id_last); //LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str()); @@ -162,7 +162,7 @@ int main(int argc, char ** argv) { // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1] { // do not waste time on small drafts - if (draft.size() < (size_t) n_draft_min) { + if (draft.size() < (size_t) params_spec.n_min) { draft.clear(); } @@ -240,7 +240,7 @@ int main(int argc, char ** argv) { LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); LOG_INF("\n"); - LOG_INF("n_draft = %d\n", n_draft); + LOG_INF("n_draft = %d\n", params_spec.n_max); LOG_INF("n_predict = %d\n", n_predict); LOG_INF("n_drafted = %d\n", n_drafted); LOG_INF("n_accept = %d\n", n_accept); @@ -249,8 +249,6 @@ int main(int argc, char ** argv) { LOG_INF("\n"); LOG_INF("draft:\n\n"); - llama_perf_context_print(ctx_dft); - LOG_INF("\n"); LOG_INF("target:\n\n"); common_perf_print(ctx_tgt, smpl); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 89d3249431..3e5cf5f46b 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -46,7 +46,7 @@ int main(int argc, char ** argv) { common_init(); - if (params.speculative.model.path.empty()) { + if (params.speculative.mparams_dft.path.empty()) { LOG_ERR("%s: --model-draft is required\n", __func__); return 1; } @@ -78,7 +78,7 @@ int main(int argc, char ** argv) { // load the draft model params.devices = params.speculative.devices; - params.model = params.speculative.model; + params.model = params.speculative.mparams_dft; params.n_gpu_layers = params.speculative.n_gpu_layers; if (params.speculative.cpuparams.n_threads > 0) { params.cpuparams.n_threads = params.speculative.cpuparams.n_threads; diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 73cb4c75b3..1ca4e3cc0e 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -48,11 +48,8 @@ enum server_state { struct server_slot { int id; - llama_batch batch_spec = {}; - // TODO: change to unique_ptrs for consistency: llama_context * ctx = nullptr; - llama_context * ctx_dft = nullptr; // multimodal mtmd_context * mctx = nullptr; @@ -259,7 +256,7 @@ struct server_slot { } bool can_speculate() const { - return ctx_dft; + return !!spec; } void add_token(const completion_token_output & token) { @@ -295,6 +292,7 @@ struct server_slot { SLT_DBG(*this, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, task->params.speculative.n_min); n_draft_max = 0; } + return n_draft_max; } @@ -397,6 +395,8 @@ struct server_slot { draft_ratio, n_draft_accepted, n_draft_total ); } + + common_speculative_print_stats(spec); } json to_json(bool only_metrics = false) const { @@ -553,18 +553,13 @@ private: // note: keep these alive - they determine the lifetime of the model, context, etc. common_init_result_ptr llama_init; - common_init_result_ptr llama_init_dft; llama_context * ctx = nullptr; - bool vocab_dft_compatible = true; - - llama_model * model_dft = nullptr; - - llama_context_params cparams_dft; - llama_batch batch {}; + llama_model_ptr model_dft; + bool add_bos_token = true; int32_t n_ctx; // total context for all clients / slots @@ -597,13 +592,8 @@ private: // Clear any sampling context for (server_slot & slot : slots) { - llama_free(slot.ctx_dft); - slot.ctx_dft = nullptr; - common_speculative_free(slot.spec); slot.spec = nullptr; - - llama_batch_free(slot.batch_spec); } llama_batch_free(batch); @@ -648,44 +638,39 @@ private: add_bos_token = llama_vocab_get_add_bos(vocab); - if (params_base.has_speculative()) { - SRV_INF("loading draft model '%s'\n", params_base.speculative.model.path.c_str()); + if (params_base.speculative.has_dft()) { + SRV_INF("loading draft model '%s'\n", params_base.speculative.mparams_dft.path.c_str()); + + const auto & params_spec = params_base.speculative; auto params_dft = params_base; - params_dft.devices = params_base.speculative.devices; - params_dft.model = params_base.speculative.model; - params_dft.n_ctx = params_base.speculative.n_ctx == 0 ? llama_n_ctx_seq(ctx) : params_base.speculative.n_ctx; - params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers; params_dft.n_parallel = 1; - params_dft.cache_type_k = params_base.speculative.cache_type_k; - params_dft.cache_type_v = params_base.speculative.cache_type_v; + params_dft.n_ctx = params_spec.n_ctx == 0 ? llama_n_ctx_seq(ctx) : params_spec.n_ctx; + params_dft.n_batch = llama_n_ctx_seq(ctx); + params_dft.devices = params_spec.devices; + params_dft.model = params_spec.mparams_dft; + params_dft.n_gpu_layers = params_spec.n_gpu_layers; + params_dft.cache_type_k = params_spec.cache_type_k; + params_dft.cache_type_v = params_spec.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; + if (params_spec.cpuparams.n_threads > 0) { + params_dft.cpuparams.n_threads = params_spec.cpuparams.n_threads; + params_dft.cpuparams_batch.n_threads = params_spec.cpuparams_batch.n_threads; + } - llama_init_dft = common_init_from_params(params_dft); + params_dft.tensor_buft_overrides = params_spec.tensor_buft_overrides; - model_dft = llama_init_dft->model(); + auto mparams_dft = common_model_params_to_llama(params_dft); + model_dft.reset(llama_model_load_from_file(params_dft.model.path.c_str(), mparams_dft)); if (model_dft == nullptr) { - SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str()); + SRV_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str()); return false; } - vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft->context()); - if (!vocab_dft_compatible) { - SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str()); - } - - const int n_ctx_dft = llama_n_ctx(llama_init_dft->context()); - - cparams_dft = common_context_params_to_llama(params_dft); - cparams_dft.n_batch = n_ctx_dft; - - // the context is not needed - we will create one for each slot - llama_init_dft->free_context(); + params_base.speculative.model_dft = model_dft.get(); + params_base.speculative.cparams_dft = common_context_params_to_llama(params_dft); } std::string & mmproj_path = params_base.mmproj.path; @@ -695,6 +680,7 @@ private: } mtmd_context_params mparams = mtmd_context_params_default(); + mparams.use_gpu = params_base.mmproj_use_gpu; mparams.print_timings = false; mparams.n_threads = params_base.cpuparams.n_threads; @@ -702,6 +688,7 @@ private: mparams.warmup = params_base.warmup; mparams.image_min_tokens = params_base.image_min_tokens; mparams.image_max_tokens = params_base.image_max_tokens; + mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams); if (mctx == nullptr) { SRV_ERR("failed to load multimodal model, '%s'\n", mmproj_path.c_str()); @@ -718,11 +705,6 @@ private: params_base.n_cache_reuse = 0; SRV_WRN("%s\n", "cache_reuse is not supported by multimodal, it will be disabled"); } - - if (params_base.has_speculative()) { - SRV_ERR("%s\n", "err: speculative decode is not supported by multimodal"); - return false; - } } if (!llama_memory_can_shift(llama_get_memory(ctx))) { @@ -757,29 +739,24 @@ private: for (int i = 0; i < params_base.n_parallel; i++) { server_slot slot; - slot.id = i; - slot.ctx = ctx; + slot.id = i; + slot.ctx = ctx; slot.n_ctx = n_ctx_slot; - slot.mctx = mctx; + + slot.mctx = mctx; slot.prompt.tokens.has_mtmd = mctx != nullptr; - if (model_dft) { - slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1); - - // TODO: rework speculative decoding [TAG_SERVER_SPEC_REWORK] - slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft); - if (slot.ctx_dft == nullptr) { - SRV_ERR("%s", "failed to create draft context\n"); - return false; - } - - slot.spec = common_speculative_init(slot.ctx, slot.ctx_dft); - if (slot.spec == nullptr) { - SRV_ERR("%s", "failed to create speculator\n"); - return false; - } - for (auto & pair : params_base.speculative.replacements) { - common_speculative_add_replacement_tgt_dft(slot.spec, pair.first.c_str(), pair.second.c_str()); + // try speculative decoding + { + slot.spec = common_speculative_init(params_base.speculative, slot.ctx); + if (slot.spec) { + if (mctx) { + SRV_ERR("%s\n", "speculative decoding is not supported with multimodal"); + return false; + } + SRV_WRN("%s", "speculative decoding context initialized\n"); + } else { + SRV_WRN("%s", "speculative decoding context not initialized\n"); } } @@ -1059,7 +1036,7 @@ private: return res; } - std::vector construct_lora_list(const std::map & config) { + std::vector construct_lora_list(const std::map & config) const { std::vector output = params_base.lora_adapters; // copy for (size_t i = 0; i < output.size(); ++i) { auto it = config.find(i); @@ -1162,7 +1139,7 @@ private: backend_sampling &= task.params.sampling.backend_sampling; // TODO: speculative decoding requires multiple samples per batch - not supported yet - backend_sampling &= !(slot.ctx_dft && task.params.speculative.n_max > 0); + backend_sampling &= !(slot.spec && task.params.speculative.n_max > 0); // TODO: getting post/pre sampling logits is not yet supported with backend sampling backend_sampling &= !need_logits; @@ -1179,14 +1156,6 @@ private: slot.smpl.reset(); } - // initialize draft batch - // TODO: rework speculative decoding [TAG_SERVER_SPEC_REWORK] - if (slot.ctx_dft) { - llama_batch_free(slot.batch_spec); - - slot.batch_spec = llama_batch_init(task.params.speculative.n_max + 1, 0, 1); - } - slot.task = std::make_unique(std::move(task)); slot.state = slot.task->is_child() @@ -2059,19 +2028,23 @@ private: // generate draft tokens in speculative decoding mode // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK] // perform the speculative drafting for all sequences at the same time in a single batch - int n_draft_max = slot.get_n_draft_max(); + const int n_draft_max = slot.get_n_draft_max(); if (n_draft_max > 0) { if (mctx) { // we should never reach this, as speculative is automatically disabled if mmproj is loaded GGML_ABORT("not supported by multimodal"); } - struct common_speculative_params params_spec; - params_spec.n_draft = n_draft_max; - params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.task->params.speculative.n_max; - params_spec.p_min = slot.task->params.speculative.p_min; const llama_tokens & cached_text_tokens = slot.prompt.tokens.get_text_tokens(); - llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, slot.sampled); + + const auto & params_spec = slot.task->params.speculative; + + llama_tokens draft = common_speculative_draft(slot.spec, params_spec, cached_text_tokens, slot.sampled); + + if (draft.size() > (size_t) n_draft_max) { + SLT_WRN(slot, "draft size %d exceeds max %d, truncating\n", (int) draft.size(), n_draft_max); + draft.resize(n_draft_max); + } // add the sampled token to the batch slot.i_batch_dft.push_back(batch.n_tokens); @@ -2742,6 +2715,10 @@ private: // prompt evaluated for next-token prediction slot.state = SLOT_STATE_GENERATING; + + if (slot.can_speculate()) { + common_speculative_begin(slot.spec, slot.prompt.tokens.get_text_tokens()); + } } else if (slot.state != SLOT_STATE_GENERATING) { continue; // continue loop of slots } @@ -2813,6 +2790,9 @@ private: // update how many tokens out of those tested were accepted slot.n_draft_accepted += ids.size() - 1; + // inform the speculative decoding about the number of accepted tokens + common_speculative_accept(slot.spec, ids.size() - 1); + // rollback to the state before sampling the draft tokens slot.prompt.tokens.keep_first(slot.prompt.n_tokens() - n_draft); diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 799e341d37..2d25db63b7 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -5,6 +5,7 @@ #include "llama.h" #include "chat.h" #include "sampling.h" +#include "speculative.h" #include "json-schema-to-grammar.h" using json = nlohmann::ordered_json; @@ -76,6 +77,11 @@ json task_params::to_json(bool only_metrics) const { {"speculative.n_max", speculative.n_max}, {"speculative.n_min", speculative.n_min}, {"speculative.p_min", speculative.p_min}, + {"speculative.type", common_speculative_type_to_str(speculative.type)}, + {"speculative.ngram_size_n", speculative.ngram_size_n}, + {"speculative.ngram_size_m", speculative.ngram_size_m}, + {"speculative.ngram_c_rate", speculative.ngram_check_rate}, + {"speculative.ngram_m_hits", speculative.ngram_min_hits}, {"timings_per_token", timings_per_token}, {"post_sampling_probs", post_sampling_probs}, {"backend_sampling", sampling.backend_sampling}, @@ -135,6 +141,11 @@ json task_params::to_json(bool only_metrics) const { {"speculative.n_max", speculative.n_max}, {"speculative.n_min", speculative.n_min}, {"speculative.p_min", speculative.p_min}, + {"speculative.type", common_speculative_type_to_str(speculative.type)}, + {"speculative.ngram_size_n", speculative.ngram_size_n}, + {"speculative.ngram_size_m", speculative.ngram_size_m}, + {"speculative.ngram_c_rate", speculative.ngram_check_rate}, + {"speculative.ngram_m_hits", speculative.ngram_min_hits}, {"timings_per_token", timings_per_token}, {"post_sampling_probs", post_sampling_probs}, {"backend_sampling", sampling.backend_sampling}, @@ -242,6 +253,18 @@ task_params server_task::params_from_json_cmpl( params.speculative.n_min = std::max(params.speculative.n_min, 0); params.speculative.n_max = std::max(params.speculative.n_max, 0); + params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type))); + + params.speculative.ngram_size_n = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n); + params.speculative.ngram_size_m = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m); + params.speculative.ngram_check_rate = json_value(data, "speculative.ngram_c_rate", defaults.speculative.ngram_check_rate); + params.speculative.ngram_min_hits = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits); + + params.speculative.ngram_size_n = std::max(std::min(1, (int) params.speculative.ngram_size_n), 1024); + params.speculative.ngram_size_m = std::max(std::min(1, (int) params.speculative.ngram_size_m), 1024); + params.speculative.ngram_check_rate = std::max(std::min(1, (int) params.speculative.ngram_check_rate), 1024); + params.speculative.ngram_min_hits = std::max(std::min(1, (int) params.speculative.ngram_min_hits), 1024); + // Use OpenAI API logprobs only if n_probs wasn't provided if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){ params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs); From f6b533d898ce84bae8d9fa8dfc6697ac087800bf Mon Sep 17 00:00:00 2001 From: Ruben Ortlam Date: Wed, 28 Jan 2026 18:52:45 +0100 Subject: [PATCH 049/206] Vulkan Flash Attention Coopmat1 Refactor (#19075) * vulkan: use coopmat for flash attention p*v matrix multiplication * fix P loading issue * fix barrier position * remove reduction that is no longer needed * move max thread reduction into loop * remove osh padding * add bounds checks and padding * remove unused code * fix shmem sizes, loop duration and accesses * don't overwrite Qf, add new shared psh buffer instead * add missing bounds checks * use subgroup reductions * optimize * move bounds check, reduce barriers * support other Bc values and other subgroup sizes * remove D_split * replace Of register array with shared memory Ofsh array * parallelize HSV across the rowgroups * go back to Of in registers, not shmem * vectorize sfsh * don't store entire K tile in shmem * fixes * load large k tiles to shmem on Nvidia * adapt shared memory host check function to shader changes * remove Bc 32 case * remove unused variable * fix missing mask reduction tmspsh barrier * fix mask bounds check * fix rowmax f16 under/overflow to inf * fix flash_attn_cm2 BLOCK_SIZE preprocessor directives --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 64 ++- .../vulkan-shaders/flash_attn_base.glsl | 6 + .../vulkan-shaders/flash_attn_cm1.comp | 438 +++++++++++------- .../vulkan-shaders/flash_attn_cm2.comp | 6 +- 4 files changed, 329 insertions(+), 185 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 514f290d09..3852867c29 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -3162,17 +3162,31 @@ static void ggml_vk_load_shaders(vk_device& device) { // For scalar, use 128 (arbitrary) // The same D_split value is used for both HSK and HSV, so just base it on the union of the LSBs. const uint32_t D = (hsk|hsv); - uint32_t wg_size = (path == FA_SCALAR || path == FA_COOPMAT1) - ? scalar_flash_attention_workgroup_size - : ((small_rows && (D % 32) == 0) ? 256 : 128); auto rows_cols = fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache); + uint32_t wg_size; + switch (path) { + case FA_COOPMAT2: + wg_size = ((small_rows && (D % 32) == 0) ? 256 : 128); + break; + case FA_COOPMAT1: + wg_size = (rows_cols[1] / 16) * device->subgroup_size; // enough subgroups for Bc/MatBc + break; + default: + wg_size = scalar_flash_attention_workgroup_size; + break; + } + // D_split can't be larger than a subgroup because we use subgroupShuffle to reduce it. // D_split can't be larger than the LSB of D divided by 4 due to vectorization in the shader. const uint32_t D_lsb = D ^ (D & (D-1)); uint32_t D_split = std::min(std::min(device->subgroup_size, 8u), D_lsb / 4); - return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split}; + // Nvidia prefers shared memory use to load large tiles of K + // AMD prefers loading K directly from global memory + const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA ? 1 : 0; + + return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem}; }; #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \ @@ -3187,15 +3201,15 @@ static void ggml_vk_load_shaders(vk_device& device) { 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \ } \ } \ } \ @@ -8344,41 +8358,49 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; - VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", total_size=" << total_size << ", supported=" << supported); + VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", total_size=" << total_size << ", supported=" << supported); return supported; } -static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv, bool f32acc) { +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type kv_type) { // Needs to be kept up to date on shader changes GGML_UNUSED(hsv); - const uint32_t wg_size = scalar_flash_attention_workgroup_size; - const uint32_t Br = coopmat1_flash_attention_num_large_rows; - const uint32_t Bc = scalar_flash_attention_Bc; + const auto rows_cols = fa_rows_cols(FA_COOPMAT1, hsk, hsv, 0, kv_type, false, false); + const uint32_t Br = rows_cols[0]; + const uint32_t Bc = rows_cols[1]; + + const uint32_t MatBr = 16, MatBc = 16; + + const uint32_t row_split = Bc / MatBc; 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 tmpsh = (Bc / MatBc) * sizeof(float); const uint32_t qstride = hsk_pad / 4 + 2; const uint32_t Qf = Br * qstride * f16vec4; + const uint32_t psh_stride = Br / 4 + 2; + const uint32_t Psh = Bc * psh_stride * f16vec4; + const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br; const uint32_t sfsh = Bc * sfshstride * acctype; - const uint32_t kshstride = hsk_pad / 4 + 2; - const uint32_t ksh = Bc * kshstride * f16vec4; + const bool k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA; + const uint32_t kshstride = (k_load_shmem ? hsk_pad : MatBr) / 4 + 2; + const uint32_t vsh_stride = MatBc / 4 * row_split; + const uint32_t ksh = ((kshstride >= vsh_stride) ? (Bc * kshstride) : (Bc * vsh_stride)) * f16vec4; - const uint32_t slope = Br * sizeof(float); + const uint32_t slope = Br * acctype; - const uint32_t total_size = tmpsh + tmpshv4 + Qf + sfsh + ksh + slope; + const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + slope; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; - VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported); + VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", kv_type=" << kv_type << ", total_size=" << total_size << ", supported=" << supported); return supported; } @@ -8442,7 +8464,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx const bool coopmat_shape_supported = (dst->op_params[3] == GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f32acc) || (dst->op_params[3] != GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f16acc); - const bool coopmat_shmem_supported = ggml_vk_flash_attn_coopmat_shmem_support(ctx->device, HSK, HSV, dst->op_params[3] == GGML_PREC_F32); + const bool coopmat_shmem_supported = ggml_vk_flash_attn_coopmat_shmem_support(ctx->device, HSK, HSV, dst->op_params[3] == GGML_PREC_F32, k->type); if (!coopmat_shape_supported || !coopmat_shmem_supported) { path = FA_SCALAR; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl index 29b5c7c3a4..23a4d2c005 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -8,6 +8,8 @@ layout (constant_id = 3) const uint32_t HSK = 32; 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; +layout (constant_id = 7) const uint32_t SubGroupSize = 32; +layout (constant_id = 8) const uint32_t K_LOAD_SHMEM = 0; // Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths const uint32_t HSK_pad = (HSK + 15) & ~15; @@ -74,6 +76,10 @@ layout (binding = 1) readonly buffer K_PACKED16 {A_TYPE_PACKED16 k_data_packed16 layout (binding = 2) readonly buffer V_PACKED16 {A_TYPE_PACKED16 v_data_packed16[];} v_packed; #endif +#ifndef BLOCK_SIZE +#define BLOCK_SIZE 1 +#endif + #if defined(DATA_A_F32) #undef BLOCK_SIZE #define BLOCK_SIZE 4 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 0eb50fe58f..83d52d19d6 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -7,6 +7,7 @@ #extension GL_EXT_shader_explicit_arithmetic_types_int32 : require #extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable #extension GL_KHR_shader_subgroup_vote : enable #extension GL_KHR_memory_scope_semantics : enable #extension GL_KHR_cooperative_matrix : enable @@ -14,12 +15,13 @@ #include "types.glsl" #include "flash_attn_base.glsl" -const uint32_t HSK_per_thread = HSK / D_split; -const uint32_t HSV_per_thread = HSV / D_split; +// These need to be supported N,M values for a MatBc x MatBr x 16 coopmatmuladd +const uint32_t MatBr = 16; +const uint32_t MatBc = 16; -const uint32_t row_split = 4; +const uint32_t row_split = Bc / MatBc; const uint32_t rows_per_thread = Br / row_split; -const uint32_t cols_per_iter = gl_WorkGroupSize.x / D_split / row_split; +const uint32_t cols_per_iter = gl_WorkGroupSize.x / row_split; const uint32_t cols_per_thread = Bc / cols_per_iter; @@ -40,24 +42,24 @@ D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TY return elem; } -// These need to be supported N,M values for a MatBc x MatBr x 16 coopmatmuladd -const uint32_t MatBr = 16; -const uint32_t MatBc = 16; - -shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x]; -shared ACC_TYPEV4 tmpshv4[gl_WorkGroupSize.x]; +shared float tmpsh[row_split]; const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4 shared f16vec4 Qf[Br * qstride]; +const uint psh_stride = Br / 4 + 2; +shared f16vec4 Psh[Bc * psh_stride]; + // 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 sfshstride = (HSK <= 128) ? (Br / 4 + 2) : Br / 4; +shared ACC_TYPEV4 sfsh[Bc * sfshstride]; -const uint32_t kshstride = HSK_pad / 4 + 2; // in units of f16vec4 -shared f16vec4 ksh[Bc * kshstride]; +const uint32_t kshstride = (K_LOAD_SHMEM != 0 ? HSK_pad : MatBr) / 4 + 2; // in units of f16vec4 +const uint v_cols = MatBc / 4 * row_split; // total cols, 4 vec4s per MatBc * number of subgroups +const uint vsh_stride = v_cols; +shared f16vec4 ksh[(kshstride >= vsh_stride) ? (Bc * kshstride) : (Bc * vsh_stride)]; -shared float slope[Br]; +shared ACC_TYPE slope[Br]; void main() { #ifdef NEEDS_INIT_IQ_SHMEM @@ -69,9 +71,9 @@ void main() { const uint32_t tid = gl_LocalInvocationIndex; const uint32_t threads_per_rowgroup = gl_WorkGroupSize.x / row_split; + const uint32_t d_per_thread = (HSV/4 + threads_per_rowgroup - 1) / threads_per_rowgroup; const uint32_t row_tid = gl_LocalInvocationIndex / threads_per_rowgroup; - const uint32_t d_tid = gl_LocalInvocationIndex % D_split; - const uint32_t col_tid = (gl_LocalInvocationIndex % threads_per_rowgroup) / D_split; + const uint32_t col_tid = gl_LocalInvocationIndex % threads_per_rowgroup; #define tile_row(r) (row_tid * rows_per_thread + (r)) @@ -102,9 +104,9 @@ void main() { } barrier(); - ACC_TYPEV4 Of[rows_per_thread][HSV_per_thread / 4]; - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + ACC_TYPEV4 Of[rows_per_thread][d_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + [[unroll]] for (uint32_t d = 0; d < d_per_thread; ++d) { Of[r][d] = ACC_TYPEV4(0.0); } } @@ -125,13 +127,11 @@ void main() { uint r = tid; slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2); } - barrier(); } else { if (tid < Br) { uint r = tid; - slope[r] = 1.0; + slope[r] = ACC_TYPE(1.0); } - barrier(); } #if BLOCK_SIZE > 1 @@ -149,19 +149,45 @@ void main() { [[dont_unroll]] for (uint32_t j = start_j; j < end_j; ++j) { - float mask_cache[Bc * Br / WorkGroupSize]; + f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize]; if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) { bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; float max_mask = NEG_FLT_MAX_OVER_2; - [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { - uint32_t c = (idx + tid) % Bc; - uint32_t r = (idx + tid) / Bc; - if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) { - if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) { - float m = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]); + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + if ((!KV_bounds_check || j * Bc + c < KV)) { + f16vec4 m; + if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]); + max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3])); + } else if (i * Br + r * 4 + 2 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)], + 0.0); + max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])); + } else if (i * Br + r * 4 + 1 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + 0.0, + 0.0); + max_mask = max(max(max_mask, float(m[0])), float(m[1])); + } else if (i * Br + r * 4 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + 0.0, + 0.0, + 0.0); + max_mask = max(max_mask, float(m[0])); + } else { + m = f16vec4(0.0); + } mask_cache[idx / WorkGroupSize] = m; - max_mask = max(max_mask, m); } } } @@ -180,26 +206,28 @@ void main() { } } - [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK / 4; idx += gl_WorkGroupSize.x) { - uint32_t d = (idx + tid) % (HSK / 4); - uint32_t c = (idx + tid) / (HSK / 4); - if (c < Bc && d < HSK / 4) { - f16vec4 K_Tf = f16vec4(0); - if (!KV_bounds_check || j * Bc + c < KV) { + if (K_LOAD_SHMEM != 0) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK / 4); + uint32_t c = (idx + tid) / (HSK / 4); + if (c < Bc && d < HSK / 4) { + f16vec4 K_Tf = f16vec4(0); + if (!KV_bounds_check || j * Bc + c < KV) { #if BLOCK_SIZE > 1 - uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d; - uint ib = coord / BLOCK_SIZE; - uint iqs = (coord % BLOCK_SIZE); - K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K)); + uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d; + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K)); #else - K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); + K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); #endif - } + } - ksh[c * kshstride + d] = K_Tf; + ksh[c * kshstride + d] = K_Tf; + } } + barrier(); } - barrier(); // 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 @@ -208,11 +236,55 @@ void main() { coopmat KMat; coopmat QMat; - for (uint32_t d = 0; d < HSK_pad / 16; ++d) { - coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor); + [[unroll]] for (uint32_t d = 0; d < HSK_pad / 16; ++d) { + if (K_LOAD_SHMEM == 0) { +#if BLOCK_SIZE == 1 + if (KV_bounds_check || d * 16 + 16 > HSK) { +#endif + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * MatBr / 4; idx += gl_WorkGroupSize.x) { + uint32_t col_vec = (idx + tid) % (MatBr / 4); + uint32_t row = (idx + tid) / (MatBr / 4); + if (idx + tid < Bc * MatBr / 4) { + f16vec4 K_Tf = f16vec4(0); + if ((!KV_bounds_check || j * Bc + row < KV) && (HSK == HSK_pad || d * 16 + col_vec * 4 < HSK)) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + row) * k_stride * BLOCK_SIZE + d * 16 + col_vec * 4; + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K)); +#else + K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + row) * k_stride / 4 + d * 16 / 4 + col_vec]); +#endif + } - uint coord = (gl_SubgroupID * MatBc) * kshstride + d * 16 / 4; - coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor); + ksh[row * kshstride + col_vec] = K_Tf; + } + } + barrier(); +#if BLOCK_SIZE == 1 + } +#endif + +#if BLOCK_SIZE == 1 + if (KV_bounds_check || d * 16 + 16 > HSK) +#endif + { + uint coord = (gl_SubgroupID * MatBc) * kshstride; + coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor); + } +#if BLOCK_SIZE == 1 + else { + const uint coord = k_offset / 4 + (j * Bc + gl_SubgroupID * MatBc) * k_stride / 4 + d * 16 / 4; + coopMatLoad(KMat, data_kv4, coord, k_stride / 4, gl_CooperativeMatrixLayoutRowMajor); + } +#endif + } else { + uint coord = (gl_SubgroupID * MatBc) * kshstride + d * 16 / 4; + coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor); + } + + coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor); SfMat = coopMatMulAdd(KMat, QMat, SfMat); } @@ -222,26 +294,26 @@ void main() { barrier(); if (p.logit_softcap != 0.0f) { - [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { - uint32_t c = (idx + tid) / Br; - uint32_t r = (idx + tid) % Br; - if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) { - sfsh[c * sfshstride + r] = ACC_TYPE(p.logit_softcap * tanh(sfsh[c * sfshstride + r])); + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + sfsh[c * sfshstride + r] = ACC_TYPEV4(p.logit_softcap * tanh(sfsh[c * sfshstride + r])); } } barrier(); } if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) { - bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; - - [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { - uint32_t c = (idx + tid) % Bc; - uint32_t r = (idx + tid) / Bc; - if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) { - if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) { - float f = mask_cache[idx / WorkGroupSize]; - sfsh[c * sfshstride + r] += ACC_TYPE(slope[r] * f); + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + if (!KV_bounds_check || j * Bc + c < KV) { + // Mask nem1 bounds check is handled when loading masks + ACC_TYPEV4 masks = ACC_TYPEV4(mask_cache[idx / WorkGroupSize]); + ACC_TYPEV4 slopes = ACC_TYPEV4(slope[r * 4], slope[r * 4 + 1], slope[r * 4 + 2], slope[r * 4 + 3]); + sfsh[c * sfshstride + r] += slopes * masks; } } } @@ -250,51 +322,145 @@ void main() { float eMf[rows_per_thread]; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint r_vec = tile_row(r) / 4; + const uint r_comp = tile_row(r) % 4; + float rowmaxf = NEG_FLT_MAX_OVER_2; [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { continue; } - rowmaxf = max(rowmaxf, float(sfsh[tile_row(r) + (c * cols_per_iter + col_tid) * sfshstride])); + rowmaxf = max(rowmaxf, float(sfsh[r_vec + (c * cols_per_iter + col_tid) * sfshstride][r_comp])); } float Moldf = Mf[r]; + // Compute max across the row + rowmaxf = subgroupMax(rowmaxf); + // M = max(rowmax, Mold) // P = e^(S - M) // eM = e^(Mold - M) Mf[r] = max(rowmaxf, Moldf); eMf[r] = exp(Moldf - Mf[r]); - } - [[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(eMf[r]) * Of[r][d]; - } - } - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { Lf[r] = eMf[r]*Lf[r]; } - [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { - continue; - } - float Pf[rows_per_thread]; + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Pf[r] = exp(sfsh[tile_row(r) + (c * cols_per_iter + col_tid) * sfshstride] - Mf[r]); - Lf[r] += Pf[r]; + Of[r][d_local] = ACC_TYPE(eMf[r]) * Of[r][d_local]; } - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { -#if BLOCK_SIZE > 1 - uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); - uint ib = coord / BLOCK_SIZE; - uint iqs = (coord % BLOCK_SIZE); - vec4 Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); -#else - vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); + } + + // Calculate and store Pf in Psh + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + const uint col = c * cols_per_iter + col_tid; + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; r += 4) { + const uint row = tile_row(r); + if (KV_bounds_check && j * Bc + col >= KV) { + Psh[col * psh_stride + row / 4] = f16vec4(0.0f); + } else { + const vec4 mfvec = vec4(Mf[r], Mf[r + 1], Mf[r + 2], Mf[r + 3]); + const f16vec4 Pf = f16vec4(exp(vec4(sfsh[row / 4 + col * sfshstride]) - mfvec)); + [[unroll]] for (uint32_t vec_idx = 0; vec_idx < 4; ++vec_idx) { + Lf[r + vec_idx] += Pf[vec_idx]; + } + Psh[col * psh_stride + row / 4] = Pf; + } + } + } + + const uint num_hsv_tiles = (HSV + MatBc * row_split - 1) / (MatBc * row_split); // round up + + // Each subgroup handles HSV/4 columns + [[unroll]] for (uint32_t hsv_tile = 0; hsv_tile < num_hsv_tiles; ++hsv_tile) { + const uint hsv_offset = (hsv_tile * row_split + gl_SubgroupID) * 16; + + SfMat = coopmat(0); + + // Preload V tiles for [Bc, 16 * num subgroups] + const uint v_rows = Bc; + const uint v_total = v_rows * v_cols; + const uint v_loads_per_thread = v_total / gl_WorkGroupSize.x; + +#if BLOCK_SIZE == 1 + // For f16, only preload if not aligned + if (KV_bounds_check) { #endif - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Of[r][d] += ACC_TYPE(Pf[r]) * ACC_TYPEV4(Vf); + [[unroll]] for (uint32_t i = 0; i < v_loads_per_thread; ++i) { + const uint idx = i * gl_WorkGroupSize.x + tid; + const uint row = idx / v_cols; + const uint col = idx % v_cols; + + const uint v_row = j * Bc + row; + const uint v_col = hsv_tile * MatBc * row_split + col * 4; + + const uint coord = v_row * v_stride * BLOCK_SIZE + v_col; + const uint ib = coord / BLOCK_SIZE; + const uint iqs = coord % BLOCK_SIZE; + + if (!KV_bounds_check || (v_row < KV && v_col < HSV)) { +#if BLOCK_SIZE > 1 + ksh[row * vsh_stride + col] = f16vec4(dequantize4(ib, iqs, v_offset, BINDING_IDX_V)); +#else + ksh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4]; +#endif + } else { + ksh[row * vsh_stride + col] = f16vec4(0.0f); + } + } +#if BLOCK_SIZE == 1 + } +#endif + + barrier(); + + [[unroll]] for (uint32_t bc_chunk = 0; bc_chunk < Bc / MatBc; ++bc_chunk) { + coopMatLoad(KMat, Psh, bc_chunk * MatBc * psh_stride, psh_stride, gl_CooperativeMatrixLayoutColumnMajor); + +#if BLOCK_SIZE == 1 + if (!KV_bounds_check) { + // F16 values can be loaded directly from global memory + const uint v_tile_row = j * Bc + bc_chunk * MatBc; + const uint v_tile_offset = v_offset / 4 + v_tile_row * v_stride / 4 + hsv_offset / 4; + coopMatLoad(QMat, data_vv4, v_tile_offset, v_stride / 4, gl_CooperativeMatrixLayoutRowMajor); + } else +#endif + { + const uint v_tile_offset = bc_chunk * MatBr * v_cols + gl_SubgroupID * (MatBc / 4); + coopMatLoad(QMat, ksh, v_tile_offset, vsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + + SfMat = coopMatMulAdd(KMat, QMat, SfMat); + } + + // Store SfMat to sfsh and load into Of + const uint osh_stride = row_split * MatBc / 4; + const uint o_offset = gl_SubgroupID * MatBc / 4; + coopMatStore(SfMat, sfsh, o_offset, osh_stride, gl_CooperativeMatrixLayoutRowMajor); + + barrier(); + + const uint hsv_per_tile = row_split * MatBc; + const uint hsv_base = hsv_tile * hsv_per_tile; + const uint d_values_per_tile = hsv_per_tile / 4; + + const uint d_start = hsv_tile * d_values_per_tile; + const uint d_end = min(d_start + d_values_per_tile, HSV / 4); + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + + [[unroll]] for (uint32_t d_local = 0; d_local < d_per_thread; ++d_local) { + const uint d = d_local * threads_per_rowgroup + col_tid; + const uint hsv_col = 4 * d; + + if (hsv_col >= hsv_base && hsv_col < hsv_base + hsv_per_tile && hsv_col < HSV) { + const uint local_hsv = (hsv_col - hsv_base) / 4; + Of[r][d_local] += ACC_TYPEV4(sfsh[row * osh_stride + local_hsv]); + } } } } @@ -302,69 +468,8 @@ void main() { barrier(); } - // prevent race on tmpsh - barrier(); - - // reduce across threads - - float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread]; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - FLOAT_TYPE M = Mf[r]; - tmpsh[tid] = M; - // Compute max across the row - barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) { - M = max(M, tmpsh[tid ^ s]); - barrier(); - tmpsh[tid] = M; - barrier(); - } - rowmaxf[r] = tmpsh[d_tid + row_tid * threads_per_rowgroup]; - barrier(); - } - - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Moldf[r] = Mf[r]; - - // M = max(rowmax, Mold) - // eM = e^(Mold - M) - Mf[r] = max(rowmaxf[r], Moldf[r]); - eMf[r] = exp(Moldf[r] - Mf[r]); - - Lf[r] = eMf[r]*Lf[r]; - } - - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - FLOAT_TYPE L = Lf[r]; - tmpsh[tid] = L; - // Compute sum across the row - barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) { - L += tmpsh[tid ^ s]; - barrier(); - tmpsh[tid] = L; - barrier(); - } - Lf[r] = tmpsh[d_tid + row_tid * threads_per_rowgroup]; - barrier(); - } - - [[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] = ACC_TYPE(eMf[r]) * Of[r][d]; - tmpshv4[tid] = Of[r][d]; - - barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) { - Of[r][d] += tmpshv4[tid ^ s]; - barrier(); - tmpshv4[tid] = Of[r][d]; - barrier(); - } - Of[r][d] = tmpshv4[d_tid + row_tid * threads_per_rowgroup]; - barrier(); - } + Lf[r] = subgroupAdd(Lf[r]); } // If there is split_k, then the split_k resolve shader does the final @@ -375,9 +480,12 @@ void main() { [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { if (tile_row(r) < N) { - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV/4) break; + const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(tile_row(r), 4*(d * D_split + d_tid) + comp, float(Of[r][d][comp]), o_offset, iq2, N); + perElemOpGqaStore(tile_row(r), 4 * d + comp, float(Of[r][d_local][comp]), o_offset, iq2, N); } } } @@ -404,8 +512,9 @@ void main() { 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); + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; + Of[r][d_local] *= ACC_TYPE(ms); } } else { vs = exp(sink - Mf[r]); @@ -420,11 +529,12 @@ void main() { Lfrcp[r] = (Lf[r] == 0.0) ? 0.0 : (1.0 / Lf[r]); } - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Of[r][d] *= ACC_TYPE(Lfrcp[r]); + Of[r][d_local] *= ACC_TYPE(Lfrcp[r]); #if defined(ACC_TYPE_MAX) - Of[r][d] = clamp(Of[r][d], -ACC_TYPE_MAX, ACC_TYPE_MAX); + Of[r][d_local] = clamp(Of[r][d_local], -ACC_TYPE_MAX, ACC_TYPE_MAX); #endif } } @@ -434,9 +544,12 @@ void main() { if (p.gqa_ratio > 1) { [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { if (tile_row(r) < N) { - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV / 4) break; + const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(tile_row(r), 4*(d * D_split + d_tid) + comp, float(Of[r][d][comp]), o_offset, iq2, N); + perElemOpGqaStore(tile_row(r), 4 * d + comp, float(Of[r][d_local][comp]), o_offset, iq2, N); } } } @@ -444,9 +557,12 @@ void main() { } else { [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { if (i * Br + tile_row(r) < N) { - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV / 4) break; + const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - data_o[o_offset + iq2 * HSV + (i * Br + tile_row(r)) * p.ne1 * HSV + 4*(d * D_split + d_tid) + comp] = D_TYPE(Of[r][d][comp]); + data_o[o_offset + iq2 * HSV + (i * Br + tile_row(r)) * p.ne1 * HSV + 4 * d + comp] = D_TYPE(Of[r][d_local][comp]); } } } 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 d49a8da65f..54f1b0b622 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -55,7 +55,7 @@ ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE ele return max(elem0, elem1); } -#if defined(BLOCK_SIZE) +#if BLOCK_SIZE > 1 #define DECODEFUNC , DEQUANTFUNC #else #define DECODEFUNC @@ -85,7 +85,7 @@ void main() { tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0); -#if defined(BLOCK_SIZE) +#if BLOCK_SIZE > 1 tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, BLOCK_SIZE); tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, BLOCK_SIZE); #endif @@ -98,7 +98,7 @@ void main() { if (Clamp != gl_CooperativeMatrixClampModeConstantNV) { q_stride &= ~7; -#if !defined(BLOCK_SIZE) +#if BLOCK_SIZE == 1 k_stride &= ~7; v_stride &= ~7; #endif From 50e8962f79e3fcb4bf44777194bd4324908d5737 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Wed, 28 Jan 2026 22:05:39 +0100 Subject: [PATCH 050/206] ci : find latest release with asset for winget (#19161) --- .github/workflows/winget.yml | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/.github/workflows/winget.yml b/.github/workflows/winget.yml index 7506091647..2047c276f8 100644 --- a/.github/workflows/winget.yml +++ b/.github/workflows/winget.yml @@ -28,16 +28,17 @@ jobs: owner: context.repo.owner, repo: context.repo.repo, }); - console.log("Latest release:", releases[0].tag_name); - return releases[0].tag_name; + const { tag_name: version, assets: assets } = releases.find(({assets}) => assets.find(asset => asset.name.includes('win-vulkan'))); + const { browser_download_url: asset_url } = assets.find(asset => asset.name.includes('win-vulkan')); + console.log("Latest release:", version); + core.setOutput('VERSION', version); + core.setOutput('ASSETURL', asset_url); - name: Update manifest - env: - VERSION: ${{ steps.find_latest_release.outputs.result }} run: | echo "Updating manifest..." - komac update --version ${{ env.VERSION }} \ - --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \ + komac update --version ${{ steps.find_latest_release.outputs.VERSION }} \ + --urls "${{ steps.find_latest_release.outputs.ASSETURL }}" \ --token ${{ secrets.WINGET_GITHUB_TOKEN }} \ --submit \ ggml.llamacpp From d4964a7c66c4ff935d86c9ac92abeb12073723bf Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Thu, 29 Jan 2026 09:20:22 +0800 Subject: [PATCH 051/206] sycl: fix norm kernels: l2_norm, group_norm, rms_norm by remove assert to support more cases (#19154) Co-authored-by: Neo Zhang Jianyu --- docs/ops/SYCL.csv | 1344 +++++++++++++++++------------- ggml/src/ggml-sycl/ggml-sycl.cpp | 6 +- ggml/src/ggml-sycl/norm.cpp | 3 - 3 files changed, 753 insertions(+), 600 deletions(-) diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 91b442bde8..091a5caed7 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -878,6 +878,54 @@ "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=0,p1=1","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=0","support","1","yes","SYCL" "SYCL0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=1","support","1","yes","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=0","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","0","no","SYCL" +"SYCL0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","0","no","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","SYCL" @@ -965,6 +1013,7 @@ "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" +"SYCL0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL" "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL" @@ -5696,35 +5745,58 @@ "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","1","yes","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000000","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","1","yes","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000001","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","1","yes","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000100","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","1","yes","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","SYCL" +"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.100000","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","SYCL" -"SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" -"SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" @@ -5734,6 +5806,15 @@ "SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" @@ -6593,6 +6674,30 @@ "SYCL0","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],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q5_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q6_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq1_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq1_m,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","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],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","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],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","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],k_v=0,o=1","support","0","no","SYCL" @@ -8917,6 +9022,11 @@ "SYCL0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","SYCL" "SYCL0","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","SYCL" "SYCL0","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","1","yes","SYCL" @@ -8969,6 +9079,7 @@ "SYCL0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" @@ -8978,6 +9089,7 @@ "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" @@ -8988,11 +9100,13 @@ "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" @@ -9002,6 +9116,7 @@ "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" @@ -9012,11 +9127,13 @@ "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" @@ -9026,6 +9143,7 @@ "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" @@ -9036,11 +9154,13 @@ "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" @@ -9050,6 +9170,7 @@ "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" @@ -9060,6 +9181,7 @@ "SYCL0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","SYCL" @@ -9185,6 +9307,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" @@ -9194,6 +9317,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" @@ -9204,11 +9328,13 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" @@ -9218,6 +9344,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" @@ -9228,11 +9355,13 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" @@ -9242,6 +9371,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" @@ -9252,11 +9382,13 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" @@ -9266,6 +9398,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" @@ -9276,6 +9409,7 @@ "SYCL0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" "SYCL0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" @@ -9850,26 +9984,26 @@ "SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=nearest,flags=none","support","1","yes","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","1","yes","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=nearest","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=0","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=1","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=0","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=align_corners","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic,flags=align_corners","support","0","no","SYCL" -"SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic,flags=align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=0","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=1","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|antialias","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear|antialias","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","SYCL" "SYCL0","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","0","no","SYCL" @@ -9892,8 +10026,9 @@ "SYCL0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" "SYCL0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","SYCL" @@ -9915,6 +10050,7 @@ "SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","SYCL" "SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","SYCL" "SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","SYCL" "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","SYCL" @@ -9924,19 +10060,41 @@ "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","SYCL" +"SYCL0","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","SYCL" +"SYCL0","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","SYCL" +"SYCL0","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","SYCL" -"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","SYCL" +"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -14097,86 +14255,86 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" 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"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -14337,46 +14495,46 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" 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"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -15017,86 +15175,86 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" 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"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -15457,46 +15615,46 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" 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"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -16057,46 +16215,46 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" 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"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" @@ -16257,46 +16415,46 @@ "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index ce2f0d41c9..3a4c092af5 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -4606,14 +4606,12 @@ 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: - 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); + return true; case GGML_OP_RMS_NORM_BACK: - return ((op->src[0]->ne[0] % WARP_SIZE) == 0); + return ggml_is_contiguous(op->src[0]); case GGML_OP_SCALE: return true; case GGML_OP_CONT: diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index 823d3a4828..00702b5d09 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -251,7 +251,6 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i const float eps, queue_ptr stream, int device) { const sycl::range<3> global_dims(nsamples, nchannels, nrows); - GGML_ASSERT(ncols % WARP_SIZE == 0); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); stream->submit([&](sycl::handler& cgh) { @@ -334,7 +333,6 @@ static void group_norm_f32_sycl(const float* x, float* dst, static void rms_norm_f32_sycl(const float* x, 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 float eps, queue_ptr stream, int device) { - GGML_ASSERT(ncols % WARP_SIZE == 0); // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); const sycl::range<3> global_dims(nsamples, nchannels, nrows); @@ -374,7 +372,6 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols, const int nrows, const float eps, queue_ptr stream, int device) { - GGML_ASSERT(ncols % WARP_SIZE == 0); // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); From 3bcc990997f201114ee6b6abdec5eb43683d7af2 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Thu, 29 Jan 2026 10:31:28 +0800 Subject: [PATCH 052/206] CUDA: refactor topk-moe to enable more models (GLM 4.7, Nemotron etc.) (#19126) --- ggml/src/ggml-cuda/ggml-cuda.cu | 287 ++++++++++++++++++++------- ggml/src/ggml-cuda/topk-moe.cu | 330 ++++++++++++++++++-------------- ggml/src/ggml-cuda/topk-moe.cuh | 34 ++-- 3 files changed, 426 insertions(+), 225 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index e9df0ea4a7..76d0f12550 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -3080,63 +3080,166 @@ static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope, return true; } -static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list ops, std::initializer_list unary_ops) { +static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) { + args.sigmoid = false; + args.softmax = false; + args.delayed_softmax = false; + args.prob_bias = false; + args.norm = false; + + const int n_nodes = cgraph->n_nodes; + ggml_tensor ** nodes = cgraph->nodes; + + if (nodes[node_idx]->op == GGML_OP_SOFT_MAX) { + args.softmax = true; + } + + if (nodes[node_idx]->op == GGML_OP_UNARY) { + if (ggml_get_unary_op(nodes[node_idx]) != GGML_UNARY_OP_SIGMOID) { + return false; + } + args.sigmoid = true; + } + + if (nodes[node_idx]->op == GGML_OP_ARGSORT) { + args.delayed_softmax = true; + } + + node_idx++; + + if (args.sigmoid || args.softmax) { + // SOFTMAX -> RESHAPE + if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_RESHAPE || + nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + return false; + } + ggml_tensor * probs_reshaped = nodes[node_idx]; + node_idx++; + + if (node_idx >= n_nodes) { + return false; + } + + // src of bias add is the unreshaped probs (-2 instead of -1) + if (nodes[node_idx]->op == GGML_OP_ADD && nodes[node_idx]->src[0] == nodes[node_idx - 2]) { + args.prob_bias = true; + node_idx++; + } + // RESHAPE/ADD -> ARGSORT + if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_ARGSORT) { + return false; + } + + if (args.prob_bias && nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + return false; + } else if (!args.prob_bias && nodes[node_idx]->src[0] != nodes[node_idx - 2]) { + return false; + } + + node_idx++; + + // ARGSORT-> VIEW + if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_VIEW || + nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + return false; + } + node_idx++; + + if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_GET_ROWS) { + return false; + } + + // GET_ROWS + if (nodes[node_idx]->src[0] != probs_reshaped || nodes[node_idx]->src[1] != nodes[node_idx - 1]) { + return false; + } + node_idx++; + } else if (args.delayed_softmax) { + if (node_idx - 2 < 0) { + return false; + } + ggml_tensor * probs_reshaped = nodes[node_idx - 2]; + + // VIEW->ARGSORT + if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_VIEW || + nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + return false; + } + node_idx++; + + // GET_ROWS + if (node_idx >= n_nodes || nodes[node_idx]->src[1] != nodes[node_idx - 1] || + nodes[node_idx]->src[0] != probs_reshaped) { + return false; + } + node_idx++; + + static const std::vector remaining_ops = { GGML_OP_RESHAPE, GGML_OP_SOFT_MAX, GGML_OP_RESHAPE }; + + for (const ggml_op op : remaining_ops) { + if (node_idx >= n_nodes || nodes[node_idx]->op != op || nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + return false; + } + node_idx++; + } + } + + // At this point we can check for norm + scale. Everything is now at least valid till the norm + if (node_idx >= n_nodes) { + return true; + } + + if (nodes[node_idx]->op == GGML_OP_RESHAPE) { + //check RESHAPE->SUM_ROWS->CLAMP->DIV->RESHAPE + static const std::vector norm_ops = { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP }; + + args.norm = true; + for (const ggml_op op : norm_ops) { + if (nodes[node_idx]->op == op && nodes[node_idx]->src[0] == nodes[node_idx - 1]) { + node_idx++; + } else { + args.norm = false; + return true; + } + } + + // DIV <- CLAMP, RESHAPE + if (nodes[node_idx]->op != GGML_OP_DIV || nodes[node_idx]->src[1] != nodes[node_idx - 1] || + nodes[node_idx]->src[0] != nodes[node_idx - 3]) { + args.norm = false; + return true; + } + node_idx++; + + if (nodes[node_idx]->op != GGML_OP_RESHAPE || nodes[node_idx]->src[0] != nodes[node_idx - 1]) { + args.norm = false; + return true; + } + + node_idx++; + } + + if (nodes[node_idx]->op == GGML_OP_SCALE && nodes[node_idx]->src[0] == nodes[node_idx - 1]) { + args.scale = true; + } + + return true; +} + +static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, + int node_idx, + std::initializer_list ops, + std::initializer_list unary_ops) { #ifndef NDEBUG const size_t num_unary = std::count(ops.begin(), ops.end(), GGML_OP_UNARY); GGML_ASSERT(unary_ops.size() == num_unary); #endif - //TODO: remove special case once ggml_can_fuse can handle empty nodes - std::initializer_list topk_moe_ops = - ggml_cuda_topk_moe_ops(/*with_norm*/ false, /*delayed_softmax=*/false); - std::initializer_list topk_moe_ops_with_norm = - ggml_cuda_topk_moe_ops(/*with_norm=*/true, /*delayed_softmax=*/false); - std::initializer_list topk_moe_ops_delayed_softmax = - ggml_cuda_topk_moe_ops(/*with_norm=*/false, /*delayed_softmax=*/true); - const auto is_equal = [](const std::initializer_list & list1, const std::initializer_list & list2) { return std::equal(list1.begin(), list1.end(), list2.begin(), list2.end()); }; - if (is_equal(topk_moe_ops_with_norm, ops) && - ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 9 })) { - ggml_tensor * softmax = cgraph->nodes[node_idx]; - ggml_tensor * weights = cgraph->nodes[node_idx + 9]; - ggml_tensor * get_rows = cgraph->nodes[node_idx + 4]; - ggml_tensor * argsort = cgraph->nodes[node_idx + 2]; - int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0]; - - if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) { - return true; - } - } - - if (is_equal(topk_moe_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 4 })) { - ggml_tensor * softmax = cgraph->nodes[node_idx]; - ggml_tensor * weights = cgraph->nodes[node_idx + 4]; - ggml_tensor * get_rows = cgraph->nodes[node_idx + 4]; - ggml_tensor * argsort = cgraph->nodes[node_idx + 2]; - int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0]; - - if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) { - return true; - } - } - - if (is_equal(topk_moe_ops_delayed_softmax, ops) && - ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 1, node_idx + 5 })) { - ggml_tensor * softmax = cgraph->nodes[node_idx + 4]; - ggml_tensor * weights = cgraph->nodes[node_idx + 5]; - ggml_tensor * get_rows = cgraph->nodes[node_idx + 2]; - ggml_tensor * argsort = cgraph->nodes[node_idx + 0]; - int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0]; - - if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) { - return true; - } - } - std::initializer_list mul_mat_bias_glu_ops = { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_GLU }; std::initializer_list mul_mat_id_bias_glu_ops = { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_GLU }; @@ -3398,35 +3501,75 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud // start of fusion operations static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr); if (!disable_fusion) { + ggml_cuda_topk_moe_args args; - if (ggml_cuda_can_fuse(cgraph, i, ggml_cuda_topk_moe_ops(/*with norm*/ true), {})) { - ggml_tensor * weights = cgraph->nodes[i + 9]; - ggml_tensor * selected_experts = cgraph->nodes[i + 3]; - ggml_tensor * clamp = cgraph->nodes[i + 7]; - ggml_cuda_op_topk_moe(*cuda_ctx, node->src[0], weights, selected_experts, /*with norm*/ true, - /*delayed softmax*/ false, clamp); - i += 9; - continue; - } + if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX || + cgraph->nodes[i]->op == GGML_OP_ARGSORT) { + const bool can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args); - if (ggml_cuda_can_fuse(cgraph, i, ggml_cuda_topk_moe_ops(/*with norm*/ false), {})) { - ggml_tensor * weights = cgraph->nodes[i + 4]; - ggml_tensor * selected_experts = cgraph->nodes[i + 3]; - ggml_cuda_op_topk_moe(*cuda_ctx, node->src[0], weights, selected_experts, /*with norm*/ false, - /*delayed softmax*/ false); - i += 4; - continue; - } + std::vector ops; - if (ggml_cuda_can_fuse(cgraph, i, - ggml_cuda_topk_moe_ops(/*with norm*/ false, /*delayed softmax*/ true), {})) { - ggml_tensor * weights = cgraph->nodes[i + 5]; - ggml_tensor * ids = cgraph->nodes[i + 1]; + if (can_fuse) { + const ggml_tensor * logits = node->src[0]; + ggml_tensor * weights = nullptr; + ggml_tensor * ids = nullptr; + const ggml_tensor * bias = nullptr; + const ggml_tensor * clamp = nullptr; + const ggml_tensor * scale = nullptr; - ggml_cuda_op_topk_moe(*cuda_ctx, node->src[0], weights, ids, /*with norm*/ false, - /*delayed_softmax*/ true); - i += 5; - continue; + if (!args.delayed_softmax) { + ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX; + int out_nodes[2]; // nodes which can't be elided + + if (args.prob_bias) { + bias = cgraph->nodes[i + 2]->src[1]; + ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, + GGML_OP_VIEW, GGML_OP_GET_ROWS }); + out_nodes[0] = i + 4; + ids = cgraph->nodes[i + 4]; + } else { + ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, + GGML_OP_GET_ROWS }); + out_nodes[0] = i + 3; + ids = cgraph->nodes[i + 3]; + } + + if (args.norm) { + ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, + GGML_OP_DIV, GGML_OP_RESHAPE }); + clamp = cgraph->nodes[i + ops.size() - 3]; + } + if (args.scale) { + ops.insert(ops.end(), { GGML_OP_SCALE }); + scale = cgraph->nodes[i + ops.size() - 1]; + } + + weights = cgraph->nodes[i + ops.size() - 1]; + out_nodes[1] = i + ops.size() - 1; + + if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) && + ggml_cuda_should_use_topk_moe(node, logits, weights, ids)) { + ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args); + i += ops.size() - 1; + continue; + } + } else if (!args.norm && !args.prob_bias) { + //special case gpt-oss, no norm, no bias. + ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS, + GGML_OP_RESHAPE, GGML_OP_SOFT_MAX, GGML_OP_RESHAPE }); + weights = cgraph->nodes[i + 5]; + ids = cgraph->nodes[i + 1]; + const ggml_tensor * softmax = cgraph->nodes[i + 4]; + + int out_nodes[2] = { i + 1, i + 5 }; + if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) && + ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids)) { + ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args); + i += ops.size() - 1; + continue; + } + } + } } if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) { diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu index 48e569efa0..08a88990dd 100644 --- a/ggml/src/ggml-cuda/topk-moe.cu +++ b/ggml/src/ggml-cuda/topk-moe.cu @@ -5,6 +5,13 @@ #include #include +// Kernel config struct - passed by value to CUDA kernel +struct topk_moe_config { + bool use_sigmoid; + bool with_norm; + bool delayed_softmax; +}; + // Warp-local softmax used for both the pre-top-k logits and the post-top-k delayed path. template __device__ void softmax_warp_inplace(float (&vals)[experts_per_thread], const int limit, const int lane) { @@ -50,6 +57,16 @@ __device__ void softmax_warp_inplace(float (&vals)[experts_per_thread], const in } } +template +__device__ void sigmoid_warp_inplace(float (&vals)[experts_per_thread], const int limit, const int lane) { +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + const int idx = lane + i * WARP_SIZE; + const bool active = !use_limit || (idx < limit); + vals[i] = active ? 1.f / (1.f + expf(-vals[i])) : -INFINITY; + } +} + /* This kernel does the following: 1. optionally softmax over the logits per token [n_experts, n_tokens] @@ -59,13 +76,16 @@ __device__ void softmax_warp_inplace(float (&vals)[experts_per_thread], const in It is intended as fusion of softmax->top-k->get_rows pipeline for MoE models */ -template -__launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * logits, - float * weights, - int32_t * ids, - const int n_rows, - const int n_expert_used, - const float clamp_val) { +template +__launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * logits, + float * weights, + int32_t * ids, + float * bias, + const int n_rows, + const int n_expert_used, + const float clamp_val, + const float scale_val, + const topk_moe_config config) { const int row = blockIdx.x * blockDim.y + threadIdx.y; if (row >= n_rows) { return; @@ -79,14 +99,41 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * float wt[experts_per_thread]; + // Initialize all slots to -INFINITY +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + wt[i] = -INFINITY; + } + #pragma unroll for (int i = 0; i < n_experts; i += WARP_SIZE) { const int expert = i + threadIdx.x; wt[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[expert] : -INFINITY; } - if constexpr (!delayed_softmax) { - softmax_warp_inplace(wt, n_experts, threadIdx.x); + if (!config.delayed_softmax) { + if (config.use_sigmoid) { + sigmoid_warp_inplace(wt, n_experts, threadIdx.x); + } else { + softmax_warp_inplace(wt, n_experts, threadIdx.x); + } + } + + // selection_wt is only needed when bias is present (selection uses wt + bias) + // when no bias, we use wt directly for both selection and weight values + float selection_wt[has_bias ? experts_per_thread : 1]; + + if constexpr (has_bias) { +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + selection_wt[i] = -INFINITY; + } +#pragma unroll + for (int i = 0; i < n_experts; i += WARP_SIZE) { + const int expert = i + threadIdx.x; + selection_wt[i / WARP_SIZE] = + (n_experts % WARP_SIZE == 0 || expert < n_experts) ? wt[i / WARP_SIZE] + bias[expert] : -INFINITY; + } } //at this point, each thread holds either a portion of the softmax distribution @@ -106,22 +153,56 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * float max_val = wt[0]; int max_expert = threadIdx.x; -#pragma unroll - for (int i = 1; i < experts_per_thread; i++) { - const int expert = threadIdx.x + i * WARP_SIZE; - if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) { - max_val = wt[i]; - max_expert = expert; - } - } + if constexpr (has_bias) { + float max_val_s = selection_wt[0]; #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) { - const float val = __shfl_xor_sync(0xFFFFFFFF, max_val, mask, WARP_SIZE); - const int expert = __shfl_xor_sync(0xFFFFFFFF, max_expert, mask, WARP_SIZE); - if (val > max_val || (val == max_val && expert < max_expert)) { - max_val = val; - max_expert = expert; + for (int i = 1; i < experts_per_thread; i++) { + const int expert = threadIdx.x + i * WARP_SIZE; + if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && selection_wt[i] > max_val_s) { + max_val = wt[i]; + max_val_s = selection_wt[i]; + max_expert = expert; + } + } + +#pragma unroll + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) { + const float val = __shfl_xor_sync(0xFFFFFFFF, max_val, mask, WARP_SIZE); + const float val_s = __shfl_xor_sync(0xFFFFFFFF, max_val_s, mask, WARP_SIZE); + const int expert = __shfl_xor_sync(0xFFFFFFFF, max_expert, mask, WARP_SIZE); + if (val_s > max_val_s || (val_s == max_val_s && expert < max_expert)) { + max_val = val; + max_val_s = val_s; + max_expert = expert; + } + } + + if ((max_expert & (WARP_SIZE - 1)) == threadIdx.x) { + selection_wt[max_expert / WARP_SIZE] = -INFINITY; + } + } else { +#pragma unroll + for (int i = 1; i < experts_per_thread; i++) { + const int expert = threadIdx.x + i * WARP_SIZE; + if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) { + max_val = wt[i]; + max_expert = expert; + } + } + +#pragma unroll + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) { + const float val = __shfl_xor_sync(0xFFFFFFFF, max_val, mask, WARP_SIZE); + const int expert = __shfl_xor_sync(0xFFFFFFFF, max_expert, mask, WARP_SIZE); + if (val > max_val || (val == max_val && expert < max_expert)) { + max_val = val; + max_expert = expert; + } + } + + if ((max_expert & (WARP_SIZE - 1)) == threadIdx.x) { + wt[max_expert / WARP_SIZE] = -INFINITY; } } @@ -130,16 +211,14 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * } if ((max_expert & (WARP_SIZE - 1)) == threadIdx.x) { - wt[max_expert / WARP_SIZE] = -INFINITY; - ids[k] = max_expert; - if constexpr (with_norm) { + if (config.with_norm) { wt_sum += max_val; } } } - if constexpr (with_norm) { + if (config.with_norm) { wt_sum = warp_reduce_sum(wt_sum); wt_sum = max(wt_sum, clamp_val); const float inv_sum = 1.0f / wt_sum; @@ -149,7 +228,7 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * } } - if constexpr (delayed_softmax) { + if (config.delayed_softmax) { softmax_warp_inplace(output_weights, n_expert_used, threadIdx.x); } @@ -157,25 +236,25 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * for (int i = 0; i < experts_per_thread; i++) { const int idx = i * WARP_SIZE + threadIdx.x; if (idx < n_expert_used) { - weights[idx] = output_weights[i]; + weights[idx] = output_weights[i] * scale_val; } } - - if (!with_norm) { - GGML_UNUSED(clamp_val); - } } -template +template static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx, const float * logits, float * weights, int32_t * ids, + float * bias, const int n_rows, const int n_expert, const int n_expert_used, - const float clamp_val) { - static_assert(!(with_norm && delayed_softmax), "delayed softmax is not supported with weight normalization"); + const float clamp_val, + const float scale_val, + const topk_moe_config config) { + GGML_ASSERT(!(config.with_norm && config.delayed_softmax) && + "delayed softmax is not supported with weight normalization"); const int rows_per_block = 4; dim3 grid_dims((n_rows + rows_per_block - 1) / rows_per_block, 1, 1); dim3 block_dims(WARP_SIZE, rows_per_block, 1); @@ -183,44 +262,48 @@ static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx, switch (n_expert) { case 1: - topk_moe_cuda<1, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<1, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 2: - topk_moe_cuda<2, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<2, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 4: - topk_moe_cuda<4, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<4, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 8: - topk_moe_cuda<8, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<8, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 16: - topk_moe_cuda<16, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<16, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 32: - topk_moe_cuda<32, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<32, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 64: - topk_moe_cuda<64, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<64, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 128: - topk_moe_cuda<128, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<128, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 256: - topk_moe_cuda<256, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<256, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; case 512: - topk_moe_cuda<512, with_norm, delayed_softmax> - <<>>(logits, weights, ids, n_rows, n_expert_used, clamp_val); + topk_moe_cuda<512, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); + break; + case 576: + topk_moe_cuda<576, has_bias><<>>(logits, weights, ids, bias, n_rows, n_expert_used, + clamp_val, scale_val, config); break; default: GGML_ASSERT(false && "fatal error"); @@ -228,13 +311,14 @@ static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx, } } -void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, - const ggml_tensor * logits, - ggml_tensor * weights, - ggml_tensor * ids, - const bool with_norm, - const bool delayed_softmax, - ggml_tensor * clamp) { +void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, + const ggml_tensor * logits, + ggml_tensor * weights, + ggml_tensor * ids, + const ggml_tensor * clamp, + const ggml_tensor * scale, + const ggml_tensor * bias, + const ggml_cuda_topk_moe_args & args) { GGML_ASSERT(logits->type == GGML_TYPE_F32); GGML_ASSERT(weights->type == GGML_TYPE_F32); GGML_ASSERT(ids->type == GGML_TYPE_I32); @@ -245,107 +329,75 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, const float * logits_d = (const float *) logits->data; float * weights_d = (float *) weights->data; int32_t * ids_d = (int32_t *) ids->data; + float * bias_d = bias ? (float *) bias->data : nullptr; + + float scale_val = scale ? ggml_get_op_params_f32(scale, 0) : 1.0f; GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts); const int n_expert_used = weights->ne[1]; + const bool with_norm = clamp != nullptr; + float clamp_val = -INFINITY; - if (with_norm) { - if (clamp) { - clamp_val = ggml_get_op_params_f32(clamp, 0); - } - launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, n_rows, n_experts, n_expert_used, clamp_val); + if (clamp) { + clamp_val = ggml_get_op_params_f32(clamp, 0); + } + + topk_moe_config config; + config.use_sigmoid = args.sigmoid; + config.with_norm = with_norm; + config.delayed_softmax = args.delayed_softmax; + + if (bias) { + launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, bias_d, n_rows, n_experts, n_expert_used, clamp_val, + scale_val, config); } else { - GGML_ASSERT(clamp == nullptr); - if (delayed_softmax) { - launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, n_rows, n_experts, n_expert_used, - clamp_val); - } else { - launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, n_rows, n_experts, n_expert_used, - clamp_val); - } + launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, bias_d, n_rows, n_experts, n_expert_used, clamp_val, + scale_val, config); } } -bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, +bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op, const ggml_tensor * weights, - const ggml_tensor * get_rows, - const ggml_tensor * argsort, - const ggml_tensor * clamp, - int n_expert) { - ggml_tensor * probs = get_rows->src[0]; - if (probs->op != GGML_OP_RESHAPE) { - return false; - } - probs = probs->src[0]; - ggml_tensor * selection_probs = argsort->src[0]; - - if (probs != selection_probs) { + const ggml_tensor * logits, + const ggml_tensor * ids) { + const int n_expert = ids->nb[1] / ids->nb[0]; + if (((n_expert & (n_expert - 1)) != 0 || n_expert > 512) && n_expert != 576) { return false; } - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (const float *) softmax->op_params + 0, sizeof(float)); - memcpy(&max_bias, (const float *) softmax->op_params + 1, sizeof(float)); - - if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) { + if (!ggml_is_contiguous(weights) || !ggml_is_contiguous(logits)) { return false; } - if (scale != 1.0f || max_bias != 0.0f) { - return false; - } + if (gating_op->op == GGML_OP_SOFT_MAX) { + const ggml_tensor * softmax = gating_op; + float scale = 1.0f; + float max_bias = 0.0f; - // don't fuse when masks or sinks are present - if (softmax->src[1] || softmax->src[2]) { - return false; - } + memcpy(&scale, (const float *) softmax->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) softmax->op_params + 1, sizeof(float)); - // n_expert must be a power of 2 - if ((n_expert & (n_expert - 1)) != 0 || n_expert > 512) { - return false; - } - - if (clamp) { - if (clamp->op != GGML_OP_CLAMP) { + if (!ggml_is_contiguous(softmax->src[0])) { return false; } - float max_val = ggml_get_op_params_f32(clamp, 1); - if (max_val != INFINITY) { + if (scale != 1.0f || max_bias != 0.0f) { + return false; + } + + // don't fuse when masks or sinks are present + if (softmax->src[1] || softmax->src[2]) { + return false; + } + } else if (gating_op->op == GGML_OP_UNARY) { + ggml_unary_op op = ggml_get_unary_op(gating_op); + + if (op != GGML_UNARY_OP_SIGMOID) { return false; } } - return true; } - -std::initializer_list ggml_cuda_topk_moe_ops(bool norm, bool delayed_softmax) { - static std::initializer_list norm_ops = { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, - GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE, - GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, - GGML_OP_RESHAPE }; - - static std::initializer_list no_norm_ops = { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, - GGML_OP_VIEW, GGML_OP_GET_ROWS }; - - static std::initializer_list delayed_softmax_ops = { GGML_OP_ARGSORT, GGML_OP_VIEW, - GGML_OP_GET_ROWS, GGML_OP_RESHAPE, - GGML_OP_SOFT_MAX, GGML_OP_RESHAPE }; - - GGML_ASSERT(!norm || !delayed_softmax); - - if (delayed_softmax) { - return delayed_softmax_ops; - } - - if (norm) { - return norm_ops; - } - - return no_norm_ops; -} diff --git a/ggml/src/ggml-cuda/topk-moe.cuh b/ggml/src/ggml-cuda/topk-moe.cuh index 6b6c13c587..243dc2f1c4 100644 --- a/ggml/src/ggml-cuda/topk-moe.cuh +++ b/ggml/src/ggml-cuda/topk-moe.cuh @@ -3,19 +3,25 @@ #include -void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, - const ggml_tensor * logits, - ggml_tensor * weights, - ggml_tensor * ids, - const bool with_norm, - const bool delayed_softmax = false, - ggml_tensor * weight_clamp = nullptr); +struct ggml_cuda_topk_moe_args { + bool sigmoid{}; + bool softmax{}; + bool delayed_softmax{}; + bool prob_bias{}; + bool norm{}; + bool scale{}; +}; -bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, +void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, + const ggml_tensor * logits, + ggml_tensor * weights, + ggml_tensor * ids, + const ggml_tensor * clamp, + const ggml_tensor * scale, + const ggml_tensor * bias, + const ggml_cuda_topk_moe_args & args); + +bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op, const ggml_tensor * weights, - const ggml_tensor * get_rows, - const ggml_tensor * argsort, - const ggml_tensor * clamp, - int n_expert); - -std::initializer_list ggml_cuda_topk_moe_ops(bool with_norm, bool delayed_softmax = false); + const ggml_tensor * logits, + const ggml_tensor * ids); From b33df266d0a53f800c47513386920cff1019d70e Mon Sep 17 00:00:00 2001 From: Vishal Singh Date: Thu, 29 Jan 2026 09:58:57 +0530 Subject: [PATCH 053/206] ggml-zendnn : resolve ZenDNN backend cross-module symbol dependency (#19159) --- ggml/src/ggml-zendnn/ggml-zendnn.cpp | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/ggml/src/ggml-zendnn/ggml-zendnn.cpp index afbecde7a5..551c15bb4a 100644 --- a/ggml/src/ggml-zendnn/ggml-zendnn.cpp +++ b/ggml/src/ggml-zendnn/ggml-zendnn.cpp @@ -2,7 +2,6 @@ #include "ggml-backend-impl.h" #include "ggml-impl.h" -#include "ggml-cpu.h" #include "zendnnl.hpp" #include @@ -122,8 +121,8 @@ static void ggml_zendnn_compute_forward_mul_mat( GGML_TENSOR_BINARY_OP_LOCALS - ggml_type const vec_dot_type = ggml_get_type_traits_cpu(src0->type)->vec_dot_type; - ggml_from_float_t const from_float = ggml_get_type_traits_cpu(vec_dot_type)->from_float; + ggml_type const vec_dot_type = src0->type; + ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float_ref; GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); From eed25bc6b052c363aa760d0055282cc2222ccf6e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 29 Jan 2026 08:50:47 +0200 Subject: [PATCH 054/206] arg : add -kvu to llama-batched-bench (#19172) --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index a685c418bf..218418f070 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1301,7 +1301,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, bool value) { params.kv_unified = value; } - ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED})); + ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH})); add_opt(common_arg( {"--context-shift"}, {"--no-context-shift"}, From f3dd7b8e680e37342649024c0ee282c5d60b83da Mon Sep 17 00:00:00 2001 From: yulo <77381088+zhang-hui-yulo@users.noreply.github.com> Date: Thu, 29 Jan 2026 18:10:53 +0800 Subject: [PATCH 055/206] HIP: add mmf for CDNA (#18896) * refactor mmf rows_per_block * speed up compile * pass cdna compile * fix cuda error * clean up mmf * f32 mmf * clean float mma * fix mmf error * faster mmf * extend tile k * fix compile error * Revert "extend tile k" This reverts commit 4d2ef3d483932659801a59a5af0b6b48f6ffd5c7. * fix smem overflow * speed up compiling mmf * speed up compile for hip * 512 block for cdna * config pad size * fix as comment * update select logic * move some code to cuh * fix as comment * correct cdna3 config --------- Co-authored-by: zhang hui --- ggml/src/ggml-cuda/mma.cuh | 102 ++++++++++++- ggml/src/ggml-cuda/mmf.cu | 40 +++-- ggml/src/ggml-cuda/mmf.cuh | 243 ++++++++++++++++++++----------- ggml/src/ggml-hip/CMakeLists.txt | 2 + 4 files changed, 288 insertions(+), 99 deletions(-) diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index 42085d1002..dd45d6c78f 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -333,7 +333,33 @@ namespace ggml_cuda_mma { static __device__ __forceinline__ int get_j(const int l) { if constexpr (I == 16 && J == 8) { - return 4 * (threadIdx.x / 16) + l; + return ne * (threadIdx.x / 16) + l; + } else { + NO_DEVICE_CODE; + return -1; + } + } +#elif defined(AMD_MFMA_AVAILABLE) + static constexpr int ne = I * J / 64; + half2 x[ne] = {{0.0f, 0.0f}}; + + static constexpr __device__ bool supported() { + if (I == 16 && J == 8) return true; + return false; + } + + static __device__ __forceinline__ int get_i(const int l) { + if constexpr (I == 16 && J == 8) { + return threadIdx.x % 16; + } else { + NO_DEVICE_CODE; + return -1; + } + } + + static __device__ __forceinline__ int get_j(const int l) { + if constexpr (I == 16 && J == 8) { + return ne * (threadIdx.x / 16) + l; } else { NO_DEVICE_CODE; return -1; @@ -391,7 +417,22 @@ namespace ggml_cuda_mma { static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR; #if defined(AMD_WMMA_AVAILABLE) - static constexpr int ne = I * J / 32; + static constexpr int ne = tile::ne; + nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; + + static constexpr __device__ bool supported() { + return tile::supported(); + } + + static __device__ __forceinline__ int get_i(const int l) { + return tile::get_i(l); + } + + static __device__ __forceinline__ int get_j(const int l) { + return tile::get_j(l); + } +#elif defined(AMD_MFMA_AVAILABLE) + static constexpr int ne = tile::ne; nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; static constexpr __device__ bool supported() { @@ -945,6 +986,32 @@ namespace ggml_cuda_mma { #endif // AMPERE_MMA_AVAILABLE } + template + static __device__ __forceinline__ void mma( + tile<16, 16, float, dl_d> & D, const tile<16, 8, float, dl_ab> & A, const tile<16, 8, float, dl_ab> & B) { +#ifdef AMD_MFMA_AVAILABLE + using floatx4_t = __attribute__((ext_vector_type(4))) float; + floatx4_t& acc_frag = reinterpret_cast(D.x[0]); +#if defined(CDNA3) + using floatx2_t = __attribute__((ext_vector_type(2))) float; + const floatx2_t& a_frag = reinterpret_cast(A.x[0]); + const floatx2_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_mfma_f32_16x16x8_xf32(a_frag, b_frag, acc_frag, 0, 0, 0); +#elif defined(CDNA2) || defined(CDNA1) +#pragma unroll + for (int i = 0; i < 2; ++i) { + acc_frag = __builtin_amdgcn_mfma_f32_16x16x4f32(A.x[i], B.x[i], acc_frag, 0, 0, 0); + } +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; +#endif // defined(CDNA3) +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; +#endif // AMD_MFMA_AVAILABLE + } + static __device__ __forceinline__ void mma_block_scaled(tile<16, 8, float> & D, const tile<16, 8, int> & A, const tile<8, 8, int> & B, @@ -1054,6 +1121,13 @@ namespace ggml_cuda_mma { GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; #endif // RDNA4 +#elif defined(AMD_MFMA_AVAILABLE) + using halfx4_t = __attribute__((ext_vector_type(4))) _Float16; + using floatx4_t = __attribute__((ext_vector_type(4))) float; + floatx4_t& acc_frag = reinterpret_cast(D.x[0]); + const halfx4_t& a_frag = reinterpret_cast(A.x[0]); + const halfx4_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_mfma_f32_16x16x16f16(a_frag, b_frag, acc_frag, 0, 0, 0); #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; @@ -1081,11 +1155,31 @@ namespace ggml_cuda_mma { #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; -#endif // RDNA4 +#endif // defined(RDNA4) +#elif defined(AMD_MFMA_AVAILABLE) + using floatx4_t = __attribute__((ext_vector_type(4))) float; + floatx4_t& acc_frag = reinterpret_cast(D.x[0]); +#if defined(CDNA3) || defined(CDNA2) + using bf16x4_t = __attribute__((ext_vector_type(4))) __bf16; + const bf16x4_t& a_frag = reinterpret_cast(A.x[0]); + const bf16x4_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(a_frag, b_frag, acc_frag, 0, 0, 0); +#elif defined(CDNA1) +#pragma unroll + for (int i = 0; i < 2; ++i) { + using bf16x2_t = __attribute__((ext_vector_type(2))) __bf16; + const bf16x2_t& a_frag = reinterpret_cast(A.x[i]); + const bf16x2_t& b_frag = reinterpret_cast(B.x[i]); + acc_frag = __builtin_amdgcn_mfma_f32_16x16x8bf16(a_frag, b_frag, acc_frag, 0, 0, 0); + } #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; -#endif // AMPERE_MMA_AVAILABLE +#endif // defined(CDNA3) || defined(CDNA2) +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; +#endif // defined(AMD_WMMA_AVAILABLE) } template diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index 6643f243b1..aad4c34aa6 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -2,6 +2,13 @@ #include "mmf.cuh" #include "mmid.cuh" +static __forceinline__ int mmf_get_rows_per_block(const int cc) { + if (GGML_CUDA_CC_IS_CDNA(cc)) { + return MMF_ROWS_PER_BLOCK_CDNA; + } else { + return MMF_ROWS_PER_BLOCK; + } +} 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); @@ -89,28 +96,32 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr ids_info_ptr = &ids_info; } + const int device = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[device].cc; + const int rows_per_block = mmf_get_rows_per_block(cc); + 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, stride_col_y/vals_per_T, stride_col_dst, + mul_mat_f_switch_rows_per_block( + rows_per_block, src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_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(), ids_info_ptr); } 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, stride_col_y/vals_per_T, stride_col_dst, + mul_mat_f_switch_rows_per_block( + rows_per_block, src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_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(), ids_info_ptr); } 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, stride_col_y/vals_per_T, stride_col_dst, + mul_mat_f_switch_rows_per_block( + rows_per_block, src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_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(), ids_info_ptr); } break; @@ -140,7 +151,11 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const return false; } } - if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) { + if (src0_ne[1] % mmf_get_rows_per_block(cc) != 0) { + return false; + } + + if (GGML_CUDA_CC_IS_CDNA3(cc) && type == GGML_TYPE_BF16) { return false; } @@ -153,6 +168,11 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const } else { if (GGML_CUDA_CC_IS_RDNA3_0(cc) && src1_ncols > 8) { return false; + } else if (GGML_CUDA_CC_IS_CDNA2(cc) && (type == GGML_TYPE_F16 || type == GGML_TYPE_BF16)) { + //TODO: truse CDNA2 as CDNA1, tune the perf when CDNA2 is available. + return false; + } else if (GGML_CUDA_CC_IS_CDNA1(cc) && (type == GGML_TYPE_F16 || type == GGML_TYPE_BF16)) { + return false; } else if (src1_ncols > 16) { return false; } @@ -160,11 +180,11 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const switch (type) { case GGML_TYPE_F32: - return ampere_mma_available(cc); + return ampere_mma_available(cc) || amd_mfma_available(cc); case GGML_TYPE_F16: - return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc); + return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc) || amd_mfma_available(cc); case GGML_TYPE_BF16: - return ampere_mma_available(cc) || amd_wmma_available(cc); + return ampere_mma_available(cc) || amd_wmma_available(cc) || amd_mfma_available(cc); default: return false; } diff --git a/ggml/src/ggml-cuda/mmf.cuh b/ggml/src/ggml-cuda/mmf.cuh index e36730948f..c2a8d54c95 100644 --- a/ggml/src/ggml-cuda/mmf.cuh +++ b/ggml/src/ggml-cuda/mmf.cuh @@ -7,6 +7,31 @@ using namespace ggml_cuda_mma; #define MMF_ROWS_PER_BLOCK 32 +#define MMF_ROWS_PER_BLOCK_CDNA 64 + +static __forceinline__ int64_t mmf_get_max_block_size(int cc) { + if (GGML_CUDA_CC_IS_CDNA(cc)) { + return 512; + } else { + return 256; + } +} + +static __forceinline__ int mmf_get_padding(int cc) { + if (GGML_CUDA_CC_IS_CDNA(cc)) { + return 2; + } else { + return 4; + } +} + +static constexpr __device__ int mmf_get_padding() { +#if defined(AMD_MFMA_AVAILABLE) + return 2; +#else + return 4; +#endif // defined(AMD_MFMA_AVAILABLE) +} struct mmf_ids_data { const int32_t * ids_src_compact = nullptr; @@ -29,23 +54,25 @@ static __global__ void mul_mat_f( 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) { // TODO: handle this in a consistent and simpler way after AMD MFMA support has been added -#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) #if defined(AMD_WMMA_AVAILABLE) - // Special case for tf32, just dummy mma layout as wmma doesn't support it. - constexpr bool is_tf32 = std::is_same_v; - constexpr int tile_B_I = is_tf32 ? 8 : 16; - constexpr int tile_C_J = is_tf32 ? 8 : 16; - constexpr data_layout ab_layout = is_tf32 ? DATA_LAYOUT_I_MAJOR : get_input_data_layout(); - typedef tile<16, 8, T, ab_layout> tile_A; - typedef tile tile_B; - typedef tile<16, tile_C_J, float, DATA_LAYOUT_J_MAJOR> tile_C; + if constexpr (!(std::is_same_v || std::is_same_v) || rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { + typedef tile<16, 8, T, get_input_data_layout()> tile_A; + typedef tile<16, 8, T, get_input_data_layout()> tile_B; + typedef tile<16, 16, float, DATA_LAYOUT_J_MAJOR> tile_C; +#elif defined(AMD_MFMA_AVAILABLE) + if constexpr (rows_per_block != MMF_ROWS_PER_BLOCK_CDNA) {NO_DEVICE_CODE;} else { + typedef tile<16, 8, T, DATA_LAYOUT_I_MAJOR> tile_A; + typedef tile<16, 8, T, DATA_LAYOUT_I_MAJOR> tile_B; + typedef tile<16, 16, float, DATA_LAYOUT_J_MAJOR> tile_C; #else #ifdef VOLTA_MMA_AVAILABLE - if constexpr (!std::is_same_v) {NO_DEVICE_CODE;} else { + if constexpr (!std::is_same_v || rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { typedef tile<32, 4, T, DATA_LAYOUT_I_MAJOR> tile_A; typedef tile< 8, 4, T, DATA_LAYOUT_I_MAJOR_MIRRORED> tile_B; typedef tile<32, 8, float, DATA_LAYOUT_I_MAJOR> tile_C; #else + if constexpr (rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { typedef tile<16, 8, T> tile_A; typedef tile<8, 8, T> tile_B; typedef tile<16, 8, float> tile_C; @@ -57,7 +84,7 @@ static __global__ void mul_mat_f( } constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - constexpr int tile_k_padded = warp_size + 4; + constexpr int tile_k_padded = warp_size + mmf_get_padding(); constexpr int ntA = rows_per_block / tile_A::I; constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I; @@ -198,7 +225,7 @@ static __global__ void mul_mat_f( } float * buf_iw = (float *) compute_base; - constexpr int kiw = nwarps*rows_per_block + 4; + constexpr int kiw = nwarps*rows_per_block + mmf_get_padding(); if (nwarps > 1) { __syncthreads(); @@ -228,27 +255,34 @@ static __global__ void mul_mat_f( return; } - float sum = 0.0f; - static_assert(rows_per_block == warp_size, "need loop/check"); + float sum[rows_per_block/warp_size] = {0.0f}; + static_assert((rows_per_block % warp_size) == 0, "rows_per_block must be a multiple of warp_size."); #pragma unroll for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) { - const int i = i0 + threadIdx.x; +#pragma unroll + for (int i1 = 0; i1 < sizeof(sum)/sizeof(sum[0]); ++i1) { + const int i = i0 + i1*warp_size + threadIdx.x; - sum += buf_iw[j*kiw + i]; + sum[i1] += buf_iw[j*kiw + i]; + } } if constexpr (!has_ids) { - dst[j*stride_col_dst + row0 + threadIdx.x] = sum; +#pragma unroll + for (int i0 = 0; i0 < sizeof(sum)/sizeof(sum[0]); ++i0) { + dst[j*stride_col_dst + row0 + i0*warp_size + threadIdx.x] = sum[i0]; + } } else { const int slot = (j < cols_per_block) ? slot_map[j] : -1; if (slot >= 0 && (col_base + j) < ncols_dst_total) { - dst[slot*stride_channel_dst + j*stride_col_dst + row0 + threadIdx.x] = sum; +#pragma unroll + for (int i0 = 0; i0 < sizeof(sum)/sizeof(sum[0]); ++i0) { + dst[slot*stride_channel_dst + j*stride_col_dst + row0 + i0*warp_size + threadIdx.x] = sum[i0]; + } } } } -#ifdef VOLTA_MMA_AVAILABLE } -#endif //VOLTA_MMA_AVAILABLE #else GGML_UNUSED_VARS(x, y, ids, dst, ncols, ncols_dst_total, nchannels_dst, stride_row, stride_col_y, stride_col_dst, @@ -256,7 +290,7 @@ static __global__ void mul_mat_f( 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; -#endif // (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } //This kernel is for larger batch sizes of mul_mat_id @@ -271,23 +305,25 @@ static __global__ void mul_mat_f_ids( const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst, const uint3 sis1_fd, const uint3 nch_fd) { // TODO: handle this in a consistent and simpler way after AMD MFMA support has been added -#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) #if defined(AMD_WMMA_AVAILABLE) - // Special case for tf32, just dummy mma layout as wmma doesn't support it. - constexpr bool is_tf32 = std::is_same_v; - constexpr int tile_B_I = is_tf32 ? 8 : 16; - constexpr int tile_C_J = is_tf32 ? 8 : 16; - constexpr data_layout ab_layout = is_tf32 ? DATA_LAYOUT_I_MAJOR : get_input_data_layout(); - typedef tile<16, 8, T, ab_layout> tile_A; - typedef tile tile_B; - typedef tile<16, tile_C_J, float, DATA_LAYOUT_J_MAJOR> tile_C; + if constexpr (!(std::is_same_v || std::is_same_v) || rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { + typedef tile<16, 8, T, get_input_data_layout()> tile_A; + typedef tile<16, 8, T, get_input_data_layout()> tile_B; + typedef tile<16, 16, float, DATA_LAYOUT_J_MAJOR> tile_C; +#elif defined(AMD_MFMA_AVAILABLE) + if constexpr (rows_per_block != MMF_ROWS_PER_BLOCK_CDNA) {NO_DEVICE_CODE;} else { + typedef tile<16, 8, T, DATA_LAYOUT_I_MAJOR> tile_A; + typedef tile<16, 8, T, DATA_LAYOUT_I_MAJOR> tile_B; + typedef tile<16, 16, float, DATA_LAYOUT_J_MAJOR> tile_C; #else #ifdef VOLTA_MMA_AVAILABLE - if constexpr (!std::is_same_v) {NO_DEVICE_CODE;} else { + if constexpr (!std::is_same_v || rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { typedef tile<32, 4, T, DATA_LAYOUT_I_MAJOR> tile_A; typedef tile< 8, 4, T, DATA_LAYOUT_I_MAJOR_MIRRORED> tile_B; typedef tile<32, 8, float, DATA_LAYOUT_I_MAJOR> tile_C; #else + if constexpr (rows_per_block != MMF_ROWS_PER_BLOCK) {NO_DEVICE_CODE;} else { typedef tile<16, 8, T> tile_A; typedef tile<8, 8, T> tile_B; typedef tile<16, 8, float> tile_C; @@ -300,7 +336,7 @@ static __global__ void mul_mat_f_ids( constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - constexpr int tile_k_padded = warp_size + 4; + constexpr int tile_k_padded = warp_size + mmf_get_padding(); constexpr int ntA = rows_per_block / tile_A::I; constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I; @@ -467,7 +503,7 @@ static __global__ void mul_mat_f_ids( } float * buf_iw = (float *) compute_base; - constexpr int kiw = nwarps*rows_per_block + 4; + constexpr int kiw = nwarps*rows_per_block + mmf_get_padding(); if (nwarps > 1) { __syncthreads(); @@ -497,13 +533,16 @@ static __global__ void mul_mat_f_ids( return; } - float sum = 0.0f; - static_assert(rows_per_block == warp_size, "need loop/check"); + float sum[rows_per_block/warp_size] = {0.0f}; + static_assert((rows_per_block % warp_size) == 0, "rows_per_block must be a multiple of warp_size."); #pragma unroll for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) { - const int i = i0 + threadIdx.x; +#pragma unroll + for (int i1 = 0; i1 < sizeof(sum)/sizeof(sum[0]); ++i1) { + const int i = i0 + i1*warp_size + threadIdx.x; - sum += buf_iw[j*kiw + i]; + sum[i1] += buf_iw[j * kiw + i]; + } } const int global_j = col_base + j; @@ -513,23 +552,24 @@ static __global__ void mul_mat_f_ids( const int token = (int) qrm.x; if (token < ncols_dst_total) { const int slot = (int) qrm.y; - dst[slot*stride_channel_dst + token*stride_col_dst + row0 + threadIdx.x] = sum; +#pragma unroll + for (int i0 = 0; i0 < sizeof(sum)/sizeof(sum[0]); ++i0) { + dst[slot * stride_channel_dst + token * stride_col_dst + row0 + i0*warp_size + threadIdx.x] = sum[i0]; + } } } } -#ifdef VOLTA_MMA_AVAILABLE } -#endif // VOLTA_MMA_AVAILABLE #else GGML_UNUSED_VARS(x, y, ids_src_compact, ids_dst_compact, expert_bounds, dst, ncols, ncols_dst_total, nchannels_dst, 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, sis1_fd, nch_fd); NO_DEVICE_CODE; -#endif // (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } -template +template static inline void mul_mat_f_switch_ids( const T * x, const float * y, const int32_t * ids, float * dst, const int64_t ncols_x, const int64_t ncols_dst, const int64_t nchannels_dst, @@ -553,7 +593,7 @@ static inline void mul_mat_f_switch_ids( const uint3 sis1_fd = ids_data->sis1 > 0 ? init_fastdiv_values((uint32_t) ids_data->sis1) : make_uint3(0, 0, 1); const uint3 nch_fd = init_fastdiv_values((uint32_t) nchannels_dst); - mul_mat_f_ids<<>> + mul_mat_f_ids<<>> (x, y, ids_data->ids_src_compact, ids_data->ids_dst_compact, ids_data->expert_bounds_dev, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, @@ -564,19 +604,19 @@ static inline void mul_mat_f_switch_ids( dim3 block_nums_ids = block_nums; block_nums_ids.y *= col_tiles; - mul_mat_f<<>> + mul_mat_f<<>> (x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } else { - mul_mat_f<<>> + mul_mat_f<<>> (x, y, ids, dst, ncols_x, cols_per_block, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } } -template +template 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 ncols_dst, @@ -605,7 +645,7 @@ void mul_mat_f_cuda( int64_t nwarps_best = 1; int64_t niter_best = (ncols_x + warp_size*2 - 1) / (warp_size*2); - int64_t max_block_size = 256; + int64_t max_block_size = mmf_get_max_block_size(cc); 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) { @@ -614,10 +654,9 @@ void mul_mat_f_cuda( } } - constexpr int rows_per_block = MMF_ROWS_PER_BLOCK; - const int nbytes_shared_iter = nwarps_best * (volta_mma_available(cc) ? tile_A_32::I : tile_A_16::I) * (warp_size + 4) * 4; - const int nbytes_cols_per_block_pad = amd_wmma_available(cc) ? tile_B_16::I : tile_B_8::I; - const int nbytes_shared_combine = GGML_PAD(cols_per_block, nbytes_cols_per_block_pad) * (nwarps_best*rows_per_block + 4) * 4; + const int nbytes_shared_iter = nwarps_best * (volta_mma_available(cc) ? tile_A_32::I : tile_A_16::I) * (warp_size + mmf_get_padding(cc)) * 4; + const int nbytes_cols_per_block_pad = (amd_wmma_available(cc) || amd_mfma_available(cc)) ? tile_B_16::I : tile_B_8::I; + const int nbytes_shared_combine = GGML_PAD(cols_per_block, nbytes_cols_per_block_pad) * (nwarps_best*rows_per_block + mmf_get_padding(cc)) * 4; const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine); const int nbytes_slotmap = ids ? GGML_PAD(cols_per_block, 16) * sizeof(int) : 0; const int nbytes_shared_total = nbytes_shared + nbytes_slotmap; @@ -628,56 +667,56 @@ void mul_mat_f_cuda( switch (nwarps_best) { case 1: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 2: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 3: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 4: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 5: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 6: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 7: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, ids_data); } break; case 8: { - mul_mat_f_switch_ids( + mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, @@ -691,7 +730,7 @@ void mul_mat_f_cuda( GGML_UNUSED_VARS(nchannels_y); } -template +template 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, @@ -708,82 +747,82 @@ static void mul_mat_f_switch_cols_per_block( switch (ncols_case) { case 1: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 2: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 3: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 4: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 5: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 6: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 7: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 8: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 9: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 10: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 11: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 12: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 13: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 14: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 15: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; case 16: { - mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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, ids_data); } break; @@ -793,8 +832,36 @@ static void mul_mat_f_switch_cols_per_block( } } -#define DECL_MMF_CASE_HELPER(T, ncols_dst) \ - template void mul_mat_f_cuda( \ +template +static void mul_mat_f_switch_rows_per_block( + const int rows_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 stride_col_id, const int stride_row_id, + 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, const mmf_ids_data * ids_data) { + switch (rows_per_block) { + case MMF_ROWS_PER_BLOCK: { + mul_mat_f_switch_cols_per_block( + x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + stride_col_id, stride_row_id, 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, ids_data); + } break; + case MMF_ROWS_PER_BLOCK_CDNA: { + mul_mat_f_switch_cols_per_block( + x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, + stride_col_id, stride_row_id, 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, ids_data); + } break; + default: + GGML_ABORT("unsupported rows_per_block: %i", rows_per_block); + } +} + +#define DECL_MMF_CASE_HELPER(T, nrows_dst, ncols_dst) \ + template 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, int64_t ncols_dst_total, const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst, \ const int64_t stride_col_id, const int64_t stride_row_id, \ @@ -803,16 +870,22 @@ static void mul_mat_f_switch_cols_per_block( 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, const mmf_ids_data * ids_data); -#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_MUSA) #define DECL_MMF_CASE_EXTERN(ncols_dst) \ - extern DECL_MMF_CASE_HELPER(float, ncols_dst) \ - extern DECL_MMF_CASE_HELPER(half2, ncols_dst) \ - extern DECL_MMF_CASE_HELPER(nv_bfloat162, ncols_dst) + extern DECL_MMF_CASE_HELPER(float, MMF_ROWS_PER_BLOCK, ncols_dst) \ + extern DECL_MMF_CASE_HELPER(half2, MMF_ROWS_PER_BLOCK, ncols_dst) \ + extern DECL_MMF_CASE_HELPER(nv_bfloat162, MMF_ROWS_PER_BLOCK, ncols_dst) \ + extern DECL_MMF_CASE_HELPER(float, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) \ + extern DECL_MMF_CASE_HELPER(half2, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) \ + extern DECL_MMF_CASE_HELPER(nv_bfloat162, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) #define DECL_MMF_CASE(ncols_dst) \ - DECL_MMF_CASE_HELPER(float, ncols_dst) \ - DECL_MMF_CASE_HELPER(half2, ncols_dst) \ - DECL_MMF_CASE_HELPER(nv_bfloat162, ncols_dst) + DECL_MMF_CASE_HELPER(float, MMF_ROWS_PER_BLOCK, ncols_dst) \ + DECL_MMF_CASE_HELPER(half2, MMF_ROWS_PER_BLOCK, ncols_dst) \ + DECL_MMF_CASE_HELPER(nv_bfloat162, MMF_ROWS_PER_BLOCK, ncols_dst) \ + DECL_MMF_CASE_HELPER(float, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) \ + DECL_MMF_CASE_HELPER(half2, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) \ + DECL_MMF_CASE_HELPER(nv_bfloat162, MMF_ROWS_PER_BLOCK_CDNA, ncols_dst) DECL_MMF_CASE_EXTERN(1); DECL_MMF_CASE_EXTERN(2); diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 23b6889919..80037d2436 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -62,6 +62,8 @@ file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_ROCM ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") list(APPEND GGML_SOURCES_ROCM ${SRCS}) +file(GLOB SRCS "../ggml-cuda/template-instances/mmf*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) if (GGML_CUDA_FA_ALL_QUANTS) file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") From b45ef2702c262998d5db9887cd3c82f04761237a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Thu, 29 Jan 2026 14:06:54 +0100 Subject: [PATCH 056/206] jinja : do not pass empty tools and add some none filters (#19176) --- common/chat.cpp | 10 ++++------ common/jinja/value.cpp | 10 ++++++++++ tests/test-chat-template.cpp | 5 ++--- 3 files changed, 16 insertions(+), 9 deletions(-) diff --git a/common/chat.cpp b/common/chat.cpp index eeb38ad06a..3f75b52349 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -771,10 +771,12 @@ static std::string apply( nlohmann::ordered_json inp = nlohmann::ordered_json{ {"messages", messages_override.has_value() ? *messages_override : inputs.messages}, - {"tools", tools_override.has_value() ? *tools_override : inputs.tools}, {"bos_token", tmpl.bos_token()}, {"eos_token", tmpl.eos_token()}, }; + if (tools_override.has_value() || !inputs.tools.empty()) { + inp["tools"] = tools_override.has_value() ? *tools_override : inputs.tools; + } if (inputs.extra_context.is_object()) { // TODO: do we need to merge, or replacing is fine? for (const auto & [k, v] : inputs.extra_context.items()) { @@ -790,9 +792,6 @@ static std::string apply( if (inputs.add_generation_prompt) { inp["add_generation_prompt"] = true; } - if (inp["tools"].is_null()) { - inp["tools"] = json::array(); - } jinja::global_from_json(ctx, inp, inputs.mark_input); @@ -2219,12 +2218,11 @@ static common_chat_params common_chat_params_init_glm_4_5(const common_chat_temp static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) { LOG_DBG("%s\n", __func__); common_chat_params data; - const std::optional tools_override = json(); const std::optional additional_context = json { {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")}, {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))}, }; - data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, tools_override, additional_context); + data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override =*/ std::nullopt, additional_context); 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) { diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp index 17d43826c0..f254ae9251 100644 --- a/common/jinja/value.cpp +++ b/common/jinja/value.cpp @@ -1028,6 +1028,16 @@ const func_builtins & value_none_t::get_builtins() const { {"safe", [](const func_args &) -> value { return mk_val("None"); }}, + {"strip", [](const func_args &) -> value { + return mk_val("None"); + }}, + {"items", empty_value_fn}, + {"map", empty_value_fn}, + {"reject", empty_value_fn}, + {"rejectattr", empty_value_fn}, + {"select", empty_value_fn}, + {"selectattr", empty_value_fn}, + {"unique", empty_value_fn}, }; return builtins; } diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index d2a1437ca4..27b537a036 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -54,7 +54,6 @@ std::string DEFAULT_JSON = R"({ ], "bos_token": "", "eos_token": "", - "tools": [], "add_generation_prompt": true })"; @@ -481,7 +480,7 @@ int main_automated_tests(void) { /* .name= */ "Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start)", /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", /* .expected_output= */ "[INST] You are a helpful assistant\n\nHello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[INST] Another question[/INST]", - /* .expected_output_jinja= */ "[INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[AVAILABLE_TOOLS] [[/AVAILABLE_TOOLS][INST] You are a helpful assistant\n\nAnother question[/INST]", + /* .expected_output_jinja= */ "[INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[INST] You are a helpful assistant\n\nAnother question[/INST]", /* .bos_token= */ "", /* .eos_token= */ "", }, @@ -489,7 +488,7 @@ int main_automated_tests(void) { /* .name= */ "Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start)", /* .template_str= */ "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", /* .expected_output= */ "[INST]You are a helpful assistant\n\nHello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [INST]Another question[/INST]", - /* .expected_output_jinja= */ "[INST]Hello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [AVAILABLE_TOOLS][[/AVAILABLE_TOOLS][INST]You are a helpful assistant\n\nAnother question[/INST]", + /* .expected_output_jinja= */ "[INST]Hello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [INST]You are a helpful assistant\n\nAnother question[/INST]", /* .bos_token= */ "", /* .eos_token= */ "", }, From 84b0a983195f87351195221e4f3be1c7b456ddb7 Mon Sep 17 00:00:00 2001 From: Andrew Marshall Date: Thu, 29 Jan 2026 09:56:39 -0500 Subject: [PATCH 057/206] webui: Update Svelte to fix effect_update_depth_exceeded errors (#19144) The upstream fix is first available in 5.38.2, so constrain to at least that version. Rebuild pre-compiled webui index.html.gz based on these changes. See also: https://github.com/ggml-org/llama.cpp/issues/16347 https://github.com/huntabyte/bits-ui/issues/1687 https://github.com/sveltejs/svelte/issues/16548 --- tools/server/public/index.html.gz | Bin 1451557 -> 1453103 bytes tools/server/webui/package-lock.json | 59 +++++++++++++++++++-------- tools/server/webui/package.json | 2 +- 3 files changed, 43 insertions(+), 18 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index b2c11faefede91f165035151a5a25ac4e4ce0952..e3b06f490103a30ef41f127d35275f11b1255e02 100644 GIT binary patch delta 1412507 zcmV(`K-0ga%vi73Sbraj2mk;800062?0su@8#j{n=bc}{@tnMCB_u;ONnPl1&N!Ci zXf~ANSRRiY<>XLgi)@LvWs{Of9lgJOpDJ7$=tf^i$(eX2lU<7q6bgkxp-=z{g}?r} zQu$MuO{C`Qm3h<)^11}fTad#2) zx}(vmH1H?>yu0xGrSlaR5T$4HY0nQsDs?z5O{R;I-wzf500OS0AI$yUVzeTNlWA$( zz4A-FVRtg{!*XfvU-3i*P-9-UKPOcHRyB-Ddg6?~lEY zt8W|K4?oqqhqa({`QAGUzHOd=xLx+vwV?a`Pqp6R^ndz?M!(TtwHlpO>$*4YU3V^D z+;mVD`?`%(GXNBr)2$E%MY{;}+N!|VRb`sm~3Rqf;W??LBf{pxi5 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"tailwind-variants": "^3.2.2", @@ -88,6 +88,7 @@ "version": "2.3.0", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==", + "dev": true, "license": "Apache-2.0", "dependencies": { "@jridgewell/gen-mapping": "^0.3.5", @@ -867,6 +868,7 @@ "integrity": "sha512-oJrXtQiAXLvT9clCf1K4kxp3eKsQhIaZqxEyowkBcsvZDdZkbWrVmnGknxs5flTD0VGsxrxKgBCZty1EzoiMzA==", "dev": true, "license": "Apache-2.0", + "peer": true, "dependencies": { "@swc/helpers": "^0.5.0" } @@ -898,7 +900,6 @@ "version": "2.3.5", "resolved": "https://registry.npmjs.org/@jridgewell/remapping/-/remapping-2.3.5.tgz", "integrity": "sha512-LI9u/+laYG4Ds1TDKSJW2YPrIlcVYOwi2fUC6xB43lueCjgxV4lffOCZCtYFiH6TNOX+tQKXx97T4IKHbhyHEQ==", - "dev": true, "license": "MIT", "dependencies": { "@jridgewell/gen-mapping": "^0.3.5", @@ -2031,6 +2032,7 @@ "integrity": 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"@vitest/utils": "3.2.4", "pathe": "^2.0.3", @@ -3186,6 +3195,7 @@ "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz", "integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==", "license": "MIT", + "peer": true, "bin": { "acorn": "bin/acorn" }, @@ -3738,8 +3748,7 @@ "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.1.3.tgz", "integrity": "sha512-M1uQkMl8rQK/szD0LNhtqxIPLpimGm8sOBwU7lLnCpSbTyY3yeU1Vc7l4KT5zT4s/yOxHH5O7tIuuLOCnLADRw==", "dev": true, - "license": "MIT", - "peer": true + "license": "MIT" }, "node_modules/debug": { "version": "4.4.1", @@ -3840,10 +3849,9 @@ } }, "node_modules/devalue": { - "version": "5.3.2", - "resolved": "https://registry.npmjs.org/devalue/-/devalue-5.3.2.tgz", - "integrity": "sha512-UDsjUbpQn9kvm68slnrs+mfxwFkIflOhkanmyabZ8zOYk8SMEIbJ3TK+88g70hSIeytu4y18f0z/hYHMTrXIWw==", - "dev": true, + "version": "5.6.2", + "resolved": "https://registry.npmjs.org/devalue/-/devalue-5.6.2.tgz", + "integrity": "sha512-nPRkjWzzDQlsejL1WVifk5rvcFi/y1onBRxjaFMjZeR9mFpqu2gmAZ9xUB9/IEanEP/vBtGeGganC/GO1fmufg==", "license": "MIT" }, "node_modules/devlop": { @@ -3973,6 +3981,7 @@ "dev": true, "hasInstallScript": true, "license": "MIT", + "peer": true, "bin": { "esbuild": "bin/esbuild" }, @@ -4027,6 +4036,7 @@ "integrity": "sha512-QldCVh/ztyKJJZLr4jXNUByx3gR+TDYZCRXEktiZoUR3PGy4qCmSbkxcIle8GEwGpb5JBZazlaJ/CxLidXdEbQ==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@eslint-community/eslint-utils": "^4.2.0", "@eslint-community/regexpp": "^4.12.1", @@ -6939,6 +6949,7 @@ } ], "license": "MIT", + "peer": true, "dependencies": { "nanoid": "^3.3.11", "picocolors": "^1.1.1", @@ -7072,6 +7083,7 @@ "integrity": "sha512-I7AIg5boAr5R0FFtJ6rCfD+LFsWHp81dolrFD8S79U9tb8Az2nGrJncnMSnys+bpQJfRUzqs9hnA81OAA3hCuQ==", "dev": true, "license": "MIT", + "peer": true, "bin": { "prettier": "bin/prettier.cjs" }, @@ -7088,6 +7100,7 @@ "integrity": "sha512-pn1ra/0mPObzqoIQn/vUTR3ZZI6UuZ0sHqMK5x2jMLGrs53h0sXhkVuDcrlssHwIMk7FYrMjHBPoUSyyEEDlBQ==", "dev": true, "license": "MIT", + "peer": true, "peerDependencies": { "prettier": "^3.0.0", "svelte": "^3.2.0 || ^4.0.0-next.0 || ^5.0.0-next.0" @@ -7312,6 +7325,7 @@ "integrity": "sha512-FS+XFBNvn3GTAWq26joslQgWNoFu08F4kl0J4CgdNKADkdSGXQyTCnKteIAJy96Br6YbpEU1LSzV5dYtjMkMDg==", "dev": true, "license": "MIT", + "peer": true, "engines": { "node": ">=0.10.0" } @@ -7322,6 +7336,7 @@ "integrity": "sha512-Xs1hdnE+DyKgeHJeJznQmYMIBG3TKIHJJT95Q58nHLSrElKlGQqDTR2HQ9fx5CN/Gk6Vh/kupBTDLU11/nDk/g==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "scheduler": "^0.26.0" }, @@ -7598,6 +7613,7 @@ "integrity": "sha512-4iya7Jb76fVpQyLoiVpzUrsjQ12r3dM7fIVz+4NwoYvZOShknRmiv+iu9CClZml5ZLGb0XMcYLutK6w9tgxHDw==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@types/estree": "1.0.8" }, @@ -7704,6 +7720,7 @@ "integrity": "sha512-elOcIZRTM76dvxNAjqYrucTSI0teAF/L2Lv0s6f6b7FOwcwIuA357bIE871580AjHJuSvLIRUosgV+lIWx6Rgg==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "chokidar": "^4.0.0", "immutable": "^5.0.2", @@ -7938,6 +7955,7 @@ "integrity": "sha512-7smAu0o+kdm378Q2uIddk32pn0UdIbrtTVU+rXRVtTVTCrK/P2cCui2y4JH+Bl3NgEq1bbBQpCAF/HKrDjk2Qw==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@storybook/global": "^5.0.0", "@storybook/icons": "^1.6.0", @@ -8079,12 +8097,13 @@ } }, "node_modules/svelte": { - "version": "5.36.12", - "resolved": "https://registry.npmjs.org/svelte/-/svelte-5.36.12.tgz", - "integrity": "sha512-c3mWT+b0yBLl3gPGSHiy4pdSQCsPNTjLC0tVoOhrGJ6PPfCzD/RQpAmAfJtQZ304CAae2ph+L3C4aqds3R3seQ==", + "version": "5.48.3", + "resolved": "https://registry.npmjs.org/svelte/-/svelte-5.48.3.tgz", + "integrity": "sha512-w7QZ398cdNherTdiQ/v3SYLLGOO4948Jgjh04PYqtTYVohmBvbmFwLmo7pp8gp4/1tceRWfSTjHgjtfpCVNJmQ==", "license": "MIT", + "peer": true, "dependencies": { - "@ampproject/remapping": "^2.3.0", + "@jridgewell/remapping": "^2.3.4", "@jridgewell/sourcemap-codec": "^1.5.0", "@sveltejs/acorn-typescript": "^1.0.5", "@types/estree": "^1.0.5", @@ -8092,8 +8111,9 @@ "aria-query": "^5.3.1", "axobject-query": "^4.1.0", "clsx": "^2.1.1", + "devalue": "^5.6.2", "esm-env": "^1.2.1", - "esrap": "^2.1.0", + "esrap": "^2.2.1", "is-reference": "^3.0.3", "locate-character": "^3.0.0", "magic-string": "^0.30.11", @@ -8281,9 +8301,9 @@ } }, "node_modules/svelte/node_modules/esrap": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/esrap/-/esrap-2.1.0.tgz", - "integrity": "sha512-yzmPNpl7TBbMRC5Lj2JlJZNPml0tzqoqP5B1JXycNUwtqma9AKCO0M2wHrdgsHcy1WRW7S9rJknAMtByg3usgA==", + "version": "2.2.2", + "resolved": "https://registry.npmjs.org/esrap/-/esrap-2.2.2.tgz", + "integrity": "sha512-zA6497ha+qKvoWIK+WM9NAh5ni17sKZKhbS5B3PoYbBvaYHZWoS33zmFybmyqpn07RLUxSmn+RCls2/XF+d0oQ==", "license": "MIT", "dependencies": { "@jridgewell/sourcemap-codec": "^1.4.15" @@ -8326,6 +8346,7 @@ "integrity": "sha512-gBXpgUm/3rp1lMZZrM/w7D8GKqshif0zAymAhbCyIt8KMe+0v9DQ7cdYLR4FHH/cKpdTXb+A/tKKU3eolfsI+g==", "dev": true, "license": "MIT", + "peer": true, "funding": { "type": "github", "url": "https://github.com/sponsors/dcastil" @@ -8356,7 +8377,8 @@ "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-4.1.11.tgz", "integrity": "sha512-2E9TBm6MDD/xKYe+dvJZAmg3yxIEDNRc0jwlNyDg/4Fil2QcSLjFKGVff0lAf1jjeaArlG/M75Ey/EYr/OJtBA==", "dev": true, - "license": "MIT" + "license": "MIT", + "peer": true }, "node_modules/tapable": { "version": "2.2.2", @@ -8569,6 +8591,7 @@ "integrity": "sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ==", "dev": true, "license": "Apache-2.0", + "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" @@ -8934,6 +8957,7 @@ "integrity": "sha512-BxAKBWmIbrDgrokdGZH1IgkIk/5mMHDreLDmCJ0qpyJaAteP8NvMhkwr/ZCQNqNH97bw/dANTE9PDzqwJghfMQ==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.5.0", @@ -9094,6 +9118,7 @@ "integrity": "sha512-LUCP5ev3GURDysTWiP47wRRUpLKMOfPh+yKTx3kVIEiu5KOMeqzpnYNsKyOoVrULivR8tLcks4+lga33Whn90A==", "dev": true, "license": "MIT", + "peer": true, "dependencies": { "@types/chai": "^5.2.2", "@vitest/expect": "3.2.4", diff --git a/tools/server/webui/package.json b/tools/server/webui/package.json index 1a8c273749..a361ce76e3 100644 --- a/tools/server/webui/package.json +++ b/tools/server/webui/package.json @@ -62,7 +62,7 @@ "remark-math": "^6.0.0", "sass": "^1.93.3", "storybook": "^10.0.7", - "svelte": "^5.0.0", + "svelte": "^5.38.2", "svelte-check": "^4.0.0", "tailwind-merge": "^3.3.1", "tailwind-variants": "^3.2.2", From 7b7ae857f686a4191dbd48252ab4dda426846a29 Mon Sep 17 00:00:00 2001 From: Aldehir Rojas Date: Thu, 29 Jan 2026 09:06:15 -0600 Subject: [PATCH 058/206] chat : add parsing for solar-open-100b (#18540) * chat : add parsing for solar-open-100b * add comments to rules * cont : make assistant start optional * cont : remove assistant start prefix altogether --------- Co-authored-by: Piotr Wilkin (ilintar) --- common/chat.cpp | 162 +++++++++++++++++- .../templates/upstage-Solar-Open-100B.jinja | 156 +++++++++++++++++ tests/test-chat.cpp | 130 +++++++++++++- 3 files changed, 442 insertions(+), 6 deletions(-) create mode 100644 models/templates/upstage-Solar-Open-100B.jinja diff --git a/common/chat.cpp b/common/chat.cpp index 3f75b52349..2bf4632669 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -2571,20 +2571,165 @@ static common_chat_params common_chat_params_init_granite(const common_chat_temp static common_chat_params common_chat_params_init_solar_open(const common_chat_template & tmpl, const struct templates_params & inputs) { common_chat_params data; - // TODO: Reasoning effort - json additional_context = {}; + // Copy `reasoning_content` to `reasoning` + auto adjusted_messages = json::array(); + for (const auto & msg : inputs.messages) { + if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) { + auto adjusted_message = msg; + adjusted_message["reasoning"] = msg.at("reasoning_content"); + adjusted_message.erase("reasoning_content"); + adjusted_messages.push_back(adjusted_message); + } else { + adjusted_messages.push_back(msg); + } + } - data.prompt = apply(tmpl, inputs, std::nullopt, std::nullopt, additional_context); - data.format = COMMON_CHAT_FORMAT_SOLAR_OPEN; + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto include_grammar = true; + auto prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages); + + // Check if we need to replace the flush token with end token during inference and without generation prompt. + if (inputs.is_inference && !inputs.add_generation_prompt) { + static constexpr std::string_view return_token = "<|flush|>"; + 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_PEG_NATIVE; data.preserved_tokens = { "<|think|>", "<|content|>", "<|begin|>", "<|end|>", + "<|tool_calls|>", + "<|tool_call:begin|>", + "<|tool_call:end|>", + "<|tool_call:name|>", + "<|tool_call:args|>", }; - // TODO: Tool calling + auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { + auto lit_think = p.atomic(p.literal("<|think|>")); + auto lit_assistant_begin = p.atomic(p.literal("<|begin|>assistant")); + auto lit_content = p.atomic(p.literal("<|content|>")); + auto lit_end = p.atomic(p.literal("<|end|>")); + auto parser_until_end = p.until("<|end|>"); + + // reasoning <- "<|think|>" (!"<|end|>" .)* + auto parser_reasoning = p.rule("reasoning", lit_think + p.reasoning(parser_until_end)); + + // content <- "<|content|>" (!"<|end|>" .)* + auto parser_content = p.rule("content", lit_content + p.content(parser_until_end)); + + // wrap_choice(items) <- item-choice wrapped* + // item-choice <- items[0] / ... / items[n] + // wrapped <- "<|end|><|begin|>assistant" item-choice + auto wrap_choice = [&](const std::vector & items) { + auto choice = p.choice(items); + return choice + p.zero_or_more(lit_end + lit_assistant_begin + choice); + }; + + // wrap_seq(items) <- item[0] "<|end|><|begin|>assistant" item[1] ... + auto wrap_seq = [&](const std::vector & items) { + auto seq = p.sequence(); + for (auto i = 0u; i < items.size(); i++) { + if (i == 0) { + seq += items[i]; + continue; + } + seq += lit_end + lit_assistant_begin + items[i]; + } + return seq; + }; + + // Response format parser + if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) { + auto parser_response_format = lit_content + p.content(p.schema(p.json(), "response-format", inputs.json_schema)); + return p.choice({ + wrap_seq({parser_reasoning, parser_response_format}), + wrap_seq({parser_response_format}) + }); + } + + auto lit_tool_call_begin = p.literal("<|tool_call:begin|>"); + auto lit_tool_call_name = p.literal("<|tool_call:name|>"); + auto lit_tool_call_args = p.literal("<|tool_call:args|>"); + auto lit_tool_call_end = p.literal("<|tool_call:end|>"); + + // Tool call parser + if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { + auto parser_tool_call = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); + + // tool(name, schema) <- name "<|tool_call:args|>" schema + parser_tool_call |= p.rule("tool-" + name, + p.atomic(p.tool_name(p.literal(name)) + lit_tool_call_args) + + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))); + }); + + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + auto max_calls = inputs.parallel_tool_calls ? -1 : 1; + + // tool-calls <- "<|tool_calls|>" tool-call+ + // tool-call <- "<|tool_call:begin|> call-id "<|tool_call:name|>" &([^<]+ "<|tool_call:args|>") tool-choice "<|tool_call:end|>" + // call-id <- [a-zA-Z0-9_-]+ + // tool-choice <- tool(t[0].name, t[0].schema) / ... / tool(t[n].name, t[n].schema) + auto parser_tool_calls = p.trigger_rule("tool-calls", + p.atomic(p.literal("<|tool_calls|>")) + + p.repeat( + p.tool_open( + lit_tool_call_begin + + p.tool_id(p.chars("[a-zA-Z0-9_-]", 1, -1)) + + lit_tool_call_name + + p.peek(p.chars("[^<]", 1, -1) + lit_tool_call_args)) + + parser_tool_call + + p.tool_close(lit_tool_call_end), + /* min = */ 1, + /* max = */ max_calls)); + + if (min_calls == 1) { + // If required, then try any combination of the reasoning, content, and tool call + return p.choice({ + wrap_seq({parser_reasoning, parser_content, parser_tool_calls}), + wrap_seq({parser_reasoning, parser_tool_calls}), + wrap_seq({parser_content, parser_tool_calls}), + wrap_seq({parser_tool_calls}) + }); + } + + return wrap_choice({parser_reasoning, parser_content, parser_tool_calls}); + } + + // Content only parser + include_grammar = false; + return wrap_choice({parser_reasoning, parser_content}); + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_calls|>"} + }; + } return data; } @@ -3041,6 +3186,13 @@ static common_chat_params common_chat_templates_apply_jinja( return common_chat_params_init_apriel_1_5(tmpl, params); } + // Solar Open + if (src.find("<|tool_response:begin|>") != std::string::npos && + src.find("<|tool_response:name|>") != std::string::npos && + src.find("<|tool_response:result|>") != std::string::npos) { + return common_chat_params_init_solar_open(tmpl, params); + } + // 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())) { diff --git a/models/templates/upstage-Solar-Open-100B.jinja b/models/templates/upstage-Solar-Open-100B.jinja new file mode 100644 index 0000000000..13268c1a84 --- /dev/null +++ b/models/templates/upstage-Solar-Open-100B.jinja @@ -0,0 +1,156 @@ +{#- ======== Template Parameters ======== #} +{%- set add_generation_prompt = add_generation_prompt if add_generation_prompt is defined else true %} +{%- set default_system_prompt = default_system_prompt if default_system_prompt is defined else true %} +{%- set reasoning_effort = reasoning_effort if reasoning_effort is defined else "high" %} +{%- set think_render_option = think_render_option if think_render_option is defined else "lastthink" %} + +{#- ======== System Block State ======== #} +{%- set sys_ns = namespace(is_first_block=true) -%} + +{#- ======== Find last user message index ======== #} +{%- set last_user_idx = namespace(value=-1) -%} +{%- for message in messages -%} + {%- if message.role == 'user' -%} + {%- set last_user_idx.value = loop.index0 -%} + {%- endif -%} +{%- endfor -%} + +{#- ======== System messages renderers ======== #} +{%- macro render_system_message(user_system_messages) %} + {%- if default_system_prompt %} + {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %} + {%- set sys_ns.is_first_block = false %} + {{- "## Provider System Prompt\n\nYou are Solar Open 100B, a large language model trained by Upstage AI, a Korean startup. Your knowledge cutoff is 2025-07. The current date is " + strftime_now("%Y-%m-%d") + "." }} + {%- endif -%} + {%- if user_system_messages %} + {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %} + {%- set sys_ns.is_first_block = false %} + {{- "## System Prompt" }} + {%- for system_message in user_system_messages %} + {{- "\n\n" }} + {{- system_message }} + {%- endfor %} + {%- endif -%} +{%- endmacro %} + +{%- macro render_tool_instruction(tools) %} + {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %} + {%- set sys_ns.is_first_block = false %} + {{- "## Tools\n\n### Tool Call Instruction" }} + {{- "\nYou may invoke one or more tools to assist with the user's query. Available tools are provided in JSON Schema format: <|tools:begin|><|tool:begin|><|tool:end|>...<|tools:end|>\n" }} + {{- "\n### Available Tools\n" }} + {{- "<|tools:begin|>" }} + {%- for tool in tools %} + {{- "<|tool:begin|>" }} + {{- tool.function | tojson }} + {{- "<|tool:end|>" }} + {%- endfor %} + {{- "<|tools:end|>\n" }} + {{- "\n### Tool Call Format\n" }} + {{- "For each tool call, return a JSON object with the following structure, enclosed within <|tool_call:begin|> and <|tool_call:end|> tags: \n<|tool_call:begin|><|tool_call:name|><|tool_call:args|><|tool_call:end|>\n" }} + {{- "- The must be a randomly generated string consisting of 10 lowercase letters (a-z) and/or digits (0-9) (e.g., a1b2c3d4e5)\n" }} + {{- "\n### Tool Response Format\n" }} + {{- "Each tool is responded by `tool` with the following structure:\n<|tool_response:id|><|tool_response:name|><|tool_response:result|><|tool_response:end|>\n" }} + {{- "- Ensure the matches the corresponding tool call" -}} +{%- endmacro %} + +{%- macro render_json_response_format_instruction(response_format) %} + {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %} + {%- set sys_ns.is_first_block = false %} + {{- "## Output Format Constraint" }} + {{- "\n\nYour final response should follow the JSON schema: \n[Start of schema]" }} + {{- response_format }} + {{- "\n[End of schema]\nPlease ensure your answers adhere to this format and do not contain any unnecessary text." }} +{%- endmacro %} + +{%- macro get_tool_name(messages, tool_call_id) %} + {%- for msg in messages -%} + {%- if msg.role == 'assistant' and msg.tool_calls -%} + {%- for tool_call in msg.tool_calls -%} + {%- if tool_call.id == tool_call_id -%} + {{- tool_call.function.name }} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- endfor -%} +{%- endmacro %} + +{%- macro render_tool_arguments(tool_arguments) %} + {%- if tool_arguments is mapping -%} + {{- tool_arguments | tojson }} + {%- else -%} + {{- tool_arguments }} + {%- endif -%} +{%- endmacro %} + +{#- ======== Render system message ======== #} +{%- set ns = namespace(system_messages=[]) -%} +{%- for message in messages -%} + {%- if message.role == 'system' -%} + {%- set ns.system_messages = ns.system_messages + [message.content] -%} + {%- endif -%} +{%- endfor -%} + +{%- if ns.system_messages or default_system_prompt or tools or response_format -%} + {{- "<|begin|>system<|content|>" }} + {{- render_system_message(ns.system_messages) }} + {%- if tools -%} + {{- render_tool_instruction(tools) }} + {%- endif %} + {%- if response_format -%} + {{- render_json_response_format_instruction(response_format) }} + {%- endif %} + {{- "<|end|>" }} +{%- endif -%} + +{#- ======== Render main messages ======== #} +{%- for message in messages -%} + {%- if message.role == 'user' -%} + {{- "<|begin|>user<|content|>" + message.content + "<|end|>" }} + {%- elif message.role == 'tool' -%} + {%- set prev_is_tool = loop.index0 > 0 and messages[loop.index0 - 1].role == 'tool' -%} + {%- set next_is_tool = loop.index0 < (messages | length - 1) and messages[loop.index0 + 1].role == 'tool' -%} + {%- if not prev_is_tool -%} + {{- "<|begin|>tool<|tool_response|>" }} + {%- endif -%} + {{- "<|tool_response:begin|>" + message.tool_call_id + "<|tool_response:name|>" }} + {{- get_tool_name(messages, message.tool_call_id) }} + {{- "<|tool_response:result|>" }} + {{- message.content }} + {{- "<|tool_response:end|>" }} + {%- if not next_is_tool -%} + {{- "<|end|>" }} + {%- endif -%} + {%- elif message.role == 'assistant' -%} + {#- ======== Assistant Thinking ======== #} + {%- if think_render_option == "all" -%} + {%- if message.reasoning -%} + {{- "<|begin|>assistant<|think|>" + message.reasoning + "<|end|>" }} + {%- endif -%} + {%- elif think_render_option == "lastthink" -%} + {%- if message.reasoning and loop.index0 > last_user_idx.value -%} + {{- "<|begin|>assistant<|think|>" + message.reasoning + "<|end|>" }} + {%- endif -%} + {%- endif -%} + + {#- ======== Assistant Messages ======== #} + {%- if message.tool_calls -%} + {{- "<|begin|>assistant<|tool_calls|>" }} + {%- for tool_call in message.tool_calls -%} + {{- "<|tool_call:begin|>" + tool_call.id +"<|tool_call:name|>" + tool_call.function.name + "<|tool_call:args|>" }} + {{- render_tool_arguments(tool_call.function.arguments) }} + {{- "<|tool_call:end|>" }} + {%- endfor -%} + {{- "<|calls|>" }} + {%- else -%} + {{- "<|begin|>assistant<|content|>" + message.content + "<|end|>" }} + {%- endif -%} + {%- endif -%} +{%- endfor -%} + +{%- if add_generation_prompt -%} + {%- if reasoning_effort in ["low", "minimal"] -%} + {{- "<|begin|>assistant<|think|><|end|>" }} + {%- endif -%} + {{- "<|begin|>assistant" }} +{%- endif -%} diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index de7075e6e5..4378a8db71 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -592,7 +592,7 @@ static void test_peg_parser(common_chat_templates * tmpls, const std::function123456789" + "<|tool_call:name|>special_function" + "<|tool_call:args|>{\"arg1\":1}" + "<|tool_call:end|>"; + + t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; + t.params.tools = {special_function_tool}; + t.expect = message_assist_call_id; + }); + + // Test tool call with reasoning + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|think|>I'm\nthinking<|end|>" + "<|begin|>assistant<|tool_calls|>" + "<|tool_call:begin|>0" + "<|tool_call:name|>special_function" + "<|tool_call:args|>{\"arg1\":1}" + "<|tool_call:end|>"; + + t.params.tools = {special_function_tool}; + t.expect = message_assist_thoughts_call_idx; + }); + + // Test tool call with reasoning and tool_choice = required + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|think|>I'm\nthinking<|end|>" + "<|begin|>assistant<|tool_calls|>" + "<|tool_call:begin|>0" + "<|tool_call:name|>special_function" + "<|tool_call:args|>{\"arg1\":1}" + "<|tool_call:end|>"; + + t.params.tools = {special_function_tool}; + t.params.tool_choice = COMMON_CHAT_TOOL_CHOICE_REQUIRED; + t.expect = message_assist_thoughts_call_idx; + }); + + // Test tool call without reasoning and tool_choice = required + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|tool_calls|>" + "<|tool_call:begin|>0" + "<|tool_call:name|>special_function" + "<|tool_call:args|>{\"arg1\":1}" + "<|tool_call:end|>"; + + t.params.tools = {special_function_tool}; + t.params.tool_choice = COMMON_CHAT_TOOL_CHOICE_REQUIRED; + t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; + t.expect = message_assist_call_idx; + }); + + // Test parallel tool calls + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|think|>I'm\nthinking<|end|>" + "<|begin|>assistant<|tool_calls|>" + "<|tool_call:begin|>0" + "<|tool_call:name|>special_function" + "<|tool_call:args|>{\"arg1\":1}" + "<|tool_call:end|>" + "<|tool_call:begin|>1" + "<|tool_call:name|>special_function_with_opt" + "<|tool_call:args|>{\"arg1\": 1, \"arg2\": 2}" + "<|tool_call:end|>"; + + t.params.parallel_tool_calls = true; + t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; + + t.expect.reasoning_content = "I'm\nthinking"; + t.expect.tool_calls = {{ + /* .name = */ "special_function", + /* .arguments = */ R"({"arg1": 1})", + /* .id = */ "0", + }, { + /* .name = */ "special_function_with_opt", + /* .arguments = */ R"({"arg1": 1, "arg2": 2})", + /* .id = */ "1", + }}; + }); + + // Test response format + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|think|>I need to output the invoice details in JSON<|end|>" + "<|begin|>assistant<|content|>" + R"({"amount": 123.45, "date": "2025-12-03"})"; + + t.params.json_schema = invoice_schema; + + t.expect.reasoning_content = "I need to output the invoice details in JSON"; + t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; + }); + + // Test response format no reasoning + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = "<|content|>" + R"({"amount": 123.45, "date": "2025-12-03"})"; + + t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; + t.params.json_schema = invoice_schema; + + t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; + }); + } } static void test_msg_diffs_compute() { From 4fdbc1e4dba428ce0cf9d2ac22232dc170bbca82 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 29 Jan 2026 18:45:30 +0200 Subject: [PATCH 059/206] cuda : fix nkvo, offload and cuda graph node properties matching (#19165) * cuda : fix nkvo * cont : more robust cuda graph node property matching * cont : restore pre-leafs implementation * cont : comments + static_assert --- ggml/src/ggml-cuda/common.cuh | 12 +++++- ggml/src/ggml-cuda/fattn.cu | 5 --- ggml/src/ggml-cuda/ggml-cuda.cu | 69 +++++++++++++++++++++++---------- src/llama-graph.cpp | 5 --- 4 files changed, 59 insertions(+), 32 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 3335f443ae..43280644e4 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -1122,15 +1122,17 @@ struct ggml_tensor_extra_gpu { #endif struct ggml_cuda_graph_node_properties { - void * node_address; + void * node_data; ggml_op node_op; int32_t flags; int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; - void * src_address[GGML_MAX_SRC]; + void * src_data[GGML_MAX_SRC]; int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; }; +static_assert(std::is_trivial::value, "ggml_cuda_graph_node_properties must be trivial"); + struct ggml_cuda_graph { #ifdef USE_CUDA_GRAPH ~ggml_cuda_graph() { @@ -1150,6 +1152,12 @@ struct ggml_cuda_graph { int number_consecutive_updates = 0; std::vector props; + // these are extra tensors (inputs) that participate in the ggml graph but are not nodes + // they properties also have to match in order to be able to safely reuse a CUDA graph + // ref: https://github.com/ggml-org/llama.cpp/pull/18583 + // ref: https://github.com/ggml-org/llama.cpp/pull/19165 + std::vector extra; + void record_update(bool use_graph, bool update_required) { if (use_graph && update_required) { number_consecutive_updates++; diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 195904ee20..721edd9994 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -310,8 +310,6 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const } } - const bool V_is_K_view = V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs)); - const int cc = ggml_cuda_info().devices[device].cc; switch (K->ne[0]) { @@ -334,9 +332,6 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const if (!gqa_opt_applies) { return BEST_FATTN_KERNEL_NONE; } - if (!V_is_K_view) { - return BEST_FATTN_KERNEL_NONE; - } break; default: return BEST_FATTN_KERNEL_NONE; diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 76d0f12550..cfcffde8a2 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -70,17 +70,18 @@ #include #include #include -#include +#include #include #include #include #include #include -#include -#include -#include +#include +#include +#include #include #include +#include static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); @@ -2916,7 +2917,8 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) { } static void ggml_cuda_graph_node_set_properties(ggml_cuda_graph_node_properties * props, ggml_tensor * node) { - props->node_address = node->data; + memset(props, 0, sizeof(ggml_cuda_graph_node_properties)); + props->node_data = node->data; props->node_op = node->op; props->flags = node->flags; for (int i = 0; i < GGML_MAX_DIMS; i++) { @@ -2924,14 +2926,17 @@ static void ggml_cuda_graph_node_set_properties(ggml_cuda_graph_node_properties props->nb[i] = node->nb[i]; } for (int i = 0; i < GGML_MAX_SRC; i++) { - props->src_address[i] = node->src[i] ? node->src[i]->data : nullptr; + if (!node->src[i]) { + continue; + } + + props->src_data[i] = node->src[i]->data; } memcpy(props->op_params, node->op_params, GGML_MAX_OP_PARAMS); } static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_graph_node_properties * props) { - if (node->data != props->node_address && - node->op != GGML_OP_VIEW) { + if (node->data != props->node_data && node->op != GGML_OP_VIEW) { return false; } @@ -2948,12 +2953,18 @@ static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_ } } - for (int i = 0; i < GGML_MAX_SRC; i++) { - if (node->src[i] && - node->src[i]->data != props->src_address[i] && - node->op != GGML_OP_VIEW - ) { - return false; + if (node->op != GGML_OP_VIEW) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (!node->src[i]) { + if (props->src_data[i] != nullptr) { + return false; + } + continue; + } + + if (node->src[i]->data != props->src_data[i]) { + return false; + } } } @@ -2974,7 +2985,6 @@ static const void * ggml_cuda_graph_get_key(ggml_cgraph * cgraph) { } static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) { - bool res = false; const void * graph_key = ggml_cuda_graph_get_key(cgraph); @@ -2985,15 +2995,20 @@ static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx } // Check if the graph size has changed - if (graph->props.size() != (size_t)cgraph->n_nodes + cgraph->n_leafs) { + if (graph->props.size() != (size_t)cgraph->n_nodes) { res = true; - graph->props.resize(cgraph->n_nodes + cgraph->n_leafs); + graph->props.resize(cgraph->n_nodes); } // Loop over nodes in GGML graph to determine if CUDA graph update is required // and store properties to allow this comparison for the next token + std::unordered_set seen_node; + std::vector srcs_extra; for (int i = 0; i < cgraph->n_nodes; i++) { bool props_match = true; + + seen_node.insert(cgraph->nodes[i]); + if (!res) { props_match = ggml_cuda_graph_node_properties_match(cgraph->nodes[i], &graph->props[i]); } @@ -3001,17 +3016,31 @@ static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx res = true; } ggml_cuda_graph_node_set_properties(&graph->props[i], cgraph->nodes[i]); + + for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) { + ggml_tensor * src = cgraph->nodes[i]->src[src_idx]; + if (src && seen_node.find(src) == seen_node.end()) { + srcs_extra.push_back(src); + } + } } - for (int i = 0; i < cgraph->n_leafs; i++) { + if (graph->extra.size() != (size_t) srcs_extra.size()) { + res = true; + graph->extra.resize(srcs_extra.size()); + } + + for (size_t i = 0; i < srcs_extra.size(); ++i) { bool props_match = true; + if (!res) { - props_match = ggml_cuda_graph_node_properties_match(cgraph->leafs[i], &graph->props[cgraph->n_nodes + i]); + props_match = ggml_cuda_graph_node_properties_match(srcs_extra[i], &graph->extra[i]); } + if (!props_match) { res = true; } - ggml_cuda_graph_node_set_properties(&graph->props[cgraph->n_nodes + i], cgraph->leafs[i]); + ggml_cuda_graph_node_set_properties(&graph->extra[i], srcs_extra[i]); } return res; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index b3198b7e3a..16d42c4ae3 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1630,11 +1630,6 @@ ggml_tensor * llm_graph_context::build_attn_mha( hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f); cb(cur, LLAMA_TENSOR_NAME_FATTN, il); - if (!cparams.offload_kqv) { - // all nodes between the KV store and the attention output are run on the CPU - ggml_backend_sched_set_tensor_backend(sched, cur, backend_cpu); - } - ggml_flash_attn_ext_add_sinks(cur, sinks); ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32); From ce38a4db478b90542874cd4af5cb48b3a0fcf311 Mon Sep 17 00:00:00 2001 From: Todor Boinovski Date: Thu, 29 Jan 2026 12:33:21 -0800 Subject: [PATCH 060/206] hexagon: enable offloading to Hexagon on Windows on Snapdragon (#19150) * hexagon: updates to enable offloading to HTP on WoS * Update windows.md * Update windows.md * hexagon: enable -O3 optimizations * hexagon: move all _WINDOWS conditional compilation to _WIN32 * hexagon: updates to enable offloading to HTP on WoS * hexagon: use run-time vs load-time dynamic linking for cdsp driver interface * refactor htp-drv * hexagon: add run-bench.ps1 script * hexagon: htdrv refactor * hexagon: unify Android and Windows build readmes * hexagon: update README.md * hexagon: refactor htpdrv * hexagon: drv refactor * hexagon: more drv refactor * hexagon: fixes for android builds * hexagon: factor out dl into ggml-backend-dl * hexagon: add run-tool.ps1 script * hexagon: merge htp-utils in htp-drv and remove unused code * wos: no need for getopt_custom.h * wos: add missing CR in htpdrv * hexagon: ndev enforecement applies only to the Android devices * hexagon: add support for generating and signing .cat file * hexagon: add .inf file * hexagon: working auto-signing and improved windows builds * hexagon: futher improve skel build * hexagon: add rough WoS guide * hexagon: updated windows guide * hexagon: improve cmake handling of certs and logging * hexagon: improve windows setup/build doc * hexagon: more windows readme updates * hexagon: windows readme updates * hexagon: windows readme updates * hexagon: windows readme updates * hexagon: windows readme updates * Update windows.md * Update windows.md * snapdragon: rename docs/backend/hexagon to docs/backends/snapdragon Also added a power shell script to simplify build env setup. * hexagon: remove trailing whitespace and move cmake requirement to user-presets * hexagon: fix CMakeUserPresets path in workflow yaml * hexagon: introduce local version of libdl.h * hexagon: fix src1 reuse logic gpt-oss needs a bigger lookahead window. The check for src[1] itself being quantized was wrong. --------- Co-authored-by: Max Krasnyansky --- .github/workflows/build.yml | 2 +- .../CMakeUserPresets.json | 21 +- .../backend/{hexagon => snapdragon}/README.md | 62 ++- .../{hexagon => snapdragon}/developer.md | 0 docs/backend/snapdragon/windows.md | 161 +++++++ ggml/src/CMakeLists.txt | 1 + ggml/src/ggml-backend-dl.cpp | 48 ++ ggml/src/ggml-backend-dl.h | 45 ++ ggml/src/ggml-backend-reg.cpp | 67 +-- ggml/src/ggml-hexagon/CMakeLists.txt | 113 +++-- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 59 ++- ggml/src/ggml-hexagon/htp-drv.cpp | 418 ++++++++++++++++ ggml/src/ggml-hexagon/htp-drv.h | 121 +++++ ggml/src/ggml-hexagon/htp-utils.c | 454 ------------------ ggml/src/ggml-hexagon/htp-utils.h | 221 --------- ggml/src/ggml-hexagon/libdl.h | 79 +++ ggml/src/ggml-hexagon/libggml-htp.inf | 38 ++ scripts/snapdragon/windows/run-bench.ps1 | 40 ++ scripts/snapdragon/windows/run-cli.ps1 | 53 ++ scripts/snapdragon/windows/run-tool.ps1 | 56 +++ scripts/snapdragon/windows/setup-build.ps1 | 105 ++++ 21 files changed, 1325 insertions(+), 839 deletions(-) rename docs/backend/{hexagon => snapdragon}/CMakeUserPresets.json (70%) rename docs/backend/{hexagon => snapdragon}/README.md (84%) rename docs/backend/{hexagon => snapdragon}/developer.md (100%) create mode 100644 docs/backend/snapdragon/windows.md create mode 100644 ggml/src/ggml-backend-dl.cpp create mode 100644 ggml/src/ggml-backend-dl.h create mode 100644 ggml/src/ggml-hexagon/htp-drv.cpp create mode 100644 ggml/src/ggml-hexagon/htp-drv.h delete mode 100644 ggml/src/ggml-hexagon/htp-utils.c delete mode 100644 ggml/src/ggml-hexagon/htp-utils.h create mode 100644 ggml/src/ggml-hexagon/libdl.h create mode 100644 ggml/src/ggml-hexagon/libggml-htp.inf create mode 100644 scripts/snapdragon/windows/run-bench.ps1 create mode 100644 scripts/snapdragon/windows/run-cli.ps1 create mode 100644 scripts/snapdragon/windows/run-tool.ps1 create mode 100644 scripts/snapdragon/windows/setup-build.ps1 diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 551bdd3df0..d3a1638f16 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1371,7 +1371,7 @@ jobs: id: update_presets if: ${{ matrix.build == 'arm64-snapdragon' }} run: | - cp docs/backend/hexagon/CMakeUserPresets.json . + cp docs/backend/snapdragon/CMakeUserPresets.json . - name: Build id: ndk_build diff --git a/docs/backend/hexagon/CMakeUserPresets.json b/docs/backend/snapdragon/CMakeUserPresets.json similarity index 70% rename from docs/backend/hexagon/CMakeUserPresets.json rename to docs/backend/snapdragon/CMakeUserPresets.json index 1f2676c0bc..4cf473d05f 100644 --- a/docs/backend/hexagon/CMakeUserPresets.json +++ b/docs/backend/snapdragon/CMakeUserPresets.json @@ -1,5 +1,10 @@ { - "version": 4, + "version": 5, + "cmakeMinimumRequired": { + "major": 3, + "minor": 28, + "patch": 0 + }, "configurePresets": [ { "name": "arm64-android-snapdragon", @@ -16,7 +21,9 @@ "CMAKE_CXX_FLAGS_RELEASE": "-O3 -DNDEBUG", "CMAKE_C_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g", "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g", - "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}", + "CMAKE_PREFIX_PATH": "$env{OPENCL_SDK_ROOT}", + "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}", + "HEXAGON_TOOLS_ROOT": "$env{HEXAGON_TOOLS_ROOT}", "PREBUILT_LIB_DIR": "android_aarch64", "GGML_OPENMP": "OFF", "GGML_LLAMAFILE": "OFF", @@ -31,7 +38,15 @@ "name": "arm64-windows-snapdragon", "inherits": [ "base", "arm64-windows-llvm" ], "cacheVariables": { - "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}", + "CMAKE_C_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -flto -D_GNU_SOURCE", + "CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -flto -D_GNU_SOURCE", + "CMAKE_C_FLAGS_RELEASE": "-O3 -DNDEBUG", + "CMAKE_CXX_FLAGS_RELEASE": "-O3 -DNDEBUG", + "CMAKE_C_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g", + "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g", + "CMAKE_PREFIX_PATH": "$env{OPENCL_SDK_ROOT}", + "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}", + "HEXAGON_TOOLS_ROOT": "$env{HEXAGON_TOOLS_ROOT}", "PREBUILT_LIB_DIR": "windows_aarch64", "GGML_OPENMP": "OFF", "GGML_LLAMAFILE": "OFF", diff --git a/docs/backend/hexagon/README.md b/docs/backend/snapdragon/README.md similarity index 84% rename from docs/backend/hexagon/README.md rename to docs/backend/snapdragon/README.md index 3befdf7225..8e1f37b206 100644 --- a/docs/backend/hexagon/README.md +++ b/docs/backend/snapdragon/README.md @@ -1,6 +1,8 @@ -# Snapdragon-based Android devices +# Snapdragon-based devices -## How to Build +## Setup + +### Android The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain). This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc. @@ -12,7 +14,24 @@ This method works on Linux, macOS, and Windows. macOS and Windows users should i [d]/> cd /workspace ``` -The rest of the Android build process assumes that you're running inside the toolchain container. +Note: The rest of the **Android** build process assumes that you're running inside the toolchain container. + +### Windows On Snapdragon + +Native Windows 11 arm64 builds has the following tools dependencies: +- MS Visual Studio 2026 (Community Edition or Pro) + - MSVC arm64 standard and runtime libraries + - UCRT and Driver Kit +- LLVM core libraries and Clang compiler (winget) +- CMake, Git, Python (winget) +- Hexagon SDK Community Edition 6.4 or later (see windows.md) +- OpenCL SDK 2.3 or later (see windows.md) + +Note: The rest of the **Windows** build process assumes that you're running natively in Powershell. +Adapt below build commands accordingly. + +## How to Build + Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets: ``` @@ -49,24 +68,26 @@ Preset CMake variables: To generate an installable "package" simply use cmake --install: ``` -[d]/workspace> cmake --install build-snapdragon --prefix pkg-adb/llama.cpp +[d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon/llama.cpp -- Install configuration: "Release" --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-cpu.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-opencl.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-hexagon.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v73.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v75.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v79.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v81.so --- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-cpu.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-opencl.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-hexagon.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v73.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v75.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v79.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v81.so +-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml.so ... --- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-bench --- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-cli +-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-bench +-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-cli ... ``` ## How to Install +### Android + For this step, your device needs to be configured for on-device development. Please see https://developer.android.com/studio/debug/dev-options for details. @@ -74,10 +95,10 @@ Once ADB is enabled, use `adb push` to install `pkg-snapdragon` on the device. **Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.** ``` -~/src/llama.cpp$ adb push pkg-adb/llama.cpp /data/local/tmp/ -pkg-adb/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s) -pkg-adb/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s) -pkg-adb/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s) +~/src/llama.cpp$ adb push pkg-snapdragon/llama.cpp /data/local/tmp/ +pkg-snapdragon/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s) +pkg-snapdragon/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s) +pkg-snapdragon/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s) 102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s) ``` @@ -92,6 +113,11 @@ At this point, you should also install some models: Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s) ``` +### Windows + +All artifacts are already installed in the `pkg-snapdragon` folder. +To run, adapt below instructions to use Powershell scrits in `scripts/snapdragon/windows`. + ## How to Run The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables. diff --git a/docs/backend/hexagon/developer.md b/docs/backend/snapdragon/developer.md similarity index 100% rename from docs/backend/hexagon/developer.md rename to docs/backend/snapdragon/developer.md diff --git a/docs/backend/snapdragon/windows.md b/docs/backend/snapdragon/windows.md new file mode 100644 index 0000000000..710ad8fdf4 --- /dev/null +++ b/docs/backend/snapdragon/windows.md @@ -0,0 +1,161 @@ +## Overview + +The document covers procedures for installing the latest GPU and NPU drivers, and OpenCL and Hexagon SDKs. + + +In order to use Hexagon NPU on Snapdragon Windows devices the underlying HTP Ops libraries (e.g libggml-htp-v73.so) +must be included in the .cat file digitally signed with a trusted certificate. + +This document covers details on how to generate personal certificate files (.pfx) and how to configure the system +to allow for test signatures (aka test-signing). + +## Install the latest Adreno OpenCL SDK + +Either use the trimmed down version (optimized for CI) from + + https://github.com/snapdragon-toolchain/opencl-sdk/releases/download/v2.3.2/adreno-opencl-sdk-v2.3.2-arm64-wos.tar.xz + +Or download the complete official version from + + https://softwarecenter.qualcomm.com/catalog/item/Adreno_OpenCL_SDK?version=2.3.2 + +Unzip/untar the archive into +``` +c:\Qualcomm\OpenCL_SDK\2.3.2 +``` + +## Install the latest Hexagon SDK Community Edition + +Either use the trimmed down version (optimized for CI) from + + https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v6.4.0.2/hexagon-sdk-v6.4.0.2-arm64-wos.tar.xz + +Or download the complete official version from + + https://softwarecenter.qualcomm.com/catalog/item/Hexagon_SDK?version=6.4.0.2 + +Unzip/untar the archive into +``` +c:\Qualcomm\Hexagon_SDK\6.4.0.2 +``` + +## Install the latest Adreno GPU driver + +Download the driver from + + https://softwarecenter.qualcomm.com/catalog/item/Windows_Graphics_Driver + +After the automated installation and reboot please make sure that the GPU device shows up in the `Device Manager` (under 'Display Adapters`) + +## Install the latest Qualcomm NPU driver + +Download the driver from + + https://softwarecenter.qualcomm.com/catalog/item/Qualcomm_HND + +After the automated installation and reboot please make sure that the Hexagon NPU device shows up in the `Device Manager` (under `Neural Processors`). + +If the device is not available you can try installing all components (`qcnspmcdm8380`, `qcnspmcdm8380_ext`) manually. +The components are extracted into +``` +c:\QCDrivers\qcnspmcdm... +``` + +## Enable NPU driver test signatures + +Please note that the following steps are required only for the Hexagon NPU. +Adreno GPU backend does not require test signatures. + +### Enable testsigning + +Use `bcdedit` to enable test-signing +``` +> bcdedit /set TESTSIGNING ON +``` +(Secure Boot may need to be disabled for this to work) + +Make sure test-signing is enabled after reboot +``` +> bcdedit /enum +... +testsigning Yes +... +``` +For additional details see Microsoft guide at + + https://learn.microsoft.com/en-us/windows-hardware/drivers/install/the-testsigning-boot-configuration-option + +### Create personal certificate + +The tools required for this procedure are available as part of Windows SDK and Windows Driver Kit which should be +installed as part of the MS Visual Studio. +They are typically located at +``` +c:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0 +``` +(replace 10.0.26100.0 with correct version). + +To create personal self-signed certificate run the following commands (either from cmd or power-shell): +``` +> cd c:\Users\MyUser +> mkdir Certs +> cd Certs +> makecert -r -pe -ss PrivateCertStore -n CN=GGML.HTP.v1 -eku 1.3.6.1.5.5.7.3.3 -sv ggml-htp-v1.pvk ggml-htp-v1.cer +> pvk2pfx.exe -pvk ggml-htp-v1.pvk -spc ggml-htp-v1.cer -pfx ggml-htp-v1.pfx +``` +(replace `MyUser` with your username). + +Add this certificate to `Trusted Root Certification Authorities` and `Trusted Publishers` stores. +This can be done using `certlm` Certificate Manager tool. +Right click on the certificate store, select `All Tasks -> Import` and follow the prompts to import the certificate from the +PFX file you created above. + +For additional details see Microsoft guide at + + https://learn.microsoft.com/en-us/windows-hardware/drivers/install/introduction-to-test-signing + +Make sure to save the PFX file, you will need it for the build procedures. +Please note that the same certificate can be used for signing any number of builds. + +## Build Hexagon backend with signed HTP ops libraries + +The overall Hexagon backend build procedure for Windows on Snapdragon is the same as for other platforms. +However, additional settings are required for generating and signing HTP Ops libraries. +``` +> $env:OPENCL_SDK_ROOT="C:\Qualcomm\OpenCL_SDK\2.3.2" +> $env:HEXAGON_SDK_ROOT="C:\Qualcomm\Hexagon_SDK\6.4.0.2" +> $env:HEXAGON_TOOLS_ROOT="C:\Qualcomm\Hexagon_SDK\6.4.0.2\tools\HEXAGON_Tools\19.0.04" +> $env:HEXAGON_HTP_CERT="c:\Users\MyUsers\Certs\ggml-htp-v1.pfx" +> $env:WINDOWS_SDK_BIN="C:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0\arm64" + +> cmake --preset arm64-windows-snapdragon -B build-wos +... +> cmake --install build-wos --prefix pkg-snapdragon +``` + +Once the build is complete HTP ops libraries will be installed like this +``` +> dir pkg-snapdragon/lib +... +-a---- 1/22/2026 6:01 PM 187656 libggml-htp-v73.so +-a---- 1/22/2026 6:01 PM 191752 libggml-htp-v75.so +-a---- 1/22/2026 6:01 PM 187656 libggml-htp-v79.so +-a---- 1/22/2026 6:01 PM 187656 libggml-htp-v81.so +-a---- 1/22/2026 6:01 PM 4139 libggml-htp.cat +``` + +The .cat file, the signature and proper certicate installation can be verified with + +``` +> signtool.exe verify /v /pa .\pkg-snapdragon\lib\libggml-htp.cat +Verifying: .\pkg-snapdragon\lib\libggml-htp.cat + +Signature Index: 0 (Primary Signature) +Hash of file (sha256): 9820C664DA59D5EAE31DBB664127FCDAEF59CDC31502496BC567544EC2F401CF + +Signing Certificate Chain: + Issued to: GGML.HTP.v1 +... +Successfully verified: .\pkg-snapdragon\lib\libggml-htp.cat +... +``` diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 260ad48f0e..265023733e 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -222,6 +222,7 @@ if (GGML_SCHED_NO_REALLOC) endif() add_library(ggml + ggml-backend-dl.cpp ggml-backend-reg.cpp) add_library(ggml::ggml ALIAS ggml) diff --git a/ggml/src/ggml-backend-dl.cpp b/ggml/src/ggml-backend-dl.cpp new file mode 100644 index 0000000000..a65cf00905 --- /dev/null +++ b/ggml/src/ggml-backend-dl.cpp @@ -0,0 +1,48 @@ +#include "ggml-backend-dl.h" + +#ifdef _WIN32 + +dl_handle * dl_load_library(const fs::path & path) { + // suppress error dialogs for missing DLLs + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + HMODULE handle = LoadLibraryW(path.wstring().c_str()); + + SetErrorMode(old_mode); + + return handle; +} + +void * dl_get_sym(dl_handle * handle, const char * name) { + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + void * p = (void *) GetProcAddress(handle, name); + + SetErrorMode(old_mode); + + return p; +} + +const char * dl_error() { + return ""; +} + +#else + +dl_handle * dl_load_library(const fs::path & path) { + dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL); + return handle; +} + +void * dl_get_sym(dl_handle * handle, const char * name) { + return dlsym(handle, name); +} + +const char * dl_error() { + const char *rslt = dlerror(); + return rslt != nullptr ? rslt : ""; +} + +#endif diff --git a/ggml/src/ggml-backend-dl.h b/ggml/src/ggml-backend-dl.h new file mode 100644 index 0000000000..f74b7c9489 --- /dev/null +++ b/ggml/src/ggml-backend-dl.h @@ -0,0 +1,45 @@ +#pragma once + +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +#else +# include +# include +#endif +#include + +namespace fs = std::filesystem; + +#ifdef _WIN32 + +using dl_handle = std::remove_pointer_t; + +struct dl_handle_deleter { + void operator()(HMODULE handle) { + FreeLibrary(handle); + } +}; + +#else + +using dl_handle = void; + +struct dl_handle_deleter { + void operator()(void * handle) { + dlclose(handle); + } +}; + +#endif + +using dl_handle_ptr = std::unique_ptr; + +dl_handle * dl_load_library(const fs::path & path); +void * dl_get_sym(dl_handle * handle, const char * name); +const char * dl_error(); + diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index dd991f262e..8a693f84af 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -1,5 +1,6 @@ #include "ggml-backend-impl.h" #include "ggml-backend.h" +#include "ggml-backend-dl.h" #include "ggml-impl.h" #include #include @@ -98,72 +99,6 @@ static std::string path_str(const fs::path & path) { } } -#ifdef _WIN32 - -using dl_handle = std::remove_pointer_t; - -struct dl_handle_deleter { - void operator()(HMODULE handle) { - FreeLibrary(handle); - } -}; - -static dl_handle * dl_load_library(const fs::path & path) { - // suppress error dialogs for missing DLLs - DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); - SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); - - HMODULE handle = LoadLibraryW(path.wstring().c_str()); - - SetErrorMode(old_mode); - - return handle; -} - -static void * dl_get_sym(dl_handle * handle, const char * name) { - DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); - SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); - - void * p = (void *) GetProcAddress(handle, name); - - SetErrorMode(old_mode); - - return p; -} - -static const char * dl_error() { - return ""; -} - -#else - -using dl_handle = void; - -struct dl_handle_deleter { - void operator()(void * handle) { - dlclose(handle); - } -}; - -static void * dl_load_library(const fs::path & path) { - dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL); - - return handle; -} - -static void * dl_get_sym(dl_handle * handle, const char * name) { - return dlsym(handle, name); -} - -static const char * dl_error() { - const char *rslt = dlerror(); - return rslt != nullptr ? rslt : ""; -} - -#endif - -using dl_handle_ptr = std::unique_ptr; - struct ggml_backend_reg_entry { ggml_backend_reg_t reg; dl_handle_ptr handle; diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt index d58e287823..2b69197017 100644 --- a/ggml/src/ggml-hexagon/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/CMakeLists.txt @@ -1,7 +1,17 @@ +file(TO_CMAKE_PATH "${HEXAGON_SDK_ROOT}" HEXAGON_SDK_ROOT) +file(TO_CMAKE_PATH "${HEXAGON_TOOLS_ROOT}" HEXAGON_TOOLS_ROOT) + +if (NOT IS_DIRECTORY "${HEXAGON_SDK_ROOT}" OR NOT IS_DIRECTORY "${HEXAGON_TOOLS_ROOT}") + message(FATAL_ERROR "Make sure HEXAGON_SDK_ROOT and HEXAGON_TOOLS_ROOT point to the correct Hexagon SDK installation.") +endif() + +message(STATUS "hexagon: using ${HEXAGON_SDK_ROOT} and ${HEXAGON_TOOLS_ROOT} for building libggml-htp skels") + include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake) include(ExternalProject) option(GGML_HEXAGON_HTP_DEBUG "ggml-hexagon: enable HTP debug output" OFF) +set(GGML_HEXAGON_HTP_CERT "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate") set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)") add_library(htp_iface OBJECT @@ -25,56 +35,71 @@ else() target_link_options(htp_iface PUBLIC -ldl) endif() -link_custom_library(htp_iface cdsprpc) -link_custom_library(htp_iface rpcmem) - set(TARGET_NAME ggml-hexagon) ggml_add_backend_library(${TARGET_NAME} - ggml-hexagon.cpp htp-utils.c htp-utils.h ../../include/ggml-hexagon.h) + ggml-hexagon.cpp + htp-drv.cpp + htp-drv.h + libdl.h + ../../include/ggml-hexagon.h) target_link_libraries(${TARGET_NAME} PRIVATE htp_iface) target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/htp ${CMAKE_CURRENT_BINARY_DIR}) -# Build HTP bits -set(HTP_CMAKE_ARGS - -DCMAKE_TOOLCHAIN_FILE=${CMAKE_CURRENT_SOURCE_DIR}/htp/cmake-toolchain.cmake - -DCMAKE_BUILD_TYPE=Release - -DCMAKE_INSTALL_LIBDIR=${CMAKE_CURRENT_BINARY_DIR} - -DHEXAGON_SDK_ROOT=$ENV{HEXAGON_SDK_ROOT} - -DHEXAGON_TOOLS_ROOT=$ENV{HEXAGON_TOOLS_ROOT} - -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG} - -DGGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE}) +# Build HTP skels +set(HTP_SKELS) +function(build_htp_skel V) + ExternalProject_Add(htp-${V} + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON + BUILD_BYPRODUCTS ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-${V}.so + CMAKE_ARGS + -DCMAKE_BUILD_TYPE=Release + -DCMAKE_TOOLCHAIN_FILE=${CMAKE_CURRENT_SOURCE_DIR}/htp/cmake-toolchain.cmake + -DCMAKE_INSTALL_LIBDIR=${CMAKE_CURRENT_BINARY_DIR} + -DHEXAGON_SDK_ROOT=${HEXAGON_SDK_ROOT} + -DHEXAGON_TOOLS_ROOT=${HEXAGON_TOOLS_ROOT} + -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG} + -DGGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE} + -DDSP_VERSION=${V} + -DPREBUILT_LIB_DIR="toolv19_${V}") + list(APPEND HTP_SKELS ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-${V}.so) + set(HTP_SKELS ${HTP_SKELS} PARENT_SCOPE) +endfunction() -ExternalProject_Add(htp-v68 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v68 -DPREBUILT_LIB_DIR="toolv19_v68") - -ExternalProject_Add(htp-v69 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v69 -DPREBUILT_LIB_DIR="toolv19_v69") - -ExternalProject_Add(htp-v73 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v73 -DPREBUILT_LIB_DIR="toolv19_v73") - -ExternalProject_Add(htp-v75 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v75 -DPREBUILT_LIB_DIR="toolv19_v75") - -ExternalProject_Add(htp-v79 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v79 -DPREBUILT_LIB_DIR="toolv19_v79") - -ExternalProject_Add(htp-v81 - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON - CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v81 -DPREBUILT_LIB_DIR="toolv19_v81") +build_htp_skel(v68) +build_htp_skel(v69) +build_htp_skel(v73) +build_htp_skel(v75) +build_htp_skel(v79) +build_htp_skel(v81) # Install Hexagon skels required at runtime -install(FILES - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v68.so - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v69.so - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v73.so - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v75.so - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v79.so - ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v81.so - TYPE LIB) +install(FILES ${HTP_SKELS} TYPE LIB) + +if (CMAKE_SYSTEM_NAME MATCHES Windows AND GGML_HEXAGON_HTP_CERT) + file(TO_CMAKE_PATH "$ENV{WINDOWS_SDK_BIN}/arm64" WINSDK_BIN0_ARM64) + file(TO_CMAKE_PATH "$ENV{WINDOWS_SDK_BIN}/x86" WINSDK_BIN0_X86) + file(TO_CMAKE_PATH "$ENV{WindowsSdkVerBinPath}/arm64" WINSDK_BIN1_ARM64) + file(TO_CMAKE_PATH "$ENV{WindowsSdkVerBinPath}/x86" WINSDK_BIN1_X86) + + set(WINSDK_PATHS ${WINSDK_BIN0_ARM64} ${WINSDK_BIN0_X86} ${WINSDK_BIN1_ARM64} ${WINSDK_BIN1_X86}) + + find_program(INF2CAT NAMES inf2cat.exe PATHS ${WINSDK_PATHS} REQUIRED) + find_program(SIGNTOOL NAMES signtool.exe PATHS ${WINSDK_PATHS} REQUIRED) + + message(STATUS "hexagon: using ${GGML_HEXAGON_HTP_CERT} to sign libggml-htp skels") + + set(LIBGGML_HTP_CAT ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp.cat) + add_custom_target(libggml-htp-cat + BYPRODUCTS ${LIBGGML_HTP_CAT} + DEPENDS libggml-htp.inf ${HTP_SKELS} + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/libggml-htp.inf ${CMAKE_CURRENT_BINARY_DIR} + COMMAND ${INF2CAT} /driver:${CMAKE_CURRENT_BINARY_DIR} /os:10_25H2_ARM64 + COMMAND ${SIGNTOOL} sign /fd sha256 /f ${GGML_HEXAGON_HTP_CERT} ${LIBGGML_HTP_CAT} + COMMENT "generating and signing libggml-htp.cat file" + VERBATIM + ) + + add_dependencies(${TARGET_NAME} libggml-htp-cat) + install(FILES ${LIBGGML_HTP_CAT} TYPE LIB) +endif() diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 5b835c11c7..4f0a1620fb 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -14,9 +14,6 @@ #ifdef _WIN32 # include -# ifndef _WINDOWS -# define _WINDOWS -# endif #else # include # include @@ -25,8 +22,6 @@ #pragma clang diagnostic ignored "-Wnested-anon-types" #pragma clang diagnostic ignored "-Wgnu-anonymous-struct" -#include "htp-utils.h" - #include #include #include @@ -40,6 +35,7 @@ #include "op-desc.h" #include "htp-msg.h" #include "htp_iface.h" +#include "htp-drv.h" static size_t opt_ndev = 1; static size_t opt_nhvx = 0; // use all @@ -150,9 +146,9 @@ void ggml_hexagon_session::enqueue(struct htp_general_req &req, struct dspqueue_ 0, // flags - the framework will autoset this n_bufs, // number of buffers bufs, // buffer references - sizeof(req), + sizeof(req), // Message length (const uint8_t *) &req, // Message - 1000000 // Timeout + DSPQUEUE_TIMEOUT // Timeout ); if (err != 0) { @@ -182,13 +178,13 @@ void ggml_hexagon_session::flush() { // Read response packet from queue int err = dspqueue_read(q, &flags, - HTP_MAX_PACKET_BUFFERS, // Maximum number of buffer references - &n_bufs, // Number of buffer references - bufs, // Buffer references - sizeof(rsp), // Max message length - &rsp_size, // Message length - (uint8_t *) &rsp, - 1000000); // Timeout + HTP_MAX_PACKET_BUFFERS, // Maximum number of buffer references + &n_bufs, // Number of buffer references + bufs, // Buffer references + sizeof(rsp), // Max message length + &rsp_size, // Message length + (uint8_t *) &rsp, // Message + DSPQUEUE_TIMEOUT); // Timeout if (err == AEE_EEXPIRED) { // TODO: might need to bail out if the HTP is stuck on something @@ -269,13 +265,7 @@ struct ggml_backend_hexagon_buffer_context { ggml_backend_hexagon_buffer_context(ggml_hexagon_session * sess, size_t size, bool repack) { size += 4 * 1024; // extra page for padding - if (rpcmem_alloc2) { - this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size); - } else { - GGML_LOG_INFO("ggml-hex: %s rpcmem_alloc2 not found, falling back to rpcmem_alloc\n", sess->name.c_str()); - this->base = (uint8_t *) rpcmem_alloc(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size); - } - + this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size); if (!this->base) { GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer : size %zu\n", sess->name.c_str(), size); throw std::runtime_error("ggml-hex: rpcmem_alloc failed (see log for details)"); @@ -2461,12 +2451,12 @@ static void ggml_backend_hexagon_free(ggml_backend_t backend) { } static inline bool op_reuse_src1(const ggml_tensor * op1, const ggml_tensor * op0) { - return (op0 && op0->src[1] == op1->src[1] && ggml_is_quantized(op0->src[0]->type) && ggml_is_quantized(op1->src[1]->type)); + return (op0 && op0->src[1] == op1->src[1] && ggml_is_quantized(op0->src[0]->type)); } static inline bool is_compute_op(ggml_tensor *node) { - return !(ggml_op_is_empty(node->op) || ggml_is_empty(node)); + return !ggml_op_is_empty(node->op) && !ggml_is_empty(node) && (node->flags & GGML_TENSOR_FLAG_COMPUTE); } // scan the graph and figure out last compute op index @@ -2488,7 +2478,7 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg const int last = last_compute_op(graph); - const struct ggml_tensor * prev_quant_op = nullptr; // prev executed op with quantizer + const struct ggml_tensor * prev_op = nullptr; // prev executed op for (int i = 0; i < graph->n_nodes; ++i) { ggml_tensor * node = graph->nodes[i]; @@ -2497,17 +2487,15 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg continue; } - if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { - continue; - } - uint32_t flags = 0; // skip quantizer if src1 is reused - if (op_reuse_src1(node, prev_quant_op)) { + if (op_reuse_src1(node, prev_op)) { flags |= HTP_OPFLAGS_SKIP_QUANTIZE; } + prev_op = node; + // ask for early notification for the last Op if (i == last) { flags |= HTP_OPFLAGS_EARLY_WAKEUP; @@ -2520,7 +2508,6 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg } else { ggml_hexagon_dispatch_op>(sess, node, flags); } - prev_quant_op = node; break; case GGML_OP_MUL_MAT_ID: if (ggml_is_quantized(node->src[0]->type)) { @@ -2528,7 +2515,6 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg } else { ggml_hexagon_dispatch_op>(sess, node, flags); } - prev_quant_op = node; break; case GGML_OP_MUL: case GGML_OP_ADD: @@ -2670,7 +2656,7 @@ static std::vector ggml_hexagon_graph_optimize_reorder(const std::vectorcontext = new ggml_hexagon_registry(reg); HEX_VERBOSE("ggml-hex: size-of-general-req %zu size-of-general-rsp %zu\n", sizeof(struct htp_general_req), @@ -3180,6 +3170,11 @@ ggml_backend_reg_t ggml_backend_hexagon_reg(void) { static std::mutex mutex; std::lock_guard lock(mutex); if (!initialized) { + auto nErr = htpdrv_init(); + if (nErr != AEE_SUCCESS) { + return NULL; + } + ggml_hexagon_init(®); } diff --git a/ggml/src/ggml-hexagon/htp-drv.cpp b/ggml/src/ggml-hexagon/htp-drv.cpp new file mode 100644 index 0000000000..2530bb06d6 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp-drv.cpp @@ -0,0 +1,418 @@ +// sample drv interface + +#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" +#pragma clang diagnostic ignored "-Wmissing-prototypes" +#pragma clang diagnostic ignored "-Wsign-compare" + +#include +#include +#include +#include +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +#else +# include +# include +#endif +#include "ggml-impl.h" +#include "htp-drv.h" +#include "libdl.h" + +#include + +// +// Driver API types +// + +typedef void * (*rpcmem_alloc_pfn_t)(int heapid, uint32_t flags, int size); +typedef void * (*rpcmem_alloc2_pfn_t)(int heapid, uint32_t flags, size_t size); +typedef void (*rpcmem_free_pfn_t)(void * po); +typedef int (*rpcmem_to_fd_pfn_t)(void * po); + +typedef AEEResult (*dspqueue_create_pfn_t)(int domain, + uint32_t flags, + uint32_t req_queue_size, + uint32_t resp_queue_size, + dspqueue_callback_t packet_callback, + dspqueue_callback_t error_callback, + void * callback_context, + dspqueue_t * queue); +typedef AEEResult (*dspqueue_close_pfn_t)(dspqueue_t queue); +typedef AEEResult (*dspqueue_export_pfn_t)(dspqueue_t queue, uint64_t *queue_id); +typedef AEEResult (*dspqueue_write_pfn_t)(dspqueue_t queue, uint32_t flags, + uint32_t num_buffers, + struct dspqueue_buffer *buffers, + uint32_t message_length, + const uint8_t *message, + uint32_t timeout_us); +typedef AEEResult (*dspqueue_read_pfn_t)(dspqueue_t queue, uint32_t *flags, + uint32_t max_buffers, uint32_t *num_buffers, + struct dspqueue_buffer *buffers, + uint32_t max_message_length, + uint32_t *message_length, uint8_t *message, + uint32_t timeout_us); + +typedef int (*fastrpc_mmap_pfn_t)(int domain, int fd, void *addr, int offset, size_t length, enum fastrpc_map_flags flags); +typedef int (*fastrpc_munmap_pfn_t)(int domain, int fd, void *addr, size_t length); + +typedef int (*remote_handle64_open_pfn_t)(const char* name, remote_handle64 *ph); +typedef int (*remote_handle64_invoke_pfn_t)(remote_handle64 h, uint32_t dwScalars, remote_arg *pra); +typedef int (*remote_handle64_close_pfn_t)(remote_handle h); +typedef int (*remote_handle_control_pfn_t)(uint32_t req, void* data, uint32_t datalen); +typedef int (*remote_handle64_control_pfn_t)(remote_handle64 h, uint32_t req, void* data, uint32_t datalen); +typedef int (*remote_session_control_pfn_t)(uint32_t req, void *data, uint32_t datalen); + +// +// Driver API pfns +// + +rpcmem_alloc_pfn_t rpcmem_alloc_pfn = nullptr; +rpcmem_alloc2_pfn_t rpcmem_alloc2_pfn = nullptr; +rpcmem_free_pfn_t rpcmem_free_pfn = nullptr; +rpcmem_to_fd_pfn_t rpcmem_to_fd_pfn = nullptr; + +fastrpc_mmap_pfn_t fastrpc_mmap_pfn = nullptr; +fastrpc_munmap_pfn_t fastrpc_munmap_pfn = nullptr; + +dspqueue_create_pfn_t dspqueue_create_pfn = nullptr; +dspqueue_close_pfn_t dspqueue_close_pfn = nullptr; +dspqueue_export_pfn_t dspqueue_export_pfn = nullptr; +dspqueue_write_pfn_t dspqueue_write_pfn = nullptr; +dspqueue_read_pfn_t dspqueue_read_pfn = nullptr; + +remote_handle64_open_pfn_t remote_handle64_open_pfn = nullptr; +remote_handle64_invoke_pfn_t remote_handle64_invoke_pfn = nullptr; +remote_handle64_close_pfn_t remote_handle64_close_pfn = nullptr; +remote_handle_control_pfn_t remote_handle_control_pfn = nullptr; +remote_handle64_control_pfn_t remote_handle64_control_pfn = nullptr; +remote_session_control_pfn_t remote_session_control_pfn = nullptr; + +// +// Driver API +// + +void * rpcmem_alloc(int heapid, uint32_t flags, int size) { + return rpcmem_alloc_pfn(heapid, flags, size); +} + +void * rpcmem_alloc2(int heapid, uint32_t flags, size_t size) { + if (rpcmem_alloc2_pfn) { + return rpcmem_alloc2_pfn(heapid, flags, size); + } else { + GGML_LOG_INFO("ggml-hex: rpcmem_alloc2 not found, falling back to rpcmem_alloc\n"); + return rpcmem_alloc_pfn(heapid, flags, size); + } +} + +void rpcmem_free(void * po) { + return rpcmem_free_pfn(po); +} + +int rpcmem_to_fd(void * po) { + return rpcmem_to_fd_pfn(po); +} + +HTPDRV_API int fastrpc_mmap(int domain, int fd, void * addr, int offset, size_t length, enum fastrpc_map_flags flags) { + return fastrpc_mmap_pfn(domain, fd, addr, offset, length, flags); +} + +HTPDRV_API int fastrpc_munmap(int domain, int fd, void * addr, size_t length) { + return fastrpc_munmap_pfn(domain, fd, addr, length); +} + +AEEResult dspqueue_create(int domain, + uint32_t flags, + uint32_t req_queue_size, + uint32_t resp_queue_size, + dspqueue_callback_t packet_callback, + dspqueue_callback_t error_callback, + void * callback_context, + dspqueue_t * queue) { + return dspqueue_create_pfn(domain, flags, req_queue_size, resp_queue_size, packet_callback, error_callback, + callback_context, queue); +} + +AEEResult dspqueue_close(dspqueue_t queue) { + return dspqueue_close_pfn(queue); +} + +AEEResult dspqueue_export(dspqueue_t queue, uint64_t * queue_id) { + return dspqueue_export_pfn(queue, queue_id); +} + +AEEResult dspqueue_write(dspqueue_t queue, + uint32_t flags, + uint32_t num_buffers, + struct dspqueue_buffer * buffers, + uint32_t message_length, + const uint8_t * message, + uint32_t timeout_us) { + return dspqueue_write_pfn(queue, flags, num_buffers, buffers, message_length, message, timeout_us); +} + +AEEResult dspqueue_read(dspqueue_t queue, + uint32_t * flags, + uint32_t max_buffers, + uint32_t * num_buffers, + struct dspqueue_buffer * buffers, + uint32_t max_message_length, + uint32_t * message_length, + uint8_t * message, + uint32_t timeout_us) { + return dspqueue_read_pfn(queue, flags, max_buffers, num_buffers, buffers, max_message_length, message_length, + message, timeout_us); +} + +HTPDRV_API int remote_handle64_open(const char * name, remote_handle64 * ph) { + return remote_handle64_open_pfn(name, ph); +} + +HTPDRV_API int remote_handle64_invoke(remote_handle64 h, uint32_t dwScalars, remote_arg * pra) { + return remote_handle64_invoke_pfn(h, dwScalars, pra); +} + +HTPDRV_API int remote_handle64_close(remote_handle64 h) { + return remote_handle64_close_pfn(h); +} + +HTPDRV_API int remote_handle_control(uint32_t req, void * data, uint32_t datalen) { + return remote_handle_control_pfn(req, data, datalen); +} + +HTPDRV_API int remote_handle64_control(remote_handle64 h, uint32_t req, void * data, uint32_t datalen) { + return remote_handle64_control_pfn(h, req, data, datalen); +} + +HTPDRV_API int remote_session_control(uint32_t req, void * data, uint32_t datalen) { + return remote_session_control_pfn(req, data, datalen); +} + +#ifdef _WIN32 + +static std::string wstr_to_str(std::wstring_view wstr) { + std::string result; + if (wstr.empty()) { + return result; + } + auto bytes_needed = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, + wstr.data(), (int) wstr.size(), + nullptr, 0, nullptr, nullptr); + if (bytes_needed == 0) { + GGML_LOG_ERROR("ggml-hex: WideCharToMultiByte failed. Error %lu\n", GetLastError()); + throw std::runtime_error("Invalid wstring input"); + } + + result.resize(bytes_needed, '\0'); + int bytes_written = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, + wstr.data(), (int) wstr.size(), + result.data(), bytes_needed, + nullptr, nullptr); + if (bytes_written == 0) { + GGML_LOG_ERROR("ggml-hex: WideCharToMultiByte failed. Error %lu\n", GetLastError()); + throw std::runtime_error("Wstring conversion failed"); + } + return result; +} + +static std::string get_driver_path() { + std::wstring serviceName = L"qcnspmcdm"; + std::string result; + + // Get a handle to the SCM database. + SC_HANDLE schSCManager = OpenSCManagerW(NULL, NULL, STANDARD_RIGHTS_READ); + if (nullptr == schSCManager) { + GGML_LOG_ERROR("ggml-hex: Failed to open SCManager. Error: %lu\n", GetLastError()); + return result; + } + + // Get a handle to the service. + SC_HANDLE schService = OpenServiceW(schSCManager, // SCM database + serviceName.c_str(), // name of service + SERVICE_QUERY_CONFIG); // need query config access + + if (nullptr == schService) { + GGML_LOG_ERROR("ggml-hex: Failed to open qcnspmcdm service. Error: %lu\n", GetLastError()); + CloseServiceHandle(schSCManager); + return result; + } + + // Store the size of buffer used as an output. + DWORD bufferSize; + if (!QueryServiceConfigW(schService, NULL, 0, &bufferSize) && + (GetLastError() != ERROR_INSUFFICIENT_BUFFER)) { + GGML_LOG_ERROR("ggml-hex: Failed to query service config. Error: %lu\n", GetLastError()); + CloseServiceHandle(schService); + CloseServiceHandle(schSCManager); + return result; + } + // Get the configuration of the service. + LPQUERY_SERVICE_CONFIGW serviceConfig = + static_cast(LocalAlloc(LMEM_FIXED, bufferSize)); + if (!QueryServiceConfigW(schService, serviceConfig, bufferSize, &bufferSize)) { + fprintf(stderr, "ggml-hex: Failed to query service config. Error: %lu\n", GetLastError()); + LocalFree(serviceConfig); + CloseServiceHandle(schService); + CloseServiceHandle(schSCManager); + return result; + } + + // Read the driver file path get its parent directory + std::wstring driverPath = std::wstring(serviceConfig->lpBinaryPathName); + driverPath = driverPath.substr(0, driverPath.find_last_of(L"\\")); + + // Clean up resources + LocalFree(serviceConfig); + CloseServiceHandle(schService); + CloseServiceHandle(schSCManager); + + // Driver path would contain invalid path string, like: + // \SystemRoot\System32\DriverStore\FileRepository\qcadsprpc8280.inf_arm64_c2b9460c9a072f37 + // "\SystemRoot" should be replace with a correct one (e.g. C:\Windows) + const std::wstring systemRootPlaceholder = L"\\SystemRoot"; + if (0 != driverPath.compare(0, systemRootPlaceholder.length(), systemRootPlaceholder)) { + GGML_LOG_ERROR("ggml-hex: String pattern not found in driver path.\n"); + return result; + } + + // Replace \SystemRoot with an absolute path from system ENV windir + const std::wstring systemRootEnv = L"windir"; + + // Query the number of wide charactors this variable requires + DWORD numWords = GetEnvironmentVariableW(systemRootEnv.c_str(), NULL, 0); + if (numWords == 0) { + GGML_LOG_ERROR("ggml-hex: Failed get systemRoot environment variable\n"); + return result; + } + + // Query the actual system root name from environment variable + std::vector systemRoot(numWords + 1); + numWords = GetEnvironmentVariableW(systemRootEnv.c_str(), systemRoot.data(), numWords + 1); + if (numWords == 0) { + GGML_LOG_ERROR("ggml-hex: Failed to read windir environment variable\n"); + return result; + } + driverPath.replace(0, systemRootPlaceholder.length(), std::wstring(systemRoot.data())); + + return wstr_to_str(driverPath); +} + +#endif + +using dl_handle_ptr = std::unique_ptr; + +int htpdrv_init() { + static dl_handle_ptr lib_cdsp_rpc_handle = nullptr; + static bool initialized = false; +#ifdef _WIN32 + std::string drv_path = get_driver_path() + "\\" + "libcdsprpc.dll"; +#else + std::string drv_path = "libcdsprpc.so"; +#endif + if (initialized) { + GGML_LOG_INFO("ggml-hex: Driver already loaded\n"); + return AEE_SUCCESS; + } + GGML_LOG_INFO("ggml-hex: Loading driver %s\n", drv_path.c_str()); + + fs::path path{ drv_path.c_str() }; + dl_handle_ptr handle { dl_load_library(path) }; + if (!handle) { + GGML_LOG_ERROR("ggml-hex: failed to load %s: %s\n", path.u8string().c_str(), dl_error()); + return AEE_EUNABLETOLOAD; + } + +#define dlsym(drv, type, pfn, symbol, ignore) \ + do { \ + pfn = (type) dl_get_sym(drv, #symbol); \ + if (!ignore && nullptr == pfn) { \ + GGML_LOG_ERROR("ggml-hex: failed to dlsym %s\n", #symbol); \ + return AEE_EUNABLETOLOAD; \ + } \ + } while (0) + + dlsym(handle.get(), rpcmem_alloc_pfn_t, rpcmem_alloc_pfn, rpcmem_alloc, false); + dlsym(handle.get(), rpcmem_alloc2_pfn_t, rpcmem_alloc2_pfn, rpcmem_alloc2, true); + dlsym(handle.get(), rpcmem_free_pfn_t, rpcmem_free_pfn, rpcmem_free, false); + dlsym(handle.get(), rpcmem_to_fd_pfn_t, rpcmem_to_fd_pfn, rpcmem_to_fd, false); + dlsym(handle.get(), fastrpc_mmap_pfn_t, fastrpc_mmap_pfn, fastrpc_mmap, false); + dlsym(handle.get(), fastrpc_munmap_pfn_t, fastrpc_munmap_pfn, fastrpc_munmap, false); + dlsym(handle.get(), dspqueue_create_pfn_t, dspqueue_create_pfn, dspqueue_create, false); + dlsym(handle.get(), dspqueue_close_pfn_t, dspqueue_close_pfn, dspqueue_close, false); + dlsym(handle.get(), dspqueue_export_pfn_t, dspqueue_export_pfn, dspqueue_export, false); + dlsym(handle.get(), dspqueue_write_pfn_t, dspqueue_write_pfn, dspqueue_write, false); + dlsym(handle.get(), dspqueue_read_pfn_t, dspqueue_read_pfn, dspqueue_read, false); + dlsym(handle.get(), remote_handle64_open_pfn_t, remote_handle64_open_pfn, remote_handle64_open, false); + dlsym(handle.get(), remote_handle64_invoke_pfn_t, remote_handle64_invoke_pfn, remote_handle64_invoke, false); + dlsym(handle.get(), remote_handle_control_pfn_t, remote_handle_control_pfn, remote_handle_control, false); + dlsym(handle.get(), remote_handle64_control_pfn_t, remote_handle64_control_pfn, remote_handle64_control, false); + dlsym(handle.get(), remote_session_control_pfn_t, remote_session_control_pfn, remote_session_control, false); + dlsym(handle.get(), remote_handle64_close_pfn_t, remote_handle64_close_pfn, remote_handle64_close, false); + + lib_cdsp_rpc_handle = std::move(handle); + initialized = true; + + return AEE_SUCCESS; +} + +domain * get_domain(int domain_id) { + int i = 0; + int size = sizeof(supported_domains) / sizeof(domain); + + for (i = 0; i < size; i++) { + if (supported_domains[i].id == domain_id) { + return &supported_domains[i]; + } + } + + return NULL; +} + +int get_hex_arch_ver(int domain, int * arch) { + if (!remote_handle_control_pfn) { + GGML_LOG_ERROR("ggml-hex: remote_handle_control is not supported on this device\n"); + return AEE_EUNSUPPORTEDAPI; + } + + struct remote_dsp_capability arch_ver; + arch_ver.domain = (uint32_t) domain; + arch_ver.attribute_ID = ARCH_VER; + arch_ver.capability = (uint32_t) 0; + + int err = remote_handle_control(DSPRPC_GET_DSP_INFO, &arch_ver, sizeof(arch_ver)); + if ((err & 0xff) == (AEE_EUNSUPPORTEDAPI & 0xff)) { + GGML_LOG_ERROR("ggml-hex: FastRPC capability API is not supported on this device\n"); + return AEE_EUNSUPPORTEDAPI; + } + + if (err != AEE_SUCCESS) { + GGML_LOG_ERROR("ggml-hex: FastRPC capability query failed (err %d)\n", err); + return err; + } + + switch (arch_ver.capability & 0xff) { + case 0x68: + *arch = 68; + return 0; + case 0x69: + *arch = 69; + return 0; + case 0x73: + *arch = 73; + return 0; + case 0x75: + *arch = 75; + return 0; + case 0x79: + *arch = 79; + return 0; + case 0x81: + *arch = 81; + return 0; + } + return -1; +} diff --git a/ggml/src/ggml-hexagon/htp-drv.h b/ggml/src/ggml-hexagon/htp-drv.h new file mode 100644 index 0000000000..6eba7ba17d --- /dev/null +++ b/ggml/src/ggml-hexagon/htp-drv.h @@ -0,0 +1,121 @@ +#pragma once + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef _WIN32 +# pragma clang diagnostic ignored "-Wignored-attributes" +#endif + +#include +#include +#include +#include + +#if defined(_WIN32) && !defined(__MINGW32__) +# ifdef GGML_BACKEND_BUILD +# define HTPDRV_API __declspec(dllexport) extern +# else +# define HTPDRV_API __declspec(dllimport) extern +# endif +#else +# define HTPDRV_API __attribute__ ((visibility ("default"))) extern +#endif + +/* Offset to differentiate HLOS and Hexagon error codes. + Stores the value of AEE_EOFFSET for Hexagon. */ +#ifndef DSP_OFFSET +# define DSP_OFFSET 0x80000400 +#endif + +/* Errno for connection reset by peer. */ +#ifndef ECONNRESET +# ifdef __hexagon__ +# define ECONNRESET 104 +# endif +#endif + +/* Abstraction of different OS specific sleep APIs. + SLEEP accepts input in seconds. */ +#ifndef SLEEP +# ifdef __hexagon__ +# define SLEEP(x) \ + { /* Do nothing for simulator. */ \ + } +# else +# ifdef _WIN32 +# define SLEEP(x) Sleep(1000 * x) /* Sleep accepts input in milliseconds. */ +# else +# define SLEEP(x) sleep(x) /* sleep accepts input in seconds. */ +# endif +# endif +#endif + +/* Include windows specific header files. */ +#ifdef _WIN32 +# include +# include +# define _CRT_SECURE_NO_WARNINGS 1 +# define _WINSOCK_DEPRECATED_NO_WARNINGS 1 +#endif + +/* Includes and defines for all HLOS except windows */ +#if !defined(__hexagon__) && !defined(_WIN32) +# include "unistd.h" + +# include +#endif + +/* Includes and defines for Hexagon and all HLOS except Windows. */ +#if !defined(_WIN32) +/* Weak reference to remote symbol for compilation. */ +# pragma weak remote_session_control +# pragma weak remote_handle_control +# pragma weak remote_handle64_control +# pragma weak fastrpc_mmap +# pragma weak fastrpc_munmap +# pragma weak rpcmem_alloc2 +#endif + +#if !defined(_WIN32) +# pragma weak remote_system_request +#endif + +#ifdef _WIN32 +# define DSPQUEUE_TIMEOUT DSPQUEUE_TIMEOUT_NONE +#else +# define DSPQUEUE_TIMEOUT 1000000 +#endif + +/** + * htpdrv_init API: driver interface entry point + * + * @return Return AEE error codes as defined in Hexagon SDK. + */ +HTPDRV_API int htpdrv_init(void); + +/** + * get_domain API: get domain struct from domain value. + * + * @param[in] domain value of a domain + * @return Returns domain struct of the domain if it is supported or else + * returns NULL. + * + */ +HTPDRV_API domain * get_domain(int domain_id); + +/** + * get_hex_arch_ver API: query the Hexagon processor architecture version information + * + * @param[in] domain_id value of a domain + * @param[out] Arch version (73, 75, ...) + * @return 0 if query is successful. + * non-zero if error, return value points to the error. + * + */ +HTPDRV_API int get_hex_arch_ver(int domain, int * arch); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-hexagon/htp-utils.c b/ggml/src/ggml-hexagon/htp-utils.c deleted file mode 100644 index 3f335bf71c..0000000000 --- a/ggml/src/ggml-hexagon/htp-utils.c +++ /dev/null @@ -1,454 +0,0 @@ - -#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" -#pragma clang diagnostic ignored "-Wmissing-prototypes" -#pragma clang diagnostic ignored "-Wsign-compare" - -#define GGML_COMMON_IMPL_C -#include "ggml-backend-impl.h" -#include "ggml-common.h" -#include "ggml-hexagon.h" -#include "ggml-impl.h" - -#include "htp-utils.h" - -#include -#include -#include -#include -#include -#include -#include - -domain * get_domain(int domain_id) { - int i = 0; - int size = sizeof(supported_domains) / sizeof(domain); - - for (i = 0; i < size; i++) { - if (supported_domains[i].id == domain_id) { - return &supported_domains[i]; - } - } - - return NULL; -} - -bool is_valid_domain_id(int domain_id, int compute_only) { - int i = 0; - int size = sizeof(supported_domains) / sizeof(domain); - - if (compute_only) { - return is_CDSP(domain_id); - } - - for (i = 0; i < size; i++) { - if (supported_domains[i].id == domain_id) { - return true; - } - } - - return false; -} - -int get_domains_info(char * domain_type, int * num_domains, fastrpc_domain ** domains_info) { - int nErr = AEE_SUCCESS; - int ss_info = 0; - if (domain_type != NULL) { - if (strcmp(domain_type, "LPASS") == 0) { - ss_info = FASTRPC_LPASS; - } else if (strcmp(domain_type, "HPASS") == 0) { - ss_info = FASTRPC_HPASS; - } else { - ss_info = FASTRPC_NSP; - } - } - system_req_payload req = { 0 }; - req.id = FASTRPC_GET_DOMAINS; - req.sys.domains = NULL; - fastrpc_domain * domain = NULL; - if (ss_info != 0) { - req.sys.flags = DOMAINS_LIST_FLAGS_SET_TYPE(req.sys.flags, ss_info); - } else { - req.sys.flags = 0; - } -#ifdef _WIN32 - nErr = AEE_EUNSUPPORTED; - goto bail; -#endif - if (remote_system_request) { - nErr = remote_system_request(&req); - if (nErr != AEE_SUCCESS) { - GGML_LOG_ERROR("Failure in remote_system_request call: %d.\n", nErr); - goto bail; - } - // Allocate memory for domain-info array - req.sys.max_domains = req.sys.num_domains; - if ((req.sys.domains = calloc(req.sys.num_domains, sizeof(fastrpc_domain))) == NULL) { - nErr = AEE_ENOMEMORY; - GGML_LOG_ERROR("Unable to allocate memory for req.sys.domains"); - goto bail; - } - - nErr = remote_system_request(&req); - if (nErr != AEE_SUCCESS) { - GGML_LOG_ERROR("Failure in remote_system_request call: %d.\n", nErr); - goto bail; - } - - for (int i = 0; i < req.sys.num_domains; i++) { - // Verify that only requested type domains were returned - domain = &req.sys.domains[i]; - if (domain->type != ss_info && domain_type != NULL) { - nErr = -1; - GGML_LOG_ERROR("Incorrect data received from remote_system_request.\n"); - goto bail; - } - } - *domains_info = req.sys.domains; - *num_domains = req.sys.num_domains; - } else { - nErr = AEE_EUNSUPPORTED; - goto bail; - } -bail: - if (nErr && !req.sys.domains) { - free(req.sys.domains); - } - return nErr; -} - -int get_effective_domain_id(char * domain_name, int session_id, int * effec_domain_id) { - int err = 0; - remote_rpc_effective_domain_id_t sess = { 0 }; - - sess.domain_name = domain_name; - sess.domain_name_len = strlen(domain_name); - sess.session_id = session_id; - - err = remote_session_control(FASTRPC_GET_EFFECTIVE_DOMAIN_ID, &sess, sizeof(sess)); - if (err) { - GGML_LOG_ERROR("Error 0x%x: failed to get effective domain id for %s, session id %d\n", err, sess.domain_name, - session_id); - return err; - } - - *effec_domain_id = sess.effective_domain_id; - return err; -} - -int get_dsp_support(int * domain) { - int nErr = AEE_SUCCESS; - *domain = CDSP_DOMAIN_ID; // DSP domain default value is CDSP_DOMAIN_ID - - if (remote_handle_control) { - struct remote_dsp_capability dsp_capability_domain = { CDSP_DOMAIN_ID, DOMAIN_SUPPORT, 0 }; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_domain, sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - goto bail; - } - - if (dsp_capability_domain.capability == 0) { - dsp_capability_domain.domain = ADSP_DOMAIN_ID; // Check for ADSP support. - dsp_capability_domain.attribute_ID = DOMAIN_SUPPORT; - dsp_capability_domain.capability = 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_domain, - sizeof(struct remote_dsp_capability)); - if (dsp_capability_domain.capability) { - *domain = ADSP_DOMAIN_ID; // For targets like Agatti (not having cDSP), domain is ADSP_DOMAIN_ID - } - } - - if (nErr != AEE_SUCCESS) { - GGML_LOG_ERROR("\nget_dsp_support failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return nErr; -} - -int get_vtcm_info(int domain, uint32_t * capability, uint32_t attr) { - int nErr = AEE_SUCCESS; - *capability = 0; - - if (attr == VTCM_PAGE || attr == VTCM_COUNT) { - } else { - nErr = AEE_EBADPARM; - GGML_LOG_ERROR("Unsupported attr. Only VTCM_PAGE and VTCM_COUNT supported\n"); - goto bail; - } - if (remote_handle_control) { - if (domain == ADSP_DOMAIN_ID || domain == CDSP_DOMAIN_ID) { - /* - * Query the DSP for VTCM information - * Since the ADSP does not have a dedicated VTCM, we expect the output to be 0 - */ - struct remote_dsp_capability dsp_capability_vtcm_dsp; - dsp_capability_vtcm_dsp.domain = (uint32_t) domain; - dsp_capability_vtcm_dsp.attribute_ID = attr; - dsp_capability_vtcm_dsp.capability = (uint32_t) 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_vtcm_dsp, - sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - GGML_LOG_ERROR("Running the usecase without checking the capability\n"); - nErr = AEE_SUCCESS; - goto bail; - } else if (nErr == AEE_SUCCESS) { - *capability = dsp_capability_vtcm_dsp.capability; - } else { - GGML_LOG_ERROR("\nget_vtcm_info failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTED; - GGML_LOG_ERROR("Unsupported domain %d\n", domain); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return nErr; -} - -bool is_unsignedpd_supported(int domain_id) { - int nErr = AEE_SUCCESS; - if (remote_handle_control) { - struct remote_dsp_capability dsp_capability_domain = { domain_id, UNSIGNED_PD_SUPPORT, 0 }; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_domain, sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device. Falling back to signed pd.\n"); - return false; - } - if (nErr) { - GGML_LOG_ERROR("\nERROR 0x%x: FastRPC Capability API failed. Falling back to signed pd.", nErr); - return false; - } - if (dsp_capability_domain.capability == 1) { - return true; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device. Falling back to signed pd.\n"); - return false; - } - return false; -} - -bool get_unsignedpd_support(void) { - return is_unsignedpd_supported(CDSP_DOMAIN_ID); -} - -bool is_async_fastrpc_supported(int domain) { - int nErr = AEE_SUCCESS; - if (remote_handle_control) { - if (domain == CDSP_DOMAIN_ID) { - /* - * Query the DSP for ASYNC_FASTRPC_SUPPORT information - * Async fastrpc is supported only on CDSP - */ - struct remote_dsp_capability dsp_capability_async_support; - dsp_capability_async_support.domain = (uint32_t) domain; - dsp_capability_async_support.attribute_ID = ASYNC_FASTRPC_SUPPORT; - dsp_capability_async_support.capability = (uint32_t) 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_async_support, - sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - GGML_LOG_ERROR("Running the usecase without checking the capability\n"); - nErr = AEE_SUCCESS; - goto bail; - } else if (dsp_capability_async_support.capability == 1) { - return true; - } - if (nErr != AEE_SUCCESS) { - GGML_LOG_ERROR("\nis_async_fastrpc_supported failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTED; - GGML_LOG_ERROR("Async fastrpc is not supported on domain %d\n", domain); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return false; -} - -bool is_status_notification_supported(int domain) { - int nErr = AEE_SUCCESS; - - if (remote_handle_control) { - /* - * Query the DSP for STATUS_NOTIFICATION_SUPPORT information - * DSP User PD status notification Support - */ - struct remote_dsp_capability dsp_capability_status_notification_support; - dsp_capability_status_notification_support.domain = (uint32_t) domain; - dsp_capability_status_notification_support.attribute_ID = STATUS_NOTIFICATION_SUPPORT; - dsp_capability_status_notification_support.capability = (uint32_t) 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_status_notification_support, - sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - GGML_LOG_ERROR("Running the usecase without checking the capability\n"); - nErr = AEE_SUCCESS; - goto bail; - } else if (dsp_capability_status_notification_support.capability == 1) { - return true; - } - if (nErr != AEE_SUCCESS) { - GGML_LOG_ERROR("\nis_status_notification_supported failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return false; -} - -int get_hmx_support_info(int domain, uint32_t * capability, uint32_t attr) { - int nErr = AEE_SUCCESS; - *capability = 0; - - if (attr != HMX_SUPPORT_SPATIAL && attr != HMX_SUPPORT_DEPTH) { - nErr = AEE_EBADPARM; - GGML_LOG_ERROR("Unsupported attr. Only HMX_SUPPORT_SPATIAL and HMX_SUPPORT_DEPTH supported\n"); - goto bail; - } - if (remote_handle_control) { - if (domain == CDSP_DOMAIN_ID) { - /* - * Query the DSP for HMX SUPPORT information - * HMX is supported on CDSP only - */ - struct remote_dsp_capability dsp_capability_hmx_dsp; - dsp_capability_hmx_dsp.domain = (uint32_t) domain; - dsp_capability_hmx_dsp.attribute_ID = attr; - dsp_capability_hmx_dsp.capability = (uint32_t) 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_hmx_dsp, - sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - GGML_LOG_ERROR("Running the usecase without checking the capability\n"); - nErr = AEE_SUCCESS; - goto bail; - } else if (nErr == AEE_SUCCESS) { - *capability = dsp_capability_hmx_dsp.capability; - } else { - GGML_LOG_ERROR("\nget_hmx_support_info failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTED; - GGML_LOG_ERROR("HMX support is not there for domain %d\n", domain); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return nErr; -} - -int get_hex_arch_ver(int domain, int * arch) { - if (!remote_handle_control) { - GGML_LOG_ERROR("ggml-hex: remote_handle_control is not supported on this device\n"); - return AEE_EUNSUPPORTEDAPI; - } - - struct remote_dsp_capability arch_ver; - arch_ver.domain = (uint32_t) domain; - arch_ver.attribute_ID = ARCH_VER; - arch_ver.capability = (uint32_t) 0; - - int err = remote_handle_control(DSPRPC_GET_DSP_INFO, &arch_ver, sizeof(arch_ver)); - if ((err & 0xff) == (AEE_EUNSUPPORTEDAPI & 0xff)) { - GGML_LOG_ERROR("ggml-hex: FastRPC capability API is not supported on this device\n"); - return AEE_EUNSUPPORTEDAPI; - } - - if (err != AEE_SUCCESS) { - GGML_LOG_ERROR("ggml-hex: FastRPC capability query failed (err %d)\n", err); - return err; - } - - switch (arch_ver.capability & 0xff) { - case 0x68: - *arch = 68; - return 0; - case 0x69: - *arch = 69; - return 0; - case 0x73: - *arch = 73; - return 0; - case 0x75: - *arch = 75; - return 0; - case 0x79: - *arch = 79; - return 0; - case 0x81: - *arch = 81; - return 0; - } - return -1; -} - -int get_hvx_support_info(int domain, uint32_t * capability, uint32_t attr) { - int nErr = AEE_SUCCESS; - *capability = 0; - - if (remote_handle_control) { - if (domain == CDSP_DOMAIN_ID) { - /* - * Query the DSP for HVX SUPPORT information - * HVX is supported on CDSP only - */ - struct remote_dsp_capability dsp_capability_hvx_dsp; - dsp_capability_hvx_dsp.domain = (uint32_t) domain; - dsp_capability_hvx_dsp.attribute_ID = attr; - dsp_capability_hvx_dsp.capability = (uint32_t) 0; - nErr = remote_handle_control(DSPRPC_GET_DSP_INFO, &dsp_capability_hvx_dsp, - sizeof(struct remote_dsp_capability)); - if ((nErr & 0xFF) == (AEE_EUNSUPPORTEDAPI & 0xFF)) { - GGML_LOG_ERROR("\nFastRPC Capability API is not supported on this device\n"); - GGML_LOG_ERROR("Running the usecase without checking the capability\n"); - nErr = AEE_SUCCESS; - goto bail; - } else if (nErr == AEE_SUCCESS) { - *capability = dsp_capability_hvx_dsp.capability; - } else { - GGML_LOG_ERROR("\nget_hvx_support_info failed with Error 0x%x\n", nErr); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTED; - GGML_LOG_ERROR("HVX support is not available on domain %d\n", domain); - goto bail; - } - } else { - nErr = AEE_EUNSUPPORTEDAPI; - GGML_LOG_ERROR("remote_dsp_capability interface is not supported on this device\n"); - } - -bail: - return nErr; -} diff --git a/ggml/src/ggml-hexagon/htp-utils.h b/ggml/src/ggml-hexagon/htp-utils.h deleted file mode 100644 index 7bbae3a0b7..0000000000 --- a/ggml/src/ggml-hexagon/htp-utils.h +++ /dev/null @@ -1,221 +0,0 @@ -#ifndef HTP_UTILS_H -#define HTP_UTILS_H - -#ifdef __cplusplus -extern "C" { -#endif - -#include -#include -#include -#include -#include - -/* Offset to differentiate HLOS and Hexagon error codes. - Stores the value of AEE_EOFFSET for Hexagon. */ -#ifndef DSP_OFFSET -# define DSP_OFFSET 0x80000400 -#endif - -/* Errno for connection reset by peer. */ -#ifndef ECONNRESET -# ifdef __hexagon__ -# define ECONNRESET 104 -# endif -#endif - -/* Abstraction of different OS specific sleep APIs. - SLEEP accepts input in seconds. */ -#ifndef SLEEP -# ifdef __hexagon__ -# define SLEEP(x) \ - { /* Do nothing for simulator. */ \ - } -# else -# ifdef _WINDOWS -# define SLEEP(x) Sleep(1000 * x) /* Sleep accepts input in milliseconds. */ -# else -# define SLEEP(x) sleep(x) /* sleep accepts input in seconds. */ -# endif -# endif -#endif - -/* Include windows specific header files. */ -#ifdef _WINDOWS -# include -# include -# define _CRT_SECURE_NO_WARNINGS 1 -# define _WINSOCK_DEPRECATED_NO_WARNINGS 1 -/* Including this file for custom implementation of getopt function. */ -# include "getopt_custom.h" -#endif - -/* Includes and defines for all HLOS except windows */ -#if !defined(__hexagon__) && !defined(_WINDOWS) -# include "unistd.h" - -# include -#endif - -/* Includes and defines for Hexagon and all HLOS except Windows. */ -#if !defined(_WINDOWS) -/* Weak reference to remote symbol for compilation. */ -# pragma weak remote_session_control -# pragma weak remote_handle_control -# pragma weak remote_handle64_control -# pragma weak fastrpc_mmap -# pragma weak fastrpc_munmap -# pragma weak rpcmem_alloc2 -#endif - -#if !defined(_WINDOWS) -# pragma weak remote_system_request -#endif -/** - * Wrapper for FastRPC Capability API: query DSP support. - * - * @param[out] domain pointer to supported domain. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - */ -int get_dsp_support(int * domain); - -/** - * Wrapper for FastRPC Capability API: query VTCM information. - * - * @param[in] domain value of domain in the queried. - * @param[out] capability capability value of the attribute queried. - * @param[in] attr value of the attribute to the queried. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - */ -int get_vtcm_info(int domain, uint32_t * capability, uint32_t attr); - -/** - * Wrapper for FastRPC Capability API: query unsigned pd support on CDSP domain. - * - * @return true if unsigned pd is supported. - * false if unsigned pd is not supported, capability query failed. - */ - -bool get_unsignedpd_support(void); - -/** - * Wrapper for FastRPC Capability API: query unsigned pd support. - * - * @param[in] domain value of domain in the queried. - * @return true if unsigned pd is supported. - * false if unsigned pd is not supported, capability query failed. - */ - -bool is_unsignedpd_supported(int domain_id); - -/** - * is_valid_domain_id API: query a domain id is valid. - * - * @param[in] domain value of domain in the queried. - * @param[in] compute_only value of domain is only compared with CDSP domains supported by the target when enabled. - * @return true if value of domain is valid. - * false if value of domain is not valid. - */ - -bool is_valid_domain_id(int domain_id, int compute_only); - -/** - * get_domain API: get domain struct from domain value. - * - * @param[in] domain value of a domain - * @return Returns domain struct of the domain if it is supported or else - * returns NULL. - * - */ - -domain * get_domain(int domain_id); - -/** - * get_domains_info API: get information for all the domains available on the device - * - * @param[in] domain_type pointer to domain type - * @param[in] num_domains pointer to number of domains - * @param[in] domains_info pointer to save discovered domains information. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - * - * It is user's responsibility to free the memory used to store the domains info whose address is present in domains_info before closing the application. - * - */ - -int get_domains_info(char * domain_type, int * num_domains, fastrpc_domain ** domains_info); - -/** - * get_effective_domain_id API: get effective domain id for given session id - * - * @param[in] domain_name pointer to domain name - * @param[in] session_id - * @param[in] effec_domain_id pointer to save obtained effective domain id. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - * - */ - -int get_effective_domain_id(char * domain_name, int session_id, int * effec_domain_id); - -/** - * is_async_fastrpc_supported API: query a domain id has async fastrpc supported or not - * - * @param[in] domain_id value of a domain - * @return Returns true or false stating support of Async FastRPC - * - */ - -bool is_async_fastrpc_supported(int domain_id); - -/** - * is_status_notification_supported API: query the DSP for STATUS_NOTIFICATION_SUPPORT information - * - * @param[in] domain_id value of a domain - * @return Returns true or false stating status notification support information - * - */ -bool is_status_notification_supported(int domain_id); - -/** - * get_hmx_support_info API: query the DSP for HMX SUPPORT information - * - * @param[in] domain_id value of a domain - * @param[out] capability capability value of the attribute queried. - * @param[in] attr value of the attribute to the queried. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - * - */ -int get_hmx_support_info(int domain, uint32_t * capability, uint32_t attr); - -/** - * get_hex_arch_ver API: query the Hexagon processor architecture version information - * - * @param[in] domain_id value of a domain - * @param[out] Arch version (73, 75, ...) - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - * - */ -int get_hex_arch_ver(int domain, int * arch); - -/** - * get_hvx_support_info API: query the DSP for HVX SUPPORT information - * - * @param[in] domain_id value of a domain - * @param[out] capability capability value of the attribute queried. - * @param[in] attr value of the attribute to the queried. - * @return 0 if query is successful. - * non-zero if error, return value points to the error. - * - */ -int get_hvx_support_info(int domain, uint32_t * capability, uint32_t attr); - -#ifdef __cplusplus -} -#endif - -#endif //DSP_CAPABILITIES_UTILS_H diff --git a/ggml/src/ggml-hexagon/libdl.h b/ggml/src/ggml-hexagon/libdl.h new file mode 100644 index 0000000000..8ca5016f03 --- /dev/null +++ b/ggml/src/ggml-hexagon/libdl.h @@ -0,0 +1,79 @@ +#pragma once + +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +#else +# include +# include +#endif +#include + +namespace fs = std::filesystem; + +#ifdef _WIN32 + +using dl_handle = std::remove_pointer_t; + +struct dl_handle_deleter { + void operator()(HMODULE handle) { + FreeLibrary(handle); + } +}; + +static inline dl_handle * dl_load_library(const fs::path & path) { + // suppress error dialogs for missing DLLs + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + HMODULE handle = LoadLibraryW(path.wstring().c_str()); + + SetErrorMode(old_mode); + + return handle; +} + +static inline void * dl_get_sym(dl_handle * handle, const char * name) { + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + void * p = (void *) GetProcAddress(handle, name); + + SetErrorMode(old_mode); + + return p; +} + +static inline const char * dl_error() { + return ""; +} + +#else + +using dl_handle = void; + +struct dl_handle_deleter { + void operator()(void * handle) { + dlclose(handle); + } +}; + +static inline dl_handle * dl_load_library(const fs::path & path) { + dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL); + return handle; +} + +static inline void * dl_get_sym(dl_handle * handle, const char * name) { + return dlsym(handle, name); +} + +static inline const char * dl_error() { + const char *rslt = dlerror(); + return rslt != nullptr ? rslt : ""; +} + +#endif diff --git a/ggml/src/ggml-hexagon/libggml-htp.inf b/ggml/src/ggml-hexagon/libggml-htp.inf new file mode 100644 index 0000000000..656d2d9ab2 --- /dev/null +++ b/ggml/src/ggml-hexagon/libggml-htp.inf @@ -0,0 +1,38 @@ +[Version] +Signature = "$WINDOWS NT$" +Class = ComputeAccelerator +ClassGuid = {F01A9D53-3FF6-48D2-9F97-C8A7004BE10C} +Provider = %GGML% +DriverVer = 01/01/2026,1.0.0.0 +CatalogFile = libggml-htp.cat +PnpLockDown = 1 + +[DestinationDirs] +Drivers_Dir = 6 + +[SourceDisksNames] +1 = %DiskId% + +[SourceDisksFiles] +libggml-htp-v68.so = 1 +libggml-htp-v69.so = 1 +libggml-htp-v73.so = 1 +libggml-htp-v75.so = 1 +libggml-htp-v81.so = 1 + +[ControlFlags] +ExcludeFromSelect = * + +[DefaultInstall.NTarm64] +CopyFiles=Drivers_Dir + +[Drivers_Dir] +libggml-htp-v68.so,,,0x10 ;COPYFLG_NO_OVERWRITE +libggml-htp-v69.so,,,0x10 ;COPYFLG_NO_OVERWRITE +libggml-htp-v73.so,,,0x10 ;COPYFLG_NO_OVERWRITE +libggml-htp-v75.so,,,0x10 ;COPYFLG_NO_OVERWRITE +libggml-htp-v81.so,,,0x10 ;COPYFLG_NO_OVERWRITE + +[Strings] +GGML = 'GGML' +DiskId = 'GGML HTP library' diff --git a/scripts/snapdragon/windows/run-bench.ps1 b/scripts/snapdragon/windows/run-bench.ps1 new file mode 100644 index 0000000000..21fd063ebe --- /dev/null +++ b/scripts/snapdragon/windows/run-bench.ps1 @@ -0,0 +1,40 @@ + +#!/usr/bin/env pwsh + +# Basedir on device +$basedir=".\pkg-snapdragon" + +$cli_opts=$args + +$model="Llama-3.2-3B-Instruct-Q4_0.gguf" +if ($null -ne $env:M) { + $model=$env:M +} + +$device="HTP0" +if ($null -ne $env:D) { + $device=$env:D +} + +if ($null -ne $env:V) { + $env:GGML_HEXAGON_VERBOSE=$env:V +} + +if ($null -ne $env:OPMASK) { + $env:GGML_HEXAGON_OPMASK=$env:OPMASK +} + +if ($null -ne $env:NHVX) { + $env:GGML_HEXAGON_NHVX=$env:NHVX +} + +if ($null -ne $env:NDEV) { + $env:GGML_HEXAGON_NDEV=$env:NDEV +} + +$env:ADSP_LIBRARY_PATH="$basedir\lib" + +& "$basedir\bin\llama-bench.exe" ` + --mmap 0 -m $basedir\..\..\gguf\$model ` + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 ` + --batch-size 128 -ngl 99 --device $device $cli_opts diff --git a/scripts/snapdragon/windows/run-cli.ps1 b/scripts/snapdragon/windows/run-cli.ps1 new file mode 100644 index 0000000000..b13161aa63 --- /dev/null +++ b/scripts/snapdragon/windows/run-cli.ps1 @@ -0,0 +1,53 @@ + +#!/usr/bin/env pwsh + +# Basedir on device +$basedir=".\pkg-snapdragon" + +$cli_opts=$args + +$model="Llama-3.2-3B-Instruct-Q4_0.gguf" +if ($null -ne $env:M) { + $model=$env:M +} + +$device="HTP0" +if ($null -ne $env:D) { + $device=$env:D +} + +if ($null -ne $env:V) { + $env:GGML_HEXAGON_VERBOSE=$env:V +} + +if ($null -ne $env:E) { + $env:GGML_HEXAGON_EXPERIMENTAL=$env:E +} + +if ($null -ne $env:SCHED) { + $env:GGML_SCHED_DEBUG=$env:SCHED; $cli_opts="$cli_opts -v" +} + +if ($null -ne $env:PROF) { + $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1 +} + +if ($null -ne $env:OPMASK) { + $env:GGML_HEXAGON_OPMASK=$env:OPMASK +} + +if ($null -ne $env:NHVX) { + $env:GGML_HEXAGON_NHVX=$env:NHVX +} + +if ($null -ne $env:NDEV) { + $env:GGML_HEXAGON_NDEV=$env:NDEV +} + +$env:ADSP_LIBRARY_PATH="$basedir\lib" + +& "$basedir\bin\llama-completion.exe" ` + --no-mmap -no-cnv -m $basedir\..\..\gguf\$model ` + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 ` + --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on ` + -ngl 99 --device $device $cli_opts diff --git a/scripts/snapdragon/windows/run-tool.ps1 b/scripts/snapdragon/windows/run-tool.ps1 new file mode 100644 index 0000000000..70094af9bc --- /dev/null +++ b/scripts/snapdragon/windows/run-tool.ps1 @@ -0,0 +1,56 @@ + +#!/usr/bin/env pwsh + +# Basedir on device +$basedir=".\pkg-snapdragon" + +if ($args.Count -eq 0) { + Write-Host "No arguments provided.Expected the tool and argument to run." + exit -1 +} + +$tool=$args[0] +$cli_opts=@() + +if ($args.Count -gt 1) { + $cli_opts=$args[1..($args.Count - 1)] + $remainingArgs = $args[1..($args.Count - 1)] +} + +$device="HTP0" +if ($null -ne $env:D) { + $device=$env:D +} + +if ($null -ne $env:V) { + $env:GGML_HEXAGON_VERBOSE=$env:V +} + +if ($null -ne $env:E) { + $env:GGML_HEXAGON_EXPERIMENTAL=$env:E +} + +if ($null -ne $env:SCHED) { + $env:GGML_SCHED_DEBUG=$env:SCHED; $cli_opts="$cli_opts -v" +} + +if ($null -ne $env:PROF) { + $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1 +} + +if ($null -ne $env:OPMASK) { + $env:GGML_HEXAGON_OPMASK=$env:OPMASK +} + +if ($null -ne $env:NHVX) { + $env:GGML_HEXAGON_NHVX=$env:NHVX +} + +if ($null -ne $env:NDEV) { + $env:GGML_HEXAGON_NDEV=$env:NDEV +} + +$env:ADSP_LIBRARY_PATH="$basedir\lib" + +& "$basedir\bin\$tool" ` + $cli_opts diff --git a/scripts/snapdragon/windows/setup-build.ps1 b/scripts/snapdragon/windows/setup-build.ps1 new file mode 100644 index 0000000000..0f3244cc9d --- /dev/null +++ b/scripts/snapdragon/windows/setup-build.ps1 @@ -0,0 +1,105 @@ +# Requires Run as Administrator is NOT strictly necessary for User-scope env vars, +# but recommended for creating directories in C:\ root if permissions are restricted. + +$ErrorActionPreference = "Stop" + +# --- Configuration --- +$BaseDir = "C:\Qualcomm" + +# SDK 1: Hexagon +$HexagonUrl = "https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v6.4.0.2/hexagon-sdk-v6.4.0.2-arm64-wos.tar.xz" +$HexagonParent = Join-Path $BaseDir "Hexagon_SDK" +$HexagonSdkVersion = "6.4.0.2" +$HexagonToolsVersion = "19.0.04" +$HexagonSdkTarget = Join-Path $HexagonParent $HexagonSdkVersion +$HexagonToolsTarget = Join-Path $HexagonSdkTarget "\tools\HEXAGON_Tools\$HexagonToolsVersion" + +# SDK 2: OpenCL +$OpenCLUrl = "https://github.com/snapdragon-toolchain/opencl-sdk/releases/download/v2.3.2/adreno-opencl-sdk-v2.3.2-arm64-wos.tar.xz" +$OpenCLParent = Join-Path $BaseDir "OpenCL_SDK" +$OpenCLVersion = "2.3.2" +$OpenCLTarget = Join-Path $OpenCLParent $OpenCLVersion + +# --- Helper Function --- +function Install-QualcommSDK { + param ( + [string]$Url, + [string]$ParentDir, + [string]$TargetDir, + [string]$Name + ) + + # 1. Create Parent Directory + if (-not (Test-Path -Path $ParentDir)) { + Write-Host "Creating directory: $ParentDir" -ForegroundColor Cyan + New-Item -Path $ParentDir -ItemType Directory -Force | Out-Null + } + + # 2. Check for Specific Version Directory + if (Test-Path -Path $TargetDir) { + Write-Host "$Name ($TargetDir) already exists. Skipping download." -ForegroundColor Green + } + else { + Write-Host "$Name not found. preparing to download..." -ForegroundColor Yellow + + # Create the target directory to extract into + New-Item -Path $TargetDir -ItemType Directory -Force | Out-Null + + # Define temporary archive path + $TempFile = Join-Path $ParentDir "temp_sdk.tar.xz" + + try { + # Download + Write-Host "Downloading from: $Url" + Invoke-WebRequest -Uri $Url -OutFile $TempFile + + # Untar + # Note: We assume Windows includes tar.exe (Win 10 build 17063+) + Write-Host "Extracting archive to $TargetDir..." + + # We use -C to extract contents INTO the target directory created above + tar -xJvf $TempFile -C $TargetDir\.. + + Write-Host "Extraction complete." -ForegroundColor Green + } + catch { + Write-Error "Failed to download or extract $Name. Error: $_" + # Cleanup target dir if failed so script tries again next time + Remove-Item -Path $TargetDir -Recurse -Force -ErrorAction SilentlyContinue + } + finally { + # Cleanup Archive + if (Test-Path $TempFile) { Remove-Item $TempFile -Force } + } + } +} + +# --- Execution --- + +# 1. Ensure Base C:\Qualcomm exists +if (-not (Test-Path $BaseDir)) { + New-Item -Path $BaseDir -ItemType Directory -Force | Out-Null +} + +# 2. Run Install Logic +Install-QualcommSDK -Url $HexagonUrl -ParentDir $HexagonParent -TargetDir $HexagonSdkTarget -Name "Hexagon SDK" +Install-QualcommSDK -Url $OpenCLUrl -ParentDir $OpenCLParent -TargetDir $OpenCLTarget -Name "OpenCL SDK" + +# --- Environment Variables --- + +Write-Host "`nSetting Environment Variables..." -ForegroundColor Cyan + +# Set OPENCL_SDK_ROOT +[System.Environment]::SetEnvironmentVariable('OPENCL_SDK_ROOT', $OpenCLTarget, [System.EnvironmentVariableTarget]::User) +$env:OPENCL_SDK_ROOT = $OpenCLTarget # Set for current session as well +Write-Host "OPENCL_SDK_ROOT set to: $OpenCLTarget" + +# Set HEXAGON_SDK_ROOT +[System.Environment]::SetEnvironmentVariable('HEXAGON_SDK_ROOT', $HexagonSdkTarget, [System.EnvironmentVariableTarget]::User) +$env:HEXAGON_SDK_ROOT = $HexagonSdkTarget # Set for current session as well +Write-Host "HEXAGON_SDK_ROOT set to: $HexagonSdkTarget" + +# Set HEXAGON_SDK_ROOT +[System.Environment]::SetEnvironmentVariable('HEXAGON_TOOLS_ROOT', $HexagonToolsTarget, [System.EnvironmentVariableTarget]::User) +$env:HEXAGON_TOOLS_ROOT = $HexagonToolsTarget # Set for current session as well +Write-Host "HEXAGON_TOOLS_ROOT set to: $HexagonToolsTarget" From bd90fc74c3fecd18f36e26a91b3c3282578bf680 Mon Sep 17 00:00:00 2001 From: Zheyuan Chen Date: Thu, 29 Jan 2026 14:05:30 -0800 Subject: [PATCH 061/206] ggml-webgpu: improve flastAttention performance by software pipelining (#19151) * webgpu : pipeline flash_attn Q/K loads in WGSL * ggml-webgpu: unroll Q*K accumlation inner loop * ggml-webgpu: vectorization * ggml-webgpu: unrolling * ggml-webgpu: remove redundant unrolling * ggml-webgpu: restore the config * ggml-webgpu: remove redundant comments * ggml-webgpu: formatting * ggml-webgpu: formatting and remove vectorization * ggml-webgpu: remove unnecessary constants * ggml-webgpu: change QKV buffer to read_write to pass validation * ggml-webgpu: add explanation for the additional bracket around Q K accumulate * Indentation and for -> if for tail * Kick off CI on wgsl only commits --------- Co-authored-by: Reese Levine --- .github/workflows/build.yml | 6 +- .../ggml-webgpu/wgsl-shaders/flash_attn.wgsl | 165 +++++++++++------- 2 files changed, 109 insertions(+), 62 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index d3a1638f16..f738edefc4 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -21,7 +21,8 @@ on: '**/*.m', '**/*.metal', '**/*.comp', - '**/*.glsl' + '**/*.glsl', + '**/*.wgsl' ] pull_request: @@ -42,7 +43,8 @@ on: '**/*.m', '**/*.metal', '**/*.comp', - '**/*.glsl' + '**/*.glsl', + '**/*.wgsl' ] concurrency: diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl index de7c132a62..b682216146 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl @@ -114,7 +114,7 @@ struct Params { #define PARAMS_BINDING 4 #endif -@group(0) @binding(DST_BINDING) var dst: array; +@group(0) @binding(DST_BINDING) var dst: array>; @group(0) @binding(PARAMS_BINDING) var params: Params; // Just a very small float value. @@ -160,14 +160,21 @@ fn calc_softmax_term(kv_idx: u32, q_tile_row: u32, slope: f32) -> f32 { return v; } +fn load_f32x4(buf: ptr>, read_write>, scalar_index: u32) -> vec4 { + return (*buf)[scalar_index >> 2u]; +} + +fn load_kvx4(buf: ptr>, read_write>, scalar_index: u32) -> vec4 { + return (*buf)[scalar_index >> 2u]; +} @compute @workgroup_size(WG_SIZE) fn main(@builtin(workgroup_id) wg_id: vec3, - @builtin(local_invocation_id) local_id: vec3, - @builtin(subgroup_id) subgroup_id: u32, - @builtin(subgroup_size) subgroup_size: u32, - @builtin(num_subgroups) num_subgroups: u32, - @builtin(subgroup_invocation_id) sg_inv_id: u32) { + @builtin(local_invocation_id) local_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_size) subgroup_size: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_invocation_id) sg_inv_id: u32) { // initialize row max for online softmax for (var i = local_id.x; i < Q_TILE; i += WG_SIZE) { @@ -231,9 +238,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3, for (var kv_tile = 0u; kv_tile < params.seq_len_kv; kv_tile += KV_TILE) { // clear inter_shmem to ensure zero-initialized accumulators - for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) { - inter_shmem[elem_idx] = 0.0; - } + for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) { + inter_shmem[elem_idx] = 0.0; + } // load k tile into shared memory #if defined(KV_Q4_0) @@ -309,48 +316,77 @@ fn main(@builtin(workgroup_id) wg_id: vec3, // accumulate q block * k block into registers across the entire KV tile // TODO: this loop seems to be the current largest bottleneck - for (var kv_block = subgroup_id; kv_block < KV_BLOCKS; kv_block += num_subgroups) { - let inter_offset = kv_block * SG_MAT_N; - var acc: subgroup_matrix_result = subgroupMatrixLoad< - subgroup_matrix_result>(&inter_shmem, inter_offset, false, KV_TILE); + // this bracket exists to scope the lifetime of variables, reducing register pressure + { #ifdef KV_DIRECT - let k_block_row = kv_tile + kv_block * SG_MAT_N; - let k_global_offset = k_head_offset + k_block_row * params.stride_k1; + let k_block_row = kv_tile + subgroup_id * SG_MAT_N; + var k_global_offset = k_head_offset + k_block_row * params.stride_k1; #else - let k_block_offset = kv_block * SG_MAT_N * HEAD_DIM_QK; + var k_block_offset = subgroup_id * SG_MAT_N * HEAD_DIM_QK; #endif - for (var head_dim_block = 0u; head_dim_block < HEAD_DIM_QK; head_dim_block += SG_MAT_K) { - // load q submatrix from shared memory - var q_sg_mat: subgroup_matrix_left = subgroupMatrixLoad>( - &q_shmem, - head_dim_block, - false, - HEAD_DIM_QK - ); + for (var kv_block = subgroup_id; kv_block < KV_BLOCKS; kv_block += num_subgroups) { + let inter_offset = kv_block * SG_MAT_N; + var acc: subgroup_matrix_result = subgroupMatrixLoad>(&inter_shmem, inter_offset, false, KV_TILE); + + var q_cur = subgroupMatrixLoad>(&q_shmem, 0u, false, HEAD_DIM_QK); - // load k submatrix from device or shared memory #ifdef KV_DIRECT - var k_sg_mat: subgroup_matrix_right = subgroupMatrixLoad>( - &K, - k_global_offset + head_dim_block, - true, - params.stride_k1 - ); + var k_cur = subgroupMatrixLoad>(&K, k_global_offset + 0u, true, params.stride_k1); #else - var k_sg_mat: subgroup_matrix_right = subgroupMatrixLoad>( - &kv_shmem, - k_block_offset + head_dim_block, - true, - HEAD_DIM_QK - ); + var k_cur = subgroupMatrixLoad>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK); #endif - acc = subgroupMatrixMultiplyAccumulate(q_sg_mat, k_sg_mat, acc); + + var t: u32 = 1u; + for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) { + let h0 = t * SG_MAT_K; + var q0 = subgroupMatrixLoad>(&q_shmem, h0, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var k0 = subgroupMatrixLoad>(&K, k_global_offset + h0, true, params.stride_k1); +#else + var k0 = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = q0; + k_cur = k0; + + let h1 = (t + 1u) * SG_MAT_K; + var q1g = subgroupMatrixLoad>(&q_shmem, h1, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var k1g = subgroupMatrixLoad>(&K, k_global_offset + h1, true, params.stride_k1); +#else + var k1g = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = q1g; + k_cur = k1g; + } + + // handle odd tail + if (t < HEAD_DIM_QK / SG_MAT_K) { + let h = t * SG_MAT_K; + var qn = subgroupMatrixLoad>(&q_shmem, h, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var kn = subgroupMatrixLoad>(&K, k_global_offset + h, true, params.stride_k1); +#else + var kn = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = qn; + k_cur = kn; + } + + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + +#ifdef KV_DIRECT + k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1; +#else + k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK; +#endif + subgroupMatrixStore(&inter_shmem, inter_offset, acc, false, KV_TILE); } - - // store acc to shared memory for softmax (S matrix from paper) - subgroupMatrixStore(&inter_shmem, inter_offset, acc, false, KV_TILE); } + #ifdef MASK // load mask tile into shared memory for this KV block // TODO: optimize and skip if mask is -INF for the entire tile @@ -495,7 +531,6 @@ fn main(@builtin(workgroup_id) wg_id: vec3, false, HEAD_DIM_V ); - for (var kv_block = 0u; kv_block < KV_BLOCKS; kv_block++) { let p_offset = kv_block * SG_MAT_N; var p_sg_mat: subgroup_matrix_left = subgroupMatrixLoad>( @@ -527,11 +562,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3, // O += P * V o_sg_mat = subgroupMatrixMultiplyAccumulate(p_sg_mat, v_sg_mat, o_sg_mat); } - // store O back to shared memory subgroupMatrixStore(&o_shmem, head_dim_block, o_sg_mat, false, HEAD_DIM_V); } - workgroupBarrier(); } @@ -566,26 +599,38 @@ fn main(@builtin(workgroup_id) wg_id: vec3, o_shmem[idx] = f16(val); } } - workgroupBarrier(); #endif - - // write output back to global memory for (var q_tile_row = subgroup_id; - q_tile_row < Q_TILE; - q_tile_row += num_subgroups) { - let global_q_row = q_row_start + q_tile_row; - if (global_q_row >= params.seq_len_q) { - break; - } + q_tile_row < Q_TILE; + q_tile_row += num_subgroups) { - let exp_sum = exp_sum_shmem[q_tile_row]; - let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0); + let global_q_row = q_row_start + q_tile_row; + if (global_q_row >= params.seq_len_q) { break; } - for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { - let o_val = o_shmem[q_tile_row * HEAD_DIM_V + elem_idx]; - let scaled = f32(o_val) * scale; - dst[dst_global_offset + q_tile_row * dst2_stride + elem_idx] = scaled; - } + let exp_sum = exp_sum_shmem[q_tile_row]; + let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0.0); + + let row_base: u32 = dst_global_offset + q_tile_row * dst2_stride; + + for (var elem_base = sg_inv_id * 4u; + elem_base < HEAD_DIM_V; + elem_base += subgroup_size * 4u) { + + let i0 = q_tile_row * HEAD_DIM_V + (elem_base + 0u); + let i1 = q_tile_row * HEAD_DIM_V + (elem_base + 1u); + let i2 = q_tile_row * HEAD_DIM_V + (elem_base + 2u); + let i3 = q_tile_row * HEAD_DIM_V + (elem_base + 3u); + + let v = vec4( + f32(o_shmem[i0]) * scale, + f32(o_shmem[i1]) * scale, + f32(o_shmem[i2]) * scale, + f32(o_shmem[i3]) * scale + ); + + let dst_vec_index: u32 = (row_base + elem_base) >> 2u; + dst[dst_vec_index] = v; + } } } From d284baf1b5969b74177e705ee6bca63c1bbfb4b7 Mon Sep 17 00:00:00 2001 From: DDXDB <38449595+DDXDB@users.noreply.github.com> Date: Fri, 30 Jan 2026 09:46:57 +0800 Subject: [PATCH 062/206] Fix typos in SYCL documentation (#19162) * Fix typos in SYCL documentation * Update SYCL.md * Update SYCL.md * Update SYCL.md * Update docs/backend/SYCL.md Co-authored-by: Neo Zhang Jianyu * Update SYCL.md --------- Co-authored-by: Neo Zhang Jianyu --- docs/backend/SYCL.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index bcb3ce6743..c0a422b3dc 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -35,9 +35,9 @@ The following releases are verified and recommended: |Commit ID|Tag|Release|Verified Platform| Update date| |-|-|-|-|-| -|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1
LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15| -|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| -|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| +|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |Arc B580/Linux/oneAPI 2025.1
LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15| +|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| ## News @@ -51,7 +51,7 @@ The following releases are verified and recommended: |-|-|-|-| |PVC 1550|39|73|+87%| |Flex 170|39|50|+28%| - |Arc770|42|55|+30%| + |Arc A770|42|55|+30%| |MTL|13|16|+23%| |ARL-H|14|17|+21%| @@ -62,7 +62,7 @@ The following releases are verified and recommended: - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs. - 2024.5 - - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770. + - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc A770. - Arch Linux is verified successfully. - 2024.4 @@ -111,7 +111,8 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the |-------------------------------|---------|---------------------------------------| | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | -| Intel Arc Series | Support | Arc 770, 730M, Arc A750, B580 | +| Intel Arc A-Series | Support | Arc A770, Arc A730M, Arc A750 | +| Intel Arc B-Series | Support | Arc B580 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake | | Intel iGPU | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7 | From c7358ddf6458ffaf45dc1d9ca447b354c919fcff Mon Sep 17 00:00:00 2001 From: RachelMantel Date: Fri, 30 Jan 2026 06:00:49 +0200 Subject: [PATCH 063/206] sycl: implement GGML_OP_TRI (#19089) * sycl: implement GGML_OP_TRI * docs: update ops.md for SYCL TRI * docs: regenerate ops.md * docs: update SYCL support for GGML_OP_TRI --- docs/ops.md | 2 +- docs/ops/SYCL.csv | 8 ++-- ggml/src/ggml-sycl/ggml-sycl.cpp | 69 ++++++++++++++++++++++++++++++++ 3 files changed, 74 insertions(+), 5 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index c066ab5a85..b8e0347803 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -114,7 +114,7 @@ Legend: | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | -| TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 091a5caed7..255b4ef473 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -10052,10 +10052,10 @@ "SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL" "SYCL0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","SYCL" "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" -"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","SYCL" -"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","SYCL" -"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","0","no","SYCL" -"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","0","no","SYCL" +"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","SYCL" +"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","SYCL" +"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","SYCL" +"SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","1","yes","SYCL" "SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL" diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 3a4c092af5..d20b7ec57d 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -2263,6 +2263,65 @@ inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_ten diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream); } +static void tri_f32_sycl( + const float * src, + float * dst, + const int64_t ne0, + const int64_t ne1, + const int64_t ne2, + const int64_t ne3, + const ggml_tri_type ttype, + dpct::queue_ptr main_stream +) { + const size_t total = (size_t) ne0 * (size_t) ne1 * (size_t) ne2 * (size_t) ne3; + + main_stream->parallel_for(sycl::range<1>(total), [=](sycl::id<1> tid) { + const int64_t idx = (int64_t) tid[0]; + + const int64_t i0 = idx % ne0; + const int64_t t1 = idx / ne0; + const int64_t i1 = t1 % ne1; + + bool keep = false; + switch (ttype) { + case GGML_TRI_TYPE_LOWER: keep = (i0 < i1); break; + case GGML_TRI_TYPE_LOWER_DIAG: keep = (i0 <= i1); break; + case GGML_TRI_TYPE_UPPER: keep = (i0 > i1); break; + case GGML_TRI_TYPE_UPPER_DIAG: keep = (i0 >= i1); break; + default: keep = false; break; + } + + dst[idx] = keep ? src[idx] : 0.0f; + }); +} + +static void ggml_sycl_op_tri(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + GGML_ASSERT(src0); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + + const float * src0_dd = static_cast(src0->data); + float * dst_dd = static_cast(dst->data); + + const ggml_tri_type ttype = (ggml_tri_type) ggml_get_op_params_i32(dst, 0); + + const int64_t ne0 = src0->ne[0]; + const int64_t ne1 = src0->ne[1]; + const int64_t ne2 = src0->ne[2]; + const int64_t ne3 = src0->ne[3]; + + tri_f32_sycl(src0_dd, dst_dd, ne0, ne1, ne2, ne3, ttype, main_stream); +} + + inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -3912,6 +3971,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_TRANSPOSE: GGML_SYCL_DEBUG("%s: Tensor NO-OP\n", __func__); break; + case GGML_OP_TRI: + ggml_sycl_op_tri(ctx, dst); + break; case GGML_OP_DIAG_MASK_INF: ggml_sycl_diag_mask_inf(ctx, dst); break; @@ -4616,6 +4678,13 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return true; case GGML_OP_CONT: return op->src[0]->type != GGML_TYPE_BF16; + case GGML_OP_TRI: + { + const ggml_tensor * src0 = op->src[0]; + return src0 && + op->type == GGML_TYPE_F32 && + ggml_is_contiguous(src0); + } case GGML_OP_DIAG_MASK_INF: return true; case GGML_OP_SOFT_MAX: From 1025fd2c09b1d4f9aa3fb2f7349084eecd04f958 Mon Sep 17 00:00:00 2001 From: s8322 Date: Fri, 30 Jan 2026 06:01:38 +0200 Subject: [PATCH 064/206] sycl: implement GGML_UNARY_OP_SOFTPLUS (#19114) * sycl: add softplus unary op implementation * sycl: add softplus unary op implementation * docs(ops): mark SYCL SOFTPLUS as supported * docs: update SYCL status for SOFTPLUS --- docs/ops.md | 2 +- docs/ops/SYCL.csv | 16 ++++++++-------- ggml/src/ggml-sycl/element_wise.cpp | 20 ++++++++++++++++++++ ggml/src/ggml-sycl/element_wise.hpp | 2 ++ ggml/src/ggml-sycl/ggml-sycl.cpp | 4 ++++ 5 files changed, 35 insertions(+), 9 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index b8e0347803..2c7c60dcca 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -97,7 +97,7 @@ Legend: | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 255b4ef473..c1d22e65d4 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -29,8 +29,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" "SYCL0","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" @@ -71,8 +71,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","EXPM1","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","EXPM1","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -113,8 +113,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","EXPM1","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" "SYCL0","EXPM1","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" @@ -155,8 +155,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","EXPM1","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","EXPM1","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" -"SYCL0","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" +"SYCL0","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 8d83b2446b..651b875b63 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -123,6 +123,15 @@ static __dpct_inline__ T op_log(T x) { return sycl::log(x); } +template +static __dpct_inline__ T op_softplus(T x) { + const float xf = (float) x; + const float ax = sycl::fabs(xf); + const float m = sycl::fmax(xf, 0.0f); + const float y = m + sycl::log1p(sycl::exp(-ax)); + return (T) y; +} + template static __dpct_inline__ T op_neg(T x) { return -x; @@ -695,6 +704,12 @@ static inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, ggml_tensor }); } +static inline void ggml_sycl_op_softplus(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) { + return op_softplus(x); + }); +} + static inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) { return op_neg(x); @@ -1101,6 +1116,11 @@ void ggml_sycl_log(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_log(ctx, dst); } +void ggml_sycl_softplus(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_softplus(ctx, dst); +} + void ggml_sycl_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); ggml_sycl_op_neg(ctx, dst); diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index 0913a2e529..7c71974687 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -61,6 +61,8 @@ void ggml_sycl_exp(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_log(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_softplus(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + void ggml_sycl_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_step(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index d20b7ec57d..74b4ed91cc 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3845,6 +3845,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_UNARY_OP_EXP: ggml_sycl_exp(ctx, dst); break; + case GGML_UNARY_OP_SOFTPLUS: + ggml_sycl_softplus(ctx, dst); + break; case GGML_UNARY_OP_SGN: ggml_sycl_sgn(ctx, dst); break; @@ -4466,6 +4469,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_GELU_ERF: case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_ELU: return true; case GGML_UNARY_OP_FLOOR: From ecbf01d44186fe78aa69d22fd6ee7790e2b87dc5 Mon Sep 17 00:00:00 2001 From: bssrdf Date: Thu, 29 Jan 2026 23:57:52 -0500 Subject: [PATCH 065/206] add tensor type checking as part of cuda graph properties (#19186) --- ggml/src/ggml-cuda/common.cuh | 1 + ggml/src/ggml-cuda/ggml-cuda.cu | 5 +++++ 2 files changed, 6 insertions(+) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 43280644e4..a3256d59dd 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -1124,6 +1124,7 @@ struct ggml_tensor_extra_gpu { struct ggml_cuda_graph_node_properties { void * node_data; ggml_op node_op; + enum ggml_type node_type; int32_t flags; int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index cfcffde8a2..e9e9592eba 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -2920,6 +2920,7 @@ static void ggml_cuda_graph_node_set_properties(ggml_cuda_graph_node_properties memset(props, 0, sizeof(ggml_cuda_graph_node_properties)); props->node_data = node->data; props->node_op = node->op; + props->node_type = node->type; props->flags = node->flags; for (int i = 0; i < GGML_MAX_DIMS; i++) { props->ne[i] = node->ne[i]; @@ -2944,6 +2945,10 @@ static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_ return false; } + if (node->type != props->node_type) { + return false; + } + for (int i = 0; i < GGML_MAX_DIMS; i++) { if (node->ne[i] != props->ne[i]) { return false; From b316895ff95b39cc5b7494427e9dfca9e231b23e Mon Sep 17 00:00:00 2001 From: Antonis Makropoulos Date: Fri, 30 Jan 2026 08:54:28 +0200 Subject: [PATCH 066/206] docs: Add LlamaLib to UI projects (#19181) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0783e43e5c..dac020ad37 100644 --- a/README.md +++ b/README.md @@ -213,6 +213,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo - [llama.vim](https://github.com/ggml-org/llama.vim) (MIT) - [LARS](https://github.com/abgulati/LARS) (AGPL) - [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [LlamaLib](https://github.com/undreamai/LlamaLib) (Apache-2.0) - [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) - [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) - [LMStudio](https://lmstudio.ai/) (proprietary) From 83bcdf7217dc06ac67ff5f7322bdd89f46664c04 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 30 Jan 2026 10:37:06 +0100 Subject: [PATCH 067/206] memory : remove unused tmp_buf (#19199) This commit removes the unused tmp_buf variable from llama-kv-cache.cpp and llama-memory-recurrent.cpp. The tmp_buf variable was declared but never used but since it has a non-trivial constructor/desctuctor we don't get an unused variable warning about it. --- src/llama-kv-cache.cpp | 8 +++----- src/llama-memory-recurrent.cpp | 8 +++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index f3c9b49f30..c35cd6761b 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -1772,8 +1772,6 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t io.write(&v_trans, sizeof(v_trans)); io.write(&n_layer, sizeof(n_layer)); - std::vector tmp_buf; - // Iterate and write all the keys first, each row is a cell // Get whole range at a time for (const auto & layer : layers) { @@ -1791,7 +1789,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t const uint64_t k_size_row = ggml_row_size(k->type, n_embd_k_gqa); io.write(&k_size_row, sizeof(k_size_row)); - // Read each range of cells of k_size length each into tmp_buf and write out + // Read each range of cells of k_size length and write out for (const auto & range : cr.data) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * k_size_row; @@ -1818,7 +1816,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t const uint64_t v_size_row = ggml_row_size(v->type, n_embd_v_gqa); io.write(&v_size_row, sizeof(v_size_row)); - // Read each range of cells of v_size length each into tmp_buf and write out + // Read each range of cells of v_size length and write out for (const auto & range : cr.data) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * v_size_row; @@ -1852,7 +1850,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t // For each row, we get the element values of each cell for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - // Read each range of cells of v_size_el length each into tmp_buf and write out + // Read each range of cells of v_size_el length and write out for (const auto & range : cr.data) { const size_t range_size = range.second - range.first; const size_t src_offset = (range.first + j * kv_size) * v_size_el; diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index 812bf25304..c8361b1812 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -785,8 +785,6 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: io.write(&s_trans, sizeof(s_trans)); io.write(&n_layer, sizeof(n_layer)); - std::vector tmp_buf; - // Iterate and write all the keys first, each row is a cell // Get whole range at a time for (uint32_t il = 0; il < n_layer; ++il) { @@ -801,7 +799,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: const uint64_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r()); io.write(&r_size_row, sizeof(r_size_row)); - // Read each range of cells of k_size length each into tmp_buf and write out + // Read each range of cells of k_size length and write out for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * r_size_row; @@ -822,7 +820,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: const uint64_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s()); io.write(&s_size_row, sizeof(s_size_row)); - // Read each range of cells of s_size length each into tmp_buf and write out + // Read each range of cells of s_size length and write out for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * s_size_row; @@ -851,7 +849,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: // For each row, we get the element values of each cell for (uint32_t j = 0; j < n_embd_s; ++j) { - // Read each range of cells of v_size_el length each into tmp_buf and write out + // Read each range of cells of v_size_el length and write out for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t src_offset = (range.first + j * mem_size) * s_size_el; From 05625031547d95f7335abd012cad4cd07986819b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 30 Jan 2026 11:12:53 +0100 Subject: [PATCH 068/206] convert : add missing return statement for GraniteMoeModel (#19202) This commit adds a missing return statement to the GraniteMoeModel class to fix an issue in the model conversion process. Resolves: https://github.com/ggml-org/llama.cpp/issues/19201 --- convert_hf_to_gguf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index a391717e32..eb43520f98 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -8806,6 +8806,7 @@ class GraniteMoeModel(GraniteModel): gate, up = data_torch.split(ffn_dim, dim=-2) yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid) yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid) + return has_experts = bool(self.hparams.get('num_local_experts')) From c3b87cebff7dfdd73b850b8a194cd0a0c6bfcf49 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 13:52:57 +0200 Subject: [PATCH 069/206] tests : add GQA=20 FA test (#19095) --- tests/test-backend-ops.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d4c1f525c6..411467e968 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -8213,11 +8213,13 @@ static std::vector> make_test_cases_eval() { if (!mask && max_bias > 0.0f) continue; for (float logit_softcap : {0.0f, 10.0f}) { if (hsk != 128 && logit_softcap != 0.0f) continue; - for (int nh : { 4, }) { + for (int nh : { 1, 4 }) { + if (nh == 1 && hsk != 576) continue; // GLM 4.7 Flash for (int nr3 : { 1, 3, }) { if (hsk > 64 && nr3 > 1) continue; // skip broadcast for large head sizes - for (int nr2 : { 1, 4, 12 }) { + for (int nr2 : { 1, 4, 12, 20 }) { if (nr2 == 12 && hsk != 128) continue; + if (nr2 == 20 && (nh != 1 || hsk != 576)) continue; //for (int kv : { 1, 17, 31, 33, 61, 113, 65, 127, 129, 130, 255, 260, 371, 380, 407, 512, 1024, }) { for (int kv : { 113, 512, 1024, }) { if (nr2 != 1 && kv != 512) continue; From f3bc98890c2b07f66c0ee20cd539abbfe9ea7639 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 30 Jan 2026 15:18:41 +0100 Subject: [PATCH 070/206] memory : clarify comments for r_l and s_l tensors [no ci] (#19203) This commit updates the comments in state_write_data to clarify that it is handling the R and S tensors and not Key and Value tensors. --- src/llama-memory-recurrent.cpp | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index c8361b1812..f0038036dc 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -785,21 +785,21 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: io.write(&s_trans, sizeof(s_trans)); io.write(&n_layer, sizeof(n_layer)); - // Iterate and write all the keys first, each row is a cell + // Iterate and write all the R tensors first, each row is a cell // Get whole range at a time for (uint32_t il = 0; il < n_layer; ++il) { // skip null layers (read_data will handle this by checking "r_l" and "s_l" for null) if (r_l[il] == nullptr) continue; - // Write key type + // Write R tensor type const int32_t r_type_i = (int32_t)r_l[il]->type; io.write(&r_type_i, sizeof(r_type_i)); - // Write row size of key + // Write row size of R tensor const uint64_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r()); io.write(&r_size_row, sizeof(r_size_row)); - // Read each range of cells of k_size length and write out + // Write each range of cells of r_size_row length for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * r_size_row; @@ -812,15 +812,15 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: // skip null layers (read_data will handle this by checking "r_l" and "s_l" for null) if (s_l[il] == nullptr) continue; - // Write value type + // Write S tensor type const int32_t s_type_i = (int32_t)s_l[il]->type; io.write(&s_type_i, sizeof(s_type_i)); - // Write row size of value + // Write row size of S tensor const uint64_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s()); io.write(&s_size_row, sizeof(s_size_row)); - // Read each range of cells of s_size length and write out + // Write each range of S tensor rows for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t buf_size = range_size * s_size_row; @@ -828,7 +828,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: } } } else { - // When v is transposed, we also need the element size and get the element ranges from each row + // When S tensor is transposed, we also need the element size and get the element ranges from each row const uint32_t mem_size = size; for (uint32_t il = 0; il < n_layer; ++il) { // skip null layers (read_data will handle this by checking "r_l" and "s_l" for null) @@ -836,7 +836,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: const uint32_t n_embd_s = hparams.n_embd_s(); - // Write value type + // Write S tensor type const int32_t s_type_i = (int32_t)s_l[il]->type; io.write(&s_type_i, sizeof(s_type_i)); @@ -849,7 +849,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std:: // For each row, we get the element values of each cell for (uint32_t j = 0; j < n_embd_s; ++j) { - // Read each range of cells of v_size_el length and write out + // Write each range of cells of s_size_el length for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; const size_t src_offset = (range.first + j * mem_size) * s_size_el; From 2e916f996a965a911eb79168b5fb959c1ea0c57b Mon Sep 17 00:00:00 2001 From: Marcello Seri Date: Fri, 30 Jan 2026 16:09:44 +0100 Subject: [PATCH 071/206] jinja : add unordered_map include to value.h [no ci] (#19205) On macos Sequoia 15.7.3, x86_64, the build has recently started failing with ``` In file included from .../code/cpp/llama.cpp/common/jinja/string.cpp:2: .../code/cpp/llama.cpp/common/./jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std' 478 | std::unordered_map unordered; | ~~~~~^ In file included from .../code/cpp/llama.cpp/common/jinja/caps.cpp:1: .../code/cpp/llama.cpp/common/jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std' 478 | std::unordered_map unordered; | ~~~~~^ In file included from .../code/cpp/llama.cpp/common/jinja/value.cpp:1: In file included from .../code/cpp/llama.cpp/common/jinja/runtime.h:4: .../code/cpp/llama.cpp/common/jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std' 478 | std::unordered_map unordered; [...] ``` After a bit of digging to make sure all the appropriate flags were used, I notifced that the necessary header was not included. This fixes the build for me and should not affect negatively other builds that for some reasons were already succeeding --- common/jinja/value.h | 1 + 1 file changed, 1 insertion(+) diff --git a/common/jinja/value.h b/common/jinja/value.h index a2f92d2c69..1c04760a08 100644 --- a/common/jinja/value.h +++ b/common/jinja/value.h @@ -12,6 +12,7 @@ #include #include #include +#include #include namespace jinja { From dabaa2e77a9d94ee1466ce55f9ff47d5c1daae43 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 18:21:48 +0200 Subject: [PATCH 072/206] spec : add ngram-mod (#19164) * spec : add ngram-mod * cont : simplify + keep track of occupancy * cont : cleanup * cont : move initialization to common/speculative * cont : cleanup * cont : cleanup * cont : fix --- common/CMakeLists.txt | 2 + common/arg.cpp | 4 +- common/common.h | 5 + common/ngram-map.cpp | 35 +++---- common/ngram-map.h | 1 + common/ngram-mod.cpp | 60 ++++++++++++ common/ngram-mod.h | 37 +++++++ common/speculative.cpp | 164 +++++++++++++++++++++++++++++++- common/speculative.h | 4 +- tools/server/server-context.cpp | 9 +- 10 files changed, 292 insertions(+), 29 deletions(-) create mode 100644 common/ngram-mod.cpp create mode 100644 common/ngram-mod.h diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 3bc7bc6210..295ae9ea25 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -75,6 +75,8 @@ add_library(${TARGET} STATIC ngram-cache.h ngram-map.cpp ngram-map.h + ngram-mod.cpp + ngram-mod.h peg-parser.cpp peg-parser.h preset.cpp diff --git a/common/arg.cpp b/common/arg.cpp index 218418f070..5fbc9022c0 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -3396,7 +3396,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( - {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v]", + {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]", string_format("type of speculative decoding to use when no draft model is provided (default: %s)\n", common_speculative_type_to_str(params.speculative.type).c_str()), [](common_params & params, const std::string & value) { @@ -3410,6 +3410,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K; } else if (value == "ngram-map-k4v") { params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V; + } else if (value == "ngram-mod") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD; } else { throw std::invalid_argument("unknown speculative decoding type without draft model"); } diff --git a/common/common.h b/common/common.h index fd3ab8cd18..398ebb0960 100644 --- a/common/common.h +++ b/common/common.h @@ -171,6 +171,7 @@ enum common_speculative_type { COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, // simple self-speculative decoding COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, // self-speculative decoding with n-gram keys only COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values + COMMON_SPECULATIVE_TYPE_NGRAM_MOD, COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, // self-speculative decoding with 3-level n-gram cache COMMON_SPECULATIVE_TYPE_COUNT // number of types, unknown type }; @@ -252,6 +253,8 @@ struct common_params_model { std::string name = ""; // in format /[:] (tag is optional) // NOLINT }; +struct common_ngram_mod; + struct common_params_speculative { common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding @@ -269,6 +272,8 @@ struct common_params_speculative { uint16_t ngram_check_rate = 1; // check rate for ngram lookup uint16_t ngram_min_hits = 1; // minimum hits at ngram/mgram lookup for mgram to be proposed + std::shared_ptr ngram_mod; + std::string lookup_cache_static; // path of static ngram cache file for lookup decoding // NOLINT std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding // NOLINT diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp index 930e7a3c10..84fd761367 100644 --- a/common/ngram-map.cpp +++ b/common/ngram-map.cpp @@ -7,6 +7,21 @@ #include #include +// Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...]. +static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) { + std::ostringstream oss; + oss << '['; + for (size_t i = 0; i < length; ++i) { + if (i > 0) { + oss << ", "; + } + oss << inp[start + i]; + } + oss << ']'; + return oss.str(); +} + + // n-gram simple // @@ -100,8 +115,6 @@ llama_tokens common_ngram_simple_draft( // maximum number of counted values of a ngram map value. #define COMMON_NGRAM_MAX_VALUE_COUNT 16380 -static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length); - void common_ngram_map_draft(common_ngram_map & map, const llama_tokens & inp, llama_token sampled, llama_tokens & draft) { @@ -347,21 +360,3 @@ void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted) { n_accepted, curr_value.n_accepted); curr_value.n_accepted = n_accepted; } - -// Helper functions. -// - -// Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...]. -std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) { - std::ostringstream oss; - oss << '['; - for (size_t i = 0; i < length; ++i) { - if (i > 0) { - oss << ", "; - } - oss << inp[start + i]; - } - oss << ']'; - return oss.str(); -} - diff --git a/common/ngram-map.h b/common/ngram-map.h index bf91883f0c..b365034ac5 100644 --- a/common/ngram-map.h +++ b/common/ngram-map.h @@ -11,6 +11,7 @@ // #include "llama.h" +#include "common.h" #include diff --git a/common/ngram-mod.cpp b/common/ngram-mod.cpp new file mode 100644 index 0000000000..76f7257f61 --- /dev/null +++ b/common/ngram-mod.cpp @@ -0,0 +1,60 @@ +#include "ngram-mod.h" + +// +// common_ngram_mod +// + +common_ngram_mod::common_ngram_mod(uint16_t n, size_t size) : n(n), used(0) { + entries.resize(size); + + reset(); +} + +size_t common_ngram_mod::idx(const entry_t * tokens) const { + size_t res = 0; + + for (size_t i = 0; i < n; ++i) { + res = res*6364136223846793005ULL + tokens[i]; + } + + res = res % entries.size(); + + return res; +} + +void common_ngram_mod::add(const entry_t * tokens) { + const size_t i = idx(tokens); + + if (entries[i] == EMPTY) { + used++; + } + + entries[i] = tokens[n]; +} + +common_ngram_mod::entry_t common_ngram_mod::get(const entry_t * tokens) const { + const size_t i = idx(tokens); + + return entries[i]; +} + +void common_ngram_mod::reset() { + std::fill(entries.begin(), entries.end(), EMPTY); + used = 0; +} + +size_t common_ngram_mod::get_n() const { + return n; +} + +size_t common_ngram_mod::get_used() const { + return used; +} + +size_t common_ngram_mod::size() const { + return entries.size(); +} + +size_t common_ngram_mod::size_bytes() const { + return entries.size() * sizeof(entries[0]); +} diff --git a/common/ngram-mod.h b/common/ngram-mod.h new file mode 100644 index 0000000000..cf3c89c915 --- /dev/null +++ b/common/ngram-mod.h @@ -0,0 +1,37 @@ +#pragma once + +#include +#include + +// +// common_ngram_mod +// ref: https://github.com/ggml-org/llama.cpp/pull/19164 +// + +// basic n-gram hasher +struct common_ngram_mod { + using entry_t = int32_t; + + static constexpr entry_t EMPTY = -1; + + common_ngram_mod(uint16_t n, size_t size); + + size_t idx(const entry_t * tokens) const; + void add(const entry_t * tokens); + entry_t get(const entry_t * tokens) const; // return -1 if not found + + void reset(); + + size_t get_n() const; + size_t get_used() const; + + size_t size() const; + size_t size_bytes() const; + +private: + size_t n; // ngram size to hash + + size_t used; + + std::vector entries; +}; diff --git a/common/speculative.cpp b/common/speculative.cpp index 3f314b5d57..a1a3b51c13 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -6,6 +6,7 @@ #include "log.h" #include "ngram-cache.h" #include "ngram-map.h" +#include "ngram-mod.h" #include "sampling.h" #include @@ -23,6 +24,7 @@ const std::vector common_speculative_types = { COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, + COMMON_SPECULATIVE_TYPE_NGRAM_MOD, COMMON_SPECULATIVE_TYPE_NGRAM_CACHE }; @@ -33,6 +35,7 @@ const std::map common_speculative_typ {"ngram_simple", COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE}, {"ngram_map_k", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K}, {"ngram_map_k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V}, + {"ngram_mod", COMMON_SPECULATIVE_TYPE_NGRAM_MOD}, {"ngram_cache", COMMON_SPECULATIVE_TYPE_NGRAM_CACHE} }; @@ -110,6 +113,8 @@ static bool common_speculative_are_compatible( struct common_speculative_state { const enum common_speculative_type type; + // TODO: rename to n_call_draft, n_gen_drafts, n_acc_drafts, n_gen_tokens, n_acc_tokens + // TODO: add n_call_begin, n_call_accept size_t drafts_call_count = 0; // number of times this implementation was called. size_t drafts_generated_count = 0; // number of times a draft or part was generated by this implementation. size_t drafts_accepted_count = 0; // number of times a draft or part was accepted by the target model. @@ -119,6 +124,8 @@ struct common_speculative_state { // TODO: track performance of most recent calls const bool gen_perf = true; // whether to generate performance stats. + // TODO: rename to t_draft_us + // TODO: add t_begin_us, t_accept_us int64_t gen_duration_us = 0; // total time spent in this implementation in microseconds. common_speculative_state(enum common_speculative_type type) : type(type) {} @@ -509,6 +516,132 @@ struct common_speculative_state_ngram_map_k : public common_speculative_state { } }; +struct common_speculative_state_ngram_mod : public common_speculative_state { + common_ngram_mod & mod; + + // the last position in the prompt that was added to the ngram container + size_t i_last = 0; + + // length of the last drafted n‑gram (number of tokens returned by draft) + size_t n_draft_last = 0; + + // consecutive accept rounds with low acceptance fraction (< 0.5) + int n_low = 0; + + // enable trace logging if LLAMA_TRACE is set + const bool verbose; + + common_speculative_state_ngram_mod(enum common_speculative_type type, common_ngram_mod & mod) + : common_speculative_state(type), mod(mod), verbose(std::getenv("LLAMA_TRACE") != nullptr) { + static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t)); + } + + void begin(const llama_tokens & prompt) override { + i_last = 0; + + n_draft_last = 0; + + const size_t n = mod.get_n(); + + if (prompt.size() < n) { + return; + } + + for (size_t i = 0; i < prompt.size() - n; ++i) { + mod.add(prompt.data() + i); + } + + i_last = prompt.size() - n; + + const double f = (double)mod.get_used() / (double)mod.size(); + LOG_INF("%s: ngram_mod occupancy = %zu/%zu (%.2f)\n", __func__, mod.get_used(), mod.size(), f); + + constexpr double f_thold = 0.25; + if (f > f_thold) { + LOG_WRN("%s: ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", __func__, f, f_thold); + + mod.reset(); + } + } + + void draft( + const common_params_speculative & params, + const llama_tokens & prompt_tgt, + llama_token id_last, + llama_tokens & result) override { + GGML_UNUSED(params); + + n_draft_last = 0; + + const size_t cur_len = prompt_tgt.size(); + if (cur_len < mod.get_n()) { + return; + } + + const size_t n = mod.get_n(); + + // add new ngrams in chunks + if (i_last + 32 < cur_len) { + for (size_t i = i_last; i < cur_len - n; ++i) { + mod.add(prompt_tgt.data() + i); + } + + i_last = cur_len - n; + } + + result.resize(n + params.n_max); + for (size_t i = 0; i < n - 1; ++i) { + result[i] = prompt_tgt[cur_len - n + 1 + i]; + } + result[n - 1] = id_last; + + for (int i = 0; i < params.n_max; ++i) { + const llama_token token = mod.get(result.data() + i); + if (token == common_ngram_mod::EMPTY) { + if (i < params.n_min) { + result.clear(); + return; + } + + result.resize(n + i); + break; + } + result[n + i] = token; + } + + // only return the m tokens that were drafted + for (size_t i = 0; n + i < result.size(); ++i) { + result[i] = result[n + i]; + } + result.resize(result.size() - n); + + // store length of drafted n‑gram for later acceptance analysis + n_draft_last = result.size(); + } + + void accept(uint16_t n_accepted) override { + if (verbose) { + LOG_INF("%s: accepted %d tokens from %zu drafted tokens\n", __func__, n_accepted, n_draft_last); + } + + // compute acceptance fraction if we have a recorded draft length + if (n_draft_last > 0) { + const double f_acc = (double)n_accepted / (double)n_draft_last; + if (f_acc < 0.5) { + n_low++; + if (n_low >= 3) { + LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, n_low); + + mod.reset(); + n_low = 0; + } + } else { + n_low = 0; + } + } + } +}; + struct common_speculative_state_ngram_cache : public common_speculative_state { uint16_t n_draft; bool save_dynamic; @@ -650,6 +783,7 @@ std::string common_speculative_type_to_str(enum common_speculative_type type) { case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: return "ngram_simple"; case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: return "ngram_map_k"; case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram_map_k4v"; + case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: return "ngram_mod"; case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: return "ngram_cache"; default: return "unknown"; } @@ -666,8 +800,8 @@ enum common_speculative_type common_speculative_type_from_name(const std::string // initialization of the speculative decoding system // common_speculative * common_speculative_init( - const common_params_speculative & params, - llama_context * ctx_tgt) { + common_params_speculative & params, + llama_context * ctx_tgt) { llama_context * ctx_dft = nullptr; if (params.model_dft) { ctx_dft = llama_init_from_model(params.model_dft, params.cparams_dft); @@ -687,6 +821,7 @@ common_speculative * common_speculative_init( bool has_ngram_simple = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE); bool has_ngram_map_k = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K); bool has_ngram_map_k4v = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V); + bool has_ngram_mod = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MOD); // In a more complex implementation we could use the same implementation but with different parameters. // This was initially used in PR-18471 but removed to simplify the code. @@ -701,6 +836,22 @@ common_speculative * common_speculative_init( // This implementation can guess tokens with high acceptance rate but is more expensive. configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params)); } + if (has_ngram_mod) { + // shared instance for all speculative decoding contexts + if (!params.ngram_mod) { + params.ngram_mod = std::make_shared(params.ngram_size_n, 4*1024*1024); + + LOG_INF("%s: initialized ngram_mod with n=%d, size=%zu (%.3f MB)\n", __func__, + params.ngram_size_n, params.ngram_mod->size(), + (float)(params.ngram_mod->size_bytes())/1024/1024); + + if (params.ngram_size_n < 16) { + LOG_WRN("%s: ngram_mod n=%d is too small - poor quality is possible, see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, params.ngram_size_n); + } + } + + configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params)); + } if (has_ngram_cache) { configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params)); } @@ -758,6 +909,11 @@ common_speculative * common_speculative_init( )); break; } + case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: { + GGML_ASSERT(config.params.ngram_mod); + impls.push_back(std::make_unique(config.type, *config.params.ngram_mod)); + break; + } case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: { auto state = create_state_ngram_cache( params.lookup_cache_static, params.lookup_cache_dynamic, config); @@ -822,8 +978,7 @@ llama_tokens common_speculative_draft( if (!result.empty()) { LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__, - common_speculative_type_to_str(impl.get()->type).c_str(), - prompt_tgt.size(), + common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(), impl.get()->drafts_call_count, result.size()); spec->curr_impl = impl.get(); // set current implementation for stats @@ -869,6 +1024,7 @@ void common_speculative_print_stats(const common_speculative * spec) { str_perf = ""; } + // TODO: report time for begin() and accept() LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n", common_speculative_type_to_str(impl->type).c_str(), impl->drafts_call_count, diff --git a/common/speculative.h b/common/speculative.h index 9e1888e4be..76fe6bb7bc 100644 --- a/common/speculative.h +++ b/common/speculative.h @@ -15,8 +15,8 @@ enum common_speculative_type common_speculative_type_from_name(const std::string std::string common_speculative_type_to_str(enum common_speculative_type type); common_speculative * common_speculative_init( - const common_params_speculative & params, - llama_context * ctx_tgt); + common_params_speculative & params, + llama_context * ctx_tgt); void common_speculative_free(common_speculative * spec); diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 1ca4e3cc0e..6f26fc9a9b 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -705,6 +705,11 @@ private: params_base.n_cache_reuse = 0; SRV_WRN("%s\n", "cache_reuse is not supported by multimodal, it will be disabled"); } + + if (params_base.speculative.type != COMMON_SPECULATIVE_TYPE_NONE) { + params_base.speculative.type = COMMON_SPECULATIVE_TYPE_NONE; + SRV_WRN("%s\n", "speculative decoding is not supported by multimodal, it will be disabled"); + } } if (!llama_memory_can_shift(llama_get_memory(ctx))) { @@ -754,9 +759,9 @@ private: SRV_ERR("%s\n", "speculative decoding is not supported with multimodal"); return false; } - SRV_WRN("%s", "speculative decoding context initialized\n"); + SLT_INF(slot, "%s", "speculative decoding context initialized\n"); } else { - SRV_WRN("%s", "speculative decoding context not initialized\n"); + SLT_INF(slot, "%s", "speculative decoding context not initialized\n"); } } From 13f3ebfae118e54716658d44ca9884e726c749c2 Mon Sep 17 00:00:00 2001 From: Simon Redman Date: Fri, 30 Jan 2026 11:27:16 -0500 Subject: [PATCH 073/206] Correctly fetch q8_1 quantize pipeline in test as needed by 8a3519b (#19194) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 3852867c29..a99375c088 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -11956,7 +11956,8 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, } } if (mmq) { - ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_quantize_q8_1, num_it); + vk_pipeline pipeline_quantize_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline_quantize_q8_1, num_it); } ggml_pipeline_allocate_descriptor_sets(ctx); From bbada8bfb9bce17eafb0af5ded22bf96ba57f778 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 19:46:10 +0200 Subject: [PATCH 074/206] server : wrap around the "id_slot" parameter (#19207) * server : wrap around the "id_slot" parameter * cont : minor --- tools/server/server-context.cpp | 41 ++++++++++++++++++--------------- tools/server/server-task.h | 2 +- 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 6f26fc9a9b..7f9c3c566b 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -155,7 +155,7 @@ struct server_slot { double t_prompt_processing; // ms double t_token_generation; // ms - std::function callback_on_release; + std::function callback_on_release; // Speculative decoding stats int32_t n_draft_total = 0; // Total draft tokens generated @@ -767,8 +767,8 @@ private: SLT_INF(slot, "new slot, n_ctx = %d\n", slot.n_ctx); - slot.callback_on_release = [this](int slot_id) { - queue_tasks.pop_deferred_task(slot_id); + slot.callback_on_release = [this](int id_slot) { + queue_tasks.pop_deferred_task(id_slot); }; slot.reset(); @@ -896,6 +896,9 @@ private: } server_slot * get_slot_by_id(int id_slot) { + // note: allow id_slot to be out of bounds (wrap around) + id_slot = id_slot % slots.size(); + for (server_slot & slot : slots) { if (slot.id == id_slot) { return &slot; @@ -1765,7 +1768,7 @@ private: break; } - int id_slot = task.slot_action.slot_id; + const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1803,7 +1806,7 @@ private: case SERVER_TASK_TYPE_SLOT_RESTORE: { if (!check_no_mtmd(task.id)) break; - int id_slot = task.slot_action.slot_id; + const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1852,7 +1855,7 @@ private: if (!check_no_mtmd(task.id)) { break; } - int id_slot = task.slot_action.slot_id; + const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -3317,7 +3320,7 @@ void server_routes::init_routes() { } // TODO: get rid of this dynamic_cast - auto res_task = dynamic_cast(result.get()); + auto * res_task = dynamic_cast(result.get()); GGML_ASSERT(res_task != nullptr); // optionally return "fail_on_no_slot" error @@ -3340,8 +3343,8 @@ void server_routes::init_routes() { } std::string id_slot_str = req.get_param("id_slot"); - int id_slot; + int id_slot; try { id_slot = std::stoi(id_slot_str); } catch (const std::exception &) { @@ -3353,14 +3356,16 @@ void server_routes::init_routes() { if (action == "save") { return handle_slots_save(req, id_slot); - } else if (action == "restore") { - return handle_slots_restore(req, id_slot); - } else if (action == "erase") { - return handle_slots_erase(req, id_slot); - } else { - res->error(format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST)); - return res; } + if (action == "restore") { + return handle_slots_restore(req, id_slot); + } + if (action == "erase") { + return handle_slots_erase(req, id_slot); + } + + res->error(format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST)); + return res; }; this->get_props = [this](const server_http_req &) { @@ -3903,7 +3908,7 @@ std::unique_ptr server_routes::handle_slots_save(const ser { server_task task(SERVER_TASK_TYPE_SLOT_SAVE); task.id = rd.get_new_id(); - task.slot_action.slot_id = id_slot; + task.slot_action.id_slot = id_slot; task.slot_action.filename = filename; task.slot_action.filepath = filepath; rd.post_task(std::move(task)); @@ -3939,7 +3944,7 @@ std::unique_ptr server_routes::handle_slots_restore(const { server_task task(SERVER_TASK_TYPE_SLOT_RESTORE); task.id = rd.get_new_id(); - task.slot_action.slot_id = id_slot; + task.slot_action.id_slot = id_slot; task.slot_action.filename = filename; task.slot_action.filepath = filepath; rd.post_task(std::move(task)); @@ -3968,7 +3973,7 @@ std::unique_ptr server_routes::handle_slots_erase(const se { server_task task(SERVER_TASK_TYPE_SLOT_ERASE); task.id = rd.get_new_id(); - task.slot_action.slot_id = id_slot; + task.slot_action.id_slot = id_slot; rd.post_task(std::move(task)); } diff --git a/tools/server/server-task.h b/tools/server/server-task.h index 244470596b..a69e8f1a3d 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -153,7 +153,7 @@ struct server_task { // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE struct slot_action { - int slot_id; + int id_slot; std::string filename; std::string filepath; }; From dfd6106c84045f819760c86cc21a27804d2faf32 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 15:56:15 +0200 Subject: [PATCH 075/206] cuda : fix compile warnings (whisper/0) --- ggml/src/ggml-cuda/ggml-cuda.cu | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index e9e9592eba..08383edb40 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -3910,14 +3910,14 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud // Launch graph CUDA_CHECK(cudaGraphLaunch(graph->instance, cuda_ctx->stream())); #else + GGML_UNUSED(graph_key); graph_evaluated_or_captured = true; #endif // USE_CUDA_GRAPH } } -static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, const void * graph_key) { - #ifdef USE_CUDA_GRAPH +static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, const void * graph_key) { ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); if (graph->graph == nullptr) { @@ -3930,12 +3930,8 @@ static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, co } return graph->is_enabled(); -#else - GGML_UNUSED(cuda_ctx); - GGML_UNUSED(graph_key); - return false; -#endif // USE_CUDA_GRAPH } +#endif // USE_CUDA_GRAPH static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context; From d9a2a4bcaa071d730bb1ab4fb411a9c93b50dd13 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 16:27:14 +0200 Subject: [PATCH 076/206] 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 c838276158..81e79a9470 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -ebc3a0f4a56be1c9424a89fbec09962ac34fde85 +a8db410a252c8c8f2d120c6f2e7133ebe032f35d From 971facc38e2544fcf2cc09368de5d1a68e33c10f Mon Sep 17 00:00:00 2001 From: shaofeiqi Date: Fri, 30 Jan 2026 10:19:27 -0800 Subject: [PATCH 077/206] opencl: add optimized q8_0 mm kernel for adreno (#18871) * Add Q8_0 OpenCL kernel Co-authored-by: yunjie * opencl: fix build for non-adreno * opencl: refactor q8_0 * opencl: enforce subgroup size of 64 for adreno for q8_0 * For A750 and older generations, subgroup size can be 64 or 128. This kernel assumes subgroup size 64. * opencl: suppress warning when adreno kernels are disabled --------- Co-authored-by: yunjie Co-authored-by: Li He --- ggml/src/ggml-opencl/CMakeLists.txt | 2 + ggml/src/ggml-opencl/ggml-opencl.cpp | 464 +++++++++++++++++- ggml/src/ggml-opencl/kernels/cvt.cl | 31 ++ .../gemv_noshuffle_general_q8_0_f32.cl | 195 ++++++++ .../kernels/mul_mm_q8_0_f32_8x4.cl | 129 +++++ 5 files changed, 819 insertions(+), 2 deletions(-) create mode 100644 ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl create mode 100644 ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 0259474b6e..fa5fadd112 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -101,6 +101,8 @@ set(GGML_OPENCL_KERNELS mul_mm_f32_f32_l4_lm mul_mm_f16_f32_l4_lm mul_mm_q8_0_f32_l4_lm + mul_mm_q8_0_f32_8x4 + gemv_noshuffle_general_q8_0_f32 mul norm relu diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 678e40965a..4850c11d14 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -226,7 +226,8 @@ static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) { return ADRENO_GPU_GEN::A7X; } - if (strstr(device_name, "830")) { + if (strstr(device_name, "830") || + strstr(device_name, "840")) { return ADRENO_GPU_GEN::A8X; } @@ -529,7 +530,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v; cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0; cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans; - cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0; + cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans; cl_kernel kernel_mul_mat_q4_0_f32_8x_flat; cl_kernel kernel_convert_block_q4_0_noshuffle; cl_kernel kernel_restore_block_q4_0_noshuffle; @@ -696,6 +697,8 @@ struct ggml_backend_opencl_context { cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096; cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096; cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096; + cl_kernel kernel_mul_mm_q8_0_f32_8x4; + cl_kernel CL_mul_mat_vec_q8_0_f32; #endif // GGML_OPENCL_USE_ADRENO_KERNELS void free() { @@ -894,6 +897,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err)); + CL_CHECK((backend_ctx->kernel_restore_block_q8_0_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0_trans", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err)); GGML_LOG_CONT("."); @@ -2290,6 +2294,46 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // mul_mm_q8_0_f32_8x4 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src_q8_8x4_gemm { + #include "mul_mm_q8_0_f32_8x4.cl.h" + }; +#else + const std::string kernel_src_q8_8x4_gemm = read_file("mul_mm_q8_0_f32_8x4.cl"); +#endif + backend_ctx->program_CL_gemm = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_q8_8x4_gemm.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_mul_mm_q8_0_f32_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mm_q8_0_f32_8x4", &err), err)); + GGML_LOG_CONT("."); + } + + // gemv_noshuffle_general_q8_0_f32 + { + std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std + + " -cl-mad-enable " + " -DSIMDGROUP_WIDTH=" + + std::to_string(backend_ctx->adreno_wave_size); + if (backend_ctx->has_vector_subgroup_broadcast) { + CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT "; + } + +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src_CL_gemv_general { + #include "gemv_noshuffle_general_q8_0_f32.cl.h" + }; +#else + const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_general_q8_0_f32.cl"); +#endif + + cl_program prog = build_program_from_source( + backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + + CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + std::string CL_moe_compile_opts = std::string("-cl-std=") + opencl_c_std + " -cl-mad-enable " " -cl-fast-relaxed-math"; @@ -3745,6 +3789,15 @@ inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ct return ((strstr(tensor->name, "ffn") != NULL) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 64 == 0); } +inline bool enable_adreno_trans_weight(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) { + + bool adreno_kernel = use_adreno_kernels(backend_ctx, tensor); + + size_t elem_num = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3]; + + return ((elem_num < 128 * 1024 * 1024) && adreno_kernel); // max element num: 2**27 +} + static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device); @@ -4159,6 +4212,130 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, tensor->extra = extra; + // Transpose the weights and scales +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (enable_adreno_trans_weight(backend_ctx, tensor)) { + + int M = tensor->ne[1]; // ne01 + int K = tensor->ne[0]; // ne00 + + GGML_ASSERT(K % 32 == 0); + GGML_ASSERT(M % 4 == 0); + GGML_ASSERT(tensor->ne[2] == 1); + GGML_ASSERT(tensor->ne[3] == 1); + + // Transpose weights + size_t q_size_bytes = K * M / 4 * sizeof(float); + cl_buffer_region region; + region.origin = 0; + region.size = q_size_bytes; + cl_mem qT_d = clCreateSubBuffer( + backend_ctx->prealloc_quant_trans.buffer, + 0, + CL_BUFFER_CREATE_TYPE_REGION, + ®ion, + &err); + CL_CHECK(err); + + cl_mem q_d_image1D; + cl_mem qT_d_image1D; + + cl_image_format img_fmt_1d; + cl_image_desc img_desc_1d; + + img_fmt_1d = { CL_RGBA, CL_FLOAT }; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * K / 4 / 4; + img_desc_1d.buffer = extra->q; + q_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err); + CL_CHECK(err); + + img_fmt_1d = { CL_RGBA, CL_FLOAT }; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * K / 4 / 4; + img_desc_1d.buffer = qT_d; + qT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err); + CL_CHECK(err); + + int height_q = M / 4; + int width_q = K / 4 / 4; + kernel = backend_ctx->kernel_transpose_32; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_d_image1D)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &qT_d_image1D)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_q)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_q)); + + size_t local_size_q[3] = {4, 16, 1}; + size_t global_size_q[3] = {static_cast(width_q), static_cast(height_q), 1}; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_q, local_size_q, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + + // Transpose scales + size_t d_size_bytes = M * (K / 32) * 2; + region.origin = 0; + region.size = d_size_bytes; + cl_mem dT_d = clCreateSubBuffer( + backend_ctx->prealloc_scales_trans.buffer, + 0, + CL_BUFFER_CREATE_TYPE_REGION, + ®ion, + &err); + CL_CHECK(err); + + cl_mem d_d_image1D; + cl_mem dT_d_image1D; + + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + 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.buffer = extra->d; + d_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)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * K / 32 / 4; + img_desc_1d.buffer = dT_d; + dT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err); + CL_CHECK(err); + + int height_s = M / 4; + int width_s = K / 32; + + kernel = backend_ctx->kernel_transpose_16_4x1; + + 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)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_s)); + + size_t local_size_s[3] = {4, 16, 1}; + size_t global_size_s[3] = {static_cast(width_s), static_cast(height_s), 1}; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_s, local_size_s, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + + // copy transposed buffer contents to original buffers + CL_CHECK(clEnqueueCopyBuffer(queue, qT_d, extra->q, 0, 0, q_size_bytes, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + + CL_CHECK(clEnqueueCopyBuffer(queue, dT_d, extra->d, 0, 0, d_size_bytes, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + + CL_CHECK(clReleaseMemObject(qT_d)); + CL_CHECK(clReleaseMemObject(dT_d)); + + CL_CHECK(clReleaseMemObject(q_d_image1D)); + CL_CHECK(clReleaseMemObject(d_d_image1D)); + CL_CHECK(clReleaseMemObject(qT_d_image1D)); + CL_CHECK(clReleaseMemObject(dT_d_image1D)); + } // end transpose +#endif // GGML_OPENCL_USE_ADRENO_KERNELS + return; } if (tensor->type == GGML_TYPE_Q6_K) { @@ -4448,6 +4625,36 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, ggml_nbytes(tensor), NULL, &err); CL_CHECK(err); +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (enable_adreno_trans_weight(backend_ctx, tensor)) { + cl_kernel kernel = backend_ctx->kernel_restore_block_q8_0_trans; + + int ne00 = tensor->ne[0]; + int ne01 = tensor->ne[1]; + GGML_ASSERT(tensor->ne[2] == 1); // ??? + GGML_ASSERT(tensor->ne[3] == 1); // ??? + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &ne01)); + + size_t global_work_size[3] = {static_cast(((ne01 + 63) / 64) * 64), 1, 1}; + size_t local_work_size[3] = {64, 1, 1}; + + cl_event evt; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_work_size, local_work_size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + + CL_CHECK(clEnqueueReadBuffer( + queue, data_device, CL_TRUE, offset, + size, data, 0, NULL, NULL)); + CL_CHECK(clReleaseMemObject(data_device)); + return; + } +#endif cl_kernel kernel = backend_ctx->kernel_restore_block_q8_0; CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d)); @@ -7947,6 +8154,252 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten CL_CHECK(clReleaseMemObject(D_sub_buffer)); } +static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(src1); + GGML_ASSERT(src1->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + const enum ggml_type src0t = src0->type; + const enum ggml_type src1t = src1->type; + + GGML_ASSERT(src0t == GGML_TYPE_Q8_0); + GGML_ASSERT(src1t == GGML_TYPE_F32); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra; + + GGML_ASSERT(src1->view_offs == 0); + GGML_ASSERT(dst->view_offs == 0); + + const int ne00 = src0->ne[0]; + const int ne01 = src0->ne[1]; + const int ne02 = src0->ne[2]; + + const int ne10 = src1->ne[0]; + const int ne12 = src1->ne[2]; + + const int ne0 = dst->ne[0]; + const int ne1 = dst->ne[1]; + + GGML_ASSERT(ne00 == ne10); + GGML_ASSERT((ne00 % 32) == 0); + GGML_ASSERT(ne0 == ne01); + + cl_context context = backend_ctx->context; + cl_kernel kernel; + + // init CL objects + cl_int status; + cl_image_format img_fmt_1d; + cl_image_desc img_desc_1d; + cl_buffer_region region; + cl_mem A_image1d; + cl_mem B_image1d; + cl_mem B_sub_buffer; + cl_mem S_image1d; + + cl_mem D_image1d; + cl_mem D_sub_buffer; + + int M = ne01; + int N = ne1; + int K = ne00; + + // create an image for A + img_fmt_1d = { CL_R, CL_FLOAT}; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * K / 4; // Divide by 4 for char -> float + img_desc_1d.buffer = extra0_q8_0->q; + A_image1d = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt_1d, &img_desc_1d, NULL, &status); + CL_CHECK(status); + + // create an image for Scale + img_fmt_1d = { CL_R, CL_HALF_FLOAT}; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * K / 32; // Block size is 32 + img_desc_1d.buffer = extra0_q8_0->d; + S_image1d = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt_1d, &img_desc_1d, NULL, &status); + CL_CHECK(status); + + // create a sub_buffer for B + region.origin = (extra1->offset); // + src1->view_offs); + region.size = K * N * sizeof(float); + B_sub_buffer = clCreateSubBuffer((extra1->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); + CL_CHECK(status); + + // create an image for B from sub_buffer: RGBA (OCL) + img_fmt_1d = {CL_RGBA, CL_FLOAT}; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = K * N / 4; + img_desc_1d.buffer = B_sub_buffer; + B_image1d = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt_1d, &img_desc_1d, NULL, &status); + CL_CHECK(status); + + // Create subbuffer and image1d_buffer for dst + region.origin = (extrad->offset); // + dst->view_offs; + region.size = M * N * sizeof(float); + D_sub_buffer = clCreateSubBuffer((extrad->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); + CL_CHECK(status); + + img_fmt_1d = {CL_R, CL_FLOAT}; + memset(&img_desc_1d, 0, sizeof(img_desc_1d)); + img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc_1d.image_width = M * N; + img_desc_1d.buffer = D_sub_buffer; + D_image1d = clCreateImage(context, CL_MEM_WRITE_ONLY, &img_fmt_1d, &img_desc_1d, NULL, &status); + CL_CHECK(status); + + size_t local_work_size[3] = {1, 1, 1}; + size_t global_work_size[3] = {1, 1, 1}; + + if (N == 1) { + kernel = backend_ctx->CL_mul_mat_vec_q8_0_f32; + + int r2 = 1; + int r3 = 1; + cl_uint k_arg = 0; + + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_mem), &A_image1d)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_mem), &extra0_q8_0->d)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_mem), &B_image1d)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_ulong), &extra1->offset)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(cl_ulong), &extrad->offset)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne10)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne12)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne0)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &ne1)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &r2)); + CL_CHECK(clSetKernelArg(kernel, k_arg++, sizeof(int), &r3)); + + size_t wavesize = backend_ctx->adreno_wave_size; + local_work_size[0] = wavesize; + local_work_size[1] = 4; // reduce factor + local_work_size[2] = 1; + + global_work_size[0] = ((M + wavesize - 1) / wavesize) * wavesize; + global_work_size[1] = 4; // reduce factor + global_work_size[2] = 1; + } else { + cl_ulong offsetd = extrad->offset + dst->view_offs; + cl_mem B_image1d_trans = nullptr; + // for B transpose + cl_mem B_d = nullptr; + int padding; + + //how many extra elements beyond multiple of 8 + int extra_elements = N % 8; + + //how much padding to add + padding = 0; + if (extra_elements > 0){ + padding = 8 - extra_elements; + } + + // Specify the starting offset (in bytes) + region.origin = 0; + // Specify the size of the sub-buffer (divide by 2 for FP16) + region.size = K * (N + padding) * sizeof(float)/2; + backend_ctx->prealloc_act_trans.allocate(context, region.size); + B_d = clCreateSubBuffer( + backend_ctx->prealloc_act_trans.buffer, + 0, + CL_BUFFER_CREATE_TYPE_REGION, + ®ion, + &status); + CL_CHECK(status); + + cl_image_format image_format_B_d_output = { CL_RGBA, CL_HALF_FLOAT }; //(CL_HALF_FLOAT for FP16) + cl_image_desc image_desc_B_d_output = { + CL_MEM_OBJECT_IMAGE1D_BUFFER, + static_cast(K * (N + padding)/4), + 0, 0, 0, 0, 0, 0, 0, { B_d } + }; + B_image1d_trans = clCreateImage( + context, + 0, + &image_format_B_d_output, + &image_desc_B_d_output, + NULL, + &status); + CL_CHECK(status); + + int height_B = N/4; + if (height_B == 0) { + height_B = 1; + } + int width_B = K/4; + int padded_height_B = (N + padding)/4; + + kernel = backend_ctx->kernel_transpose_32_16; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &B_image1d)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &B_image1d_trans)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_B)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_B)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &padded_height_B)); + + size_t local_size_t[2] = { 1, 16 }; + size_t global_size_t[2] = { + static_cast(width_B), + static_cast(padded_height_B) + }; + + backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_size_t, local_size_t, dst); + + kernel = backend_ctx->kernel_mul_mm_q8_0_f32_8x4; + + int N_with_padding = N + padding; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q8_0->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &B_image1d_trans)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &K)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &M)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &N_with_padding)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &N)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &offsetd)); + + global_work_size[0] = (size_t)(N + 7) / 8; + global_work_size[1] = (size_t)(M + 3) / 4; + global_work_size[2] = 1; + + local_work_size[0] = 2; + local_work_size[1] = 128; + local_work_size[2] = 1; + } + + // enqueue kernel with profiling + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + + // deallocate sub buffers and images + CL_CHECK(clReleaseMemObject(A_image1d)); + CL_CHECK(clReleaseMemObject(B_sub_buffer)); + CL_CHECK(clReleaseMemObject(B_image1d)); + CL_CHECK(clReleaseMemObject(S_image1d)); + CL_CHECK(clReleaseMemObject(D_sub_buffer)); + CL_CHECK(clReleaseMemObject(D_image1d)); +#else + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(dst); +#endif +} + static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -8064,6 +8517,13 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co int padding; // <--------------------------------------------> // + // q8_0 x fp32 + if (src0t == GGML_TYPE_Q8_0 && src1t == GGML_TYPE_F32 && + enable_adreno_trans_weight(backend_ctx, src0)) { + ggml_cl_mul_mat_q8_0_f32_adreno(backend, src0, src1, dst); + return; + } + // q4_0 x fp32 if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) { // TODO: remove duplicate definitions of image description + format -- move to top diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl index adf576a839..9fb434713d 100644 --- a/ggml/src/ggml-opencl/kernels/cvt.cl +++ b/ggml/src/ggml-opencl/kernels/cvt.cl @@ -274,6 +274,37 @@ kernel void kernel_restore_block_q8_0( } } +kernel void kernel_restore_block_q8_0_trans( + global uchar * src_q, + global half * src_d, + global block_q8_0 * dst, + uint ne00, + uint ne01 +){ + uint num_blk_per_row = ne00 / QK8_0; + + global block_q8_0 * b = (global block_q8_0 *) dst + get_global_id(0) * num_blk_per_row; + global uchar * q = (global uchar *) src_q + get_global_id(0) * 4; // 4 8-bit packed + global half * d = (global half *) src_d + get_global_id(0); + + for (uint blk = 0; blk < num_blk_per_row; blk++) { + b->d = *d; + + for (uint i = 0; i < QK8_0; i+=4) { + b->qs[i] = q[0]; + b->qs[i+1] = q[1]; + b->qs[i+2] = q[2]; + b->qs[i+3] = q[3]; + + q += 4 * ne01; // M stride + } + + d += ne01; + + b++; + } +} + //------------------------------------------------------------------------------ // kernel_convert_block_q6_K // Convert the block_q6_K format to 3 separate arrays (AOS -> SOA). diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl new file mode 100644 index 0000000000..f944ef3a99 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl @@ -0,0 +1,195 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_khr_subgroups : enable + +#ifdef 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"))) +#endif + +#define QK8_0 32 +#define N_SIMDGROUP 4 + +#define dequantizeBlockAccum_ns_sgbroadcast_1(total_sums, bits8, scale, y) \ + float shared_y; \ + char elem; \ + \ + shared_y = sub_group_broadcast(y.s0, 0); \ + elem = (char)(bits8.s0 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 0); \ + elem = (char)((bits8.s0 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 0); \ + elem = (char)((bits8.s0 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 0); \ + elem = (char)((bits8.s0 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s4, 0); \ + elem = (char)(bits8.s1 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 0); \ + elem = (char)((bits8.s1 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 0); \ + elem = (char)((bits8.s1 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 0); \ + elem = (char)((bits8.s1 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s0, 1); \ + elem = (char)(bits8.s2 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 1); \ + elem = (char)((bits8.s2 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 1); \ + elem = (char)((bits8.s2 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 1); \ + elem = (char)((bits8.s2 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s4, 1); \ + elem = (char)(bits8.s3 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 1); \ + elem = (char)((bits8.s3 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 1); \ + elem = (char)((bits8.s3 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 1); \ + elem = (char)((bits8.s3 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s0, 2); \ + elem = (char)(bits8.s4 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 2); \ + elem = (char)((bits8.s4 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 2); \ + elem = (char)((bits8.s4 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 2); \ + elem = (char)((bits8.s4 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s4, 2); \ + elem = (char)(bits8.s5 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 2); \ + elem = (char)((bits8.s5 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 2); \ + elem = (char)((bits8.s5 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 2); \ + elem = (char)((bits8.s5 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s0, 3); \ + elem = (char)(bits8.s6 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 3); \ + elem = (char)((bits8.s6 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 3); \ + elem = (char)((bits8.s6 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 3); \ + elem = (char)((bits8.s6 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + \ + shared_y = sub_group_broadcast(y.s4, 3); \ + elem = (char)(bits8.s7 & 0x000000FF); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 3); \ + elem = (char)((bits8.s7 & 0x0000FF00) >> 8); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 3); \ + elem = (char)((bits8.s7 & 0x00FF0000) >> 16); \ + total_sums += convert_int(elem) * scale * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 3); \ + elem = (char)((bits8.s7 & 0xFF000000) >> 24); \ + total_sums += convert_int(elem) * scale * shared_y; \ + +#ifdef ADRENO_GPU +REQD_SUBGROUP_SIZE_64 +#endif +__kernel void kernel_gemv_noshuffle( + __read_only image1d_buffer_t src0_q, // quantized A + global half * src0_d, // A scales + __read_only image1d_buffer_t src1, // B + ulong offset1, // offset to B (0) + global float * dst, // C + ulong offsetd, // offset to C + int ne00, // K + int ne01, // M + int ne02, // 1 + int ne10, // K + int ne12, // 1 + int ne0, // M + int ne1, // N + int r2, // 1 + int r3) +{ + uint groupId = get_local_id(1); + uint gid = get_global_id(0); + ushort slid = get_sub_group_local_id(); + + uint K = ne00; + uint M = ne01; + + uint LINE_STRIDE_A = M; + uint BLOCK_STRIDE_A = 8 * M; // 32 / 4 = 8 + + __private uint8 regA; + __private half regS; + __private float8 regB; + + __private float totalSum = (float)(0.0f); + + // loop along K in block granularity, skip 4 blocks every iter + #pragma unroll 1 /* tell compiler not to unroll */ + for (uint k = groupId; k < (K / QK8_0); k += N_SIMDGROUP) { + regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of one rows + // first 4 fibers in each wave load 8 B values to its private scope + if (slid < 4) { + regB.s0123 = read_imagef(src1, (slid * 2 + k * 8)); + regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8)); + } + + // load weights for one block in consecutive rows + regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x; + regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x; + regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x; + regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x; + regA.s4 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x; + regA.s5 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x; + regA.s6 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x; + regA.s7 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x; + + dequantizeBlockAccum_ns_sgbroadcast_1(totalSum, regA, regS, regB); + } + + // reduction in local memory, assumes #wave=4 + __local float reduceLM[SIMDGROUP_WIDTH * 3]; + if (groupId == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = totalSum; + if (groupId == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = totalSum; + if (groupId == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = totalSum; + barrier(CLK_LOCAL_MEM_FENCE); + if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 0 + slid]; + if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 1 + slid]; + if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 2 + slid]; + + // 1 outputs per fiber in wave 0 + if (groupId == 0) { + dst = (global float*)((global char*)dst + offsetd); + dst[gid] = totalSum; + } +} diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl new file mode 100644 index 0000000000..51ce2121ce --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl @@ -0,0 +1,129 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable + +#ifdef cl_qcom_reqd_sub_group_size +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable +#define ADRENO_GPU 1 +#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full"))) +#endif + +#ifdef ADRENO_GPU +REQD_SUBGROUP_SIZE_128 +#endif + +kernel void kernel_mul_mm_q8_0_f32_8x4( + global const uint * src0_q, + global const half * src0_d, + __read_only image1d_buffer_t src1, + global float * dst, + int k, + int m, + int n, + int n_no_padding, + ulong offsetd +) { + + int m_4 = m >> 2; + int n_4 = n >> 2; + + int gy = get_global_id(0); + int gx = get_global_id(1); + int gx_2 = gx << 2; + dst = (global float *)((global char*)dst + offsetd); + + + half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0; + half8 B; + half4 deq; + + __global const uint* wptr = src0_q + gx_2; + __global const half* sptr = src0_d + gx_2; + + for (int i = 0; i < k; i += 4) { + uint4 pack4 = vload4(0, wptr + (i / 4) * m); + half4 scale = vload4(0, sptr + (i / 32) * m); + + char4 p0 = as_char4(pack4.s0); + char4 p1 = as_char4(pack4.s1); + char4 p2 = as_char4(pack4.s2); + char4 p3 = as_char4(pack4.s3); + + // ------------------- j = 0 (k = i+0) ------------------- + B.s0123 = read_imageh(src1, gy * 2 + (i + 0) * n_4); + B.s4567 = read_imageh(src1, gy * 2 + (i + 0) * n_4 + 1); + + half4 wj0 = convert_half4((char4)(p0.s0, p1.s0, p2.s0, p3.s0)) * scale; + + c0 += B * wj0.s0; + c1 += B * wj0.s1; + c2 += B * wj0.s2; + c3 += B * wj0.s3; + + // ------------------- j = 1 (k = i+1) ------------------- + B.s0123 = read_imageh(src1, gy * 2 + (i + 1) * n_4); + B.s4567 = read_imageh(src1, gy * 2 + (i + 1) * n_4 + 1); + + half4 wj1 = convert_half4((char4)(p0.s1, p1.s1, p2.s1, p3.s1)) * scale; + + c0 += B * wj1.s0; + c1 += B * wj1.s1; + c2 += B * wj1.s2; + c3 += B * wj1.s3; + + // ------------------- j = 2 (k = i+2) ------------------- + B.s0123 = read_imageh(src1, gy * 2 + (i + 2) * n_4); + B.s4567 = read_imageh(src1, gy * 2 + (i + 2) * n_4 + 1); + + half4 wj2 = convert_half4((char4)(p0.s2, p1.s2, p2.s2, p3.s2)) * scale; + + c0 += B * wj2.s0; + c1 += B * wj2.s1; + c2 += B * wj2.s2; + c3 += B * wj2.s3; + + // ------------------- j = 3 (k = i+3) ------------------- + B.s0123 = read_imageh(src1, gy * 2 + (i + 3) * n_4); + B.s4567 = read_imageh(src1, gy * 2 + (i + 3) * n_4 + 1); + + half4 wj3 = convert_half4((char4)(p0.s3, p1.s3, p2.s3, p3.s3)) * scale; + + c0 += B * wj3.s0; + c1 += B * wj3.s1; + c2 += B * wj3.s2; + c3 += B * wj3.s3; + } + + int idx = (gy << 3) * m + (gx << 2); + + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx); + } +} From 49277958109f738eb5377242e46734788ba21be8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 30 Jan 2026 21:27:27 +0200 Subject: [PATCH 078/206] ngram-mod : fix build [no ci] (#19216) --- common/ngram-mod.h | 1 + 1 file changed, 1 insertion(+) diff --git a/common/ngram-mod.h b/common/ngram-mod.h index cf3c89c915..7af92e9dde 100644 --- a/common/ngram-mod.h +++ b/common/ngram-mod.h @@ -2,6 +2,7 @@ #include #include +#include // // common_ngram_mod From 1488339138d609139c4400d1b80f8a5b1a9a203c Mon Sep 17 00:00:00 2001 From: Daniele Pinna <72076821+pestopoppa@users.noreply.github.com> Date: Fri, 30 Jan 2026 21:10:24 +0100 Subject: [PATCH 079/206] lookup, lookahead: fix crash when n_ctx not specified (#18729) * lookup, lookahead: fix crash when n_ctx not specified Since PR #16653 (Dec 15, 2025), the default n_ctx is 0 to enable automatic GPU memory fitting. This causes llama-lookup and llama-lookahead to crash when run without explicit -c flag: GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded") Root cause: Both examples use params.n_ctx directly for batch initialization, but params.n_ctx remains 0 even after the context is properly initialized to n_ctx_train internally. Bug history: - Nov 2023: lookahead.cpp created (PR #4207) with params.n_ctx pattern - Dec 2023: lookup.cpp created (PR #4484) with same pattern - Nov 2024: default n_ctx changed to 4096 (PR #10136) - bug dormant - Dec 2025: default n_ctx changed to 0 (PR #16653) - bug activated The bug was dormant for 2+ years because params.n_ctx defaulted to 512, then 4096. PR #16653 changed it to 0 for GPU auto-fitting, triggering the crash. Fix: Use llama_n_ctx(ctx) to get the actual runtime context size, matching the pattern already used elsewhere in lookup.cpp (line 72) and in speculative.cpp/speculative-simple.cpp. Tested: llama-lookup now works without -c flag (12.5% acceptance on Gemma-3-1B). Note: llama-lookahead has a separate pre-existing issue with sequence initialization (n_seq_max=1 vs W+G+1 needed) that is unrelated to this fix. * lookahead: fix n_seq_max and kv_unified configuration Lookahead decoding requires: - W + G + 1 = 31 sequences for parallel Jacobi decoding - Unified KV cache for coupled sequences in batch splitting These requirements were broken after PR #14482 changed validation logic. Consolidates fix from PR #18730 per maintainer request. Commit message drafted with Claude. --- examples/lookahead/lookahead.cpp | 8 +++++++- examples/lookup/lookup.cpp | 2 +- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index f54cfdd77f..aa6efa62b3 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -50,6 +50,12 @@ int main(int argc, char ** argv) { const int N = 5; // n-gram size const int G = 15; // max verification n-grams + // lookahead requires W + G + 1 sequences for parallel Jacobi decoding + params.n_parallel = W + G + 1; + + // unified KV cache is required for coupled sequences in batch splitting + params.kv_unified = true; + // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); @@ -115,7 +121,7 @@ int main(int argc, char ** argv) { // seq_id == 0 : the current input token // seq_id [1, W] : tokens from the past N - 1 Jacobi iterations // seq_id [W + 1, W + G] : verification n-grams - llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1); + llama_batch batch = llama_batch_init(llama_n_ctx(ctx), 0, W + G + 1); // target model sampling context struct common_sampler * smpl = common_sampler_init(model, params.sampling); diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 8e73138a5f..c7552ddde1 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -106,7 +106,7 @@ int main(int argc, char ** argv){ std::vector draft; - llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1); + llama_batch batch_tgt = llama_batch_init(llama_n_ctx(ctx), 0, 1); const auto t_dec_start = ggml_time_us(); From ec6c7421e4f3c0e1bdb9fdc6ee9670e14a490854 Mon Sep 17 00:00:00 2001 From: tc-mb <157115220+tc-mb@users.noreply.github.com> Date: Sat, 31 Jan 2026 06:19:30 +0800 Subject: [PATCH 080/206] mtmd: support MiniCPM-o 4.5(vision only) (#19211) Signed-off-by: tc-mb --- tools/mtmd/clip.cpp | 5 +++++ .../minicpmv-convert-image-encoder-to-gguf.py | 9 ++++++++- tools/mtmd/mtmd.cpp | 2 +- 3 files changed, 14 insertions(+), 2 deletions(-) diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 9b076e0c56..9fa5afc390 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1005,6 +1005,8 @@ struct clip_model_loader { hparams.minicpmv_query_num = 64; } else if (hparams.minicpmv_version == 6) { hparams.minicpmv_query_num = 64; + } else if (hparams.minicpmv_version == 100045) { + hparams.minicpmv_query_num = 64; } else { hparams.minicpmv_query_num = 96; } @@ -3209,6 +3211,9 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im } else if (params.minicpmv_version == 6) { // MiniCPM-V 4.5 n_patches = 64; + } else if (params.minicpmv_version == 100045) { + // MiniCPM-o 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 bb2cc4e4ea..944037e703 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 @@ -501,7 +501,7 @@ default_image_mean = [0.5, 0.5, 0.5] default_image_std = [0.5, 0.5, 0.5] ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) -ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4; MiniCPM-V 4.0 use 5; MiniCPM-o-4.0 use 6', default=2) +ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4; MiniCPM-V 4.0 use 5; MiniCPM-o-4.0 use 6; MiniCPM-o-4.5 use 100045', default=2) # with proper args = ap.parse_args() @@ -610,6 +610,9 @@ else: elif minicpmv_version == 6: emb_dim = 4096 block_count = 27 + elif minicpmv_version == 100045: + emb_dim = 4096 + block_count = 27 default_vision_config = { "hidden_size": 1152, @@ -637,6 +640,10 @@ elif minicpmv_version == 6: default_vision_config["model_type"] = "siglip_vision_model" vision_config = SiglipVisionConfig(**default_vision_config) model = SiglipVisionTransformer(vision_config) +elif minicpmv_version == 100045: + 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 32a24bfcea..d037e834f3 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -236,7 +236,7 @@ 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 || minicpmv_version == 6) { + } else if (minicpmv_version == 3 || minicpmv_version == 4 || minicpmv_version == 5 || minicpmv_version == 6 || minicpmv_version == 100045) { // minicpmv 2.6 format: // (overview) (slice) (slice) \n ... slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6; From 3dd95914d09b155eed84664b9abdbbffae238738 Mon Sep 17 00:00:00 2001 From: EugeoSynthesisThirtyTwo Date: Sat, 31 Jan 2026 04:39:21 +0100 Subject: [PATCH 081/206] quantize: add option --tensor-type-file to llama-quantize (#18572) * add option --tensor-type-file to llama-quantize, but it raises an error. * add error message when file not found * quantize: update help menu, fix CI Signed-off-by: Aaron Teo --------- Signed-off-by: Aaron Teo Co-authored-by: Your Name Co-authored-by: Aaron Teo --- tools/quantize/quantize.cpp | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/tools/quantize/quantize.cpp b/tools/quantize/quantize.cpp index 881f4b3dd9..0709e0bda0 100644 --- a/tools/quantize/quantize.cpp +++ b/tools/quantize/quantize.cpp @@ -119,7 +119,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp [[noreturn]] static void usage(const char * executable) { printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable); - printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv]\n"); + printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file] [--prune-layers] [--keep-split] [--override-kv]\n"); printf(" model-f32.gguf [model-quant.gguf] type [nthreads]\n\n"); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); @@ -131,6 +131,8 @@ static void usage(const char * executable) { printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n"); printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n"); printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n"); + printf(" --tensor-type-file tensor_type.txt: list of tensors to quantize to specific ggml_type. example: --tensor-type-file tensor_type_list.txt\n"); + printf(" Advanced option to selectively quantize a long list of tensors. Format to be tensor_name=ggml_type, separated by spaces/newline.\n"); printf(" --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n"); printf(" Advanced option to remove all tensors from the given layers\n"); printf(" --keep-split: will generate quantized model in the same shards as input\n"); @@ -415,6 +417,23 @@ static bool parse_tensor_type(const char * data, std::vector & tensor_type) { + std::ifstream file(filename); + if (!file) { + printf("\n%s: failed to open file '%s': %s\n\n", __func__, filename, std::strerror(errno)); + return false; + } + + std::string arg; + while (file >> arg) { + if (!parse_tensor_type(arg.c_str(), tensor_type)) { + return false; + } + } + + return true; +} + static bool parse_layer_prune(const char * data, std::vector & prune_layers) { if (!data) { printf("\n%s: no layer pruning ids provided\n\n", __func__); @@ -480,6 +499,10 @@ int main(int argc, char ** argv) { if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) { usage(argv[0]); } + } else if (strcmp(argv[arg_idx], "--tensor-type-file") == 0) { + if (arg_idx == argc-1 || !parse_tensor_type_file(argv[++arg_idx], tensor_types)) { + usage(argv[0]); + } } else if (strcmp(argv[arg_idx], "--prune-layers") == 0) { if (arg_idx == argc-1 || !parse_layer_prune(argv[++arg_idx], prune_layers)) { usage(argv[0]); @@ -686,3 +709,4 @@ int main(int argc, char ** argv) { return 0; } + From 89f10baad5a1809055d71110dff60e55561b9c62 Mon Sep 17 00:00:00 2001 From: nullname Date: Sat, 31 Jan 2026 13:14:20 +0800 Subject: [PATCH 082/206] ggml-hexagon: flash-attention and reduce-sum optimizations (#19141) * wip * ggml-hexagon: add vectorized dot product function for FP32 and FP16 accumulation * ggml-hexagon: optimize dot product functions for FP16 and FP32 with new vectorized implementations * wip * ggml-hexagon: optimize hvx_vec_dump_f32_n and hvx_vec_reduce_sum_qf32x2 functions for improved performance * ggml-hexagon: refactor dot product functions to use a common loading function for improved readability * optimize vector dot product functions to use unified reduction for improved performance * wip * ggml-hexagon: add vectorized dot product function for FP32 and FP16 accumulation * ggml-hexagon: optimize dot product functions for FP16 and FP32 with new vectorized implementations * wip * ggml-hexagon: optimize hvx_vec_dump_f32_n and hvx_vec_reduce_sum_qf32x2 functions for improved performance * ggml-hexagon: refactor dot product functions to use a common loading function for improved readability * optimize vector dot product functions to use unified reduction for improved performance * hexagon: optimize reduce-sum for v75+ * hexagon: always keep row_sums in sf/fp32 * ggml-hexagon: enhance directory checks for HEXAGON_SDK_ROOT and HEXAGON_TOOLS_ROOT * fix compiling error after rebase --------- Co-authored-by: Max Krasnyansky --- ggml/src/ggml-hexagon/CMakeLists.txt | 16 ++- ggml/src/ggml-hexagon/htp/flash-attn-ops.c | 159 ++++++++++++++++++--- ggml/src/ggml-hexagon/htp/hvx-dump.h | 9 +- ggml/src/ggml-hexagon/htp/hvx-reduce.h | 41 ++++++ ggml/src/ggml-hexagon/htp/matmul-ops.c | 107 +++++++------- ggml/src/ggml-hexagon/htp/softmax-ops.c | 4 +- ggml/src/ggml-hexagon/htp/unary-ops.c | 4 +- 7 files changed, 248 insertions(+), 92 deletions(-) diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt index 2b69197017..f3a583543c 100644 --- a/ggml/src/ggml-hexagon/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/CMakeLists.txt @@ -1,8 +1,20 @@ file(TO_CMAKE_PATH "${HEXAGON_SDK_ROOT}" HEXAGON_SDK_ROOT) file(TO_CMAKE_PATH "${HEXAGON_TOOLS_ROOT}" HEXAGON_TOOLS_ROOT) -if (NOT IS_DIRECTORY "${HEXAGON_SDK_ROOT}" OR NOT IS_DIRECTORY "${HEXAGON_TOOLS_ROOT}") - message(FATAL_ERROR "Make sure HEXAGON_SDK_ROOT and HEXAGON_TOOLS_ROOT point to the correct Hexagon SDK installation.") +if (NOT IS_DIRECTORY "${HEXAGON_SDK_ROOT}") + message(FATAL_ERROR "Make sure HEXAGON_SDK_ROOT point to the correct Hexagon SDK installation.") +endif() + +if (NOT IS_DIRECTORY "${HEXAGON_TOOLS_ROOT}") + message("Try to read HEXAGON_TOOLS_ROOT from hexagon_sdk.json") + file(READ "${HEXAGON_SDK_ROOT}/hexagon_sdk.json" HEXAGON_SDK_CONFIG_PATH) + string(JSON HEXAGON_TOOLS_PATH GET ${HEXAGON_SDK_CONFIG_PATH} "root" "tools" "info" 0 "path") + message("Found HEXAGON_TOOLS_PATH: ${HEXAGON_TOOLS_PATH}") + set(HEXAGON_TOOLS_ROOT "${HEXAGON_SDK_ROOT}/${HEXAGON_TOOLS_PATH}") + file(TO_CMAKE_PATH "${HEXAGON_TOOLS_ROOT}" HEXAGON_TOOLS_ROOT) + if (NOT IS_DIRECTORY "${HEXAGON_TOOLS_ROOT}") + message(FATAL_ERROR "Make sure HEXAGON_TOOLS_ROOT point to the correct Hexagon SDK installation.") + endif() endif() message(STATUS "hexagon: using ${HEXAGON_SDK_ROOT} and ${HEXAGON_TOOLS_ROOT} for building libggml-htp skels") diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c index c7cb2a4e0b..c184637443 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c @@ -17,6 +17,12 @@ #include "htp-msg.h" #include "htp-ops.h" +static inline HVX_Vector hvx_load_f32_to_f16(const HVX_Vector * restrict src, const HVX_Vector zero) { + HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(src[0], zero); // 32 elements + HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(src[1], zero); // 32 elements + return Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf))); +} + // Dot product of FP32 and FP16 vectors, accumulating to float static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict y, const void * restrict x, unsigned int n, float s) { const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp32 @@ -33,23 +39,19 @@ static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict #pragma unroll(4) for (i = 0; i < nvec; i++) { // Load y (fp32) and convert into fp16 - HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero); // 32 elements - HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero); // 32 elements - HVX_Vector y_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf))); + HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero); // Load x (fp16) HVX_Vector x_hf = vx[i]; HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); } if (nloe) { // Load y (fp32) and convert into fp16 - HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero); // 32 elements - HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero); // 32 elements - HVX_Vector y_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf))); + HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero); // Load x (fp16) HVX_Vector x_hf = vx[i]; @@ -62,13 +64,72 @@ static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); } - rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_f32(s)); - rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum)); + rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum)); + hvx_vec_store_u(r, 4, Q6_Vsf_equals_Vqf32(rsum)); +} - hvx_vec_store_u(r, 4, rsum); +// Dot product of FP32 and FP16 vectors, accumulating to float +static inline void hvx_dot_f32_f16_aa_rx2(float * restrict r, + const void * restrict y, + const void * restrict x0, + const void * restrict x1, + unsigned int n, + float s) { + const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp32 + const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x0; // fp16 + const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) x1; // fp16 + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + const HVX_Vector zero = Q6_V_vsplat_R(0); + HVX_Vector rsum0 = Q6_V_vsplat_R(0); + HVX_Vector rsum1 = Q6_V_vsplat_R(0); + + uint32_t i = 0; + + #pragma unroll(2) + for (i = 0; i < nvec; i++) { + // Load y (fp32) and convert into fp16 + HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero); + // Load x (fp16) + HVX_Vector x0_hf = vx0[i]; + HVX_Vector x1_hf = vx1[i]; + + HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); + HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); + + rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); + rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + } + + if (nloe) { + // Load y (fp32) and convert into fp16 + HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero); + + // Load x (fp16) + HVX_Vector x0_hf = vx0[i]; + HVX_Vector x1_hf = vx1[i]; + + // Zero-out unused elements + // Note that we need to clear both x and y because they may contain NANs + HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); + x0_hf = Q6_V_vand_QV(bmask, x0_hf); + x1_hf = Q6_V_vand_QV(bmask, x1_hf); + y_hf = Q6_V_vand_QV(bmask, y_hf); + + HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); + HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); + + rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); + rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + } + + HVX_Vector rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32x2(rsum0, rsum1)); + hvx_vec_store_u(r, 8, Q6_Vsf_equals_Vqf32(rsum)); } // Dot product of two F16 vectors, accumulating to float @@ -91,7 +152,7 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); } if (nloe) { @@ -103,12 +164,62 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); } - rsum = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(rsum), hvx_vec_splat_f32(s)); - rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum)); - hvx_vec_store_u(r, 4, rsum); + rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum)); + hvx_vec_store_u(r, 4, Q6_Vsf_equals_Vqf32(rsum)); +} + +static inline void hvx_dot_f16_f16_aa_rx2(float * restrict r, + const void * restrict y, + const void * restrict x0, + const void * restrict x1, + unsigned int n, + float s) { + const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x0; // fp16 + const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) x1; // fp16 + const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16 + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + const HVX_Vector zero = Q6_V_vsplat_R(0); + HVX_Vector rsum0 = Q6_V_vsplat_R(0); + HVX_Vector rsum1 = Q6_V_vsplat_R(0); + + uint32_t i = 0; + + #pragma unroll(4) + for (i = 0; i < nvec; i++) { + HVX_Vector y_hf = vy[i]; + HVX_Vector x0_hf = vx0[i]; + HVX_Vector x1_hf = vx1[i]; + + HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); + HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); + + rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); + rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + } + + if (nloe) { + HVX_Vector y_hf = vy[i]; + + // Load x (fp16) and zero-out unused elements + HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); + HVX_Vector x0_hf = Q6_V_vand_QV(bmask, vx0[i]); + HVX_Vector x1_hf = Q6_V_vand_QV(bmask, vx1[i]); + + HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); + HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); + + rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); + rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + } + + HVX_Vector rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32x2(rsum0, rsum1)); + hvx_vec_store_u(r, 8, Q6_Vsf_equals_Vqf32(rsum)); } // MAD: y (F32) += x (F16) * s (float) @@ -317,20 +428,22 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in // Inner loop processing the block from VTCM uint32_t ic = 0; + const bool is_q_fp32 = (q->type == HTP_TYPE_F32); + // Process in blocks of 32 (VLEN_FP32) - static_assert(FLASH_ATTN_BLOCK_SIZE / VLEN_FP32 == 4, "FLASH_ATTN_BLOCK_SIZE changed, fix HVX_Vector_x4 usage"); + static_assert(FLASH_ATTN_BLOCK_SIZE / VLEN_FP32 <= 4, "FLASH_ATTN_BLOCK_SIZE changed, fix HVX_Vector_x4 usage"); HVX_Vector_x4 scores_x4; HVX_Vector v_max = hvx_vec_splat_f32(-INFINITY); for (uint32_t iv = 0; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32, ++iv) { // 1. Compute scores - float __attribute__((aligned(VLEN))) scores_arr[FLASH_ATTN_BLOCK_SIZE]; - for (int j = 0; j < VLEN_FP32; ++j) { + float __attribute__((aligned(VLEN))) scores_arr[VLEN_FP32]; + for (int j = 0; j < VLEN_FP32; j += 2) { const uint32_t cur_ic = ic + j; const uint8_t * k_ptr = k_base + cur_ic * size_k_row_padded; - if (q->type == HTP_TYPE_F32) { - hvx_dot_f32_f16_aa(&scores_arr[j], q_ptr_vtcm, k_ptr, DK, scale); + if (is_q_fp32) { + hvx_dot_f32_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + size_k_row_padded, DK, scale); } else { - hvx_dot_f16_f16_aa(&scores_arr[j], q_ptr_vtcm, k_ptr, DK, scale); + hvx_dot_f16_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + size_k_row_padded, DK, scale); } } @@ -403,7 +516,7 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in float s_val; const uint8_t * k_ptr = k_base + ic * size_k_row_padded; - if (q->type == HTP_TYPE_F32) { + if (is_q_fp32) { hvx_dot_f32_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, scale); } else { hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, scale); diff --git a/ggml/src/ggml-hexagon/htp/hvx-dump.h b/ggml/src/ggml-hexagon/htp/hvx-dump.h index e882227893..85201fc345 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-dump.h +++ b/ggml/src/ggml-hexagon/htp/hvx-dump.h @@ -28,19 +28,16 @@ static void hvx_vec_dump_f16(char * pref, HVX_Vector v) { } static void hvx_vec_dump_f32_n(char * pref, HVX_Vector v, uint32_t n) { - union { - HVX_Vector v; - float d[32]; - } u = { .v = v }; + HVX_VectorAlias u = { .v = v }; const uint32_t n0 = n / 16; const uint32_t n1 = n % 16; int i = 0; for (; i < n0; i++) { - hex_dump_f32_line(pref, u.d + (16 * i), 16); + hex_dump_f32_line(pref, u.fp32 + (16 * i), 16); } if (n1) { - hex_dump_f32_line(pref, u.d + (16 * i), n1); + hex_dump_f32_line(pref, u.fp32 + (16 * i), n1); } } diff --git a/ggml/src/ggml-hexagon/htp/hvx-reduce.h b/ggml/src/ggml-hexagon/htp/hvx-reduce.h index 8845fe73ea..1ca7c05d98 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-reduce.h +++ b/ggml/src/ggml-hexagon/htp/hvx-reduce.h @@ -44,6 +44,45 @@ static inline HVX_Vector hvx_vec_reduce_sum_qf32(HVX_Vector in) { return hvx_vec_reduce_sum_n_qf32(in, 32); } +#if __HVX_ARCH__ > 75 + +static inline HVX_Vector hvx_vec_reduce_sum_f32x2(HVX_Vector in0, HVX_Vector in1) { + HVX_VectorPair sump = Q6_W_vshuff_VVR(in1, in0, 4); + HVX_Vector sum_sf = Q6_Vsf_vadd_VsfVsf(Q6_V_lo_W(sump), Q6_V_hi_W(sump)); + + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 2)); + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 4)); + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 8)); + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 16)); + return sum_sf; +} + +static inline HVX_Vector hvx_vec_reduce_sum_n_f32(HVX_Vector in, unsigned int n) { + unsigned int total = n * 4; // total vec nbytes + unsigned int width = 4; // fp32 nbytes + + HVX_Vector sum = in, sum_t; + while (width < total) { + sum_t = Q6_V_vror_VR(sum, width); // rotate right + sum = Q6_Vsf_vadd_VsfVsf(sum, sum_t); // elementwise sum + width = width << 1; + } + return sum; +} + +#else + +static inline HVX_Vector hvx_vec_reduce_sum_f32x2(HVX_Vector in0, HVX_Vector in1) { + HVX_VectorPair sump = Q6_W_vshuff_VVR(in1, in0, 4); + HVX_Vector sum_qf = Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(sump), Q6_V_hi_W(sump)); + + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 2)); + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 4)); + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 8)); + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 16)); + return Q6_Vsf_equals_Vqf32(sum_qf); +} + static inline HVX_Vector hvx_vec_reduce_sum_n_f32(HVX_Vector in, unsigned int n) { unsigned int total = n * 4; // total vec nbytes unsigned int width = 4; // fp32 nbytes @@ -57,6 +96,8 @@ static inline HVX_Vector hvx_vec_reduce_sum_n_f32(HVX_Vector in, unsigned int n) return sum; } +#endif + static inline HVX_Vector hvx_vec_reduce_sum_f32(HVX_Vector in) { return hvx_vec_reduce_sum_n_f32(in, 32); } diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index 1603ff2b3b..d251eeed33 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -11,6 +11,7 @@ #include "hex-dma.h" #include "hvx-utils.h" +#include "hvx-dump.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -320,7 +321,7 @@ static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void * const uint8_t * restrict y_q = ((const uint8_t *) vy + 0); // quants first const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size); // then scales - // Row sum (qf32) + // Row sum (sf) HVX_Vector r0_sum = Q6_V_vsplat_R(0); // Multiply and accumulate into int32. @@ -344,7 +345,7 @@ static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void * HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks @@ -362,14 +363,14 @@ static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void * // Zero out unused scales HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } - // Reduce and convert into fp32 - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); + r0_sum = hvx_vec_reduce_sum_f32(r0_sum); hvx_vec_store_u(&s[0], 4, r0_sum); } @@ -402,7 +403,7 @@ static void vec_dot_q4x4x2_q8x4x2_rx2(const int n, const uint8_t * restrict y_q = ((const uint8_t *) vy + 0); // quants first const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size); // then scales - // Row sum (qf32) + // Row sum (sf) HVX_Vector r0_sum = Q6_V_vsplat_R(0); HVX_Vector r1_sum = Q6_V_vsplat_R(0); @@ -432,8 +433,8 @@ static void vec_dot_q4x4x2_q8x4x2_rx2(const int n, HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks @@ -456,20 +457,18 @@ static void vec_dot_q4x4x2_q8x4x2_rx2(const int n, HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r1_dd = Q6_V_vand_QV(bmask, r1_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); + r1_ia = Q6_V_vand_QV(bmask, r1_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } - // Convert into fp32 and reduce - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); - r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum)); - HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4); - - hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0)); + HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum); + hvx_vec_store_u(&s[0], 8, rsum); } static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { @@ -493,7 +492,7 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void * const uint8_t * restrict y_q = ((const uint8_t *) vy + 0); // quants first const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size); // then scales - // Row sum (qf32) + // Row sum (sf) HVX_Vector r0_sum = Q6_V_vsplat_R(0); // Multiply and accumulate into int32. @@ -517,7 +516,7 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void * HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks @@ -535,14 +534,14 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void * // Zero out unused scales HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } - // Reduce and convert into fp32 - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); + r0_sum = hvx_vec_reduce_sum_f32(r0_sum); hvx_vec_store_u(&s[0], 4, r0_sum); } @@ -605,8 +604,8 @@ static void vec_dot_q8x4x2_q8x4x2_rx2(const int n, HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks @@ -629,20 +628,18 @@ static void vec_dot_q8x4x2_q8x4x2_rx2(const int n, HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r1_dd = Q6_V_vand_QV(bmask, r1_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); + r1_ia = Q6_V_vand_QV(bmask, r1_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } - // Convert into fp32 and reduce - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); - r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum)); - HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4); - - hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0)); + HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum); + hvx_vec_store_u(&s[0], 8, rsum); } static void vec_dot_mxfp4x4x2_q8x4x2(const int n, @@ -669,7 +666,7 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n, const uint8_t * restrict y_q = ((const uint8_t *) vy + 0); // quants first const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size); // then scales - // Row sum (qf32) + // Row sum (sf) HVX_Vector r0_sum = Q6_V_vsplat_R(0); // Multiply and accumulate into int32. @@ -708,7 +705,7 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n, HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } // Process leftovers @@ -741,14 +738,14 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n, // Zero-out unused scales HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } - // Reduce and convert into fp32 - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); + r0_sum = hvx_vec_reduce_sum_f32(r0_sum); hvx_vec_store_u(&s[0], 4, r0_sum); } @@ -781,13 +778,13 @@ static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n, const uint8_t * restrict y_q = ((const uint8_t *) vy + 0); // quants first const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size); // then scales - // Row sum (qf32) + // Row sum (sf) HVX_Vector r0_sum = Q6_V_vsplat_R(0); HVX_Vector r1_sum = Q6_V_vsplat_R(0); // Multiply and accumulate into int32. // Compute combined scale (fp32). - // Apply scale to acc and accumulate into the row sum (qf32). + // Apply scale to acc and accumulate into the row sum (f32). const uint32_t nb = n / qk; // num full blocks int32_t nloe = n % qk; // num leftover elemements (must be signed) @@ -829,8 +826,8 @@ static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n, HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } // Process leftovers @@ -867,24 +864,22 @@ static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n, HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d)); HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy_d)); - // Zero-out unused scales + // Zero-out unused values HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r1_dd = Q6_V_vand_QV(bmask, r1_dd); + r0_ia = Q6_V_vand_QV(bmask, r0_ia); + r1_ia = Q6_V_vand_QV(bmask, r1_ia); HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd); HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd); - r0_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r0_sum, r0_fa); - r1_sum = Q6_Vqf32_vadd_Vqf32Vqf32(r1_sum, r1_fa); + r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); + r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } - // Convert into fp32 and reduce - r0_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r0_sum)); - r1_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(r1_sum)); - HVX_VectorPair p0 = Q6_W_vshuff_VVR(r1_sum, r0_sum, 4); - - hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0)); + HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum); + hvx_vec_store_u(&s[0], 8, rsum); } static void vec_dot_f16_f16_aa(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { @@ -913,7 +908,7 @@ static void vec_dot_f16_f16_aa(const int n, float * restrict s, const void * res rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); } - rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum)); + rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum)); hvx_vec_store_u(&s[0], 4, rsum); } @@ -957,11 +952,8 @@ static void vec_dot_f16_f16_aa_rx2(const int n, rsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum1, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf))); } - rsum0 = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum0)); - rsum1 = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum1)); - HVX_VectorPair p0 = Q6_W_vshuff_VVR(rsum1, rsum0, 4); - - hvx_vec_store_u(&s[0], 8, Q6_V_lo_W(p0)); + HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(Q6_Vsf_equals_Vqf32(rsum0), Q6_Vsf_equals_Vqf32(rsum1)); + hvx_vec_store_u(&s[0], 8, rsum); } static void vec_dot_f16_f16_uu(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { @@ -990,7 +982,7 @@ static void vec_dot_f16_f16_uu(const int n, float * restrict s, const void * res rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); } - rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum)); + rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum)); hvx_vec_store_u(&s[0], 4, rsum); } @@ -1042,7 +1034,8 @@ static void vec_dot_f16_f32_uu(const int n, float * restrict s, const void * res rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); } - rsum = Q6_Vsf_equals_Vqf32(hvx_vec_reduce_sum_qf32(rsum)); + // Convert into fp32 and reduce + rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum)); hvx_vec_store_u(&s[0], 4, rsum); } diff --git a/ggml/src/ggml-hexagon/htp/softmax-ops.c b/ggml/src/ggml-hexagon/htp/softmax-ops.c index 1b6b2eba4a..e91a16d947 100644 --- a/ggml/src/ggml-hexagon/htp/softmax-ops.c +++ b/ggml/src/ggml-hexagon/htp/softmax-ops.c @@ -154,8 +154,8 @@ static void hvx_fast_softmax_f32(const uint8_t * restrict src, v_pad[i] = v3; } - v = hvx_vec_reduce_sum_qf32(sum_vec); - sum_vec = hvx_vec_repl4(Q6_Vsf_equals_Vqf32(v)); + v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_vec)); + sum_vec = hvx_vec_repl4(v); HVX_VectorPred pos_sum = Q6_Q_vcmp_gt_VwVw(sum_vec, zero_v); HVX_Vector v4 = hvx_vec_inverse_f32(sum_vec); diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c index be8be8c4e6..1a27cb6e63 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.c +++ b/ggml/src/ggml-hexagon/htp/unary-ops.c @@ -57,8 +57,8 @@ static void hvx_fast_rms_norm_f32(const uint8_t * restrict src, sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2); } - HVX_Vector reduced_sum = hvx_vec_reduce_sum_qf32(sum_v); - sum_v = hvx_vec_repl4(Q6_Vsf_equals_Vqf32(reduced_sum)); + HVX_Vector reduced_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v)); + sum_v = hvx_vec_repl4(reduced_sum); HVX_Vector t_v = hvx_vec_splat_f32((float) num_elems); HVX_Vector denom_v = hvx_vec_inverse_f32(t_v); From 41ea26144e55d23f37bb765f88c07588d786567f Mon Sep 17 00:00:00 2001 From: Matthieu Coudron <886074+teto@users.noreply.github.com> Date: Sat, 31 Jan 2026 17:01:46 +0100 Subject: [PATCH 083/206] nix: fix nix develop .#python-scripts (#19218) Without this I get: > * Getting build dependencies for wheel... > * Building wheel... > Successfully built gguf-0.17.1-py3-none-any.whl > Finished creating a wheel... > Finished executing pypaBuildPhase > Running phase: pythonRuntimeDepsCheckHook > Executing pythonRuntimeDepsCheck > Checking runtime dependencies for gguf-0.17.1-py3-none-any.whl > - requests not installed For full logs, run: nix log /nix/store/x0c4a251l68bvdgang9d8v2fsmqay8a4-python3.12-gguf-0.0.0.drv I changed a bit the style to make it more terse ~> more elegant in my opinion. --- .devops/nix/package-gguf-py.nix | 2 ++ .devops/nix/scope.nix | 16 +++++----------- 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/.devops/nix/package-gguf-py.nix b/.devops/nix/package-gguf-py.nix index cca2f36a5b..de3ac841fb 100644 --- a/.devops/nix/package-gguf-py.nix +++ b/.devops/nix/package-gguf-py.nix @@ -3,6 +3,7 @@ llamaVersion, numpy, tqdm, + requests, sentencepiece, pyyaml, poetry-core, @@ -20,6 +21,7 @@ buildPythonPackage { tqdm sentencepiece pyyaml + requests ]; src = lib.cleanSource ../../gguf-py; pythonImportsCheck = [ diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix index 478e8c4228..b4328a771e 100644 --- a/.devops/nix/scope.nix +++ b/.devops/nix/scope.nix @@ -7,13 +7,6 @@ let pythonPackages = python3.pkgs; - buildPythonPackage = pythonPackages.buildPythonPackage; - numpy = pythonPackages.numpy; - tqdm = pythonPackages.tqdm; - sentencepiece = pythonPackages.sentencepiece; - pyyaml = pythonPackages.pyyaml; - poetry-core = pythonPackages.poetry-core; - pytestCheckHook = pythonPackages.pytestCheckHook; in # We're using `makeScope` instead of just writing out an attrset @@ -23,17 +16,18 @@ in lib.makeScope newScope (self: { inherit llamaVersion; gguf-py = self.callPackage ./package-gguf-py.nix { - inherit - buildPythonPackage + inherit (pythonPackages) numpy tqdm sentencepiece - poetry-core pyyaml pytestCheckHook + requests + buildPythonPackage + poetry-core ; }; - python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; }; + python-scripts = self.callPackage ./python-scripts.nix { inherit (pythonPackages) buildPythonPackage poetry-core; }; llama-cpp = self.callPackage ./package.nix { }; docker = self.callPackage ./docker.nix { }; docker-min = self.callPackage ./docker.nix { interactive = false; }; From 2634ed207a17db1a54bd8df0555bd8499a6ab691 Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Sun, 1 Feb 2026 18:24:00 +0800 Subject: [PATCH 084/206] create test.sh to enhance the parameters for testing, update the guide, rm useless script (#19243) --- docs/backend/SYCL.md | 34 ++--- examples/sycl/run-llama2.sh | 5 +- examples/sycl/run-llama3.sh | 31 ----- examples/sycl/test.sh | 130 ++++++++++++++++++ examples/sycl/win-run-llama2.bat | 4 +- .../sycl/{win-run-llama3.bat => win-test.bat} | 4 +- 6 files changed, 149 insertions(+), 59 deletions(-) delete mode 100755 examples/sycl/run-llama3.sh create mode 100755 examples/sycl/test.sh rename examples/sycl/{win-run-llama3.bat => win-test.bat} (69%) diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index c0a422b3dc..10cb02ff2c 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -119,7 +119,7 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the *Notes:* - **Memory** - - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`. + - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-completion`. - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU. - **Execution Unit (EU)** @@ -423,16 +423,12 @@ Choose one of following methods to run. - Use device 0: ```sh -./examples/sycl/run-llama2.sh 0 -# OR -./examples/sycl/run-llama3.sh 0 +./examples/sycl/test.sh -mg 0 ``` - Use multiple devices: ```sh -./examples/sycl/run-llama2.sh -# OR -./examples/sycl/run-llama3.sh +./examples/sycl/test.sh ``` 2. Command line @@ -455,13 +451,13 @@ Examples: - Use device 0: ```sh -ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap ``` - Use multiple devices: ```sh -ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --mmap ``` *Notes:* @@ -577,13 +573,13 @@ Or, use CMake presets to build: ```sh cmake --preset x64-windows-sycl-release -cmake --build build-x64-windows-sycl-release -j --target llama-cli +cmake --build build-x64-windows-sycl-release -j --target llama-completion cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release -cmake --build build-x64-windows-sycl-release -j --target llama-cli +cmake --build build-x64-windows-sycl-release -j --target llama-completion cmake --preset x64-windows-sycl-debug -cmake --build build-x64-windows-sycl-debug -j --target llama-cli +cmake --build build-x64-windows-sycl-debug -j --target llama-completion ``` #### 3. Visual Studio @@ -608,7 +604,7 @@ You can use Visual Studio to open the `llama.cpp` folder directly as a CMake pro - For a minimal experimental setup, you can build only the inference executable using: ```Powershell - cmake --build build --config Release -j --target llama-cli + cmake --build build --config Release -j --target llama-completion ``` ##### - Generating a Visual Studio Solution @@ -714,13 +710,7 @@ Choose one of following methods to run. 1. Script ``` -examples\sycl\win-run-llama-2.bat -``` - -or - -``` -examples\sycl\win-run-llama-3.bat +examples\sycl\win-test.bat ``` 2. Command line @@ -744,13 +734,13 @@ Examples: - Use device 0: ``` -build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 +build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap ``` - Use multiple devices: ``` -build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer +build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --mmap ``` diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index cf23619ee0..d33f82f339 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -18,13 +18,14 @@ CONTEXT=4096 #support malloc device memory more than 4GB. export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 +LOAD_MODE='--mmap' if [ $# -gt 0 ]; then GGML_SYCL_DEVICE=$1 echo "use $GGML_SYCL_DEVICE as main GPU" #use signle GPU only - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE} else #use multiple GPUs with same max compute units - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE} fi diff --git a/examples/sycl/run-llama3.sh b/examples/sycl/run-llama3.sh deleted file mode 100755 index feee5165e9..0000000000 --- a/examples/sycl/run-llama3.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env bash - -# MIT license -# Copyright (C) 2025 Intel Corporation -# SPDX-License-Identifier: MIT - -# If you want more control, DPC++ Allows selecting a specific device through the -# following environment variable -export ONEAPI_DEVICE_SELECTOR="level_zero:0" -source /opt/intel/oneapi/setvars.sh - -#export GGML_SYCL_DEBUG=1 - -#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. - -INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:" -MODEL_FILE=models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -NGL=99 # Layers offloaded to the GPU. If the device runs out of memory, reduce this value according to the model you are using. -CONTEXT=4096 - -#support malloc device memory more than 4GB. -export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 - -if [ $# -gt 0 ]; then - GGML_SYCL_DEVICE=$1 - echo "Using $GGML_SYCL_DEVICE as the main GPU" - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none -else - #use multiple GPUs with same max compute units - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -fi diff --git a/examples/sycl/test.sh b/examples/sycl/test.sh new file mode 100755 index 0000000000..140c191466 --- /dev/null +++ b/examples/sycl/test.sh @@ -0,0 +1,130 @@ +#!/bin/bash + +# MIT license +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: MIT + +Help() { + cat << EOF +Usage: $(basename "$0") [OPTIONS] + +This script processes files with specified options. + +Options: + -h, --help Display this help message and exit. + -c, --context Set context length. Bigger need more memory. + -p, --promote Prompt to start generation with. + -m, --model Full model file path. + -mg,--main-gpu Set main GPU ID (0 - n) for single GPU mode. + -sm,--split-mode How to split the model across multiple GPUs, one of: + - none: use one GPU only + - layer (default): split layers and KV across GPUs + - row: split rows across GPUs + -ngl,--n-gpu-layers Max. number of layers to store in VRAM (default: -1) + -lv,--log-verbosity Set the verbosity threshold. Messages with a higher verbosity will be + ignored. Values: + - 0: generic output + - 1: error + - 2: warning + - 3: info + - 4: debug + + +EOF +} + +BIN_FILE=./build/bin/llama-completion +SEED=0 +GPUS_SETTING="" + +INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:" +MODEL_FILE=models/llama-2-7b.Q4_0.gguf +NGL=99 +CONTEXT=4096 +GGML_SYCL_DEVICE=-1 +SPLIT_MODE=layer +LOG_VERBOSE=3 +while [[ $# -gt 0 ]]; do + case "$1" in + -c|--context) + CONTEXT=$2 + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -p|--promote) + # Option that is a simple flag (boolean) + INPUT_PROMPT="$2" + # Shift once to consume the option flag + shift + shift + ;; + -m|--model) + MODEL_FILE="$2" + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -mg|--main-gpu) + GGML_SYCL_DEVICE=$2 + SPLIT_MODE=none + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -sm|--split-mode) + SPLIT_MODE=$2 + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -ngl|--n-gpu-layers) + NGL=$2 + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -lv|--log-verbosity) + LOG_VERBOSE=$2 + # Shift twice to consume both the option flag and its value + shift + shift + ;; + -h|--help) + Help + exit 0 + ;; + *) + # Handle unknown options or stop processing options + echo "Invalid option: $1" + # Optional: exit script or shift to treat remaining as positional args + exit 1 + ;; + esac +done + + + +source /opt/intel/oneapi/setvars.sh + +#export GGML_SYCL_DEBUG=1 + +#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. + +#support malloc device memory more than 4GB. +export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 +echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}" + +if [ $GGML_SYCL_DEVICE -ne -1 ]; then + echo "Use $GGML_SYCL_DEVICE as main GPU" + #use signle GPU only + GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}" + export ONEAPI_DEVICE_SELECTOR="level_zero:${$GGML_SYCL_DEVICE}" + echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}" +else + echo "Use all Intel GPUs, including iGPU & dGPU" + fi + +echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap " +ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap + diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index 32ff673ae2..1f2dab8d0a 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" :: support malloc device memory more than 4GB. set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 - -.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 +set LOAD_MODE="--mmap" +.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE% diff --git a/examples/sycl/win-run-llama3.bat b/examples/sycl/win-test.bat similarity index 69% rename from examples/sycl/win-run-llama3.bat rename to examples/sycl/win-test.bat index ea4ae69d6c..1f2dab8d0a 100644 --- a/examples/sycl/win-run-llama3.bat +++ b/examples/sycl/win-test.bat @@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" :: support malloc device memory more than 4GB. set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 - -.\build\bin\llama-completion.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -no-cnv -p %INPUT2% -n 400 -s 0 -e -ngl 99 +set LOAD_MODE="--mmap" +.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE% From 8a98ba4582ea961f06d350e60cf3572015489745 Mon Sep 17 00:00:00 2001 From: Alexis Williams Date: Sun, 1 Feb 2026 12:10:48 -0800 Subject: [PATCH 085/206] nix: fix allowUnfreePredicate for packages with multiple licenses (#19237) The allowUnfreePredicate in pkgsCuda was wrapping p.meta.license in a list unconditionally. This fails when meta.license is already a list of licenses, as it creates a nested list and then tries to access .free and .shortName on the inner list. Use lib.toList instead, which correctly handles both cases: - Single license attrset -> wraps in list - List of licenses -> returns unchanged --- .devops/nix/nixpkgs-instances.nix | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.devops/nix/nixpkgs-instances.nix b/.devops/nix/nixpkgs-instances.nix index 90d683a713..40cf58f196 100644 --- a/.devops/nix/nixpkgs-instances.nix +++ b/.devops/nix/nixpkgs-instances.nix @@ -4,7 +4,7 @@ # the module `{ pkgs ... }: { /* config */ }` implicitly uses # `_module.args.pkgs` (defined in this case by flake-parts). perSystem = - { system, ... }: + { lib, system, ... }: { _module.args = { # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs @@ -33,7 +33,7 @@ "CUDA EULA" "cuDNN EULA" ] - ) (p.meta.licenses or [ p.meta.license ]); + ) (p.meta.licenses or (lib.toList p.meta.license)); }; # Ensure dependencies use ROCm consistently pkgsRocm = import inputs.nixpkgs { From 3bc8d2cf23d86232bbdd9dcb60946f6a9199c15c Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Sun, 1 Feb 2026 14:13:38 -0800 Subject: [PATCH 086/206] Bump cmake max version (needed for Windows on Snapdragon builds) (#19188) * Bump max cmake version (needed for Windows on Snapdragon builds) * cmake: move max version setting into ggml/CMakeLists --- docs/backend/snapdragon/CMakeUserPresets.json | 5 ----- docs/backend/snapdragon/windows.md | 2 +- ggml/CMakeLists.txt | 2 +- 3 files changed, 2 insertions(+), 7 deletions(-) diff --git a/docs/backend/snapdragon/CMakeUserPresets.json b/docs/backend/snapdragon/CMakeUserPresets.json index 4cf473d05f..1faae2f3db 100644 --- a/docs/backend/snapdragon/CMakeUserPresets.json +++ b/docs/backend/snapdragon/CMakeUserPresets.json @@ -1,10 +1,5 @@ { "version": 5, - "cmakeMinimumRequired": { - "major": 3, - "minor": 28, - "patch": 0 - }, "configurePresets": [ { "name": "arm64-android-snapdragon", diff --git a/docs/backend/snapdragon/windows.md b/docs/backend/snapdragon/windows.md index 710ad8fdf4..e9346ccadf 100644 --- a/docs/backend/snapdragon/windows.md +++ b/docs/backend/snapdragon/windows.md @@ -128,7 +128,7 @@ However, additional settings are required for generating and signing HTP Ops lib > $env:HEXAGON_HTP_CERT="c:\Users\MyUsers\Certs\ggml-htp-v1.pfx" > $env:WINDOWS_SDK_BIN="C:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0\arm64" -> cmake --preset arm64-windows-snapdragon -B build-wos +> cmake --preset arm64-windows-snapdragon-release -B build-wos ... > cmake --install build-wos --prefix pkg-snapdragon ``` diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index b0b8e57898..71d1a7f0e3 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. +cmake_minimum_required(VERSION 3.14...3.28) # for add_link_options and implicit target directories. project("ggml" C CXX ASM) ### GGML Version From 2dc3ce2166a6c3b149402af60c15f4d80b472a6c Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Sun, 1 Feb 2026 18:47:29 -0800 Subject: [PATCH 087/206] Remove pipeline cache mutexes (#19195) * Remove mutex for pipeline caches, since they are now per-thread. * Add comment * Run clang-format * Cleanup * Run CI again * Run CI once more * Run clang-format --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 213 ++++++++++++--------------- 1 file changed, 93 insertions(+), 120 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 22e2bfeb4c..4ef50e365e 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -146,8 +146,13 @@ struct webgpu_submission_futures { struct webgpu_buf_pool { std::vector free; - std::mutex mutex; - + // The pool must be synchronized because + // 1. The memset pool is shared globally by every ggml buffer, + // since allocating a pool per ggml buffer would consume too much memory. + // 2. For the per-thread buffer pools in webgpu_context, + // buffers are allocated and freed in Dawn callbacks, + // which can run on a different thread than the calling thread. + std::mutex mutex; std::condition_variable cv; void init(wgpu::Device device, @@ -266,7 +271,7 @@ struct webgpu_command { #endif }; -struct webgpu_capabilities_base { +struct webgpu_capabilities { wgpu::Limits limits; bool supports_subgroup_matrix = false; @@ -286,11 +291,11 @@ struct webgpu_global_context_struct { wgpu::Device device; wgpu::Queue queue; - webgpu_capabilities_base capabilities; + webgpu_capabilities capabilities; // Shared buffer to move data from device to host - wgpu::Buffer get_tensor_staging_buf; + wgpu::Buffer get_tensor_staging_buf; // Global mutex for pipeline and staging buffer, will be refactored to exclude pipeline caches. - std::recursive_mutex mutex; + std::recursive_mutex mutex; webgpu_buf_pool memset_buf_pool; std::map memset_pipelines; // variant or type index @@ -361,7 +366,6 @@ struct webgpu_context_struct { std::unordered_map pad_pipelines; size_t memset_bytes_per_thread; - }; typedef std::shared_ptr webgpu_context; @@ -383,9 +387,8 @@ struct ggml_backend_webgpu_device_context { // Per-thread data required to actually run WebGPU operations in a backend instance struct ggml_backend_webgpu_context { - webgpu_context webgpu_ctx; - std::once_flag init_once; - std::string name; + webgpu_context webgpu_ctx; + std::string name; }; // Per-thread data related to buffers @@ -861,20 +864,15 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g }; webgpu_pipeline pipeline; - { - // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->pad_pipelines.find(pipeline_key); - if (it != ctx->pad_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - pipeline.context = processed.decisions; - ctx->pad_pipelines.emplace(pipeline_key, pipeline); - } + auto it = ctx->pad_pipelines.find(pipeline_key); + if (it != ctx->pad_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline.context = processed.decisions; + ctx->pad_pipelines.emplace(pipeline_key, pipeline); } ggml_webgpu_generic_shader_decisions decisions = @@ -944,20 +942,16 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, }; webgpu_pipeline pipeline; - // TODO: remove guard once pipeline caches are per-thread - { - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->set_rows_pipelines.find(key); - if (it != ctx->set_rows_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - pipeline.context = processed.decisions; - ctx->set_rows_pipelines.emplace(key, pipeline); - } + auto it = ctx->set_rows_pipelines.find(key); + if (it != ctx->set_rows_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline.context = processed.decisions; + ctx->set_rows_pipelines.emplace(key, pipeline); } ggml_webgpu_generic_shader_decisions decisions = @@ -1261,29 +1255,25 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, }; webgpu_pipeline pipeline; - // TODO: remove guard once pipeline caches are per-thread - { - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->flash_attn_pipelines.find(key); - if (it != ctx->flash_attn_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { - .key = key, - .sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m, - .sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n, - .sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k, - .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, - .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size - }; + auto it = ctx->flash_attn_pipelines.find(key); + if (it != ctx->flash_attn_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { + .key = key, + .sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m, + .sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n, + .sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k, + .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size + }; - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - pipeline.context = processed.decisions; - ctx->flash_attn_pipelines.emplace(key, pipeline); - } + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline.context = processed.decisions; + ctx->flash_attn_pipelines.emplace(key, pipeline); } ggml_webgpu_flash_attn_shader_decisions decisions = @@ -1308,20 +1298,16 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s }; webgpu_pipeline pipeline; - { - // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->unary_pipelines.find(pipeline_key); - if (it != ctx->unary_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - pipeline.context = processed.decisions; - ctx->unary_pipelines.emplace(pipeline_key, pipeline); - } + auto it = ctx->unary_pipelines.find(pipeline_key); + if (it != ctx->unary_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline.context = processed.decisions; + ctx->unary_pipelines.emplace(pipeline_key, pipeline); } ggml_webgpu_generic_shader_decisions decisions = @@ -1743,19 +1729,15 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src }; webgpu_pipeline pipeline; - { - // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->argmax_pipelines.find(shader_lib_ctx.vec4); - if (it != ctx->argmax_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax"); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline); - } + auto it = ctx->argmax_pipelines.find(shader_lib_ctx.vec4); + if (it != ctx->argmax_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax"); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline); } uint32_t wg_x = ggml_nelements(dst); return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); @@ -1772,9 +1754,8 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr .order = order }; - std::lock_guard lock(ctx->global_ctx->mutex); - webgpu_pipeline argsort_pipeline; - auto it = ctx->argsort_pipelines.find(order); + webgpu_pipeline argsort_pipeline; + auto it = ctx->argsort_pipelines.find(order); if (it != ctx->argsort_pipelines.end()) { argsort_pipeline = it->second; } else { @@ -1963,19 +1944,15 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; - // TODO: remove guard once pipeline caches are per-thread - { - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->cumsum_pipelines.find(1); - if (it != ctx->cumsum_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum"); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - ctx->cumsum_pipelines.emplace(1, pipeline); - } + auto it = ctx->cumsum_pipelines.find(1); + if (it != ctx->cumsum_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum"); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + ctx->cumsum_pipelines.emplace(1, pipeline); } uint32_t wg_x = ggml_nrows(dst); return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); @@ -2009,19 +1986,15 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s }; webgpu_pipeline pipeline; - { - // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->global_ctx->mutex); - auto it = ctx->sum_rows_pipelines.find(1); - if (it != ctx->sum_rows_pipelines.end()) { - pipeline = it->second; - } else { - ggml_webgpu_processed_shader processed = - ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows"); - pipeline = - ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); - ctx->sum_rows_pipelines.emplace(1, pipeline); - } + auto it = ctx->sum_rows_pipelines.find(1); + if (it != ctx->sum_rows_pipelines.end()) { + pipeline = it->second; + } else { + ggml_webgpu_processed_shader processed = + ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows"); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + ctx->sum_rows_pipelines.emplace(1, pipeline); } uint32_t wg_x = total_sum ? 1 : ggml_nrows(dst); return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); @@ -3016,10 +2989,10 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { #ifdef GGML_WEBGPU_GPU_PROFILE // Initialize buffer pool for timestamp queries, used for profiling - ctx->webgpu_global_ctx->timestamp_query_buf_pool.init(ctx->webgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, - WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, - wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, - wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); + ctx->webgpu_global_ctx->timestamp_query_buf_pool.init( + ctx->webgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, + wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); #endif GGML_LOG_INFO( From b4d05a3d2fc7820444ca618570a3ac76cc12fe83 Mon Sep 17 00:00:00 2001 From: Sascha Rogmann <59577610+srogmann@users.noreply.github.com> Date: Mon, 2 Feb 2026 07:26:58 +0100 Subject: [PATCH 088/206] spec : various improvements ton ngram-map + docs (#19253) * spec: ngram-map and reasoning chats * spec: add t_begin and t_accept * ngram-map : add internal hash map * docs : update ngram-map, add ngram-mod * docs : fix ngram-map-k * docs : differences between implementations --- common/ngram-map.cpp | 204 ++++++++++++++++++++++++++++++++++++++--- common/ngram-map.h | 35 ++++++- common/speculative.cpp | 25 +++-- docs/speculative.md | 74 ++++++++++++++- 4 files changed, 307 insertions(+), 31 deletions(-) diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp index 84fd761367..cab231bad7 100644 --- a/common/ngram-map.cpp +++ b/common/ngram-map.cpp @@ -7,6 +7,18 @@ #include #include +// prime number used for LCG hash function (32 bit), it is near (sqrt(5) - 1)/2 * 2^32. +#define LCG_FACTOR 2654435761UL + +// Compute the LCG hash of a n-gram of size len at offset start. +static uint32_t common_ngram_map_hash(const llama_tokens & tokens, size_t start, size_t len) { + uint32_t hash = 0; + for (size_t i = 0; i < len; ++i) { + hash = hash * LCG_FACTOR + tokens[start + i]; + } + return hash; +} + // Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...]. static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) { std::ostringstream oss; @@ -115,6 +127,100 @@ llama_tokens common_ngram_simple_draft( // maximum number of counted values of a ngram map value. #define COMMON_NGRAM_MAX_VALUE_COUNT 16380 +void common_ngram_map_begin( + common_ngram_map & map, const llama_tokens & tokens) { + size_t size_begin = tokens.size(); + + LOG_DBG("%s: begin, idx_last_draft=%zu, new begin=%zu, #keys=%zu\n", __func__, + map.idx_last_check, size_begin, map.keys.size()); + + size_t count_map_entries_upd = 0; + if (!map.key_map.empty() && size_begin < map.idx_last_check) { + if (map.show_key_map_stats) { + // Print statistics of hash map map_key. + size_t count_nonzero = 0; + uint32_t min_idx = UINT32_MAX; + uint32_t max_idx = 0; + for (size_t i = 0; i < map.key_map.size(); ++i) { + uint32_t key_idx = map.key_map[i]; + if (key_idx != 0) { + ++count_nonzero; + if (key_idx < min_idx) min_idx = key_idx; + if (key_idx > max_idx) max_idx = key_idx; + } + } + if (count_nonzero == 0) { + min_idx = 0; + } + LOG_INF("%s: key_map stats: entries=%zu, min_idx=%u, max_idx=%u, key_map_last_idx=%u\n", + __func__, count_nonzero, min_idx, max_idx, map.key_map_last_idx); + } + + // Update the map from hash to key index (clear outdated entries). + for (size_t i = 0; i < map.key_map.size(); ++i) { + uint32_t key_idx = map.key_map[i]; + if (key_idx >= map.size_last_begin) { + map.key_map[i] = 0; + count_map_entries_upd++; + } + } + map.key_map_last_idx = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0; + } + + if (size_begin < map.idx_last_check && !map.keys.empty()) { + // The next token generation will start at index size_begin. + // The tokens between map.size_last_begin and size_begin are no longer valid. + // + // Refresh map: Remove all entries with index >= map.size_last_begin. + size_t count_keys = map.keys.size(); + size_t count_keys_del = 0; + size_t count_values_del = 0; + for (int32_t i = map.keys.size() - 1; i >= 0; --i) { + common_ngram_map_key & key = map.keys[i]; + if (key.key_idx >= map.size_last_begin) { + // Delete the key. + LOG_DBG("%s: delete key %d at index %zu (>= size_last_begin=%zu)\n", __func__, i, key.key_idx, map.size_last_begin); + map.keys.erase(map.keys.begin() + i); + count_keys_del++; + continue; + } + if (map.key_only) { + continue; + } + + // Check the indices of the values. + for (int16_t j = COMMON_NGRAM_MAX_VALUES - 1; j >= 0; --j) { + common_ngram_map_value & value = key.values[j]; + if (value.value_idx >= map.size_last_begin) { + // Delete the value. + count_values_del++; + + // Move all values after this value to the left. + for (uint16_t k = j; k < COMMON_NGRAM_MAX_VALUES - 1; ++k) { + key.values[k] = key.values[k + 1]; + } + // Clear the last value. + key.values[COMMON_NGRAM_MAX_VALUES - 1].value_idx = 0; + key.values[COMMON_NGRAM_MAX_VALUES - 1].value_num = 0; + } + } + if (key.values[0].value_idx == 0) { + // No values left, delete the key. + LOG_DBG("%s: delete key %d at index %zu (no values left)\n", __func__, i, key.key_idx); + map.keys.erase(map.keys.begin() + i); + count_keys_del++; + } + } + + LOG_INF("%s: refresh map: idx_last_draft=%zu, new begin=%zu, #keys_checked=%zu, #keys_del=%zu, #values_del=%zu, #hashes_upd=%zu\n", __func__, + map.idx_last_check, size_begin, + count_keys, count_keys_del, count_values_del, count_map_entries_upd); + } + + map.idx_last_check = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0; + map.size_last_begin = size_begin; +} + void common_ngram_map_draft(common_ngram_map & map, const llama_tokens & inp, llama_token sampled, llama_tokens & draft) { @@ -129,6 +235,10 @@ void common_ngram_map_draft(common_ngram_map & map, if (cur_len < static_cast(2 * n + m)) { return; } + if (cur_len >= static_cast(UINT32_MAX)) { + // key_map uses uint32_t instead of size_t. + GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len); + } // Only check every check_rate tokens to save compute // i.e., perform check if (cur_len - idx_last_check) >= check_rate @@ -147,24 +257,92 @@ void common_ngram_map_draft(common_ngram_map & map, // search for the key in the map size_t match_pos = 0; - for (size_t j = cur_len - n - m - 1; j > 0; --j) { - bool match = true; - for (size_t k = 0; k < n; ++k) { - if (inp[j + k] != key_tokens[k]) { - match = false; - break; + if (map.size_last_begin > cur_len) { + GGML_ABORT("%s: map.size_last_begin > cur_len: %zu > %zu", __func__, map.size_last_begin, cur_len); + } + if (!map.key_map.empty()) { + // Search for the key in the map key_map from hash of ngrams to index of ngram. + uint32_t idx_hash = (common_ngram_map_hash(key_tokens, 0, n) % map.key_map.size()); + uint32_t idx_key = map.key_map[idx_hash]; + if (idx_key != 0 && idx_key < cur_len - n - m - 1) { + // Check if the key matches the key at idx_key (because of possible collisions). + bool match = true; + for (size_t k = 0; k < n; ++k) { + if (inp[idx_key + k] != key_tokens[k]) { + match = false; + break; + } + } + LOG_DBG("%s: key hash %x -> idx_key %d: match %d\n", __func__, idx_hash, idx_key, match ? 1 : 0); + if (match) { + match_pos = idx_key; } } - if (match) { - match_pos = j; - break; + } + if (match_pos == 0 && map.size_last_begin > (size_t) (n + m + 1)) { + // Search for the key in [1, map.size_last_begin - n - m -1], descending. + for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) { + // Check if the key matches the key. + bool match = true; + for (size_t k = 0; k < n; ++k) { + if (inp[j + k] != key_tokens[k]) { + match = false; + break; + } + } + if (match) { + match_pos = j; + break; + } + } + } + if (match_pos == 0) { + // In case of a reasoning chat, the part after size_last_begin may be deleted/reordered later. + // + // Search in [size_last_begin, cur_len - n - m - 1], descending. + for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) { + bool match = true; + for (size_t k = 0; k < n; ++k) { + if (inp[j + k] != key_tokens[k]) { + match = false; + break; + } + } + if (match) { + match_pos = j; + break; + } } } if (match_pos > 0) { - LOG_INF("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__, + LOG_DBG("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__, cur_len, n, m, key_tokens.size(), sampled, match_pos); } + if (!map.key_map.empty()) { + // Add hashes of new ngrams in key_map. + // + // Use the same order as above. + if (map.size_last_begin > (size_t) (n + m + 1)) { + for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) { + // compute hash and store index of ngram at idx j in the map. + uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size()); + if (map.key_map[idx_hash] == 0) { + map.key_map[idx_hash] = j; // collisions may occur + } + } + } + + for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) { + // compute hash and store index of ngram at idx j in the map. + uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size()); + if (map.key_map[idx_hash] == 0) { + map.key_map[idx_hash] = j; + } + } + map.key_map_last_idx = std::max(static_cast(cur_len - n - m - 1), map.key_map_last_idx); + } + if (match_pos == 0) { return; } @@ -215,8 +393,8 @@ void common_ngram_map_draft(common_ngram_map & map, draft.push_back(inp[match_pos + n + i]); } - LOG_INF("%s: key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__, - key_offset, curr_key.key_num, draft.size()); + LOG_DBG("%s: key_idx = %zu, key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__, + curr_key.key_idx, key_offset, curr_key.key_num, draft.size()); map.last_draft_created = false; map.last_draft_key_idx = key_offset; @@ -318,7 +496,7 @@ void common_ngram_map_draft(common_ngram_map & map, } } - if (sum_occur > 0 && max_occur < 3 * sum_occur) { + if (sum_occur > 0 && max_occur < 2 * sum_occur) { // The most frequent value is not much more frequent than the other values. // We do not use the draft. return; diff --git a/common/ngram-map.h b/common/ngram-map.h index b365034ac5..c094d513d5 100644 --- a/common/ngram-map.h +++ b/common/ngram-map.h @@ -9,6 +9,8 @@ // 2. ngram_map: lookup of n-grams followed by m-grams in token history using a map. // The map is a vector of key n-grams, and for each key n-gram there is a list of value m-grams. // +// ref: https://github.com/ggml-org/llama.cpp/pull/18471 +// #include "llama.h" #include "common.h" @@ -51,10 +53,13 @@ llama_tokens common_ngram_simple_draft( // maximum number of m-gram values stored for each key n-gram. #define COMMON_NGRAM_MAX_VALUES 4 +// number of entries in the (optional, size 0 to disable) map from ngram-hash to ngram-index. +#define COMMON_NGRAM_HASH_MAP_SIZE 262144 + // statistics of a m-gram after a known n-gram struct common_ngram_map_value { - size_t value_idx = 0; // index of value m-gram in token-history (0 if unused) - uint16_t value_num = 0; // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot) + size_t value_idx = 0; // index of value m-gram in token-history (0 if unused) + uint16_t value_num = 0; // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot) int16_t n_accepted = -1; // number of accepted tokens at last draft (-1 if unused) }; @@ -74,23 +79,43 @@ struct common_ngram_map { bool key_only; // true if only key n-grams are used, no values. - // first draft: vector only, no map. std::vector keys; // key n-grams which occur several times in token-history uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token uint16_t min_hits; // minimum number of key hits to consider a draft + bool show_key_map_stats = false; // true, if statitics of the key_map should be printed. + common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys, uint16_t check_rate, uint16_t min_hits) : size_key(sz_key), size_value(sz_value), key_only(only_keys), - check_rate(check_rate), min_hits(min_hits) {} + check_rate(check_rate), min_hits(min_hits) { + key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used + } + + // In reasoning chats the previous reasoning block will be removed from context history. + // A rebuild of the ngram map is needed after that. + + size_t size_last_begin = 0; // number of tokens at previous start of generation bool last_draft_created = false; // true if a draft was created at last call. - size_t last_draft_key_idx = 0; // index of last key used for draft generation. + size_t last_draft_key_idx = 0; // index of last key used for draft generation (0 = no draft) uint16_t last_draft_value_idx = 0; // index of last value used for draft generation. size_t idx_last_check = 0; // index of last check in context history + + // optional map "hash to ngram-index" for faster lookup of n-grams. map is empty if unused. + // + // uint32_t instead of size_t (size of current histories is << UINT32_MAX) + std::vector key_map; // key_map[hash] = index of ngram in context window + uint32_t key_map_last_idx = 0; // index of the last ngram added to key_map }; +// Initialize the n-gram map with the given token history. +// map: the ngram map to initialize. +// tokens: the token history to base the map on. +void common_ngram_map_begin( + common_ngram_map & map, + const llama_tokens & tokens); // Searches for the n-gram in the history and checks whether a draft sequence should be generated. // map: the ngram map to search in. diff --git a/common/speculative.cpp b/common/speculative.cpp index a1a3b51c13..152aaa48d4 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -124,9 +124,9 @@ struct common_speculative_state { // TODO: track performance of most recent calls const bool gen_perf = true; // whether to generate performance stats. - // TODO: rename to t_draft_us - // TODO: add t_begin_us, t_accept_us - int64_t gen_duration_us = 0; // total time spent in this implementation in microseconds. + int64_t t_begin_us = 0; // total time spent in refresh of this implementation in microseconds. + int64_t t_draft_us = 0; // total time spent in generating drafts in this implementation in microseconds. + int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds. common_speculative_state(enum common_speculative_type type) : type(type) {} @@ -499,7 +499,7 @@ struct common_speculative_state_ngram_map_k : public common_speculative_state { : common_speculative_state(type), map(std::move(map)) {} void begin(const llama_tokens & prompt) override { - GGML_UNUSED(prompt); + common_ngram_map_begin(map, prompt); } void draft( @@ -951,7 +951,12 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr } for (auto & impl : spec->impls) { + const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; + impl->begin(prompt); + + const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; + impl->t_begin_us += t_now_us - t_start_us; // accumulate duration for this refresh } } @@ -973,7 +978,7 @@ llama_tokens common_speculative_draft( const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; impl->drafts_call_count++; - impl->gen_duration_us += t_now_us - t_start_us; // accumulate duration for this implementation + impl->t_draft_us += t_now_us - t_start_us; // accumulate duration for this implementation } if (!result.empty()) { @@ -1001,12 +1006,15 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) { GGML_ASSERT(impl); + const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; if (n_accepted > 0) { impl->drafts_accepted_count++; impl->drafts_accepted_tokens += n_accepted; } impl->accept(n_accepted); + const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; + impl->t_accept_us += t_now_us - t_start_us; // accumulate duration for this acculumulation } void common_speculative_print_stats(const common_speculative * spec) { @@ -1018,13 +1026,14 @@ void common_speculative_print_stats(const common_speculative * spec) { std::string str_perf; if (impl->gen_perf) { std::ostringstream oss; - oss << std::fixed << std::setprecision(3) << impl->gen_duration_us / 1000.0; - str_perf = ", dur = " + oss.str() + " ms"; + oss << std::fixed << std::setprecision(3) << impl->t_begin_us / 1000.0 << ", "; + oss << std::fixed << std::setprecision(3) << impl->t_draft_us / 1000.0 << ", "; + oss << std::fixed << std::setprecision(3) << impl->t_accept_us / 1000.0; + str_perf = ", dur(b,g,a) = " + oss.str() + " ms"; } else { str_perf = ""; } - // TODO: report time for begin() and accept() LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n", common_speculative_type_to_str(impl->type).c_str(), impl->drafts_call_count, diff --git a/docs/speculative.md b/docs/speculative.md index 8281eaa2d3..03afab5b41 100644 --- a/docs/speculative.md +++ b/docs/speculative.md @@ -6,7 +6,7 @@ llama.cpp supports speculative decoding, a technique that can significantly acce ## Implementations -The `llama-server` application supports several implementations of speculative decoding: +The `llama-server` application supports several implementations of speculative decoding. An implementation with draft model can be mixed with an implementation without draft model. ### Draft Model (`draft`) @@ -32,12 +32,21 @@ An example to use this approach can be the rewriting of source code by a LLM. This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead. +``` +llama-server [...] --spec-type ngram-simple --draft-max 64 +``` + #### n-gram Map Key (`ngram-map-k`) -This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`) before generating drafts. +This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`, default is 1) before generating drafts. The number of accepted tokens is stored for each used n-gram. +**Example:** +``` +llama-server [...] --spec-type ngram-map-k --draft-max 64 +``` + #### n-gram Map Key-4-Values (`ngram-map-k4v`) This experimental implementation looks for the current n-gram of size n (called the _key_) in the token history. For each key, up to four _values_ (n-grams of size m, called _mgrams_) are tracked. An internal statistic counts the occurrences of each mgram after the key n-gram. If one mgram is significantly more frequent than the others, it is used as the draft. @@ -45,17 +54,65 @@ This experimental implementation looks for the current n-gram of size n (called The number of accepted tokens is stored for each used n-gram. **Example:** Server options to be used if there are a lot of longer repetitions. -```bash -llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 +``` +llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-max 64 ``` +### n-gram Mod (`ngram-mod`) + +Add basic ngram hasher for speculative decoding: + +- For each ngram, compute a hash using LCG +- For each computed hash, store the next token +- During speculation, iteratively compute the rolling hash of the last n tokens and pick the next token from the storage + +Some characteristics: + +- Lightweight (~16 MB) +- Constant memory and complexity +- Can generate variable draft lengths (i.e. m is not fixed) + +Currently, a single hash pool is shared across all server slots, so different requests can benefit from each other. + +**Sample usage:** + +``` +# notes: +# - small `n` are not recommended +# - MoEs require long drafts +# - dense models: can reduce `--draft-min` and `--draft-max` + +llama-server ... --spec-type ngram-mod --spec-ngram-size-n 24 --draft-min 48 --draft-max 64 +``` + +Applications: + +- Iterating over a block of text/code (e.g. in llama.vim) +- Reasoning models (when they have to repeat their thinking in the final answer) +- Summarization + +Example Video: + +- See #19164 + +### Differences between ngram-simple, ngram-map and ngram-mod + +- ngram-simple looks for a previous matching n-gram and inserts the following m-gram. +- ngram-map-k looks for a previous matching n-gram and inserts the following m-gram but uses an internal hash-map of n-grams in the current context window. +- ngram-mod uses a hash pool which is shared across all server slots. The hash pool is a map from n-gram hash to the next token (not the next m-gram as in ngram-map). ## Command-Line Options If a draft model is combined with a draftless decoding the draftless decoding has higher precedence. ``` ---spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v] +--draft, --draft-n, --draft-max N number of tokens to draft for speculative decoding (default: 16) + (env: LLAMA_ARG_DRAFT_MAX) +--draft-min, --draft-n-min N minimum number of draft tokens to use for speculative decoding + (default: 0) + (env: LLAMA_ARG_DRAFT_MIN) +[...] +--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod] type of speculative decoding to use when no draft model is provided (default: none) --spec-ngram-size-n N ngram size N for ngram-simple/ngram-map speculative decoding, length @@ -78,6 +135,7 @@ Specifies a type of speculative decoding without draft model. | `ngram-simple` | Use simple n-gram pattern matching | | `ngram-map-k` | Use n-gram pattern matching with n-gram-keys | | `ngram-map-k4v` | Use n-gram pattern matching with n-gram-keys and up to four m-gram values (experimental) | +| `ngram-mod` | Use basic ngram hasher for speculative decoding with shared pool | **Example:** Server-instance used to refactor source code. ```bash @@ -112,9 +170,15 @@ statistics ngram_simple: #calls = 15, #gen drafts = 5, #acc drafts = 5, #gen tok statistics draft: #calls = 10, #gen drafts = 10, #acc drafts = 10, #gen tokens = 110, #acc tokens = 98 ``` +``` +draft acceptance rate = 0.70312 ( 90 accepted / 128 generated) +statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms +``` + - `#calls`: number of calls of this implementations - `#gen drafts`: number of drafts generated by this implementation - `#acc drafts`: number of drafts accepted (partially) by the main model - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens) - `#acc tokens`: number of tokens accepted by the main model +- `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance). From 7a4ca3cbd905907ac4d12bc14b878fdfbe4fd1d6 Mon Sep 17 00:00:00 2001 From: Christian Kastner Date: Mon, 2 Feb 2026 07:38:55 +0100 Subject: [PATCH 089/206] docs : Minor cleanups (#19252) * Update old URLs to github.com/ggml-org/ * Bump copyrights --- LICENSE | 2 +- docs/multimodal/minicpmo2.6.md | 2 +- docs/multimodal/minicpmo4.0.md | 4 ++-- docs/multimodal/minicpmv2.5.md | 2 +- docs/multimodal/minicpmv2.6.md | 2 +- docs/multimodal/minicpmv4.0.md | 4 ++-- docs/multimodal/minicpmv4.5.md | 4 ++-- examples/deprecation-warning/README.md | 2 +- examples/deprecation-warning/deprecation-warning.cpp | 2 +- examples/json_schema_to_grammar.py | 2 +- ggml/include/ggml-cann.h | 2 +- ggml/include/ggml.h | 2 +- ggml/src/ggml-cann/acl_tensor.cpp | 2 +- ggml/src/ggml-cann/acl_tensor.h | 2 +- ggml/src/ggml-cann/aclnn_ops.cpp | 2 +- ggml/src/ggml-cann/aclnn_ops.h | 2 +- ggml/src/ggml-cann/common.h | 2 +- ggml/src/ggml-cann/ggml-cann.cpp | 2 +- ggml/src/ggml-metal/CMakeLists.txt | 2 +- ggml/src/ggml-opencl/ggml-opencl.cpp | 2 +- ggml/src/ggml-sycl/ggml-sycl.cpp | 2 +- .../src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 2 +- ggml/src/ggml.c | 2 +- src/llama-chat.cpp | 2 +- src/llama-hparams.h | 2 +- src/llama-vocab.cpp | 8 ++++---- src/models/deepseek2.cpp | 2 +- tests/test-autorelease.cpp | 2 +- tools/cvector-generator/pca.hpp | 2 +- tools/export-lora/export-lora.cpp | 2 +- tools/perplexity/README.md | 2 +- tools/server/public_legacy/index-new.html | 2 +- tools/server/public_legacy/index.html | 2 +- tools/server/public_legacy/theme-mangotango.css | 2 +- tools/server/themes/buttons-top/index.html | 2 +- tools/server/themes/wild/index.html | 2 +- 36 files changed, 42 insertions(+), 42 deletions(-) diff --git a/LICENSE b/LICENSE index acb96ce78e..e7dca554bc 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023-2024 The ggml authors +Copyright (c) 2023-2026 The ggml authors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/docs/multimodal/minicpmo2.6.md b/docs/multimodal/minicpmo2.6.md index 5e74058e5d..ce003b2ebc 100644 --- a/docs/multimodal/minicpmo2.6.md +++ b/docs/multimodal/minicpmo2.6.md @@ -9,7 +9,7 @@ Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch m ### Build llama.cpp Readme modification time: 20250206 -If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) Clone llama.cpp: ```bash diff --git a/docs/multimodal/minicpmo4.0.md b/docs/multimodal/minicpmo4.0.md index 49125ea05e..a5281779c2 100644 --- a/docs/multimodal/minicpmo4.0.md +++ b/docs/multimodal/minicpmo4.0.md @@ -8,11 +8,11 @@ Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model ### Build llama.cpp Readme modification time: 20250206 -If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) 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/minicpmv2.5.md b/docs/multimodal/minicpmv2.5.md index 5eb87bc969..096f070a1c 100644 --- a/docs/multimodal/minicpmv2.5.md +++ b/docs/multimodal/minicpmv2.5.md @@ -8,7 +8,7 @@ Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V- ### Build llama.cpp Readme modification time: 20250206 -If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) Clone llama.cpp: ```bash diff --git a/docs/multimodal/minicpmv2.6.md b/docs/multimodal/minicpmv2.6.md index bc874bbd8c..a7db9c58db 100644 --- a/docs/multimodal/minicpmv2.6.md +++ b/docs/multimodal/minicpmv2.6.md @@ -8,7 +8,7 @@ Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch m ### Build llama.cpp Readme modification time: 20250206 -If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) Clone llama.cpp: ```bash diff --git a/docs/multimodal/minicpmv4.0.md b/docs/multimodal/minicpmv4.0.md index d04cb338ce..1d21b8cfdf 100644 --- a/docs/multimodal/minicpmv4.0.md +++ b/docs/multimodal/minicpmv4.0.md @@ -8,11 +8,11 @@ Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model ### Build llama.cpp 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) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) 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/minicpmv4.5.md b/docs/multimodal/minicpmv4.5.md index 8fea5e611d..a102c0fa51 100644 --- a/docs/multimodal/minicpmv4.5.md +++ b/docs/multimodal/minicpmv4.5.md @@ -8,11 +8,11 @@ Download [MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) PyTorch m ### 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) +If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) 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/examples/deprecation-warning/README.md b/examples/deprecation-warning/README.md index 59918ec2bb..9a1b263e8e 100644 --- a/examples/deprecation-warning/README.md +++ b/examples/deprecation-warning/README.md @@ -1,7 +1,7 @@ # Migration notice for binary filenames > [!IMPORTANT] -[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809) +[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggml-org/llama.cpp/pull/7809) This migration was important, but it is a breaking change that may not always be immediately obvious to users. diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp index c2958ea12d..11f5147328 100644 --- a/examples/deprecation-warning/deprecation-warning.cpp +++ b/examples/deprecation-warning/deprecation-warning.cpp @@ -28,7 +28,7 @@ int main(int argc, char** argv) { fprintf(stdout, "\n"); fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str()); fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str()); - fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n"); + fprintf(stdout, " See https://github.com/ggml-org/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n"); fprintf(stdout, "\n"); return EXIT_FAILURE; diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index 886dd3d81e..9fc90a3c98 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -402,7 +402,7 @@ class SchemaConverter: Transforms a regular expression pattern into a GBNF rule. Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions - Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md + Output: https://github.com/ggml-org/llama.cpp/blob/master/grammars/README.md Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers. diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h index b469e228d0..74af465337 100644 --- a/ggml/include/ggml-cann.h +++ b/ggml/include/ggml-cann.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 1988d16dc4..f759e2d588 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -6,7 +6,7 @@ // This documentation is still a work in progress. // If you wish some specific topics to be covered, feel free to drop a comment: // -// https://github.com/ggerganov/whisper.cpp/issues/40 +// https://github.com/ggml-org/whisper.cpp/issues/40 // // ## Overview // diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp index 7b7042a1f5..e95d3c4d88 100644 --- a/ggml/src/ggml-cann/acl_tensor.cpp +++ b/ggml/src/ggml-cann/acl_tensor.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h index 7deac38342..4737773a4d 100644 --- a/ggml/src/ggml-cann/acl_tensor.h +++ b/ggml/src/ggml-cann/acl_tensor.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index 02867e4fdb..87ac05748e 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h index b76e4707ac..3effa1c289 100644 --- a/ggml/src/ggml-cann/aclnn_ops.h +++ b/ggml/src/ggml-cann/aclnn_ops.h @@ -1,5 +1,5 @@ /** - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index fb3e7572e2..0120f0dfd1 100644 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index 42c6c67a40..6b2dbdd359 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023-2024 The ggml authors + * Copyright (c) 2023-2026 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to diff --git a/ggml/src/ggml-metal/CMakeLists.txt b/ggml/src/ggml-metal/CMakeLists.txt index 9c0b3db859..42054d841a 100644 --- a/ggml/src/ggml-metal/CMakeLists.txt +++ b/ggml/src/ggml-metal/CMakeLists.txt @@ -71,7 +71,7 @@ else() # disabling fast math is needed in order to pass tests/test-backend-ops # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 # note: unfortunately, we have to call it default.metallib instead of ggml.metallib - # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 + # ref: https://github.com/ggml-org/whisper.cpp/issues/1720 # note: adding -g causes segmentation fault during compile #set(XC_FLAGS -fno-fast-math -fno-inline -g) set(XC_FLAGS -fno-fast-math -fno-inline) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 4850c11d14..0f0eb3a9d8 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -3740,7 +3740,7 @@ static enum ggml_status ggml_backend_opencl_buffer_init_tensor(ggml_backend_buff // Reuse extra of the parent tensor. The offset of this view tensor // becomes `extra->offset + view_offs` and needs to be calculated when // it is used. This changes is needed because of the change to - // ggml_alloc.c in https://github.com/ggerganov/llama.cpp/pull/7640. + // ggml_alloc.c in https://github.com/ggml-org/llama.cpp/pull/7640. // `buffer` passed in here will always be `tensor->buffer`. It is OK // to allocate extras from the same buffer context for ordinary // intermediate tensors. But for views into kv cache tensors, doing so diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 74b4ed91cc..12f1e7717b 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3390,7 +3390,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor // mmvq and mmq need the __dp4a instruction which is available for gen12+ - // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e + // Workaround in https://github.com/ggml-org/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); #ifdef SYCL_USE_XMX use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); 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 bbdbf9dcaa..ca486a288a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -330,7 +330,7 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p std::vector cmd = {GLSLC, "-fshader-stage=compute", target_env, in_path, "-o", out_path}; #endif - // disable spirv-opt for coopmat shaders for https://github.com/ggerganov/llama.cpp/issues/10734 + // disable spirv-opt for coopmat shaders for https://github.com/ggml-org/llama.cpp/issues/10734 // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344 // disable spirv-opt for rope shaders for https://github.com/ggml-org/llama.cpp/issues/16860 if (!coopmat && name.find("bf16") == std::string::npos && name.find("rope") == std::string::npos) { diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 1725ad1654..e1471b540e 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -6562,7 +6562,7 @@ static void ggml_compute_backward( case GGML_OP_DIAG_MASK_INF: { if (src0_needs_grads) { /* ggml_diag_mask_inf_impl() shouldn't be here */ - /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ + /* ref: https://github.com/ggml-org/llama.cpp/pull/4203#discussion_r1412377992 */ const int n_past = ((const int32_t *) tensor->op_params)[0]; ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); } diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 3c7e0afdae..c415a998f3 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -233,7 +233,7 @@ int32_t llm_chat_apply_template( llm_chat_template tmpl, const std::vector & chat, std::string & dest, bool add_ass) { - // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 + // Taken from the research: https://github.com/ggml-org/llama.cpp/issues/5527 std::stringstream ss; if (tmpl == LLM_CHAT_TEMPLATE_CHATML) { // chatml template diff --git a/src/llama-hparams.h b/src/llama-hparams.h index caed0ec1b7..dfbc7d95e9 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -195,7 +195,7 @@ struct llama_hparams { uint32_t n_deepstack_layers = 0; // needed by encoder-decoder models (e.g. T5, FLAN-T5) - // ref: https://github.com/ggerganov/llama.cpp/pull/8141 + // ref: https://github.com/ggml-org/llama.cpp/pull/8141 llama_token dec_start_token_id = LLAMA_TOKEN_NULL; uint32_t dec_n_layer = 0; diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index a23950d007..74a8496f9e 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -90,7 +90,7 @@ static_assert(std::is_trivially_copyable::value, "llm_symbol is not // // SPM tokenizer // original implementation: -// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4 +// https://github.com/ggml-org/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4 // struct llm_bigram_spm { @@ -285,7 +285,7 @@ struct llm_tokenizer_bpe : llm_tokenizer { // original regex from tokenizer.json //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", - // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989 + // adapted: https://github.com/ggml-org/llama.cpp/pull/6920#issuecomment-2080233989 "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }; break; @@ -2390,7 +2390,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { // maintain a list of tokens that cause end-of-generation // this is currently determined based on the token text, which is obviously not ideal - // ref: https://github.com/ggerganov/llama.cpp/issues/9606 + // ref: https://github.com/ggml-org/llama.cpp/issues/9606 special_eog_ids.clear(); if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) { @@ -3079,7 +3079,7 @@ std::vector llama_vocab::impl::tokenize( } int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) const { - // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843 + // ref: https://github.com/ggml-org/llama.cpp/pull/7587#discussion_r1620983843 static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL; const llama_token_attr attr = token_get_attr(token); if (!special && (attr & attr_special)) { diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index 297dca5136..987f449934 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -14,7 +14,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr const uint32_t kv_lora_rank = hparams.n_lora_kv; // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. - // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. + // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation. // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp index 35b09aaeac..ca87c56a8f 100644 --- a/tests/test-autorelease.cpp +++ b/tests/test-autorelease.cpp @@ -1,4 +1,4 @@ -// ref: https://github.com/ggerganov/llama.cpp/issues/4952#issuecomment-1892864763 +// ref: https://github.com/ggml-org/llama.cpp/issues/4952#issuecomment-1892864763 #include #include diff --git a/tools/cvector-generator/pca.hpp b/tools/cvector-generator/pca.hpp index e88bbdde93..afd3bf6380 100644 --- a/tools/cvector-generator/pca.hpp +++ b/tools/cvector-generator/pca.hpp @@ -290,7 +290,7 @@ static void power_iteration( ggml_gallocr_free(allocr); // TODO @ngxson : The output vector is randomly inverted - // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171 + // Solution: https://github.com/ggml-org/llama.cpp/pull/8069#issuecomment-2185328171 } static void run_pca( diff --git a/tools/export-lora/export-lora.cpp b/tools/export-lora/export-lora.cpp index f038019b00..41f426208f 100644 --- a/tools/export-lora/export-lora.cpp +++ b/tools/export-lora/export-lora.cpp @@ -190,7 +190,7 @@ struct lora_merge_ctx { gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16); // check if all lora adapters have the same tensors - // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777 + // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggml-org/llama.cpp/pull/8607#discussion_r1686027777 static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once."; if (adapters.size() > 1) { for (size_t i = 1; i < adapters.size(); ++i) { diff --git a/tools/perplexity/README.md b/tools/perplexity/README.md index 33a46d1a2e..eb3846072e 100644 --- a/tools/perplexity/README.md +++ b/tools/perplexity/README.md @@ -29,7 +29,7 @@ In addition to the KL divergence the following statistics are calculated with `- * Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. * Pearson correlation coefficient of the "correct" token probabilites between models. * Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization. -* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 . +* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggml-org/llama.cpp/discussions/2875 . * Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution. ## LLaMA 3 8b Scoreboard diff --git a/tools/server/public_legacy/index-new.html b/tools/server/public_legacy/index-new.html index cbfbbdf280..e2f39d6687 100644 --- a/tools/server/public_legacy/index-new.html +++ b/tools/server/public_legacy/index-new.html @@ -1096,7 +1096,7 @@ return html`

`; diff --git a/tools/server/public_legacy/index.html b/tools/server/public_legacy/index.html index 75f39330a7..98d56ea8b1 100644 --- a/tools/server/public_legacy/index.html +++ b/tools/server/public_legacy/index.html @@ -1281,7 +1281,7 @@ `; diff --git a/tools/server/public_legacy/theme-mangotango.css b/tools/server/public_legacy/theme-mangotango.css index e433802453..315daf734a 100755 --- a/tools/server/public_legacy/theme-mangotango.css +++ b/tools/server/public_legacy/theme-mangotango.css @@ -1,5 +1,5 @@ /* Author: Yazan Agha-Schrader */ -/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */ +/* Inspiration from llama.cpp logo/banner https://github.com/ggml-org/llama.cpp#readme */ .theme-mangotango { diff --git a/tools/server/themes/buttons-top/index.html b/tools/server/themes/buttons-top/index.html index 3fb88fcc88..cb5af587aa 100644 --- a/tools/server/themes/buttons-top/index.html +++ b/tools/server/themes/buttons-top/index.html @@ -1032,7 +1032,7 @@ `; diff --git a/tools/server/themes/wild/index.html b/tools/server/themes/wild/index.html index 73f36d4b29..601f7762cd 100644 --- a/tools/server/themes/wild/index.html +++ b/tools/server/themes/wild/index.html @@ -1036,7 +1036,7 @@ `; From 1239267cc4e5a1c9fc6546825eefe13c856e7458 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 2 Feb 2026 08:51:25 +0200 Subject: [PATCH 090/206] authors : update (#19263) [no ci] --- AUTHORS | 1085 ++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 749 insertions(+), 336 deletions(-) diff --git a/AUTHORS b/AUTHORS index 0af9f44ad4..c297f3c217 100644 --- a/AUTHORS +++ b/AUTHORS @@ -1,127 +1,228 @@ -# date: Sat Mar 8 18:23:52 EET 2025 +# date: Mon Feb 2 08:45:04 EET 2026 # this file is auto-generated by scripts/gen-authors.sh +Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> +杨朱 · Kiki +エシュナヴァリシア <148695646+eternaphia@users.noreply.github.com> +吴小白 <296015668@qq.com> +源文雨 <41315874+fumiama@users.noreply.github.com> +蕭澧邦 <45505768+shou692199@users.noreply.github.com> +도로로도로또 <60079918+dororodoroddo@users.noreply.github.com> +손희준 +谢乃闻 0cc4m +0Marble <85058989+0Marble@users.noreply.github.com> 0xspringtime <110655352+0xspringtime@users.noreply.github.com> 20kdc +2114L3 <2114L3@users.noreply.github.com> 2f38b454 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com> 44670 <44670@users.noreply.github.com> +4onen <11580688+4onen@users.noreply.github.com> 65a <10104049+65a@users.noreply.github.com> 708-145 <40387547+708-145@users.noreply.github.com> -AN Long -AT +a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com> +a3sh <38979186+A3shTnT@users.noreply.github.com> +aa956 +Aadeshveer Singh <24b0926@iitb.ac.in> +Aadeshveer Singh Aarni Koskela Aaron Miller Aaron Teo <57927438+taronaeo@users.noreply.github.com> +Aaron Teo Aaryaman Vasishta Abheek Gulati Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com> +Acly +Adam +adel boussaken Adithya Balaji AdithyanI Adrian Adrian Hesketh Adrian Kretz +Adrian Lundberg <47256989+alundb@users.noreply.github.com> Adrien Gallouët Adrien Gallouët +afrideva <95653597+afrideva@users.noreply.github.com> +ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com> +agray3 Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com> Ahmet Zeer +ai-fonsi +Aidan <99101158+gSUz92nc@users.noreply.github.com> AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> AidanBeltonS Aisuko Akarshan Biswas Akarshan Biswas Akarshan Biswas +akawrykow <142945436+akawrykow@users.noreply.github.com> Al Mochkin <14274697+amochkin@users.noreply.github.com> +Alan Gray +Alawode Oluwandabira Albert Jin Alberto <57916483+albbus-stack@users.noreply.github.com> +Alberto Cabrera Pérez <1478977+Alcpz@users.noreply.github.com> Alberto Cabrera Pérez Alberto Cabrera Pérez +Alberto Cabrera Pérez +Aldehir Rojas +alek3y 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+wooksong +Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com> +Wroclaw +wsbagnsv1 +Wu Jian Ping +Wu Jian Ping wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> wzy <32936898+Freed-Wu@users.noreply.github.com> xaedes xaedes xctan +xctan +Xiake Sun +Xiang (Kevin) Li +Xiangyan Sun +Xiao-Yong Jin xiaobing318 <71554036+xiaobing318@users.noreply.github.com> xiaofei +XiaotaoChen +Xiaoyi Chen +Xie Yanbo +Xingchen Song(宋星辰) +Xinpeng Dou <15529241576@163.com> +Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com> xloem <0xloem@gmail.com> +Xuan Son Nguyen +Xuan-Son Nguyen +Xuan-Son Nguyen +yael-works <106673277+yael-works@users.noreply.github.com> +YaelGitAccount <38328157276@mby.co.il> +YaelLogic +Yaiko +YangLe yangli2 +Yann Follet <131855179+YannFollet@users.noreply.github.com> +Yaroslav +Yavor Ivanov +Yazan Agha-Schrader +Ycros <18012+ycros@users.noreply.github.com> +YehuditE +Yibo Cai +Yibo Cai +yifant-code +Yiming Cui +Yishuo Wang ymcki <84055651+ymcki@users.noreply.github.com> +Yoshi Suhara +Yoshi Suhara +Yoshi_likes_e4 <104140648+pt13762104@users.noreply.github.com> +Younes Belkada <49240599+younesbelkada@users.noreply.github.com> +Yuanhao Ji +Yuannan +Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com> +Yüg +Yui +Yuichiro Utsumi <81412151+utsumi-fj@users.noreply.github.com> yuiseki +yulo <77381088+zhang-hui-yulo@users.noreply.github.com> +yumeyao +yummy <57988893+jk3456a@users.noreply.github.com> +Yun Dou +Yuri Khrustalev yuri@FreeBSD +Yusuf Kağan Hanoğlu +Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com> +Yuxuan Zhang <2448370773@qq.com> +Z +Zagaj zakkor +Zane Shannon +Zay <95888118+isaiahbjork@users.noreply.github.com> +Zenix +Zhang Peiyuan zhangkaihuo +ZHAOKAI WANG +Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com> zhentaoyu +Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> +Zheyuan Chen +Zhiyong Wang <85110830+ravenouse@users.noreply.github.com> +Zhiyuan Li +Zhiyuan Li zhouwg <6889919+zhouwg@users.noreply.github.com> zhouwg +ZhouYuChen +Ziad Ben Hadj-Alouane +Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> zrm -Ștefan-Gabriel Muscalu -杨朱 · Kiki -源文雨 <41315874+fumiama@users.noreply.github.com> -蕭澧邦 <45505768+shou692199@users.noreply.github.com> -谢乃闻 -Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> +Zsapi From 59377a6c870be95e4c71715933e4e9ada71b8356 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 2 Feb 2026 10:00:05 +0100 Subject: [PATCH 091/206] ggml-backend: fix async set/get fallback sync (#19179) --- ggml/src/ggml-backend.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 354876574a..22c656996c 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -258,6 +258,7 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); if (backend->iface.set_tensor_async == NULL) { + ggml_backend_synchronize(backend); ggml_backend_tensor_set(tensor, data, offset, size); } else { backend->iface.set_tensor_async(backend, tensor, data, offset, size); @@ -271,6 +272,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); if (backend->iface.get_tensor_async == NULL) { + ggml_backend_synchronize(backend); ggml_backend_tensor_get(tensor, data, offset, size); } else { backend->iface.get_tensor_async(backend, tensor, data, offset, size); From 6156ae51114337ffb6bb46cb65f99227b255089f Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 2 Feb 2026 11:29:57 +0100 Subject: [PATCH 092/206] model-conversion : add debug option to conversion script (#19265) This commit adds a debug option to the model conversion script to enable using the Python debugger (pdb) during model conversion. The motivation for this is that I've found myself adding this a few times now and it would be quicker to have this flag as an option and a makefile target/recipe for it. --- examples/model-conversion/Makefile | 5 ++++- .../model-conversion/scripts/causal/convert-model.sh | 12 +++++++++++- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile index 3b0505911d..342de63bd0 100644 --- a/examples/model-conversion/Makefile +++ b/examples/model-conversion/Makefile @@ -33,11 +33,14 @@ DEVICE ?= auto causal-convert-model-bf16: OUTTYPE=bf16 causal-convert-model-bf16: causal-convert-model +causal-convert-model-debug: DEBUG=--debug +causal-convert-model-debug: 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 + ./scripts/causal/convert-model.sh $(DEBUG) causal-convert-mm-model-bf16: OUTTYPE=bf16 causal-convert-mm-model-bf16: MM_OUTTYPE=f16 diff --git a/examples/model-conversion/scripts/causal/convert-model.sh b/examples/model-conversion/scripts/causal/convert-model.sh index 32ffe132e7..a5865f6acd 100755 --- a/examples/model-conversion/scripts/causal/convert-model.sh +++ b/examples/model-conversion/scripts/causal/convert-model.sh @@ -4,12 +4,17 @@ set -e # Parse command line arguments MMPROJ="" +DEBUG="" while [[ $# -gt 0 ]]; do case $1 in --mmproj) MMPROJ="--mmproj" shift ;; + --debug) + DEBUG="1" + shift + ;; *) shift ;; @@ -28,7 +33,12 @@ echo "Data type: ${TYPE}" echo "Converted model path:: ${CONVERTED_MODEL}" echo "Metadata override: ${METADATA_OVERRIDE}" -CMD_ARGS=("python" "../../convert_hf_to_gguf.py" "--verbose") +if [[ -n "$DEBUG" ]]; then + CMD_ARGS=("python" "-m" "pdb") +else + CMD_ARGS=("python") +fi +CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose") CMD_ARGS+=("${MODEL_PATH}") CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}") CMD_ARGS+=("--outtype" "${TYPE}") From 6fdddb498780dbda2a14f8b49b92d25601e14764 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 2 Feb 2026 14:29:44 +0200 Subject: [PATCH 093/206] metal : support virtual devices (#18919) * metal : support virtual devices * cont : manage buffer type context memory * metal : add events * cont : implement cpy_tensor_async --- ggml/src/ggml-metal/ggml-metal-context.h | 8 + ggml/src/ggml-metal/ggml-metal-context.m | 105 +++++- ggml/src/ggml-metal/ggml-metal-device.cpp | 8 +- ggml/src/ggml-metal/ggml-metal-device.h | 16 +- ggml/src/ggml-metal/ggml-metal-device.m | 71 +++- ggml/src/ggml-metal/ggml-metal.cpp | 426 ++++++++++++++++------ src/llama-context.cpp | 1 + 7 files changed, 508 insertions(+), 127 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-context.h b/ggml/src/ggml-metal/ggml-metal-context.h index ec2b686b73..abf4b06ed2 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.h +++ b/ggml/src/ggml-metal/ggml-metal-context.h @@ -15,14 +15,22 @@ typedef struct ggml_metal * ggml_metal_t; ggml_metal_t ggml_metal_init(ggml_metal_device_t dev); void ggml_metal_free(ggml_metal_t ctx); +const char * ggml_metal_get_name(ggml_metal_t ctx); + void ggml_metal_synchronize(ggml_metal_t ctx); void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); +bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, const struct ggml_tensor * src, struct ggml_tensor * dst); enum ggml_status ggml_metal_graph_compute (ggml_metal_t ctx, struct ggml_cgraph * gf); void ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf); +void ggml_metal_event_record(ggml_metal_t ctx, ggml_metal_event_t ev); +void ggml_metal_event_wait (ggml_metal_t ctx, ggml_metal_event_t ev); + +ggml_metal_event_t ggml_metal_get_ev_cpy(ggml_metal_t ctx); + void ggml_metal_set_n_cb (ggml_metal_t ctx, int n_cb); void ggml_metal_set_abort_callback (ggml_metal_t ctx, ggml_abort_callback abort_callback, void * user_data); bool ggml_metal_supports_family (ggml_metal_t ctx, int family); diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m index 42a35736ee..a412d70aed 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.m +++ b/ggml/src/ggml-metal/ggml-metal-context.m @@ -24,9 +24,13 @@ struct ggml_metal_command_buffer { }; struct ggml_metal { + char name[128]; + ggml_metal_device_t dev; ggml_metal_library_t lib; + ggml_metal_event_t ev_cpy; // for async copies + dispatch_queue_t d_queue; // additional, inference-time compiled pipelines @@ -117,7 +121,11 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) { } } - //const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev); + res->ev_cpy = ggml_metal_device_event_init(dev); + + const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev); + + snprintf(res->name, sizeof(res->name), "%s", props_dev->name); res->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); @@ -206,9 +214,15 @@ void ggml_metal_free(ggml_metal_t ctx) { dispatch_release(ctx->d_queue); + ggml_metal_device_event_free(ctx->dev, ctx->ev_cpy); + free(ctx); } +const char * ggml_metal_get_name(ggml_metal_t ctx) { + return ctx->name; +} + void ggml_metal_synchronize(ggml_metal_t ctx) { // wait for any backend operations to finish if (ctx->cmd_buf_last) { @@ -273,8 +287,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, // wrap the source data into a Metal buffer id device = ggml_metal_device_get_obj(ctx->dev); id buf_src = [device newBufferWithBytes:data - length:size - options:MTLResourceStorageModeShared]; + length:size + options:MTLResourceStorageModeShared]; GGML_ASSERT(buf_src); @@ -316,9 +330,9 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te @autoreleasepool { id device = ggml_metal_device_get_obj(ctx->dev); id buf_dst = [device newBufferWithBytesNoCopy:data - length:size - options:MTLResourceStorageModeShared - deallocator:nil]; + length:size + options:MTLResourceStorageModeShared + deallocator:nil]; GGML_ASSERT(buf_dst); @@ -356,6 +370,49 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te } } +bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) { + @autoreleasepool { + struct ggml_metal_buffer_id bid_src = ggml_metal_get_buffer_id(src); + struct ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(dst); + + if (bid_src.metal == nil || bid_dst.metal == nil) { + return false; + } + + // queue the copy operation into the Metal context + // this will be queued at the end, after any currently ongoing GPU operations + id queue = ggml_metal_device_get_queue(ctx_src->dev); + id cmd_buf = [queue commandBuffer]; + id encoder = [cmd_buf blitCommandEncoder]; + + [encoder copyFromBuffer:bid_src.metal + sourceOffset:bid_src.offs + toBuffer:bid_dst.metal + destinationOffset:bid_dst.offs + size:ggml_nbytes(src)]; + + [encoder endEncoding]; + + ggml_metal_event_t ev_cpy = ggml_metal_get_ev_cpy(ctx_src); + ggml_metal_event_record(ctx_src, ev_cpy); + + [cmd_buf commit]; + + // do not wait here for completion + //[cmd_buf waitUntilCompleted]; + + // instead, remember a reference to the command buffer and wait for it later if needed + [ctx_src->cmd_bufs_ext addObject:cmd_buf]; + ctx_src->cmd_buf_last = cmd_buf; + + [cmd_buf retain]; + + ggml_metal_event_wait(ctx_dst, ev_cpy); + + return true; + } +} + enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) { // number of nodes encoded by the main thread (empirically determined) const int n_main = 64; @@ -530,6 +587,42 @@ void ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf) { //printf("%s: graph optimize took %.3f ms\n", __func__, (ggml_time_us() - t_start) / 1000.0); } +void ggml_metal_event_record(ggml_metal_t ctx, ggml_metal_event_t ev) { + @autoreleasepool { + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; + + ggml_metal_event_encode_signal(ev, cmd_buf); + + [cmd_buf commit]; + + [ctx->cmd_bufs_ext addObject:cmd_buf]; + ctx->cmd_buf_last = cmd_buf; + + [cmd_buf retain]; + } +} + +void ggml_metal_event_wait(ggml_metal_t ctx, ggml_metal_event_t ev) { + @autoreleasepool { + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; + + ggml_metal_event_encode_wait(ev, cmd_buf); + + [cmd_buf commit]; + + [ctx->cmd_bufs_ext addObject:cmd_buf]; + ctx->cmd_buf_last = cmd_buf; + + [cmd_buf retain]; + } +} + +ggml_metal_event_t ggml_metal_get_ev_cpy(ggml_metal_t ctx) { + return ctx->ev_cpy; +} + void ggml_metal_set_n_cb(ggml_metal_t ctx, int n_cb) { if (ctx->n_cb != n_cb) { ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS); diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 04c6137c5a..377b0d3eb8 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -17,10 +17,12 @@ struct ggml_metal_device_deleter { typedef std::unique_ptr ggml_metal_device_ptr; -ggml_metal_device_t ggml_metal_device_get(void) { - static ggml_metal_device_ptr ctx { ggml_metal_device_init() }; +ggml_metal_device_t ggml_metal_device_get(int device) { + static std::vector devs; - return ctx.get(); + devs.emplace_back(ggml_metal_device_init(device)); + + return devs.back().get(); } struct ggml_metal_pipelines { diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 3d01c56fb8..afb091e725 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -205,7 +205,9 @@ void ggml_metal_rsets_free(ggml_metal_rsets_t rsets); // struct ggml_metal_device_props { + int device; char name[128]; + char desc[128]; size_t max_buffer_size; size_t max_working_set_size; @@ -224,11 +226,15 @@ struct ggml_metal_device_props { int op_offload_min_batch_size; }; -ggml_metal_device_t ggml_metal_device_init(void); +typedef struct ggml_metal_event * ggml_metal_event_t; + +void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf); +void ggml_metal_event_encode_wait (ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf); + +ggml_metal_device_t ggml_metal_device_init(int device); void ggml_metal_device_free(ggml_metal_device_t dev); -// return a singleton that is automatically destroyed when the program exits -ggml_metal_device_t ggml_metal_device_get(void); +ggml_metal_device_t ggml_metal_device_get(int device); void * ggml_metal_device_get_obj (ggml_metal_device_t dev); // id void * ggml_metal_device_get_queue(ggml_metal_device_t dev); // id @@ -240,6 +246,10 @@ void ggml_metal_device_rsets_rm (ggml_metal_device_t dev, ggml_metal_rset_t rset void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev); +ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev); +void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev); +void ggml_metal_device_event_synchronize(ggml_metal_device_t dev, ggml_metal_event_t ev); + void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total); bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_tensor * op); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 7f9c384c34..285dd1630e 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -24,9 +24,6 @@ static const NSInteger MTLGPUFamilyMetal3_GGML = 5001; static const NSInteger MTLGPUFamilyMetal4_GGML = 5002; -// virtual address for GPU memory allocations -static atomic_uintptr_t g_addr_device = 0x000000400ULL; - #if !GGML_METAL_EMBED_LIBRARY // Here to assist with NSBundle Path Hack @interface GGMLMetalClass : NSObject @@ -523,6 +520,9 @@ struct ggml_metal_device { ggml_metal_library_t library; struct ggml_metal_device_props props; + + // virtual address for GPU memory allocations + atomic_uintptr_t addr_virt; }; // @@ -618,7 +618,7 @@ void ggml_metal_rsets_free(ggml_metal_rsets_t rsets) { free(rsets); } -ggml_metal_device_t ggml_metal_device_init(void) { +ggml_metal_device_t ggml_metal_device_init(int device) { ggml_metal_device_t dev = calloc(1, sizeof(struct ggml_metal_device)); assert(dev != NULL); @@ -632,6 +632,9 @@ ggml_metal_device_t ggml_metal_device_init(void) { GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__); } + dev->addr_virt = 0x000000400ULL; + + dev->props.device = device; dev->props.has_simdgroup_reduction = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7]; dev->props.has_simdgroup_reduction |= [dev->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; @@ -792,7 +795,8 @@ ggml_metal_device_t ggml_metal_device_init(void) { dev->props.max_working_set_size = dev->mtl_device.maxBufferLength; } - strncpy(dev->props.name, [[dev->mtl_device name] UTF8String], sizeof(dev->props.name) - 1); + snprintf(dev->props.name, sizeof(dev->props.name), "%s%d", "MTL", device); + snprintf(dev->props.desc, sizeof(dev->props.desc), "%s", [[dev->mtl_device name] UTF8String]); dev->library = ggml_metal_library_init(dev); if (!dev->library) { @@ -922,6 +926,59 @@ void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev) { atomic_store_explicit(&dev->rsets->d_loop, 2*dev->rsets->keep_alive_s, memory_order_relaxed); } +struct ggml_metal_event { + void * obj; // id + + atomic_int value; +}; + +void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) { + id event = (id)ev->obj; + + id cmd_buf = (id) cmd_buf_raw; + + [cmd_buf encodeSignalEvent:event value:atomic_fetch_add_explicit(&ev->value, 1, memory_order_relaxed) + 1]; +} + +void ggml_metal_event_encode_wait(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) { + id event = (id)ev->obj; + + id cmd_buf = (id) cmd_buf_raw; + + [cmd_buf encodeWaitForEvent:event value:atomic_load_explicit(&ev->value, memory_order_relaxed)]; +} + +ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev) { + id event = [dev->mtl_device newEvent]; + + ggml_metal_event_t ev = calloc(1, sizeof(struct ggml_metal_event)); + + ev->obj = (__bridge void *)event; + ev->value = 0; + + return ev; +} + +void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev) { + id event = ev->obj; + [event release]; + + free(ev); + + GGML_UNUSED(dev); +} + +void ggml_metal_device_event_synchronize(ggml_metal_device_t dev, ggml_metal_event_t ev) { + @autoreleasepool { + id event = ev->obj; + + id cmd_buf = [dev->mtl_queue commandBuffer]; + [cmd_buf encodeWaitForEvent:event value:atomic_load_explicit(&ev->value, memory_order_relaxed)]; + [cmd_buf commit]; + [cmd_buf waitUntilCompleted]; + } +} + void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total) { if (@available(macOS 10.12, iOS 16.0, *)) { *total = dev->mtl_device.recommendedMaxWorkingSetSize; @@ -1344,8 +1401,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, res->all_data = ggml_metal_host_malloc(size_aligned); res->is_shared = true; } else { - // use virtual address from g_addr_device counter - res->all_data = (void *) atomic_fetch_add_explicit(&g_addr_device, size_aligned, memory_order_relaxed); + // use virtual address + res->all_data = (void *) atomic_fetch_add_explicit(&dev->addr_virt, size_aligned, memory_order_relaxed); res->is_shared = false; } res->all_size = size_aligned; diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp index 56b59f0afd..a616dcdb46 100644 --- a/ggml/src/ggml-metal/ggml-metal.cpp +++ b/ggml/src/ggml-metal/ggml-metal.cpp @@ -7,11 +7,12 @@ #include "ggml-metal-context.h" #include "ggml-metal-ops.h" -// globals +#define GGML_METAL_NAME "MTL" +#define GGML_METAL_MAX_DEVICES 16 -// initialized in ggml_backend_metal_reg -static ggml_backend_reg g_ggml_metal_reg; -static ggml_backend_device g_ggml_metal_device; +// number of Metal devices +// note: can be overriden with GGML_METAL_DEVICES env to simulate virtual devices +static int g_devices = 1; //////////////////////////////////////////////////////////////////////////////// // backend interface @@ -165,10 +166,28 @@ static ggml_backend_buffer_i ggml_backend_metal_buffer_private_i = { /* .reset = */ NULL, }; +static bool ggml_backend_buffer_is_metal(ggml_backend_buffer_t buffer) { + return buffer->iface.free_buffer == ggml_backend_metal_buffer_shared_free_buffer || + buffer->iface.free_buffer == ggml_backend_metal_buffer_private_free_buffer; +} + // // buffer types // +struct ggml_backend_metal_buffer_type { + int device; + std::string name; +}; + +struct ggml_backend_metal_buffer_type_deleter { + void operator()(ggml_backend_metal_buffer_type * ctx) const { + delete ctx; + } +}; + +typedef std::unique_ptr ggml_backend_metal_buffer_type_ptr; + // common method for allocating shread or private Metal buffers static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size, bool shared) { ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context; @@ -218,9 +237,9 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_ // default (shared) buffer type static const char * ggml_backend_metal_buffer_type_shared_get_name(ggml_backend_buffer_type_t buft) { - return "Metal"; + ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context; - GGML_UNUSED(buft); + return ctx->name.c_str(); } static ggml_backend_buffer_t ggml_backend_metal_buffer_type_shared_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -249,29 +268,54 @@ static bool ggml_backend_metal_buffer_type_shared_is_host(ggml_backend_buffer_ty GGML_UNUSED(buft); } -static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_shared(void) { - static ggml_backend_buffer_type ggml_backend_buffer_type_metal = { - /* .iface = */ { - /* .get_name = */ ggml_backend_metal_buffer_type_shared_get_name, - /* .alloc_buffer = */ ggml_backend_metal_buffer_type_shared_alloc_buffer, - /* .get_alignment = */ ggml_backend_metal_buffer_type_shared_get_alignment, - /* .get_max_size = */ ggml_backend_metal_buffer_type_shared_get_max_size, - /* .get_alloc_size = */ ggml_backend_metal_buffer_type_shared_get_alloc_size, - /* .is_host = */ ggml_backend_metal_buffer_type_shared_is_host, - }, - /* .device = */ &g_ggml_metal_device, - /* .context = */ NULL, - }; +static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_shared(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); - return &ggml_backend_buffer_type_metal; + static std::vector bufts; + static std::vector ctxs; + + static bool initialized = false; + if (!initialized) { + bufts.reserve(g_devices); + ctxs.reserve(g_devices); + + for (int i = 0; i < g_devices; ++i) { + ggml_backend_metal_buffer_type * raw_ctx = + new ggml_backend_metal_buffer_type { + /* .device = */ i, + /* .name = */ GGML_METAL_NAME + std::to_string(i), + }; + ctxs.emplace_back(raw_ctx); + + ggml_backend_buffer_type buft = { + /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_type_shared_get_name, + /* .alloc_buffer = */ ggml_backend_metal_buffer_type_shared_alloc_buffer, + /* .get_alignment = */ ggml_backend_metal_buffer_type_shared_get_alignment, + /* .get_max_size = */ ggml_backend_metal_buffer_type_shared_get_max_size, + /* .get_alloc_size = */ ggml_backend_metal_buffer_type_shared_get_alloc_size, + /* .is_host = */ ggml_backend_metal_buffer_type_shared_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i), + /* .context = */ raw_ctx, + }; + + bufts.emplace_back(buft); + } + + initialized = true; + } + + return &bufts[device]; } // default (private) buffer type static const char * ggml_backend_metal_buffer_type_private_get_name(ggml_backend_buffer_type_t buft) { - return "Metal_Private"; + ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context; - GGML_UNUSED(buft); + return ctx->name.c_str(); } static ggml_backend_buffer_t ggml_backend_metal_buffer_type_private_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -300,29 +344,53 @@ static bool ggml_backend_metal_buffer_type_private_is_host(ggml_backend_buffer_t GGML_UNUSED(buft); } -static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_private(void) { - static ggml_backend_buffer_type ggml_backend_buffer_type_metal = { - /* .iface = */ { - /* .get_name = */ ggml_backend_metal_buffer_type_private_get_name, - /* .alloc_buffer = */ ggml_backend_metal_buffer_type_private_alloc_buffer, - /* .get_alignment = */ ggml_backend_metal_buffer_type_private_get_alignment, - /* .get_max_size = */ ggml_backend_metal_buffer_type_private_get_max_size, - /* .get_alloc_size = */ ggml_backend_metal_buffer_type_private_get_alloc_size, - /* .is_host = */ ggml_backend_metal_buffer_type_private_is_host, - }, - /* .device = */ &g_ggml_metal_device, - /* .context = */ NULL, - }; +static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_private(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); - return &ggml_backend_buffer_type_metal; + static std::vector bufts; + static std::vector ctxs; + + static bool initialized = false; + if (!initialized) { + bufts.reserve(g_devices); + ctxs.reserve(g_devices); + + for (int i = 0; i < g_devices; ++i) { + ggml_backend_metal_buffer_type * raw_ctx = new ggml_backend_metal_buffer_type{ + /* .device = */ i, + /* .name = */ GGML_METAL_NAME + std::to_string(i) + "_Private" + }; + ctxs.emplace_back(raw_ctx); + + ggml_backend_buffer_type buft = { + /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_type_private_get_name, + /* .alloc_buffer = */ ggml_backend_metal_buffer_type_private_alloc_buffer, + /* .get_alignment = */ ggml_backend_metal_buffer_type_private_get_alignment, + /* .get_max_size = */ ggml_backend_metal_buffer_type_private_get_max_size, + /* .get_alloc_size = */ ggml_backend_metal_buffer_type_private_get_alloc_size, + /* .is_host = */ ggml_backend_metal_buffer_type_private_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i), + /* .context = */ raw_ctx, + }; + + bufts.emplace_back(buft); + } + + initialized = true; + } + + return &bufts[device]; } // mapped buffer type static const char * ggml_backend_metal_buffer_type_mapped_get_name(ggml_backend_buffer_type_t buft) { - return "Metal_Mapped"; + ggml_backend_metal_buffer_type * ctx = (ggml_backend_metal_buffer_type *)buft->context; - GGML_UNUSED(buft); + return ctx->name.c_str(); } static ggml_backend_buffer_t ggml_backend_metal_buffer_type_mapped_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -352,31 +420,55 @@ static bool ggml_backend_metal_buffer_type_mapped_is_host(ggml_backend_buffer_ty GGML_UNUSED(buft); } -static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_mapped(void) { - // note: not obvious, but this buffer type still needs to implement .alloc_buffer: - // https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099 - static ggml_backend_buffer_type ggml_backend_buffer_type_mapped_metal = { - /* .iface = */ { - /* .get_name = */ ggml_backend_metal_buffer_type_mapped_get_name, - /* .alloc_buffer = */ ggml_backend_metal_buffer_type_mapped_alloc_buffer, - /* .get_alignment = */ ggml_backend_metal_buffer_type_mapped_get_alignment, - /* .get_max_size = */ ggml_backend_metal_buffer_type_mapped_get_max_size, - /* .get_alloc_size = */ ggml_backend_metal_buffer_type_mapped_get_alloc_size, - /* .is_host = */ ggml_backend_metal_buffer_type_mapped_is_host, - }, - /* .device = */ &g_ggml_metal_device, - /* .context = */ NULL, - }; +static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_mapped(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); - return &ggml_backend_buffer_type_mapped_metal; + static std::vector bufts; + static std::vector ctxs; + + static bool initialized = false; + if (!initialized) { + bufts.reserve(g_devices); + ctxs.reserve(g_devices); + + for (int i = 0; i < g_devices; ++i) { + ggml_backend_metal_buffer_type * raw_ctx = new ggml_backend_metal_buffer_type{ + /* .device = */ i, + /* .name = */ GGML_METAL_NAME + std::to_string(i) + "_Mapped" + }; + ctxs.emplace_back(raw_ctx); + + // note: not obvious, but this buffer type still needs to implement .alloc_buffer: + // https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099 + ggml_backend_buffer_type buft = { + /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_type_mapped_get_name, + /* .alloc_buffer = */ ggml_backend_metal_buffer_type_mapped_alloc_buffer, + /* .get_alignment = */ ggml_backend_metal_buffer_type_mapped_get_alignment, + /* .get_max_size = */ ggml_backend_metal_buffer_type_mapped_get_max_size, + /* .get_alloc_size = */ ggml_backend_metal_buffer_type_mapped_get_alloc_size, + /* .is_host = */ ggml_backend_metal_buffer_type_mapped_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_metal_reg(), i), + /* .context = */ raw_ctx, + }; + + bufts.emplace_back(buft); + } + + initialized = true; + } + + return &bufts[device]; } // backend static const char * ggml_backend_metal_name(ggml_backend_t backend) { - return "Metal"; + ggml_metal_t ctx = (ggml_metal_t)backend->context; - GGML_UNUSED(backend); + return ggml_metal_get_name(ctx); } static void ggml_backend_metal_free(ggml_backend_t backend) { @@ -409,12 +501,24 @@ static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const gg } static bool ggml_backend_metal_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { - return false; + if (!ggml_backend_is_metal(backend_src) || !ggml_backend_is_metal(backend_dst)) { + return false; + } - GGML_UNUSED(backend_src); - GGML_UNUSED(backend_dst); - GGML_UNUSED(src); - GGML_UNUSED(dst); + if (!ggml_backend_buffer_is_metal(src->buffer) || !ggml_backend_buffer_is_metal(dst->buffer)) { + return false; + } + + ggml_metal_t ctx_src = (ggml_metal_t)backend_src->context; + ggml_metal_t ctx_dst = (ggml_metal_t)backend_dst->context; + + //ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer; + //ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer; + + //ggml_metal_buffer_t buf_ctx_src = (ggml_metal_buffer_t)buf_src->context; + //ggml_metal_buffer_t buf_ctx_dst = (ggml_metal_buffer_t)buf_dst->context; + + return ggml_metal_cpy_tensor_async(ctx_src, ctx_dst, src, dst); } static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { @@ -423,6 +527,20 @@ static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, return ggml_metal_graph_compute(ctx, cgraph); } +static void ggml_backend_metal_event_record(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_metal_t ctx = (ggml_metal_t)backend->context; + ggml_metal_event_t ev = (ggml_metal_event_t)event->context; + + ggml_metal_event_record(ctx, ev); +} + +static void ggml_backend_metal_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_metal_t ctx = (ggml_metal_t)backend->context; + ggml_metal_event_t ev = (ggml_metal_event_t)event->context; + + ggml_metal_event_wait(ctx, ev); +} + static void ggml_backend_metal_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_metal_t ctx = (ggml_metal_t)backend->context; @@ -435,7 +553,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { ggml_metal_t ctx = (ggml_metal_t)backend->context; ggml_metal_set_n_cb(ctx, n_cb); - } static ggml_backend_i ggml_backend_metal_i = { @@ -450,12 +567,8 @@ static ggml_backend_i ggml_backend_metal_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, - - // the events API is needed only for multi-GPU setups, so likely no need to implement it for Metal - // in any case, these docs seem relevant if we ever decide to implement it: - // https://developer.apple.com/documentation/metal/mtlcommandbuffer#Synchronizing-Passes-with-Events - /* .event_record = */ NULL, - /* .event_wait = */ NULL, + /* .event_record = */ ggml_backend_metal_event_record, + /* .event_wait = */ ggml_backend_metal_event_wait, /* .graph_optimize = */ ggml_backend_metal_graph_optimize, }; @@ -519,15 +632,17 @@ void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) { // backend device static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) { - return "Metal"; + ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; - GGML_UNUSED(dev); + const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev); + + return props_dev->name; } static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) { ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; - return ggml_metal_device_get_props(ctx_dev)->name; + return ggml_metal_device_get_props(ctx_dev)->desc; } static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { @@ -550,14 +665,14 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total); props->caps = { - /* .async = */ true, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, + /* .async = */ true, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ true, + /* .events = */ true, }; } -static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_metal_device_init_backend(ggml_backend_dev_t dev, const char * params) { ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; ggml_metal_t ctx = ggml_metal_init(ctx_dev); @@ -587,7 +702,7 @@ static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev); - return props_dev->use_shared_buffers ? ggml_backend_metal_buffer_type_shared() : ggml_backend_metal_buffer_type_private(); + return props_dev->use_shared_buffers ? ggml_backend_metal_buffer_type_shared(props_dev->device) : ggml_backend_metal_buffer_type_private(props_dev->device); } static ggml_backend_buffer_t ggml_backend_metal_device_buffer_mapped(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { @@ -595,7 +710,9 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_mapped(ggml_backen ggml_metal_buffer_t res = ggml_metal_buffer_map(ctx_dev, ptr, size, max_tensor_size); - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type_mapped(), ggml_backend_metal_buffer_shared_i, res, size); + const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev); + + return ggml_backend_buffer_init(ggml_backend_metal_buffer_type_mapped(props_dev->device), ggml_backend_metal_buffer_shared_i, res, size); } static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { @@ -606,9 +723,10 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { return + buft->device == dev && ( buft->iface.get_name == ggml_backend_metal_buffer_type_shared_get_name || buft->iface.get_name == ggml_backend_metal_buffer_type_private_get_name || - buft->iface.get_name == ggml_backend_metal_buffer_type_mapped_get_name; + buft->iface.get_name == ggml_backend_metal_buffer_type_mapped_get_name); GGML_UNUSED(dev); } @@ -632,45 +750,97 @@ static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const g get_op_batch_size(op) >= ggml_metal_device_get_props(ctx_dev)->op_offload_min_batch_size; } +static ggml_backend_event_t ggml_backend_metal_device_event_new(ggml_backend_dev_t dev) { + ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; + + ggml_metal_event_t event = ggml_metal_device_event_init(ctx_dev); + GGML_ASSERT(event); + + ggml_backend_event_t ev = new ggml_backend_event { + /* .device = */ dev, + /* .context = */ event, + }; + + return ev; +} + +static void ggml_backend_metal_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; + + ggml_metal_event_t ev = (ggml_metal_event_t)event->context; + + ggml_metal_device_event_free(ctx_dev, ev); + + delete event; +} + +static void ggml_backend_metal_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context; + + ggml_metal_event_t evt = (ggml_metal_event_t)event->context; + + ggml_metal_device_event_synchronize(ctx_dev, evt); +} + static ggml_backend_device_i ggml_backend_metal_device_i = { /* .get_name = */ ggml_backend_metal_device_get_name, /* .get_description = */ ggml_backend_metal_device_get_description, /* .get_memory = */ ggml_backend_metal_device_get_memory, /* .get_type = */ ggml_backend_metal_device_get_type, /* .get_props = */ ggml_backend_metal_device_get_props, - /* .init_backend = */ ggml_backend_metal_device_init, + /* .init_backend = */ ggml_backend_metal_device_init_backend, /* .get_buffer_type = */ ggml_backend_metal_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_mapped, /* .supports_op = */ ggml_backend_metal_device_supports_op, /* .supports_buft = */ ggml_backend_metal_device_supports_buft, /* .offload_op = */ ggml_backend_metal_device_offload_op, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, + /* .event_new = */ ggml_backend_metal_device_event_new, + /* .event_free = */ ggml_backend_metal_device_event_free, + /* .event_synchronize = */ ggml_backend_metal_device_event_synchronize, }; // backend registry +struct ggml_backend_metal_reg { + std::vector devices; +}; + +typedef struct ggml_backend_metal_reg * ggml_backend_metal_reg_t; + +static ggml_backend_metal_reg_t ggml_backend_metal_reg_init(void) { + ggml_backend_metal_reg_t ctx = new struct ggml_backend_metal_reg; + + return ctx; +} + +static void ggml_backend_metal_reg_free(ggml_backend_metal_reg_t ctx) { + delete ctx; +} + +struct ggml_backend_metal_reg_deleter { + void operator()(ggml_backend_metal_reg_t ctx) { + ggml_backend_metal_reg_free(ctx); + } +}; + +typedef std::unique_ptr ggml_backend_metal_reg_ptr; + static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) { - return "Metal"; + return GGML_METAL_NAME; GGML_UNUSED(reg); } static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); + ggml_backend_metal_reg_t ctx = (ggml_backend_metal_reg_t)reg->context; + return ctx->devices.size(); } static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - return &g_ggml_metal_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); + ggml_backend_metal_reg_t ctx = (ggml_backend_metal_reg_t)reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; } static ggml_backend_feature g_ggml_backend_metal_features[] = { @@ -698,27 +868,67 @@ static void * ggml_backend_metal_get_proc_address(ggml_backend_reg_t reg, const static ggml_backend_reg_i ggml_backend_metal_reg_i = { /* .get_name = */ ggml_backend_metal_reg_get_name, - /* .device_count = */ ggml_backend_metal_reg_device_count, - /* .device_get = */ ggml_backend_metal_reg_device_get, + /* .get_device_count = */ ggml_backend_metal_reg_device_count, + /* .get_device = */ ggml_backend_metal_reg_device_get, /* .get_proc_address = */ ggml_backend_metal_get_proc_address, }; -ggml_backend_reg_t ggml_backend_metal_reg(void) { - { - g_ggml_metal_reg = { - /* .api_version = */ GGML_BACKEND_API_VERSION, - /* .iface = */ ggml_backend_metal_reg_i, - /* .context = */ NULL, - }; +static ggml_backend_dev_t ggml_backend_metal_device_init(ggml_backend_reg_t reg, int device) { + return new ggml_backend_device { + /* .iface = */ ggml_backend_metal_device_i, + /* .reg = */ reg, + /* .context = */ ggml_metal_device_get(device), + }; +} - g_ggml_metal_device = { - /* .iface = */ ggml_backend_metal_device_i, - /* .reg = */ &g_ggml_metal_reg, - /* .context = */ ggml_metal_device_get(), - }; +static void ggml_backend_metal_device_free(ggml_backend_dev_t dev) { + delete dev; +} + +struct ggml_backend_device_deleter { + void operator()(ggml_backend_dev_t ctx) { + ggml_backend_metal_device_free(ctx); + } +}; + +typedef std::unique_ptr ggml_backend_device_ptr; + +ggml_backend_reg_t ggml_backend_metal_reg(void) { + static ggml_backend_reg reg; + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + + const char * env = getenv("GGML_METAL_DEVICES"); + if (env) { + g_devices = atoi(env); + } + + static std::vector devs; + + if (!initialized) { + static ggml_backend_metal_reg_ptr reg_ctx(ggml_backend_metal_reg_init()); + + for (int i = 0; i < g_devices; ++i) { + auto * dev = ggml_backend_metal_device_init(®, i); + devs.emplace_back(dev); + + reg_ctx->devices.push_back(dev); + } + + reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_metal_reg_i, + /* .context = */ reg_ctx.get(), + }; + } + + initialized = true; } - return &g_ggml_metal_reg; + return ® } GGML_BACKEND_DL_IMPL(ggml_backend_metal_reg) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 10b306a853..203852d0f1 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -317,6 +317,7 @@ llama_context::llama_context( auto dev_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get())); if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU) { // ignore CPU backend + // TODO: should we ignore ACCEL types too? continue; } auto * dev = ggml_backend_get_device(backend.get()); From 4d5e97267386ba5a9fe4c26f26df10fe1c218534 Mon Sep 17 00:00:00 2001 From: Tamar Date: Mon, 2 Feb 2026 15:05:51 +0200 Subject: [PATCH 094/206] sycl: implement GGML_OP_TOP_K (#19242) --- docs/ops.md | 2 +- docs/ops/SYCL.csv | 450 +++++++++++++++---------------- ggml/src/ggml-sycl/ggml-sycl.cpp | 140 ++++++++++ 3 files changed, 366 insertions(+), 226 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index 2c7c60dcca..ef1ebff8b0 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -113,7 +113,7 @@ Legend: | SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | -| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index c1d22e65d4..2aa51304b3 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -9677,168 +9677,168 @@ "SYCL0","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[13,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[13,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[4,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[15,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[4,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[15,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[4,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[15,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[19,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[19,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[19,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[19,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[27,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[27,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[27,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[27,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[27,1,2,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[43,1,2,1],k=1,ties=0","support","0","no","SYCL" 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+"SYCL0","TOP_K","type=f32,ne=[4096,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[4107,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[4096,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[4107,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[4096,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[4107,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[4096,1,1,1],k=500,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[4107,1,2,1],k=500,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[4096,1,1,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[4107,1,2,1],k=1023,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=500,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=500,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[8192,1,1,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[8203,1,2,1],k=1023,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9847,16 +9847,16 @@ "SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[16395,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9865,16 +9865,16 @@ "SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[32768,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[32779,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9883,16 +9883,16 @@ "SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[65536,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[65547,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9901,16 +9901,16 @@ "SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[131072,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[131083,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9919,16 +9919,16 @@ "SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[262144,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[262155,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=15,ties=0","support","1","yes","SYCL" "SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=100,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=500,ties=0","support","0","no","SYCL" @@ -9937,51 +9937,51 @@ "SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=1023,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[524288,1,1,1],k=9999,ties=0","support","0","no","SYCL" "SYCL0","TOP_K","type=f32,ne=[524299,1,2,1],k=9999,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=1,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=2,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=3,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","0","no","SYCL" -"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=1,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=2,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=3,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","1","yes","SYCL" diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 12f1e7717b..c5139fd3dd 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -1840,6 +1840,110 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, } } +static void top_k_f32_sycl( + const float * src, + int32_t * dst_indices, + const int64_t ncols, + const int64_t nrows, + const int k, + dpct::queue_ptr main_stream +) { + const int block_size = 128; + + const sycl::range<1> block_dims(block_size); + const sycl::range<1> grid_dims(nrows); + + main_stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor shared_vals(sycl::range<1>(block_size * k), cgh); + sycl::local_accessor shared_idx(sycl::range<1>(block_size * k), cgh); + + cgh.parallel_for( + sycl::nd_range<1>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<1> item_ct1) { + const int row = item_ct1.get_group(0); + const int tid = item_ct1.get_local_id(0); + + if (row >= nrows) return; + + const float * src_row = src + row * ncols; + int32_t * dst_idx_row = dst_indices + row * k; + + float local_vals[32]; + int local_idx[32]; + + for (int i = 0; i < k; i++) { + local_vals[i] = -FLT_MAX; + local_idx[i] = -1; + } + + for (int col = tid; col < ncols; col += block_size) { + float val = src_row[col]; + + if (val > local_vals[k-1]) { + int pos = k - 1; + while (pos > 0 && val > local_vals[pos - 1]) { + pos--; + } + + for (int i = k - 1; i > pos; i--) { + local_vals[i] = local_vals[i - 1]; + local_idx[i] = local_idx[i - 1]; + } + local_vals[pos] = val; + local_idx[pos] = col; + } + } + + for (int i = 0; i < k; i++) { + shared_vals[tid * k + i] = local_vals[i]; + shared_idx[tid * k + i] = local_idx[i]; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + if (tid == 0) { + float final_vals[32]; + int final_idx[32]; + + for (int i = 0; i < k; i++) { + final_vals[i] = -FLT_MAX; + final_idx[i] = -1; + } + + for (int t = 0; t < block_size; t++) { + for (int i = 0; i < k; i++) { + float val = shared_vals[t * k + i]; + int idx = shared_idx[t * k + i]; + + if (val > final_vals[k-1]) { + int pos = k - 1; + while (pos > 0 && val > final_vals[pos - 1]) { + pos--; + } + + for (int j = k - 1; j > pos; j--) { + final_vals[j] = final_vals[j - 1]; + final_idx[j] = final_idx[j - 1]; + } + final_vals[pos] = val; + final_idx[pos] = idx; + } + } + } + + for (int i = 0; i < k; i++) { + dst_idx_row[i] = final_idx[i]; + } + + if (k > 1) { + int32_t temp = dst_idx_row[0]; + dst_idx_row[0] = dst_idx_row[1]; + dst_idx_row[1] = temp; + } + } + }); + }); +} + static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols, const int nrows, queue_ptr stream) { const sycl::range<3> block_dims(1, 1, SYCL_ARGMAX_BLOCK_SIZE); @@ -2231,6 +2335,30 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * main_stream, ctx.device); } +static void ggml_sycl_op_top_k(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_is_contiguous(src0)); + + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + + const float * src0_dd = static_cast(src0->data); + int32_t * dst_dd = static_cast(dst->data); + + const int k = dst->ne[0]; + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + GGML_ASSERT(k > 0 && k <= 32); + GGML_ASSERT(k <= ncols); + + top_k_f32_sycl(src0_dd, dst_dd, ncols, nrows, k, main_stream); +} + inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_I32); @@ -4007,6 +4135,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_ARGSORT: ggml_sycl_argsort(ctx, dst); break; + case GGML_OP_TOP_K: + ggml_sycl_op_top_k(ctx, dst); + break; case GGML_OP_TIMESTEP_EMBEDDING: ggml_sycl_op_timestep_embedding(ctx, dst); break; @@ -4710,6 +4841,15 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ARGSORT: return op->src[0]->ne[0] * sizeof(int) <= ggml_sycl_info().devices[device].smpbo; + case GGML_OP_TOP_K: { + const ggml_tensor * src0 = op->src[0]; + const int k = op->ne[0]; + return src0 && + op->type == GGML_TYPE_I32 && + src0->type == GGML_TYPE_F32 && + ggml_is_contiguous(src0) && + k > 0 && k <= 32; + } case GGML_OP_POOL_2D: case GGML_OP_ACC: return true; From bf38346d136c6121732c36c60b079bde18d63a0e Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Mon, 2 Feb 2026 21:06:21 +0800 Subject: [PATCH 095/206] Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. (#19246) User can't build up the software for Nvidia & AMD GPU. rm the oneMath since it is only used in NV and AMD code path. --- docs/backend/SYCL.md | 126 ++++------------------------- ggml/src/ggml-sycl/CMakeLists.txt | 99 +++-------------------- ggml/src/ggml-sycl/dpct/helper.hpp | 65 +++++---------- ggml/src/ggml-sycl/ggml-sycl.cpp | 35 +++----- ggml/src/ggml-sycl/outprod.cpp | 6 +- ggml/src/ggml-sycl/rope.cpp | 1 - ggml/src/ggml-sycl/wkv.cpp | 2 +- 7 files changed, 60 insertions(+), 274 deletions(-) diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index 10cb02ff2c..b3cff96604 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -22,12 +22,11 @@ - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers. - **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*. - **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs. -- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets. ### Llama.cpp + SYCL The llama.cpp SYCL backend is primarily designed for **Intel GPUs**. -SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD. +SYCL cross-platform capabilities enable support for other vendor GPUs as well. ## Recommended Release @@ -42,6 +41,9 @@ The following releases are verified and recommended: ## News +- 2026.02 + - Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU. + - 2025.11 - Support malloc memory on device more than 4GB. @@ -111,8 +113,8 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the |-------------------------------|---------|---------------------------------------| | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | -| Intel Arc A-Series | Support | Arc A770, Arc A730M, Arc A750 | -| Intel Arc B-Series | Support | Arc B580 | +| Intel Arc A-Series | Support | Arc A770, Arc A730M, Arc A750 | +| Intel Arc B-Series | Support | Arc B580 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake | | Intel iGPU | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7 | @@ -127,20 +129,7 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the ### Other Vendor GPU -**Verified devices** - -| Nvidia GPU | Status | Verified Model | -|--------------------------|-----------|----------------| -| Ampere Series | Supported | A100, A4000 | -| Ampere Series *(Mobile)* | Supported | RTX 40 Series | - -| AMD GPU | Status | Verified Model | -|--------------------------|--------------|----------------| -| Radeon Pro | Experimental | W6800 | -| Radeon RX | Experimental | 6700 XT | - -Note: AMD GPU support is highly experimental and is incompatible with F16. -Additionally, it only supports GPUs with a sub_group_size (warp size) of 32. +NA ## Docker @@ -149,11 +138,11 @@ The docker build option is currently limited to *Intel GPU* targets. ### Build image ```sh -# Using FP16 -docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile . - # Using FP32 docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile . + +# Using FP16 +docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile . ``` *Notes*: @@ -212,14 +201,6 @@ Platform #0: Intel(R) OpenCL HD Graphics `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] ``` -- **Nvidia GPU** - -In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. - -- **AMD GPU** - -To target AMD GPUs with SYCL, the ROCm stack must be installed first. - 2. **Install Intel® oneAPI Base toolkit** SYCL backend depends on: @@ -248,23 +229,6 @@ Upon a successful installation, SYCL is enabled for the available intel devices, |2025.1| |2024.1| -- **Adding support to Nvidia GPUs** - -**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. - -**oneDNN**: The current oneDNN releases *(shipped with the oneAPI base-toolkit)* do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target: - -```sh -git clone https://github.com/oneapi-src/oneDNN.git -cd oneDNN -cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -cmake --build build-nvidia --config Release -``` - -- **Adding support to AMD GPUs** - -**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit. - 3. **Verify installation and environment** In order to check the available SYCL devices on the machine, please use the `sycl-ls` command. @@ -285,25 +249,6 @@ When targeting an intel GPU, the user should expect one or more devices among th [opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO [24.39.31294] ``` -- **Nvidia GPU** - -Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below: - -``` -[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix] -[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix] -[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5] -``` - -- **AMD GPU** - -For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]: - -``` -[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000] -[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9] -``` - ### II. Build llama.cpp #### Intel GPU @@ -332,47 +277,6 @@ It is possible to come across some precision issues when running tests that stem instructions, which can be circumvented by setting the environment variable `SYCL_PROGRAM_COMPILE_OPTIONS` as `-cl-fp32-correctly-rounded-divide-sqrt` -#### Nvidia GPU - -The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices. -By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`. - -```sh -# Build LLAMA with Nvidia BLAS acceleration through SYCL -# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance -GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture - -# Option 1: Use FP32 (recommended for better performance in most cases) -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl - -# Option 2: Use FP16 -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl - -# build all binary -cmake --build build --config Release -j -v -``` - -It is possible to come across some precision issues when running tests that stem from using faster -instructions, which can be circumvented by passing the `-fno-fast-math` flag to the compiler. - -#### AMD GPU - -The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices. -By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`. - -```sh -# Build LLAMA with rocBLAS acceleration through SYCL - -## AMD -# Use FP32, FP16 is not supported -# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:' -GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx - -# build all binary -cmake --build build --config Release -j -v -``` - ### III. Run the inference #### Retrieve and prepare model @@ -766,15 +670,15 @@ use 1 SYCL GPUs: [0] with Max compute units:512 | Name | Value | Function | |--------------------|---------------------------------------|---------------------------------------------| | GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. | -| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. | -| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. | +| GGML_SYCL_TARGET | INTEL *(default)* | Set the SYCL target device type. | +| GGML_SYCL_DEVICE_ARCH | Optional | Set the SYCL device architecture. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. | | GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. (1.) | -| GGML_SYCL_GRAPH | ON *(default)* \|OFF *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). | +| GGML_SYCL_GRAPH | OFF *(default)* \|ON *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). | | GGML_SYCL_DNN | ON *(default)* \|OFF *(Optional)* | Enable build with oneDNN. | | CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. | | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | -1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds. +1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change `GGML_SYCL_F16=OFF/ON`. #### Runtime @@ -782,7 +686,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| | GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) | -| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. | +| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. | | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. | | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.| diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index 5a89d8dd68..eefdd9725c 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -1,7 +1,7 @@ message(STATUS "GGML_SYCL_TARGET=${GGML_SYCL_TARGET}") -if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA|AMD)$") - message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL, NVIDIA, or AMD") +if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL)$") + message(FATAL_ERROR "GGML_SYCL_TARGET: Invalid target, the supported options are [INTEL]") endif() check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL) @@ -125,25 +125,22 @@ endif() target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_DNNL=${GGML_SYCL_DNNL}) if (GGML_SYCL_F16) - if (GGML_SYCL_TARGET STREQUAL "AMD") - message(WARNING "AMD target does not entirely support FP16 in the SYCL backend.") - endif() add_compile_definitions(GGML_SYCL_F16) endif() if (GGML_SYCL_TARGET STREQUAL "INTEL") add_compile_definitions(GGML_SYCL_WARP_SIZE=16) target_link_options(ggml-sycl PRIVATE -Xs -ze-intel-greater-than-4GB-buffer-required) -elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA") - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) -elseif (GGML_SYCL_TARGET STREQUAL "AMD") - # INFO: Allowed Sub_group_sizes are not consistent through all - # hip targets. For example, 64 is used for certain models, but the backend - # does not support it. - # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32) - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) + + # Link against Intel oneMKL + if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + set(SYCL_COMPILER ON) + endif() + find_package(MKL REQUIRED) + target_link_libraries(ggml-sycl PRIVATE MKL::MKL_SYCL::BLAS) else() # default for other target + message(FATAL_ERROR "GGML_SYCL_TARGET is not supported") add_compile_definitions(GGML_SYCL_WARP_SIZE=32) endif() @@ -151,82 +148,6 @@ if (GGML_SYCL_GRAPH) target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GRAPH) endif() -# Link against Intel oneMKL or oneMath -if (GGML_SYCL_TARGET STREQUAL "INTEL") - # Intel devices use Intel oneMKL directly instead of oneMath to avoid the limitation of linking Intel oneMKL statically - # See https://github.com/uxlfoundation/oneMath/issues/654 - if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - set(SYCL_COMPILER ON) - endif() - find_package(MKL REQUIRED) - target_link_libraries(ggml-sycl PRIVATE MKL::MKL_SYCL::BLAS) - target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_USE_INTEL_ONEMKL) -else() - find_package(oneMath QUIET) - if (NOT oneMath_FOUND) - message(STATUS "oneMath not found: oneMath will be automatically downloaded") - # Use FetchContent to automatically pull and build oneMath - include(FetchContent) - set(BUILD_FUNCTIONAL_TESTS False) - set(BUILD_EXAMPLES False) - set(TARGET_DOMAINS blas) - if (GGML_SYCL_TARGET STREQUAL "NVIDIA") - set(ENABLE_MKLCPU_BACKEND False) - set(ENABLE_MKLGPU_BACKEND False) - set(ENABLE_CUBLAS_BACKEND True) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - set(ENABLE_MKLCPU_BACKEND False) - set(ENABLE_MKLGPU_BACKEND False) - set(ENABLE_ROCBLAS_BACKEND True) - # Ensure setting a string variable here is not overriden by oneMath CACHE variables - cmake_policy(SET CMP0126 NEW) - # Setting the device architecture is only needed and useful for AMD devices in oneMath - set(HIP_TARGETS ${GGML_SYCL_DEVICE_ARCH} CACHE STRING "oneMath HIP target" FORCE) - endif() - FetchContent_Declare( - ONEMATH - GIT_REPOSITORY https://github.com/uxlfoundation/oneMath.git - GIT_TAG 8efe85f5aaebb37f1d8c503b7af66315feabf142 - ) - FetchContent_MakeAvailable(ONEMATH) - # Create alias to match with find_package targets name - function(onemath_alias target) - if (TARGET ${target}_obj) - # Silence verbose warnings from external libraries - target_compile_options(${target}_obj PRIVATE -w) - endif() - if (TARGET ${target}) - add_library(ONEMATH::${target} ALIAS ${target}) - endif() - endfunction() - onemath_alias(onemath) - onemath_alias(onemath_blas_mklcpu) - onemath_alias(onemath_blas_mklgpu) - onemath_alias(onemath_blas_cublas) - onemath_alias(onemath_blas_rocblas) - endif() - - # Below oneMath compile-time dispatching is used for better performance - if (GGML_SYCL_TARGET STREQUAL "NVIDIA") - target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath_blas_cublas) - target_compile_options(ggml-sycl PRIVATE "-fsycl-targets=nvptx64-nvidia-cuda") - target_link_options(ggml-sycl PRIVATE "-fsycl-targets=nvptx64-nvidia-cuda") - target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_NVIDIA) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - if (NOT GGML_SYCL_DEVICE_ARCH) - message(FATAL_ERROR "Can't enable SYCL hip backend, GGML_SYCL_DEVICE_ARCH has not been set.") - endif() - target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath_blas_rocblas) - target_compile_options(ggml-sycl PRIVATE "-fsycl-targets=amdgcn-amd-amdhsa") - target_link_options(ggml-sycl PRIVATE "-fsycl-targets=amdgcn-amd-amdhsa") - target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_AMD) - else() - # Fallback to oneMath runtime dispatcher - target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath) - target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GENERIC) - endif() -endif() - if (GGML_SYCL_DEVICE_ARCH) target_compile_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH}) target_link_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH}) diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index 8ae8098717..ece66a7ac1 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -15,17 +15,9 @@ #include #include -#include - -#ifdef GGML_SYCL_USE_INTEL_ONEMKL #include -// Allow to use the same namespace for Intel oneMKL and oneMath -namespace oneapi { - namespace math = mkl; -} -#else -#include -#endif + +#include #include "ggml.h" @@ -91,32 +83,13 @@ inline std::string get_device_backend_and_type(const sycl::device &device) { } template struct matrix_info_t { - oneapi::math::transpose transpose_info[2]; + oneapi::mkl::transpose transpose_info[2]; Ts value_info[2]; std::int64_t size_info[3]; std::int64_t ld_info[3]; std::int64_t groupsize_info; }; -inline auto get_onemath_backend(sycl::queue& queue) -#if defined(GGML_SYCL_GENERIC) || defined(GGML_SYCL_USE_INTEL_ONEMKL) - -> sycl::queue& -#endif -{ -// If the backend is known at compile-time, use oneMath backend_selector to use -// compile-time dispatching and avoid the need to dlopen libraries. Otherwise -// fallback to runtime dispatching. -#if defined(GGML_SYCL_NVIDIA) - return oneapi::math::backend_selector{ queue }; -#elif defined(GGML_SYCL_AMD) - return oneapi::math::backend_selector{ queue }; -#elif defined(GGML_SYCL_GENERIC) || defined(GGML_SYCL_USE_INTEL_ONEMKL) - return queue; -#else - static_assert(false, "Unsupported backend"); -#endif -} - namespace dpct { typedef sycl::queue *queue_ptr; @@ -1734,7 +1707,7 @@ namespace dpct namespace detail { template - inline void gemm_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m, + inline void gemm_impl(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void * a, int lda, const void * b, int ldb, const void * beta, void * c, int ldc) { Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); @@ -1742,7 +1715,7 @@ namespace dpct auto data_a = get_memory(a); auto data_b = get_memory(b); auto data_c = get_memory(c); - oneapi::math::blas::column_major::gemm(get_onemath_backend(q), a_trans, b_trans, m, n, k, alpha_value, data_a, + oneapi::mkl::blas::column_major::gemm(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, data_b, ldb, beta_value, data_c, ldc); } @@ -1774,7 +1747,7 @@ namespace dpct }; template - inline void gemm_batch_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, + inline void gemm_batch_impl(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void ** a, int lda, const void ** b, int ldb, const void * beta, void ** c, int ldc, int batch_size, matrix_info_t * matrix_info) { @@ -1793,8 +1766,8 @@ namespace dpct matrix_info->ld_info[2] = ldc; matrix_info->groupsize_info = batch_size; - sycl::event e = oneapi::math::blas::column_major::gemm_batch( - get_onemath_backend(q), matrix_info->transpose_info, matrix_info->transpose_info + 1, + sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( + q, matrix_info->transpose_info, matrix_info->transpose_info + 1, matrix_info->size_info, matrix_info->size_info + 1, matrix_info->size_info + 2, reinterpret_cast(matrix_info->value_info), reinterpret_cast(a), matrix_info->ld_info, reinterpret_cast(b), matrix_info->ld_info + 1, @@ -1803,7 +1776,7 @@ namespace dpct } template - inline void gemm_batch_impl(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, + inline void gemm_batch_impl(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void * a, int lda, long long int stride_a, const void * b, int ldb, long long int stride_b, const void * beta, void * c, int ldc, long long int stride_c, int batch_size) { @@ -1812,7 +1785,7 @@ namespace dpct auto data_a = get_memory(a); auto data_b = get_memory(b); auto data_c = get_memory(c); - oneapi::math::blas::column_major::gemm_batch(get_onemath_backend(q), a_trans, b_trans, m, n, k, alpha_value, + oneapi::mkl::blas::column_major::gemm_batch(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, stride_a, data_b, ldb, stride_b, beta_value, data_c, ldc, stride_c, batch_size); } @@ -2299,7 +2272,7 @@ namespace dpct sycl::range<3>(x, y, 1), direction); } - inline void gemm(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m, int n, + inline void gemm(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void * a, library_data_t a_type, int lda, const void * b, library_data_t b_type, int ldb, const void * beta, void * c, library_data_t c_type, int ldc, library_data_t scaling_type) { @@ -2366,7 +2339,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float, library_data_t::real_float): { - detail::gemm_impl( + detail::gemm_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); break; } @@ -2405,7 +2378,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float): { - detail::gemm_impl( + detail::gemm_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); break; } @@ -2447,7 +2420,7 @@ namespace dpct /// \param [in] ldc Leading dimension of C. /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. /// \param [in] scaling_type Data type of the scaling factors. - inline void gemm_batch(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m, + inline void gemm_batch(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void * a[], library_data_t a_type, int lda, const void * b[], library_data_t b_type, int ldb, const void * beta, void * c[], library_data_t c_type, int ldc, int batch_size, library_data_t scaling_type, @@ -2485,7 +2458,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float): { - detail::gemm_batch_impl( + detail::gemm_batch_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info); break; } @@ -2493,7 +2466,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float, library_data_t::real_float): { - detail::gemm_batch_impl( + detail::gemm_batch_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, batch_size, matrix_info); break; } @@ -2569,7 +2542,7 @@ namespace dpct /// \param [in] stride_c Stride between the different C matrices. /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. /// \param [in] scaling_type Data type of the scaling factors. - inline void gemm_batch(sycl::queue & q, oneapi::math::transpose a_trans, oneapi::math::transpose b_trans, int m, + inline void gemm_batch(sycl::queue & q, oneapi::mkl::transpose a_trans, oneapi::mkl::transpose b_trans, int m, int n, int k, const void * alpha, const void * a, library_data_t a_type, int lda, long long int stride_a, const void * b, library_data_t b_type, int ldb, long long int stride_b, const void * beta, void * c, library_data_t c_type, int ldc, @@ -2642,7 +2615,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float): { - detail::gemm_batch_impl( + detail::gemm_batch_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c, batch_size); break; @@ -2651,7 +2624,7 @@ namespace dpct library_data_t::real_bfloat16, library_data_t::real_bfloat16, library_data_t::real_float, library_data_t::real_float): { - detail::gemm_batch_impl( + detail::gemm_batch_impl( q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, beta, c, ldc, stride_c, batch_size); break; diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index c5139fd3dd..a03d26d7f2 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -2167,8 +2167,8 @@ inline void ggml_sycl_op_mul_mat_sycl( const sycl::half alpha_f16 = 1.0f; const sycl::half beta_f16 = 0.0f; SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( - *stream, oneapi::math::transpose::trans, - oneapi::math::transpose::nontrans, row_diff, src1_ncols, ne10, + *stream, oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00, src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16, dst_f16.get(), dpct::library_data_t::real_half, ldc, @@ -2211,8 +2211,8 @@ inline void ggml_sycl_op_mul_mat_sycl( { const float alpha = 1.0f; const float beta = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(oneapi::math::blas::column_major::gemm( - get_onemath_backend(*stream), oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, row_diff, + SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( + *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), dst_dd_i, ldc))); } @@ -3165,8 +3165,8 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons const int64_t smb = ne12 == 1 ? s13 : s12; // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(*queue, oneapi::math::transpose::trans, - oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha, + SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(*queue, oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, src0_f16, dpct::library_data_t::real_half, nb01 / nb00, sma, src1_f16, dpct::library_data_t::real_half, s11, smb, beta, dst_ddf, mkl_data_type, ne0, ne1 * ne0, ne12 * ne13, mkl_compute_type))); @@ -3190,7 +3190,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons }); SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *queue, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha, + *queue, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **) (ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00, (const void **) (ptrs_src.get() + 1 * ne23), dpct::library_data_t::real_half, s11, beta, (void **) (ptrs_dst.get() + 0 * ne23), mkl_data_type, ne0, ne23, mkl_compute_type, matrix_info.get()))); @@ -3524,12 +3524,11 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); #endif // SYCL_USE_XMX - // mmvq path is faster in the CUDA backend. - if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda - // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization - // is enabled takes precedence over DMMV, the current if-else implementation - // requires disabling DMMV if both conditions are met - || (should_reorder_tensor(ctx, dst) && ggml_sycl_supports_reorder_mmvq(src0->type)))) { + // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization + // is enabled takes precedence over DMMV, the current if-else implementation + // requires disabling DMMV if both conditions are met + if (!g_ggml_sycl_prioritize_dmmv && ((should_reorder_tensor(ctx, dst) && + ggml_sycl_supports_reorder_mmvq(src0->type)))) { use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; } @@ -4189,16 +4188,6 @@ void ggml_backend_sycl_get_device_memory(int device, size_t *free, GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n"); ggml_sycl_set_device(device); - /* - DPCT1009:218: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string was - inserted. You need to rewrite this code. - */ - /* - DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for - device information which may not be supported by all compilers or runtimes. - You may need to adjust the code. - */ SYCL_CHECK(CHECK_TRY_ERROR( dpct::dev_mgr::instance().get_device(device).get_memory_info(*free, *total))); } diff --git a/ggml/src/ggml-sycl/outprod.cpp b/ggml/src/ggml-sycl/outprod.cpp index 3a17f3a1b8..f52b11f0d6 100644 --- a/ggml/src/ggml-sycl/outprod.cpp +++ b/ggml/src/ggml-sycl/outprod.cpp @@ -32,12 +32,12 @@ void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { // Handle transposition of src1 const bool src1_T = ggml_is_transposed(src1); - const oneapi::math::transpose src1_op = src1_T ? oneapi::math::transpose::nontrans : oneapi::math::transpose::trans; + const oneapi::mkl::transpose src1_op = src1_T ? oneapi::mkl::transpose::nontrans : oneapi::mkl::transpose::trans; const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float); try { - // Perform matrix multiplication using oneMath GEMM - oneapi::math::blas::column_major::gemm(get_onemath_backend(*stream), oneapi::math::transpose::nontrans, src1_op, + // Perform matrix multiplication using oneMKL GEMM + oneapi::mkl::blas::column_major::gemm(*stream, oneapi::mkl::transpose::nontrans, src1_op, ne0, ne1, ne01, alpha, src0_d, ne00, src1_d, ldb, beta, dst_d, ne0); } catch (sycl::exception const& exc) { diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp index 69140b19a4..aeaa58b95b 100644 --- a/ggml/src/ggml-sycl/rope.cpp +++ b/ggml/src/ggml-sycl/rope.cpp @@ -207,7 +207,6 @@ static void rope_vision(const T * x, T * dst, const int ne0, const int ne1, cons const int p = sector; theta_base = pos[channel_x] * sycl::pow(theta_scale, (float) p); } else { - // Simplified from CUDA backend code: if (sector >= sections.v[0] && sector < sec_w) which is just sector >= sections.v[0] const int p = sector - sections.v[0]; theta_base = pos[channel_x + ne2] * sycl::pow(theta_scale, (float) p); } diff --git a/ggml/src/ggml-sycl/wkv.cpp b/ggml/src/ggml-sycl/wkv.cpp index c10e2f7645..b56e0c2400 100644 --- a/ggml/src/ggml-sycl/wkv.cpp +++ b/ggml/src/ggml-sycl/wkv.cpp @@ -1,7 +1,7 @@ #include #include "wkv.hpp" -constexpr int WKV_BLOCK_SIZE = 64; // Matching CUDA_WKV_BLOCK_SIZE +constexpr int WKV_BLOCK_SIZE = 64; // Helper function for the main kernel template From 15818ac44c551d6d668c69bcfbf9a937c98f9e5a Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Mon, 2 Feb 2026 22:40:28 +0800 Subject: [PATCH 096/206] ci: add test-backend-ops test for CPU (#19268) --- .github/workflows/build.yml | 4 ++-- ci/run.sh | 27 +++++++++++++++++++++++++++ 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f738edefc4..fd251ac4c2 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1532,7 +1532,7 @@ jobs: - name: Test id: ggml-ci run: | - LLAMA_ARG_THREADS=$(nproc) bash ./ci/run.sh ./tmp/results ./tmp/mnt + LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt ggml-ci-arm64-cpu-high-perf: runs-on: ubuntu-22.04-arm @@ -1558,7 +1558,7 @@ jobs: - name: Test id: ggml-ci run: | - LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt + LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt ggml-ci-arm64-cpu-high-perf-sve: runs-on: ubuntu-22.04-arm diff --git a/ci/run.sh b/ci/run.sh index dfcf959661..96755ea13e 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -635,6 +635,29 @@ function gg_check_build_requirements { fi } +function gg_run_test_backend_ops_cpu { + cd ${SRC} + + cd build-ci-release + + set -e + + (time ./bin/test-backend-ops -b CPU ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops-cpu.log + + set +e +} + +function gg_sum_test_backend_ops_cpu { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test-backend-ops for CPU backend\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-test-backend-ops-cpu.log)" + gg_printf '```\n' + gg_printf '\n' +} + ## main export LLAMA_LOG_PREFIX=1 @@ -663,6 +686,10 @@ ret=0 test $ret -eq 0 && gg_run ctest_debug test $ret -eq 0 && gg_run ctest_release +if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then + test $ret -eq 0 && gg_run test_backend_ops_cpu +fi + if [ -z ${GG_BUILD_LOW_PERF} ]; then test $ret -eq 0 && gg_run embd_bge_small test $ret -eq 0 && gg_run rerank_tiny From a3fa03582240a4279ba019a3db2bb87311d5d485 Mon Sep 17 00:00:00 2001 From: Matthieu Coudron <886074+teto@users.noreply.github.com> Date: Mon, 2 Feb 2026 16:55:27 +0100 Subject: [PATCH 097/206] server: print actual model name in 'model not found" error (#19117) Experimenting with AI, my environment gets messy fast and it's not always easy to know what model my software is trying to load. This helps with troubleshooting. before: Error: { code = 400, message = "model not found", type = "invalid_request_error" } After: Error: { code = 400, message = "model 'toto' not found", type = "invalid_request_error" } --- tools/server/server-models.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 803cb02e6e..57655476af 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -767,7 +767,7 @@ static bool router_validate_model(const std::string & name, server_models & mode } auto meta = models.get_meta(name); if (!meta.has_value()) { - res_err(res, format_error_response("model not found", ERROR_TYPE_INVALID_REQUEST)); + res_err(res, format_error_response(string_format("model '%s' not found", name.c_str()), ERROR_TYPE_INVALID_REQUEST)); return false; } if (models_autoload) { From 9f682fb640765ff79ee13a7a00cdbaa15c1ed07a Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Tue, 3 Feb 2026 01:19:55 +0800 Subject: [PATCH 098/206] ggml-cpu: FA split across kv for faster TG (#19209) * ggml-cpu: split across kv for faster TG * simplify sinks application * add ref impl --- ggml/include/ggml-cpu.h | 5 + ggml/src/ggml-cpu/ggml-cpu-impl.h | 3 + ggml/src/ggml-cpu/ggml-cpu.c | 22 ++- ggml/src/ggml-cpu/ggml-cpu.cpp | 15 ++ ggml/src/ggml-cpu/ops.cpp | 237 ++++++++++++++++++++++-------- tests/test-backend-ops.cpp | 7 + 6 files changed, 220 insertions(+), 69 deletions(-) diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index 4f3b99c8d0..e3e067c916 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -19,6 +19,9 @@ extern "C" { // abort ggml_graph_compute when true ggml_abort_callback abort_callback; void * abort_callback_data; + + // use only reference implementations + bool use_ref; }; // numa strategies @@ -132,6 +135,8 @@ extern "C" { GGML_BACKEND_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool); GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); + GGML_BACKEND_API void ggml_backend_cpu_set_use_ref(ggml_backend_t backend_cpu, bool use_ref); + GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void); GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *, float *, int64_t); diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 0e8dd0ae05..88a9c9ec05 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -24,6 +24,9 @@ struct ggml_compute_params { void * wdata; struct ggml_threadpool * threadpool; + + // use reference implementation + bool use_ref; }; diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index b1de2ae871..3e5f01e3fb 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -5,7 +5,6 @@ #include "ggml-backend.h" #include "traits.h" #include "ggml-cpu-impl.h" -#include "ggml-cpu.h" #include "ggml-impl.h" #include "quants.h" #include "ggml-threading.h" @@ -2867,12 +2866,20 @@ struct ggml_cplan ggml_graph_plan( } break; case GGML_OP_FLASH_ATTN_EXT: { + const int64_t neq2 = node->src[0]->ne[2]; // number of query heads const int64_t DK = node->src[1]->ne[0]; const int64_t DV = node->src[2]->ne[0]; // Tiled flash attention scratch (tile sizes defined in common.h) // Per-thread: Q_q + KQ + mask + VKQ32 + V32 + padding - cur = sizeof(float)*(GGML_FA_TILE_Q*DK + 2*GGML_FA_TILE_Q*GGML_FA_TILE_KV + GGML_FA_TILE_Q*DV + GGML_FA_TILE_KV*DV)*n_tasks; + size_t prefill = sizeof(float)*(GGML_FA_TILE_Q*DK + 2*GGML_FA_TILE_Q*GGML_FA_TILE_KV + GGML_FA_TILE_Q*DV + GGML_FA_TILE_KV*DV)*n_tasks; + + // Decode path: n_kv_chunks = n_tasks (one chunk per thread) + // Per-thread: VKQ accmulator (DV), partial M, partial S + intra-thread scratch for V, Q and VKQ + size_t n_chunks = n_tasks; + size_t decode = sizeof(float)*(neq2*n_chunks*(2+DV) + n_tasks*(DK + 2*DV)); + + cur += MAX(prefill, decode); } break; case GGML_OP_FLASH_ATTN_BACK: { @@ -2929,11 +2936,12 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { set_numa_thread_affinity(state->ith); struct ggml_compute_params params = { - /*.ith =*/ state->ith, - /*.nth =*/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK, - /*.wsize =*/ cplan->work_size, - /*.wdata =*/ cplan->work_data, - /*.threadpool=*/ tp, + /*.ith =*/ state->ith, + /*.nth =*/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK, + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + /*.threadpool =*/ tp, + /*.use_ref =*/ cplan->use_ref, }; GGML_PRINT_DEBUG("thread #%d compute-start cplan %p last-graph %d \n", state->ith, cplan, state->last_graph); diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index f4713a4218..ddf1737a31 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -105,6 +105,8 @@ struct ggml_backend_cpu_context { ggml_abort_callback abort_callback; void * abort_callback_data; + + bool use_ref; // use reference implementation }; static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { @@ -143,6 +145,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; + cpu_plan->cplan.use_ref = cpu_ctx->use_ref; return cpu_plan; } @@ -182,6 +185,7 @@ static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, s cplan.abort_callback = cpu_ctx->abort_callback; cplan.abort_callback_data = cpu_ctx->abort_callback_data; + cplan.use_ref = cpu_ctx->use_ref; return ggml_graph_compute(cgraph, &cplan); } @@ -223,6 +227,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { ctx->work_size = 0; ctx->abort_callback = NULL; ctx->abort_callback_data = NULL; + ctx->use_ref = false; ggml_backend_t cpu_backend = new ggml_backend { /* .guid = */ ggml_backend_cpu_guid(), @@ -270,6 +275,13 @@ void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_ ctx->abort_callback_data = abort_callback_data; } +void ggml_backend_cpu_set_use_ref(ggml_backend_t backend_cpu, bool use_ref) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->use_ref = use_ref; +} + // CPU backend - device struct ggml_backend_cpu_device_context { @@ -646,6 +658,9 @@ static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const ch if (strcmp(name, "ggml_backend_cpu_is_numa") == 0) { return (void *)ggml_is_numa; } + if (strcmp(name, "ggml_backend_cpu_set_use_ref") == 0) { + return (void *)ggml_backend_cpu_set_use_ref; + } // threadpool - TODO: move to ggml-base if (strcmp(name, "ggml_threadpool_new") == 0) { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 48c8964361..ce15b18ce0 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -8042,12 +8042,14 @@ void ggml_compute_forward_top_k( } } -// ggml_compute_forward_flash_attn_ext - static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( const ggml_compute_params * params, ggml_tensor * dst, - int ir0, int ir1) { + int ir0, int ir1, + int64_t ic_start, int64_t ic_end, + float * partials, int64_t partial_stride) { + + const bool write_partials = (partials != nullptr); const ggml_tensor * q = dst->src[0]; const ggml_tensor * k = dst->src[1]; const ggml_tensor * v = dst->src[2]; @@ -8124,7 +8126,6 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( int ith = params->ith; - // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); @@ -8165,7 +8166,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf - for (int64_t ic = 0; ic < nek1; ++ic) { + for (int64_t ic = ic_start; ic < ic_end; ++ic) { const float mv = mp ? slope*GGML_CPU_FP16_TO_FP32(mp[ic]) : 0.0f; if (mv == -INFINITY) { continue; @@ -8238,8 +8239,8 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( } } - // sinks - if (sinks) { + // sinks - apply only on the first kv-chunk + if (sinks && ic_start == 0) { const float s = ((float *)((char *) sinks->data))[h]; float ms = 1.0f; @@ -8247,6 +8248,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( if (s > M) { ms = expf(M - s); + M = s; ggml_vec_scale_f32(DV, VKQ32, ms); } else { vs = expf(s - M); @@ -8255,20 +8257,26 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk( S = S*ms + vs; } - // V /= S - const float S_inv = S == 0.0f ? 0.0f : 1.0f/S; - ggml_vec_scale_f32(DV, VKQ32, S_inv); + if (write_partials) { + // Write M, S, VKQ to partials for later reduction + // partials layout: [M, S, VKQ[DV]] per query head + float * partial = partials + ir * partial_stride; + partial[0] = M; + partial[1] = S; + memcpy(partial + 2, VKQ32, DV * sizeof(float)); + } else { + // V /= S + const float S_inv = S == 0.0f ? 0.0f : 1.0f/S; + ggml_vec_scale_f32(DV, VKQ32, S_inv); - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; + // dst indices + const int i1 = iq1; + const int i2 = iq2; + const int i3 = iq3; - // original - //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); - - // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); + } } } @@ -8546,6 +8554,78 @@ static void ggml_compute_forward_flash_attn_ext_tiled( } } +// Reduction function: combines partial results across KV chunks +// Partials layout in wdata: [n_q_heads][n_chunks][2 + DV] +static void ggml_flash_attn_ext_reduce_partials( + const ggml_compute_params * params, + ggml_tensor * dst, + const int64_t n_chunks, + const int64_t chunk_size) { + + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * v = dst->src[2]; + + const int64_t DK = k->ne[0]; + const int64_t DV = v->ne[0]; + const int64_t nek1 = k->ne[1]; + const int64_t n_q_heads = q->ne[2]; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t wdata_per_thread = DK + 2*DV + CACHE_LINE_SIZE_F32; + float * thread_wdata = (float *) params->wdata + ith * wdata_per_thread; + + const int64_t partials_offset = nth * (DK + 2*DV + CACHE_LINE_SIZE_F32); + const int64_t partial_size = 2 + DV; + const float * partials_base = (const float *) params->wdata + partials_offset; + + // Output layout + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const size_t nb1 = dst->nb[1]; + + // Each thread reduces a subset of query heads + for (int64_t q_head = ith; q_head < n_q_heads; q_head += nth) { + float M_final = -INFINITY; + float S_final = 0.0f; + float * VKQ_final = thread_wdata; + memset(VKQ_final, 0, DV * sizeof(float)); + + // Combine partials from all chunks + for (int64_t chunk_idx = 0; chunk_idx < n_chunks; ++chunk_idx) { + const int64_t ic_start = chunk_idx * chunk_size; + if (ic_start >= nek1) continue; + + const float * partial = partials_base + (q_head * n_chunks + chunk_idx) * partial_size; + const float M_chunk = partial[0]; + const float S_chunk = partial[1]; + const float * VKQ_chunk = partial + 2; + + if (S_chunk == 0.0f) continue; + + const float M_new = fmaxf(M_final, M_chunk); + const float scale_old = expf(M_final - M_new); + const float scale_new = expf(M_chunk - M_new); + + for (int64_t d = 0; d < DV; ++d) { + VKQ_final[d] = VKQ_final[d] * scale_old + VKQ_chunk[d] * scale_new; + } + S_final = S_final * scale_old + S_chunk * scale_new; + M_final = M_new; + } + + // Normalize and write to output + if (S_final != 0.0f) { + const float S_inv = 1.0f / S_final; + ggml_vec_scale_f32(DV, VKQ_final, S_inv); + } + // iq1=0, iq3=0 for decode + memcpy((char *) dst->data + (0*ne2*ne1 + q_head + 0*ne1)*nb1, VKQ_final, nb1); + } +} + static void ggml_compute_forward_flash_attn_ext_f16( const ggml_compute_params * params, ggml_tensor * dst) { @@ -8567,6 +8647,7 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int64_t DV = nev0; const int64_t N = neq1; + GGML_ASSERT(ne0 == DV); GGML_ASSERT(ne2 == N); @@ -8587,60 +8668,92 @@ static void ggml_compute_forward_flash_attn_ext_f16( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int64_t nr = neq1*neq2*neq3; - - // rows per thread const int ith = params->ith; const int nth = params->nth; - // disable for NUMA - const bool disable_chunking = ggml_is_numa(); + // When use_ref is set, force the vec-only reference implementation (no tiling, no KV-chunking) + const bool use_ref = params->use_ref; - // 4x chunks per thread - int nth_scaled = nth * 4; - int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled; - int64_t nchunk = (nr + chunk_size - 1) / chunk_size; - - if (nth == 1 || nchunk < nth || disable_chunking) { - nchunk = nth; - } - - if (ith == 0) { - // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - ggml_threadpool_chunk_set(params->threadpool, nth); - } - - ggml_barrier(params->threadpool); - - // The number of elements in each chunk - const int64_t dr = (nr + nchunk - 1) / nchunk; - - static constexpr int64_t KV_TILE_SZ = ggml_fa_tile_config::KV; - static constexpr int64_t Q_TILE_SZ = ggml_fa_tile_config::Q; const bool kv_is_f32_or_f16 = (k->type == GGML_TYPE_F32 || k->type == GGML_TYPE_F16); - const bool use_tiled = (q->type == GGML_TYPE_F32 && - kv_is_f32_or_f16 && - k->type == v->type && - nek1 % KV_TILE_SZ == 0 && - neq1 >= Q_TILE_SZ); // Only use tiled for batch >= tile size + const bool use_split_kv_path = !use_ref && (neq1 == 1 && neq3 == 1) && kv_is_f32_or_f16 && (k->type == v->type) && q->type == GGML_TYPE_F32 && nek1 >= 512; - // The first chunk comes from our thread_id, the rest will get auto-assigned. - int current_chunk = ith; + if (use_split_kv_path) { + const int64_t chunk_size = (nek1 + nth - 1) / nth; - while (current_chunk < nchunk) { - const int64_t ir0 = dr * current_chunk; - const int64_t ir1 = MIN(ir0 + dr, nr); + // Partials buffer layout: [q_head][kv_chunk][M, S, VKQ] + const int64_t partial_size = 2 + DV; + float * partials_base = (float *) params->wdata + nth * (DK + 2*DV + CACHE_LINE_SIZE_F32); - if (use_tiled) { - ggml_compute_forward_flash_attn_ext_tiled(params, dst, ir0, ir1); + const int64_t ic_start = ith * chunk_size; + const int64_t ic_end = std::min(ic_start + chunk_size, nek1); + + const int64_t partial_stride = nth * partial_size; + float * chunk_partials = partials_base + ith * partial_size; + + if (ic_start < nek1) { + for (int64_t q_head = 0; q_head < neq2; q_head++) { + ggml_compute_forward_flash_attn_ext_f16_one_chunk( + params, dst, q_head, q_head + 1, ic_start, ic_end, + chunk_partials, partial_stride); + } } else { - ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1); + for (int64_t q_head = 0; q_head < neq2; q_head++) { + float * q_partials = chunk_partials + q_head * partial_stride; + q_partials[0] = -INFINITY; // M + q_partials[1] = 0.0f; // S + } } - current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1); + ggml_barrier(params->threadpool); + ggml_flash_attn_ext_reduce_partials(params, dst, nth, chunk_size); + } else { + + // total rows in q + const int64_t nr = neq1*neq2*neq3; + + // disable for NUMA + const bool disable_chunking = ggml_is_numa(); + + // 4x chunks per thread + int nth_scaled = nth * 4; + int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled; + int64_t nchunk = (nr + chunk_size - 1) / chunk_size; + + if (nth == 1 || nchunk < nth || disable_chunking) { + nchunk = nth; + } + + if (ith == 0) { + ggml_threadpool_chunk_set(params->threadpool, nth); + } + + ggml_barrier(params->threadpool); + + const int64_t dr = (nr + nchunk - 1) / nchunk; + + static constexpr int64_t KV_TILE_SZ = ggml_fa_tile_config::KV; + static constexpr int64_t Q_TILE_SZ = ggml_fa_tile_config::Q; + const bool use_tiled = !use_ref && + (q->type == GGML_TYPE_F32 && + kv_is_f32_or_f16 && + k->type == v->type && + nek1 % KV_TILE_SZ == 0 && + neq1 >= Q_TILE_SZ); + + int current_chunk = ith; + + while (current_chunk < nchunk) { + const int64_t ir0 = dr * current_chunk; + const int64_t ir1 = MIN(ir0 + dr, nr); + + if (use_tiled) { + ggml_compute_forward_flash_attn_ext_tiled(params, dst, ir0, ir1); + } else { + ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1, 0, nek1, nullptr, 0); + } + + current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1); + } } } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 411467e968..90cc0d7da2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -8591,6 +8591,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op output_printer->print_operation(info); return false; } + // Use reference implementation on the CPU backend for comparison + using ggml_backend_cpu_set_use_ref_t = void (*)(ggml_backend_t, bool); + auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend_cpu)); + auto * set_use_ref = (ggml_backend_cpu_set_use_ref_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_set_use_ref"); + if (set_use_ref) { + set_use_ref(backend_cpu, true); + } size_t n_ok = 0; size_t tests_run = 0; From 07a7412a3b7518a55bf6fe191beb754a1dd2a561 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 2 Feb 2026 20:59:06 +0100 Subject: [PATCH 099/206] mtmd: add min/max pixels gguf metadata (#19273) --- gguf-py/gguf/constants.py | 2 ++ gguf-py/gguf/gguf_writer.py | 6 ++++++ tools/mtmd/clip-impl.h | 2 ++ 3 files changed, 10 insertions(+) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 31273b2b5a..6f56d36c59 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -284,6 +284,8 @@ class Keys: class ClipVision: PROJECTOR_TYPE = "clip.vision.projector_type" # for mixed modality models IMAGE_SIZE = "clip.vision.image_size" + IMAGE_MIN_PIXELS = "clip.vision.image_min_pixels" + IMAGE_MAX_PIXELS = "clip.vision.image_max_pixels" PREPROC_IMAGE_SIZE = "clip.vision.preproc_image_size" PATCH_SIZE = "clip.vision.patch_size" EMBEDDING_LENGTH = "clip.vision.embedding_length" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 7fbb78866b..0b9c650161 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1113,6 +1113,12 @@ class GGUFWriter: def add_vision_image_size(self, value: int) -> None: self.add_uint32(Keys.ClipVision.IMAGE_SIZE, value) + def add_vision_max_pixels(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.IMAGE_MAX_PIXELS, value) + + def add_vision_min_pixels(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.IMAGE_MIN_PIXELS, value) + def add_vision_preproc_image_size(self, value: int) -> None: self.add_uint32(Keys.ClipVision.PREPROC_IMAGE_SIZE, value) diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index dd693623a2..ad232178bf 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -36,6 +36,8 @@ // vision-specific #define KEY_VISION_PROJ_TYPE "clip.vision.projector_type" // for models with mixed modalities #define KEY_IMAGE_SIZE "clip.vision.image_size" +#define KEY_IMAGE_MIN_PIXELS "clip.vision.image_min_pixels" +#define KEY_IMAGE_MAX_PIXELS "clip.vision.image_max_pixels" #define KEY_PREPROC_IMAGE_SIZE "clip.vision.preproc_image_size" #define KEY_PATCH_SIZE "clip.vision.patch_size" #define KEY_IMAGE_MEAN "clip.vision.image_mean" From 0dfcd3b60755bf9cb3c9ec726a584b8a4f20239b Mon Sep 17 00:00:00 2001 From: Sid Mohan <61345237+sidmohan0@users.noreply.github.com> Date: Mon, 2 Feb 2026 12:00:55 -0800 Subject: [PATCH 100/206] jinja : add missing 'in' test to template engine (#19004) (#19239) * jinja : add missing 'in' test to template engine (#19004) The jinja template parser was missing the 'in' test from global_builtins(), causing templates using reject("in", ...), select("in", ...), or 'x is in(y)' to fail with "selectattr: unknown test 'in'". This broke tool-calling for Qwen3-Coder and any other model whose chat template uses the 'in' test. Added test_is_in supporting array, string, and object containment checks, mirroring the existing 'in' operator logic in runtime.cpp. Includes test cases for all three containment types plus reject/select filter usage. Co-Authored-By: Claude Opus 4.5 * reuse test_is_in in binary op --------- Co-authored-by: Sid Mohan Co-authored-by: Claude Opus 4.5 Co-authored-by: Xuan Son Nguyen --- common/jinja/runtime.cpp | 34 +++++++++++++-------------- common/jinja/value.cpp | 27 ++++++++++++++++++++++ tests/test-jinja.cpp | 50 +++++++++++++++++++++++++++++++++++++++- 3 files changed, 93 insertions(+), 18 deletions(-) diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp index f234d9284f..4453d86e6d 100644 --- a/common/jinja/runtime.cpp +++ b/common/jinja/runtime.cpp @@ -144,6 +144,13 @@ value binary_expression::execute_impl(context & ctx) { return false; }; + auto test_is_in = [&]() -> bool { + func_args args(ctx); + args.push_back(left_val); + args.push_back(right_val); + return global_builtins().at("test_is_in")(args)->as_bool(); + }; + // Handle undefined and null values if (is_val(left_val) || is_val(right_val)) { if (is_val(right_val) && (op.value == "in" || op.value == "not in")) { @@ -223,19 +230,11 @@ value binary_expression::execute_impl(context & ctx) { return result; } } else if (is_val(right_val)) { - auto & arr = right_val->as_array(); - bool member = false; - for (const auto & item : arr) { - if (*left_val == *item) { - member = true; - break; - } - } + // case: 1 in [0, 1, 2] + bool member = test_is_in(); if (op.value == "in") { - JJ_DEBUG("Checking membership: %s in Array is %d", left_val->type().c_str(), member); return mk_val(member); } else if (op.value == "not in") { - JJ_DEBUG("Checking non-membership: %s not in Array is %d", left_val->type().c_str(), !member); return mk_val(!member); } } @@ -252,22 +251,23 @@ value binary_expression::execute_impl(context & ctx) { // String membership if (is_val(left_val) && is_val(right_val)) { - auto left_str = left_val->as_string().str(); - auto right_str = right_val->as_string().str(); + // case: "a" in "abc" + bool member = test_is_in(); if (op.value == "in") { - return mk_val(right_str.find(left_str) != std::string::npos); + return mk_val(member); } else if (op.value == "not in") { - return mk_val(right_str.find(left_str) == std::string::npos); + return mk_val(!member); } } // Value key in object if (is_val(right_val)) { - bool has_key = right_val->has_key(left_val); + // case: key in {key: value} + bool member = test_is_in(); if (op.value == "in") { - return mk_val(has_key); + return mk_val(member); } else if (op.value == "not in") { - return mk_val(!has_key); + return mk_val(!member); } } diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp index f254ae9251..2aa156b177 100644 --- a/common/jinja/value.cpp +++ b/common/jinja/value.cpp @@ -393,6 +393,33 @@ const func_builtins & global_builtins() { {"test_is_lt", test_compare_fn}, {"test_is_lessthan", test_compare_fn}, {"test_is_ne", test_compare_fn}, + {"test_is_in", [](const func_args & args) -> value { + args.ensure_count(2); + auto needle = args.get_pos(0); + auto haystack = args.get_pos(1); + if (is_val(haystack)) { + return mk_val(false); + } + if (is_val(haystack)) { + for (const auto & item : haystack->as_array()) { + if (*needle == *item) { + return mk_val(true); + } + } + return mk_val(false); + } + if (is_val(haystack)) { + if (!is_val(needle)) { + throw raised_exception("'in' test expects args[1] as string when args[0] is string, got args[1] as " + needle->type()); + } + return mk_val( + haystack->as_string().str().find(needle->as_string().str()) != std::string::npos); + } + if (is_val(haystack)) { + return mk_val(haystack->has_key(needle)); + } + throw raised_exception("'in' test expects iterable as first argument, got " + haystack->type()); + }}, {"test_is_test", [](const func_args & args) -> value { args.ensure_vals(); auto & builtins = global_builtins(); diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index f6114f1e2f..1f25c6ae71 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -189,12 +189,24 @@ static void test_conditionals(testing & t) { "negated" ); - test_template(t, "in operator", + test_template(t, "in operator (element in array)", "{% if 'x' in items %}found{% endif %}", {{"items", json::array({"x", "y"})}}, "found" ); + test_template(t, "in operator (substring)", + "{% if 'bc' in 'abcd' %}found{% endif %}", + json::object(), + "found" + ); + + test_template(t, "in operator (object key)", + "{% if 'key' in obj %}found{% endif %}", + {{"obj", {{"key", 1}, {"other", 2}}}}, + "found" + ); + test_template(t, "is defined", "{% if x is defined %}yes{% else %}no{% endif %}", {{"x", 1}}, @@ -1036,6 +1048,42 @@ static void test_tests(testing & t) { json::object(), "yes" ); + + test_template(t, "is in (array, true)", + "{{ 'yes' if 2 is in([1, 2, 3]) }}", + json::object(), + "yes" + ); + + test_template(t, "is in (array, false)", + "{{ 'yes' if 5 is in([1, 2, 3]) else 'no' }}", + json::object(), + "no" + ); + + test_template(t, "is in (string)", + "{{ 'yes' if 'bc' is in('abcde') }}", + json::object(), + "yes" + ); + + test_template(t, "is in (object keys)", + "{{ 'yes' if 'a' is in(obj) }}", + {{"obj", {{"a", 1}, {"b", 2}}}}, + "yes" + ); + + test_template(t, "reject with in test", + "{{ items | reject('in', skip) | join(', ') }}", + {{"items", json::array({"a", "b", "c", "d"})}, {"skip", json::array({"b", "d"})}}, + "a, c" + ); + + test_template(t, "select with in test", + "{{ items | select('in', keep) | join(', ') }}", + {{"items", json::array({"a", "b", "c", "d"})}, {"keep", json::array({"b", "c"})}}, + "b, c" + ); } static void test_string_methods(testing & t) { From 91ea44e89b30474831d5dc0ad57719a5819506db Mon Sep 17 00:00:00 2001 From: lhez Date: Mon, 2 Feb 2026 15:54:43 -0800 Subject: [PATCH 101/206] opencl: refactor some ops, concat, repeat, tanh and scale (#19226) * opencl: refactor concat * opencl: refactor repeat * opencl: refactor tanh * opencl: enable fp16 for tanh * opencl: refactor scale * opencl: fix unused variables --- ggml/src/ggml-opencl/ggml-opencl.cpp | 484 +++++++++++-------------- ggml/src/ggml-opencl/kernels/concat.cl | 140 +++---- ggml/src/ggml-opencl/kernels/repeat.cl | 63 ++-- ggml/src/ggml-opencl/kernels/scale.cl | 18 +- ggml/src/ggml-opencl/kernels/tanh.cl | 142 +++++--- 5 files changed, 400 insertions(+), 447 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 0f0eb3a9d8..508b2b8f03 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -453,7 +453,6 @@ struct ggml_backend_opencl_context { cl_program program_rms_norm; cl_program program_group_norm; cl_program program_rope; - cl_program program_scale; cl_program program_silu; cl_program program_sigmoid; cl_program program_softmax_f32; @@ -462,11 +461,8 @@ struct ggml_backend_opencl_context { cl_program program_softmax_4_f16; cl_program program_argsort_f32_i32; cl_program program_sum_rows_f32; - cl_program program_repeat; cl_program program_pad; - cl_program program_tanh; cl_program program_upscale; - cl_program program_concat; cl_program program_conv_2d_f16; cl_program program_conv_2d_f32; cl_program program_conv_2d_f16_f32; @@ -485,7 +481,7 @@ struct ggml_backend_opencl_context { 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_scale_f32, kernel_scale_f32_4; cl_kernel kernel_sqr_cont_f32, kernel_sqr_cont_f32_4, kernel_sqr_cont_f16, kernel_sqr_cont_f16_4; cl_kernel kernel_sqrt_cont_f32, kernel_sqrt_cont_f32_4, kernel_sqrt_cont_f16, kernel_sqrt_cont_f16_4; cl_kernel kernel_mean_f32; @@ -544,18 +540,17 @@ struct ggml_backend_opencl_context { cl_kernel kernel_im2col_f32, kernel_im2col_f16; cl_kernel kernel_argsort_f32_i32; cl_kernel kernel_sum_rows_f32; - cl_kernel kernel_repeat; + cl_kernel kernel_repeat_f32; cl_kernel kernel_pad; - cl_kernel kernel_tanh_f32_nd; - cl_kernel kernel_tanh_f16_nd; + cl_kernel kernel_tanh_f32, kernel_tanh_f32_4, kernel_tanh_f32_nc; + cl_kernel kernel_tanh_f16, kernel_tanh_f16_4, kernel_tanh_f16_nc; cl_kernel kernel_expm1_f32_nd; cl_kernel kernel_expm1_f16_nd; cl_kernel kernel_softplus_f32_nd; cl_kernel kernel_softplus_f16_nd; cl_kernel kernel_upscale; cl_kernel kernel_upscale_bilinear; - cl_kernel kernel_concat_f32_contiguous; - cl_kernel kernel_concat_f32_non_contiguous; + cl_kernel kernel_concat_f32; cl_kernel kernel_conv_2d_f16; cl_kernel kernel_conv_2d_f32; cl_kernel kernel_conv_2d_f16_f32; @@ -1483,10 +1478,12 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve #else const std::string kernel_src = read_file("scale.cl"); #endif - backend_ctx->program_scale = + cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); - CL_CHECK((backend_ctx->kernel_scale = clCreateKernel(backend_ctx->program_scale, "kernel_scale", &err), err)); + CL_CHECK((backend_ctx->kernel_scale_f32 = clCreateKernel(prog, "kernel_scale_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_scale_f32_4 = clCreateKernel(prog, "kernel_scale_f32_4", &err), err)); + CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -1814,16 +1811,11 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve #else const std::string kernel_src = read_file("repeat.cl"); #endif - if (!kernel_src.empty()) { - backend_ctx->program_repeat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); - CL_CHECK((backend_ctx->kernel_repeat = clCreateKernel(backend_ctx->program_repeat, "kernel_repeat", &err), err)); - GGML_LOG_CONT("."); - } else { - GGML_LOG_WARN("ggml_opencl: repeat kernel source not found or empty. Repeat operations will not be available.\n"); - backend_ctx->program_repeat = nullptr; - backend_ctx->kernel_repeat = nullptr; - } + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_repeat_f32 = clCreateKernel(prog, "kernel_repeat_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); } // pad @@ -1856,18 +1848,16 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve #else const std::string kernel_src = read_file("tanh.cl"); #endif - if (!kernel_src.empty()) { - backend_ctx->program_tanh = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); - CL_CHECK((backend_ctx->kernel_tanh_f32_nd = clCreateKernel(backend_ctx->program_tanh, "kernel_tanh_f32_nd", &err), err)); - CL_CHECK((backend_ctx->kernel_tanh_f16_nd = clCreateKernel(backend_ctx->program_tanh, "kernel_tanh_f16_nd", &err), err)); - GGML_LOG_CONT("."); - } else { - GGML_LOG_WARN("ggml_opencl: tanh kernel source not found or empty. Tanh operation will not be available.\n"); - backend_ctx->program_tanh = nullptr; - backend_ctx->kernel_tanh_f32_nd = nullptr; - backend_ctx->kernel_tanh_f16_nd = nullptr; - } + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_tanh_f32 = clCreateKernel(prog, "kernel_tanh_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_tanh_f32_4 = clCreateKernel(prog, "kernel_tanh_f32_4", &err), err)); + CL_CHECK((backend_ctx->kernel_tanh_f32_nc = clCreateKernel(prog, "kernel_tanh_f32_nc", &err), err)); + CL_CHECK((backend_ctx->kernel_tanh_f16 = clCreateKernel(prog, "kernel_tanh_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_tanh_f16_4 = clCreateKernel(prog, "kernel_tanh_f16_4", &err), err)); + CL_CHECK((backend_ctx->kernel_tanh_f16_nc = clCreateKernel(prog, "kernel_tanh_f16_nc", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); } // expm1 @@ -1959,22 +1949,13 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve #include "concat.cl.h" }; #else - const std::string kernel_src = read_file("concat.cl"); #endif - if (!kernel_src.empty()) { - backend_ctx->program_concat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); - - CL_CHECK((backend_ctx->kernel_concat_f32_contiguous = clCreateKernel(backend_ctx->program_concat, "kernel_concat_f32_contiguous", &err), err)); - CL_CHECK((backend_ctx->kernel_concat_f32_non_contiguous = clCreateKernel(backend_ctx->program_concat, "kernel_concat_f32_non_contiguous", &err), err)); - GGML_LOG_CONT("."); - } else { - GGML_LOG_WARN("ggml_opencl: concat kernel source not found or empty. Concat operations will not be available.\n"); - backend_ctx->program_concat = nullptr; - backend_ctx->kernel_concat_f32_contiguous = nullptr; - backend_ctx->kernel_concat_f32_non_contiguous = nullptr; - } + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_concat_f32 = clCreateKernel(prog, "kernel_concat_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); } // timestep_embedding @@ -3318,8 +3299,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_UNARY_OP_SIGMOID: return ggml_is_contiguous(op->src[0]); case GGML_UNARY_OP_TANH: - return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) || - (op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16); + return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_UNARY_OP_EXPM1: return (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) || (op->src[0]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16); @@ -7029,79 +7009,87 @@ static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; - cl_ulong offset0_abs = extra0->offset + src0->view_offs; - cl_ulong offsetd_abs = extrad->offset + dst->view_offs; + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + 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 cl_ulong nb0 = dst->nb[0]; + const cl_ulong nb1 = dst->nb[1]; + const cl_ulong nb2 = dst->nb[2]; + const cl_ulong nb3 = dst->nb[3]; cl_kernel kernel; - if (dst->type == GGML_TYPE_F32) { - kernel = backend_ctx->kernel_tanh_f32_nd; - } else if (dst->type == GGML_TYPE_F16) { - kernel = backend_ctx->kernel_tanh_f16_nd; - } else { - GGML_ASSERT(false && "Unsupported type for ggml_cl_tanh"); - } - GGML_ASSERT(kernel != nullptr); - 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 = dst->ne[0]; const int ne11 = dst->ne[1]; const int ne12 = dst->ne[2]; const int ne13 = dst->ne[3]; - const cl_ulong nb10 = dst->nb[0]; const cl_ulong nb11 = dst->nb[1]; const cl_ulong nb12 = dst->nb[2]; const cl_ulong nb13 = dst->nb[3]; - - CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0_abs)); - CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd_abs)); - - CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01)); - CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02)); - CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03)); - CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb00)); - 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), &ne10)); - CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne11)); - CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne12)); - CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne13)); - CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong),&nb10)); - CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong),&nb11)); - CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong),&nb12)); - CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong),&nb13)); - - size_t global_work_size[3]; - if (ne10 == 0 || ne11 == 0 || ne12 == 0 || ne13 == 0) { // Handle case of 0 elements - return; - } - global_work_size[0] = (size_t)ne10; - global_work_size[1] = (size_t)ne11; - global_work_size[2] = (size_t)ne12; - - size_t lws0 = 16, lws1 = 4, lws2 = 1; - if (ne10 < 16) lws0 = ne10; - if (ne11 < 4) lws1 = ne11; - if (ne12 < 1) lws2 = ne12 > 0 ? ne12 : 1; - - while (lws0 * lws1 * lws2 > 256 && lws0 > 1) lws0 /= 2; - while (lws0 * lws1 * lws2 > 256 && lws1 > 1) lws1 /= 2; - while (lws0 * lws1 * lws2 > 256 && lws2 > 1) lws2 /= 2; - - - size_t local_work_size[] = {lws0, lws1, lws2}; - - size_t* local_work_size_ptr = local_work_size; - if (!backend_ctx->non_uniform_workgroups) { - if (global_work_size[0] % local_work_size[0] != 0 || - global_work_size[1] % local_work_size[1] != 0 || - global_work_size[2] % local_work_size[2] != 0) { - local_work_size_ptr = NULL; + if (ggml_is_contiguous(src0)) { + // Handle contiguous input + int n = ggml_nelements(dst); + if (n % 4 == 0) { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_tanh_f32_4; + } else { + kernel = backend_ctx->kernel_tanh_f16_4; + } + n /= 4; + } else { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_tanh_f32; + } else { + kernel = backend_ctx->kernel_tanh_f16; + } } - } - if (global_work_size[0] == 0 || global_work_size[1] == 0 || global_work_size[2] == 0) return; - backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst); + 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), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + + size_t global_work_size[] = {(size_t)n, 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + size_t * local_work_size_ptr = local_work_size; + if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) { + local_work_size_ptr = nullptr; + } + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst); + } else { + // Handle non-contiguous input + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_tanh_f32_nc; + } else { + kernel = backend_ctx->kernel_tanh_f16_nc; + } + + 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), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + 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}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } } static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -7319,53 +7307,58 @@ static void ggml_cl_repeat(ggml_backend_t backend, const ggml_tensor * src0, con ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; - if (backend_ctx->kernel_repeat == nullptr) { - GGML_LOG_WARN("%s: repeat kernel not available, skipping OpenCL execution.\n", __func__); - return; - } + 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 * extra_src0 = (ggml_tensor_extra_cl *)src0->extra; - ggml_tensor_extra_cl * extra_dst = (ggml_tensor_extra_cl *)dst->extra; + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; - cl_ulong off_src0 = extra_src0->offset + src0->view_offs; - cl_ulong off_dst = extra_dst->offset + dst->view_offs; + 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 src0_ne0 = src0->ne[0]; const int src0_ne1 = src0->ne[1]; const int src0_ne2 = src0->ne[2]; const int src0_ne3 = src0->ne[3]; - const cl_ulong src0_nb0 = src0->nb[0]; const cl_ulong src0_nb1 = src0->nb[1]; const cl_ulong src0_nb2 = src0->nb[2]; const cl_ulong src0_nb3 = src0->nb[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 dst_ne0 = dst->ne[0]; const int dst_ne1 = dst->ne[1]; const int dst_ne2 = dst->ne[2]; const int dst_ne3 = dst->ne[3]; - const cl_ulong dst_nb0 = dst->nb[0]; const cl_ulong dst_nb1 = dst->nb[1]; const cl_ulong dst_nb2 = dst->nb[2]; const cl_ulong dst_nb3 = dst->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]; - cl_kernel kernel = backend_ctx->kernel_repeat; + const cl_ulong nb0 = dst->nb[0]; + const cl_ulong nb1 = dst->nb[1]; + const cl_ulong nb2 = dst->nb[2]; + const cl_ulong nb3 = dst->nb[3]; - CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_src0->data_device)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra_dst->data_device)); - CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_ulong), &off_src0)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_dst)); - CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &src0_ne0)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &src0_ne1)); - CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &src0_ne2)); - CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &src0_ne3)); - CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &src0_nb0)); - CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &src0_nb1)); - CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &src0_nb2)); - CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &src0_nb3)); - CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &dst_ne0)); - CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &dst_ne1)); - CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &dst_ne2)); - CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &dst_ne3)); - CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &dst_nb0)); - CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &dst_nb1)); - CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &dst_nb2)); - CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &dst_nb3)); + cl_kernel kernel = backend_ctx->kernel_repeat_f32; - size_t gws0 = dst_ne1 > 0 ? (size_t)dst_ne1 : 1; - size_t gws1 = dst_ne2 > 0 ? (size_t)dst_ne2 : 1; - size_t gws2 = dst_ne3 > 0 ? (size_t)dst_ne3 : 1; + 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), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb00)); + 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), &ne0)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb3)); - size_t global_work_size[] = { gws0, gws1, gws2 }; + int nth = 64; - backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst); + size_t global_work_size[] = {(size_t)ne1*nth, (size_t)ne2, (size_t)ne3}; + 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_pad(ggml_backend_t backend, const ggml_tensor * src0, ggml_tensor * dst) { @@ -7589,121 +7582,76 @@ static void ggml_cl_concat(ggml_backend_t backend, const ggml_tensor * src0, con GGML_ASSERT(dst->type == GGML_TYPE_F32); ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; - cl_command_queue queue = backend_ctx->queue; - if (backend_ctx->kernel_concat_f32_contiguous == nullptr || backend_ctx->kernel_concat_f32_non_contiguous == nullptr) { - GGML_LOG_WARN("%s: concat kernels not available, skipping OpenCL execution.\n", __func__); - return; - } + 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 * extrad = (ggml_tensor_extra_cl *)dst->extra; - ggml_tensor_extra_cl * extra0_cl = (ggml_tensor_extra_cl *)src0->extra; - ggml_tensor_extra_cl * extra1_cl = (ggml_tensor_extra_cl *)src1->extra; - ggml_tensor_extra_cl * extrad_cl = (ggml_tensor_extra_cl *)dst->extra; + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offset1 = extra1->offset + src1->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; - cl_ulong off_src0 = extra0_cl->offset + src0->view_offs; - cl_ulong off_src1 = extra1_cl->offset + src1->view_offs; - cl_ulong off_dst = extrad_cl->offset + dst->view_offs; + 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 int32_t dim = ((const int32_t *) dst->op_params)[0]; + 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 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]; + + 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]; + const cl_ulong nb2 = dst->nb[2]; + const cl_ulong nb3 = dst->nb[3]; + + const cl_int dim = ((const int32_t *) dst->op_params)[0]; GGML_ASSERT(dim >= 0 && dim <= 3); - if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) { - if (dim == 3) { + int nth = MIN(64, ne0); - size_t nbytes_src0 = ggml_nbytes(src0); - size_t nbytes_src1 = ggml_nbytes(src1); + cl_kernel kernel = backend_ctx->kernel_concat_f32; - CL_CHECK(clEnqueueCopyBuffer(queue, extra0_cl->data_device, extrad_cl->data_device, - off_src0, off_dst, nbytes_src0, 0, NULL, NULL)); - CL_CHECK(clEnqueueCopyBuffer(queue, extra1_cl->data_device, extrad_cl->data_device, - off_src1, off_dst + nbytes_src0, nbytes_src1, 0, NULL, NULL)); - } else { + 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(cl_ulong), &nb10)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12)); + CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13)); + CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &ne0)); + CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &nb3)); + CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_int), &dim)); - cl_kernel kernel = backend_ctx->kernel_concat_f32_contiguous; - size_t global_work_size[3]; + size_t global_work_size[] = {(size_t)ne1*nth, (size_t)ne2, (size_t)ne3}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; - for (int i3 = 0; i3 < dst->ne[3]; ++i3) { - cl_ulong current_off_src0 = off_src0 + (i3 * src0->nb[3]); - cl_ulong current_off_src1 = off_src1 + (i3 * src1->nb[3]); - cl_ulong current_off_dst = off_dst + (i3 * dst->nb[3]); - - int d_ne00 = src0->ne[0]; int d_ne01 = src0->ne[1]; int d_ne02 = src0->ne[2]; - int d_ne10 = src1->ne[0]; int d_ne11 = src1->ne[1]; int d_ne12 = src1->ne[2]; - int d_ne0 = dst->ne[0]; int d_ne1 = dst->ne[1]; int d_ne2 = dst->ne[2]; - - CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), ¤t_off_src0)); - CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), ¤t_off_src1)); - CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), ¤t_off_dst)); - CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &d_ne00)); - CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &d_ne01)); - CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &d_ne02)); - CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &d_ne10)); - CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &d_ne11)); - CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &d_ne12)); - CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &d_ne0)); - CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &d_ne1)); - CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &d_ne2)); - CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &dim)); - - global_work_size[0] = d_ne0; - global_work_size[1] = d_ne1; - global_work_size[2] = d_ne2; - - backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst); - } - } - } else { - cl_kernel kernel = backend_ctx->kernel_concat_f32_non_contiguous; - - cl_long ne00 = src0->ne[0], ne01 = src0->ne[1], ne02 = src0->ne[2], ne03 = src0->ne[3]; - cl_ulong nb00 = src0->nb[0], nb01 = src0->nb[1], nb02 = src0->nb[2], nb03 = src0->nb[3]; - - cl_ulong nb10 = src1->nb[0], nb11 = src1->nb[1], nb12 = src1->nb[2], nb13 = src1->nb[3]; - - cl_long d_ne0 = dst->ne[0], d_ne1 = dst->ne[1], d_ne2 = dst->ne[2], d_ne3 = dst->ne[3]; - cl_ulong d_nb0 = dst->nb[0], d_nb1 = dst->nb[1], d_nb2 = dst->nb[2], d_nb3 = dst->nb[3]; - - - CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &off_src0)); - CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &off_src1)); - CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad_cl->data_device)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &off_dst)); - - CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_long), &ne00)); - CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_long), &ne01)); - CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_long), &ne02)); - CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_long), &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(cl_ulong), &nb10)); - CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11)); - CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12)); - CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13)); - - CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_long), &d_ne0)); - CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_long), &d_ne1)); - CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_long), &d_ne2)); - CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_long), &d_ne3)); - CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &d_nb0)); - CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &d_nb1)); - CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &d_nb2)); - CL_CHECK(clSetKernelArg(kernel, 25, sizeof(cl_ulong), &d_nb3)); - CL_CHECK(clSetKernelArg(kernel, 26, sizeof(int), &dim)); - - size_t global_work_size_nc[] = { d_ne1 > 0 ? (size_t)d_ne1 : 1, - d_ne2 > 0 ? (size_t)d_ne2 : 1, - d_ne3 > 0 ? (size_t)d_ne3 : 1 }; - - backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size_nc, NULL, dst); - } + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor * src0, ggml_tensor * dst) { @@ -8394,6 +8342,7 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clReleaseMemObject(D_sub_buffer)); CL_CHECK(clReleaseMemObject(D_image1d)); #else + GGML_UNUSED(backend); GGML_UNUSED(src0); GGML_UNUSED(src1); GGML_UNUSED(dst); @@ -9913,7 +9862,16 @@ static void ggml_cl_scale(ggml_backend_t backend, const ggml_tensor * src0, cons cl_ulong offset0 = extra0->offset + src0->view_offs; cl_ulong offsetd = extrad->offset + dst->view_offs; - cl_kernel kernel = backend_ctx->kernel_scale; + cl_kernel kernel; + + int n = ggml_nelements(dst); + + if (n % 4 == 0) { + kernel = backend_ctx->kernel_scale_f32_4; + n /= 4; + } else { + kernel = backend_ctx->kernel_scale_f32; + } CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); @@ -9922,8 +9880,6 @@ static void ggml_cl_scale(ggml_backend_t backend, const ggml_tensor * src0, cons CL_CHECK(clSetKernelArg(kernel, 4, sizeof(float), &scale)); CL_CHECK(clSetKernelArg(kernel, 5, sizeof(float), &bias)); - int n = ggml_nelements(dst)/4; - size_t global_work_size[] = {(size_t)n, 1, 1}; size_t local_work_size[] = {64, 1, 1}; diff --git a/ggml/src/ggml-opencl/kernels/concat.cl b/ggml/src/ggml-opencl/kernels/concat.cl index 132758469c..0c1b3d785c 100644 --- a/ggml/src/ggml-opencl/kernels/concat.cl +++ b/ggml/src/ggml-opencl/kernels/concat.cl @@ -1,109 +1,51 @@ -kernel void kernel_concat_f32_contiguous( - global const char * p_src0, ulong off_src0, - global const char * p_src1, ulong off_src1, - global char * p_dst, ulong off_dst, - int d_ne00, int d_ne01, int d_ne02, // src0->ne[0..2] for the slice - int d_ne10, int d_ne11, int d_ne12, // src1->ne[0..2] for the slice (d_ne1X must match d_ne0X on non-concat axes) - int d_ne0, int d_ne1, int d_ne2, // dst->ne[0..2] for the slice - int dim +kernel void kernel_concat_f32( + global const char * src0, + ulong offset0, + global const char * src1, + ulong offset1, + global char * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb10, + ulong nb11, + ulong nb12, + ulong nb13, + int ne0, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3, + int dim ) { - global const float * src0 = (global const float*)((global char*)p_src0 + off_src0); - global const float * src1 = (global const float*)((global char*)p_src1 + off_src1); - global float * dst = (global float*)((global char*)p_dst + off_dst); + src0 = src0 + offset0; + src1 = src1 + offset1; + dst = dst + offsetd; - int i0 = get_global_id(0); // Index along dst's 0th dimension - int i1 = get_global_id(1); // Index along dst's 1st dimension - int i2 = get_global_id(2); // Index along dst's 2nd dimension + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); - if (i0 >= d_ne0 || i1 >= d_ne1 || i2 >= d_ne2) { - return; - } + int o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - ulong dst_idx = (ulong)i2 * d_ne0 * d_ne1 + (ulong)i1 * d_ne0 + i0; - ulong src_idx; + global const float * x; - if (dim == 0) { - if (i0 < d_ne00) { // Data from src0 - src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0; - dst[dst_idx] = src0[src_idx]; - } else { // Data from src1 - src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + (i0 - d_ne00); - dst[dst_idx] = src1[src_idx]; - } - } else if (dim == 1) { - if (i1 < d_ne01) { // Data from src0 - src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0; - dst[dst_idx] = src0[src_idx]; - } else { // Data from src1 - src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)(i1 - d_ne01) * d_ne10 + i0; - dst[dst_idx] = src1[src_idx]; - } - } else if (dim == 2) { - if (i2 < d_ne02) { // Data from src0 - src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0; - dst[dst_idx] = src0[src_idx]; - } else { // Data from src1 - - src_idx = (ulong)(i2 - d_ne02) * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + i0; - dst[dst_idx] = src1[src_idx]; - } - } -} - -kernel void kernel_concat_f32_non_contiguous( - global const char * p_src0, ulong off_src0, - global const char * p_src1, ulong off_src1, - global char * p_dst, ulong off_dst, - - long ne00, long ne01, long ne02, long ne03, - ulong nb00, ulong nb01, ulong nb02, ulong nb03, - - ulong nb10, ulong nb11, ulong nb12, ulong nb13, // Strides for src1 - - long d_ne0, long d_ne1, long d_ne2, long d_ne3, - ulong d_nb0, ulong d_nb1, ulong d_nb2, ulong d_nb3, - int dim -) { - global const char * src0_base = p_src0 + off_src0; - global const char * src1_base = p_src1 + off_src1; - global char * dst_base = p_dst + off_dst; - - long current_i1 = get_global_id(0); // Index for dst_dim_1 - long current_i2 = get_global_id(1); // Index for dst_dim_2 - long current_i3 = get_global_id(2); // Index for dst_dim_3 - - if (current_i1 >= d_ne1 || current_i2 >= d_ne2 || current_i3 >= d_ne3) { - return; - } - - global const float * x_val_ptr; - global float * y_val_ptr; - - for (long current_i0 = 0; current_i0 < d_ne0; ++current_i0) { - bool use_src0; - long s_i0 = current_i0, s_i1 = current_i1, s_i2 = current_i2, s_i3 = current_i3; - - if (dim == 0) { - use_src0 = (current_i0 < ne00); - if (!use_src0) { s_i0 = current_i0 - ne00; } - } else if (dim == 1) { - use_src0 = (current_i1 < ne01); - if (!use_src0) { s_i1 = current_i1 - ne01; } - } else if (dim == 2) { - use_src0 = (current_i2 < ne02); - if (!use_src0) { s_i2 = current_i2 - ne02; } - } else { // dim == 3 - use_src0 = (current_i3 < ne03); - if (!use_src0) { s_i3 = current_i3 - ne03; } - } - - if (use_src0) { - x_val_ptr = (global const float *)(src0_base + (ulong)s_i3*nb03 + (ulong)s_i2*nb02 + (ulong)s_i1*nb01 + (ulong)s_i0*nb00); + for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (global const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); } else { - x_val_ptr = (global const float *)(src1_base + (ulong)s_i3*nb13 + (ulong)s_i2*nb12 + (ulong)s_i1*nb11 + (ulong)s_i0*nb10); + x = (global const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); } - y_val_ptr = (global float *)(dst_base + (ulong)current_i3*d_nb3 + (ulong)current_i2*d_nb2 + (ulong)current_i1*d_nb1 + (ulong)current_i0*d_nb0); - *y_val_ptr = *x_val_ptr; + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; } } diff --git a/ggml/src/ggml-opencl/kernels/repeat.cl b/ggml/src/ggml-opencl/kernels/repeat.cl index 079498f5ab..53951a5543 100644 --- a/ggml/src/ggml-opencl/kernels/repeat.cl +++ b/ggml/src/ggml-opencl/kernels/repeat.cl @@ -1,39 +1,38 @@ -kernel void kernel_repeat( - global const char * src0_data_in, - global char * dst_data_in, - ulong src0_offset, - ulong dst_offset, - int src0_ne0, int src0_ne1, int src0_ne2, int src0_ne3, - ulong src0_nb0, ulong src0_nb1, ulong src0_nb2, ulong src0_nb3, - int dst_ne0, int dst_ne1, int dst_ne2, int dst_ne3, - ulong dst_nb0, ulong dst_nb1, ulong dst_nb2, ulong dst_nb3 +kernel void kernel_repeat_f32( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + int ne0, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 ) { - global const char * src0_data = src0_data_in + src0_offset; - global char * dst_data = dst_data_in + dst_offset; + src0 = src0 + offset0; + dst = dst + offsetd; - const int d3 = get_global_id(2); - const int d2 = get_global_id(1); - const int d1 = get_global_id(0); + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); - if (d3 >= dst_ne3 || d2 >= dst_ne2 || d1 >= dst_ne1) { - return; - } + const int i03 = i3%ne03; + const int i02 = i2%ne02; + const int i01 = i1%ne01; - const int s3 = d3 % src0_ne3; - const int s2 = d2 % src0_ne2; - const int s1 = d1 % src0_ne1; + global const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; + global char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1; - const global char * p_src0_slice = src0_data + (ulong)s3*src0_nb3 + (ulong)s2*src0_nb2 + (ulong)s1*src0_nb1; - global char * p_dst_slice = dst_data + (ulong)d3*dst_nb3 + (ulong)d2*dst_nb2 + (ulong)d1*dst_nb1; - - for (int d0 = 0; d0 < dst_ne0; ++d0) { - // Determine source index for dimension 0 based on tiling/broadcasting. - const int s0 = d0 % src0_ne0; - - const global char * restrict current_src_el_ptr = p_src0_slice + (ulong)s0*src0_nb0; - global char * restrict current_dst_el_ptr = p_dst_slice + (ulong)d0*dst_nb0; - for (int k = 0; k < src0_nb0; ++k) { - current_dst_el_ptr[k] = current_src_el_ptr[k]; - } + for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) { + const int i00 = i0%ne00; + *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i00*nb00)); } } diff --git a/ggml/src/ggml-opencl/kernels/scale.cl b/ggml/src/ggml-opencl/kernels/scale.cl index aeca8a456e..17ed97f0d6 100644 --- a/ggml/src/ggml-opencl/kernels/scale.cl +++ b/ggml/src/ggml-opencl/kernels/scale.cl @@ -1,9 +1,19 @@ #pragma OPENCL EXTENSION cl_khr_fp16 : enable -//------------------------------------------------------------------------------ -// scale -//------------------------------------------------------------------------------ -kernel void kernel_scale( +kernel void kernel_scale_f32( + global float * src0, + ulong offset0, + global float * dst, + ulong offsetd, + float scale, + float bias +) { + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + dst[get_global_id(0)] = src0[get_global_id(0)] * scale + bias; +} + +kernel void kernel_scale_f32_4( global float4 * src0, ulong offset0, global float4 * dst, diff --git a/ggml/src/ggml-opencl/kernels/tanh.cl b/ggml/src/ggml-opencl/kernels/tanh.cl index d9da86b148..2c4887ad3e 100644 --- a/ggml/src/ggml-opencl/kernels/tanh.cl +++ b/ggml/src/ggml-opencl/kernels/tanh.cl @@ -1,63 +1,109 @@ #pragma OPENCL EXTENSION cl_khr_fp16 : enable -#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 - -kernel void kernel_tanh_f32_nd( - global void * p_src0_base, ulong off_src0_abs, - global void * p_dst_base, ulong off_dst_abs, - int ne00, int ne01, int ne02, int ne03, - ulong nb00, ulong nb01, ulong nb02, ulong nb03, - int ne10, int ne11, int ne12, int ne13, - ulong nb10, ulong nb11, ulong nb12, ulong nb13 +kernel void kernel_tanh_f32( + global const float * src0, + ulong offset0, + global float * dst, + ulong offsetd ) { - int i0 = get_global_id(0); - int i1 = get_global_id(1); - int i2 = get_global_id(2); + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); - if (i0 < ne10 && i1 < ne11 && i2 < ne12) { - for (int i3 = 0; i3 < ne13; ++i3) { - ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03; - global const float *src_val_ptr = (global const float *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor); + dst[get_global_id(0)] = tanh(src0[get_global_id(0)]); +} - ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13; - global float *dst_val_ptr = (global float *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor); +kernel void kernel_tanh_f32_4( + global const float4 * src0, + ulong offset0, + global float4 * dst, + ulong offsetd +) { + src0 = (global float4*)((global char*)src0 + offset0); + dst = (global float4*)((global char*)dst + offsetd); - *dst_val_ptr = tanh(*src_val_ptr); - } + dst[get_global_id(0)] = tanh(src0[get_global_id(0)]); +} + +kernel void kernel_tanh_f16( + global const half * src0, + ulong offset0, + global half * dst, + ulong offsetd +) { + src0 = (global half*)((global char*)src0 + offset0); + dst = (global half*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = tanh(src0[get_global_id(0)]); +} + +kernel void kernel_tanh_f16_4( + global const half4 * src0, + ulong offset0, + global half4 * dst, + ulong offsetd +) { + src0 = (global half4*)((global char*)src0 + offset0); + dst = (global half4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = tanh(src0[get_global_id(0)]); +} + +kernel void kernel_tanh_f32_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = tanh(*x); } } -kernel void kernel_tanh_f16_nd( - global void * p_src0_base, ulong off_src0_abs, - global void * p_dst_base, ulong off_dst_abs, - int ne00, int ne01, int ne02, int ne03, - ulong nb00, ulong nb01, ulong nb02, ulong nb03, - int ne10, int ne11, int ne12, int ne13, - ulong nb10, ulong nb11, ulong nb12, ulong nb13 +kernel void kernel_tanh_f16_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 ) { - int i0 = get_global_id(0); - int i1 = get_global_id(1); - int i2 = get_global_id(2); + src0 = src0 + offset0; + dst = dst + offsetd; - if (i0 < ne10 && i1 < ne11 && i2 < ne12) { - for (int i3 = 0; i3 < ne13; ++i3) { - ulong src_offset_in_tensor = (ulong)i0*nb00 + (ulong)i1*nb01 + (ulong)i2*nb02 + (ulong)i3*nb03; - global const half *src_val_ptr = (global const half *)((global char *)p_src0_base + off_src0_abs + src_offset_in_tensor); + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); - ulong dst_offset_in_tensor = (ulong)i0*nb10 + (ulong)i1*nb11 + (ulong)i2*nb12 + (ulong)i3*nb13; - global half *dst_val_ptr = (global half *)((global char *)p_dst_base + off_dst_abs + dst_offset_in_tensor); + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - *dst_val_ptr = tanh(*src_val_ptr); - } + *y = tanh(*x); } } From aeb827a3ccf1db0ae6e81c318c43b3bcb47f7651 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 3 Feb 2026 08:20:15 +0200 Subject: [PATCH 102/206] spec : simplify time measurement using common_time_meas (#19262) --- common/speculative.cpp | 29 ++++++++++------------------- 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/common/speculative.cpp b/common/speculative.cpp index 152aaa48d4..80cd31e35f 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -951,12 +951,8 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr } for (auto & impl : spec->impls) { - const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; - + common_time_meas tm(impl->t_begin_us, !impl->gen_perf); impl->begin(prompt); - - const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; - impl->t_begin_us += t_now_us - t_start_us; // accumulate duration for this refresh } } @@ -971,14 +967,9 @@ llama_tokens common_speculative_draft( for (auto & impl : spec->impls) { { - const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; - + common_time_meas tm(impl->t_draft_us, !impl->gen_perf); impl->draft(params, prompt_tgt, id_last, result); - - const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; - impl->drafts_call_count++; - impl->t_draft_us += t_now_us - t_start_us; // accumulate duration for this implementation } if (!result.empty()) { @@ -1006,15 +997,15 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) { GGML_ASSERT(impl); - const int64_t t_start_us = impl->gen_perf ? ggml_time_us() : 0; - if (n_accepted > 0) { - impl->drafts_accepted_count++; - impl->drafts_accepted_tokens += n_accepted; - } + { + common_time_meas tm(impl->t_accept_us, !impl->gen_perf); + if (n_accepted > 0) { + impl->drafts_accepted_count++; + impl->drafts_accepted_tokens += n_accepted; + } - impl->accept(n_accepted); - const int64_t t_now_us = impl->gen_perf ? ggml_time_us() : 0; - impl->t_accept_us += t_now_us - t_start_us; // accumulate duration for this acculumulation + impl->accept(n_accepted); + } } void common_speculative_print_stats(const common_speculative * spec) { From 1efb5f7ae120c7cc7a33c4d1d82a05b3c50122f6 Mon Sep 17 00:00:00 2001 From: Alexey Dubrov Date: Tue, 3 Feb 2026 09:31:01 +0300 Subject: [PATCH 103/206] vocab: add Falcon-H1-Tiny-Coder FIM tokens (#19249) --- src/llama-vocab.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 74a8496f9e..38d03a8c39 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2262,6 +2262,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "
"
                         || t.first == "▁
"          // CodeLlama
                         || t.first == "<|code_prefix|>" // GLM-4.5
+                        || t.first == "<|prefix|>"      // Falcon-H1-Tiny-Coder
                         ) {
                     special_fim_pre_id = t.second;
                     if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2282,6 +2283,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == ""
                         || t.first == "▁"         // CodeLlama
                         || t.first == "<|code_suffix|>" // GLM-4.5
+                        || t.first == "<|suffix|>"      // Falcon-H1-Tiny-Coder
                         ) {
                     special_fim_suf_id = t.second;
                     if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2302,6 +2304,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == ""
                         || t.first == "▁"         // CodeLlama
                         || t.first == "<|code_middle|>" // GLM-4.5
+                        || t.first == "<|middle|>"      // Falcon-H1-Tiny-Coder
                         ) {
                     special_fim_mid_id = t.second;
                     if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {

From 41e3f02647be2976c4a302128680ca5983568ae5 Mon Sep 17 00:00:00 2001
From: Gaurav Garg 
Date: Tue, 3 Feb 2026 12:11:02 +0530
Subject: [PATCH 104/206] cuda : revert CUDA_SCALE_LAUNCH_QUEUES override until
 investigated (#19227)

Hangs were reported on Jetson Orin AGX if we set CUDA_SCALE_LAUNCH_QUEUES=4x. Reverting the previous PR (#19042) and updating the document to consider setting CUDA_SCALE_LAUNCH_QUEUES=4x for faster throughput on multi-GPU systems.
---
 docs/build.md                   |  4 +---
 ggml/src/ggml-cuda/ggml-cuda.cu | 10 ----------
 2 files changed, 1 insertion(+), 13 deletions(-)

diff --git a/docs/build.md b/docs/build.md
index 3a43f2a45a..fd447424c7 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -252,9 +252,7 @@ CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.ggu
 
 The environment variable [`CUDA_SCALE_LAUNCH_QUEUES`](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/environment-variables.html#cuda-scale-launch-queues) controls the size of CUDA's command buffer, which determines how many GPU operations can be queued before the CPU must wait for the GPU to catch up. A larger buffer reduces CPU-side stalls and allows more work to be queued on a GPU.
 
-**Default behavior:** llama.cpp automatically sets `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs.
-
-See PR [#19042](https://github.com/ggml-org/llama.cpp/pull/19042) for performance benchmarks and technical details.
+Consider setting `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs.
 
 ### Unified Memory
 
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 08383edb40..1bcd1ab1f8 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -5049,16 +5049,6 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
         static std::mutex mutex;
         std::lock_guard lock(mutex);
         if (!initialized) {
-            // Set CUDA_SCALE_LAUNCH_QUEUES before any CUDA API call to improve multi-GPU pipeline parallelism performance
-            // PR: https://github.com/ggml-org/llama.cpp/pull/19042
-            if (getenv("CUDA_SCALE_LAUNCH_QUEUES") == nullptr) {
-#ifdef _WIN32
-                _putenv_s("CUDA_SCALE_LAUNCH_QUEUES", "4x");
-#else
-                setenv("CUDA_SCALE_LAUNCH_QUEUES", "4x", 0); // don't overwrite if already set
-#endif // _WIN32
-            }
-
             ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context;
             const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
 

From e9a859db3cbad0d3dd10deb0527ddfcecce5e78d Mon Sep 17 00:00:00 2001
From: George <35490284+noctrex@users.noreply.github.com>
Date: Tue, 3 Feb 2026 08:43:39 +0200
Subject: [PATCH 105/206] ggml: added cleanups in ggml_quantize_free (#19278)

Add missing cleanup calls for IQ2_S, IQ1_M quantization types and IQ3XS with 512 blocks during quantization cleanup.
---
 ggml/src/ggml.c | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index e1471b540e..500cb6b72f 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -7517,8 +7517,11 @@ void ggml_quantize_free(void) {
 
     iq2xs_free_impl(GGML_TYPE_IQ2_XXS);
     iq2xs_free_impl(GGML_TYPE_IQ2_XS);
+    iq2xs_free_impl(GGML_TYPE_IQ2_S);
     iq2xs_free_impl(GGML_TYPE_IQ1_S);
+    iq2xs_free_impl(GGML_TYPE_IQ1_M);
     iq3xs_free_impl(256);
+    iq3xs_free_impl(512);
 
     ggml_critical_section_end();
 }

From 1f1e57f2bf76df88a668dce924de2bffa89c5c02 Mon Sep 17 00:00:00 2001
From: Oliver Simons 
Date: Tue, 3 Feb 2026 11:33:14 +0100
Subject: [PATCH 106/206] CUDA: Fix loop unrolling for BW in
 mul_mat_q_stream_k_fixup (#19053)

By providing stride_* variables as size_t (i.e., 64-bit) the compiler can
correctly unroll the [two for-loops](https://github.com/ggml-org/llama.cpp/blob/557515be1e93ed8939dd8a7c7d08765fdbe8be31/ggml/src/ggml-cuda/mmq.cuh#L3789-L3816)
on BW. This gives some perf for prefill/pp phase on BW, while not affecting
other SMs:

| GPU                                                     | Model                 | Test   |   t/s master |   t/s osimons/fix_bw_mmq_fixup_kernel |   Speedup |
|:--------------------------------------------------------|:----------------------|:-------|-------------:|--------------------------------------:|----------:|
| NVIDIA RTX 6000 Ada Generation                          | gpt-oss 20B MXFP4 MoE | pp8096 |      8404.05 |                               8375.79 |      1.00 |
| NVIDIA RTX 6000 Ada Generation                          | llama 3B Q4_K_M       | pp8096 |     16148.93 |                              16019.60 |      0.99 |
| NVIDIA RTX 6000 Ada Generation                          | llama 8B Q4_0         | pp8096 |      8008.29 |                               7978.80 |      1.00 |
| NVIDIA RTX 6000 Ada Generation                          | nemotron_h 9B BF16    | pp8096 |      4263.16 |                               4248.53 |      1.00 |
| NVIDIA RTX 6000 Ada Generation                          | nemotron_h 9B Q4_K_M  | pp8096 |      5165.11 |                               5157.43 |      1.00 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | gpt-oss 20B MXFP4 MoE | pp8096 |     12582.80 |                              12758.37 |      1.01 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | llama 3B Q4_K_M       | pp8096 |     16879.10 |                              17619.47 |      1.04 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | llama 8B Q4_0         | pp8096 |     10649.90 |                              10982.65 |      1.03 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | nemotron_h 9B BF16    | pp8096 |      7717.73 |                               7716.22 |      1.00 |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | nemotron_h 9B Q4_K_M  | pp8096 |      7301.90 |                               7370.38 |      1.01 |
---
 ggml/src/ggml-cuda/mmq.cuh | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index a382e6a697..f80f98cda2 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -3697,13 +3697,20 @@ static __global__ void mul_mat_q(
          tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);
 }
 
-
 template 
-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 ncols_max) {
+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 size_t stride_col_dst,
+                                                const int    nchannels_y,
+                                                const size_t stride_channel_dst,
+                                                const int    nsamples_y,
+                                                const size_t stride_sample_dst,
+                                                const int    ncols_max) {
     constexpr int     mmq_y           = get_mmq_y_device();
     constexpr int     qk              = ggml_cuda_type_traits::qk;
     constexpr int     ITER_K          = get_iter_k(type);

From c55bce415926c7e8484f20d854afebe0168f7513 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Tue, 3 Feb 2026 13:43:29 +0200
Subject: [PATCH 107/206] metal : minor cleanup (#19251)

---
 ggml/src/ggml-metal/ggml-metal-impl.h  |  4 +-
 ggml/src/ggml-metal/ggml-metal-ops.cpp | 38 ++++--------
 ggml/src/ggml-metal/ggml-metal.metal   | 86 +++++++-------------------
 3 files changed, 34 insertions(+), 94 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 59d88b01a5..e074f2ef3d 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -81,10 +81,10 @@
 #define FC_COUNT_EQUAL                 1000
 
 // op-specific constants
-#define OP_FLASH_ATTN_EXT_NQPTG 8
+#define OP_FLASH_ATTN_EXT_NQPSG 8
 #define OP_FLASH_ATTN_EXT_NCPSG 64
 
-#define OP_FLASH_ATTN_EXT_VEC_NQPTG 1
+#define OP_FLASH_ATTN_EXT_VEC_NQPSG 1
 #define OP_FLASH_ATTN_EXT_VEC_NCPSG 32
 
 // kernel argument structs
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 7f4cfbba22..f97c4435de 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -2295,7 +2295,7 @@ size_t ggml_metal_op_flash_attn_ext_extra_blk(const ggml_tensor * op) {
     //    return res;
     //}
 
-    const int nqptg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NQPTG : OP_FLASH_ATTN_EXT_NQPTG;
+    const int nqptg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NQPSG : OP_FLASH_ATTN_EXT_NQPSG;
     const int ncpsg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NCPSG : OP_FLASH_ATTN_EXT_NCPSG;
 
     const int64_t ne1 = (ne01 + nqptg - 1)/nqptg;
@@ -2411,7 +2411,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
 
     if (!ggml_metal_op_flash_attn_ext_use_vec(op)) {
         // half8x8 kernel
-        const int nqptg = OP_FLASH_ATTN_EXT_NQPTG; // queries per threadgroup
+        const int nqptg = OP_FLASH_ATTN_EXT_NQPSG; // queries per threadgroup
         const int ncpsg = OP_FLASH_ATTN_EXT_NCPSG; // cache values per simdgroup
 
         GGML_ASSERT(nqptg <= 32);
@@ -2578,9 +2578,9 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
 #undef FATTN_SMEM
     } else {
         // half4x4 kernel
-        const int nqptg = OP_FLASH_ATTN_EXT_VEC_NQPTG; // queries per threadgroup
+        const int nqptg = OP_FLASH_ATTN_EXT_VEC_NQPSG; // queries per threadgroup
         const int ncpsg = OP_FLASH_ATTN_EXT_VEC_NCPSG; // cache values per simdgroup !! sync with kernel template arguments !!
-        const int nkpsg = 1*ncpsg;
+        const int nhptg = 1;                           // heads per threadgroup
 
         GGML_ASSERT(nqptg <= 32);
         GGML_ASSERT(nqptg  % 1  == 0);
@@ -2632,6 +2632,9 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
             ggml_metal_op_concurrency_reset(ctx);
         }
 
+        // note: for simplicity assume the K is larger or equal than V
+        GGML_ASSERT(ne10 >= ne20);
+
         // ne00 + 2*ncpsg*(nsg)
         // for each query, we load it as f16 in shared memory (ne00)
         // and store the soft_max values and the mask
@@ -2639,28 +2642,9 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
         // 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*GGML_PAD(ne20, 128)*(nsg))*(sizeof(float)/2), 16))
-
-        int64_t nsgmax = 2;
-        while (true) {
-            const size_t smem = FATTN_SMEM(nsgmax);
-            // avoid using more than half of the threadgroup memory - can cause slow downs especially for large head sizes
-            if (smem > props_dev->max_theadgroup_memory_size/2) {
-                break;
-            }
-            nsgmax *= 2;
-        }
-        nsgmax /= 2;
-
-        // simdgroups per threadgroup (a.k.a. warps)
-        //const int64_t nsgt = MAX(2, MIN(nsgmax, MIN((ne11 + nkpsg - 1)/(nkpsg), (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)));
-        const int64_t nsgt = MAX(2, MIN(nsgmax, MIN((ne11 + nkpsg - 1)/(nkpsg), (int64_t) 1024/32)));
+#define FATTN_SMEM(nsg) (GGML_PAD(((GGML_PAD(ne00, 128) + 4*ncpsg + 2*GGML_PAD(ne20, 128))*(nsg))*(sizeof(float)/2), 16))
 
         int64_t nsg = 1;
-        while (nsg <= nsgt) {
-            nsg *= 2;
-        }
-        nsg /= 2;
 
         // workgroups
         // each workgroup handles nsg*nkpsg cache values
@@ -2673,7 +2657,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
         } else {
             nwg = 32;
             nsg = 1;
-            while (2*nwg*nsg*nkpsg < ne11 && nsg < 4) {
+            while (2*nwg*nsg*ncpsg < ne11 && nsg < 4) {
                 nsg *= 2;
             }
         }
@@ -2739,7 +2723,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
 
             ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
-            ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg, 32, nsg, 1);
+            ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, (ne02 + nhptg - 1)/nhptg, ne03*nwg, 32, nsg, 1);
         } else {
             // sanity checks
             assert(ggml_metal_op_flash_attn_ext_extra_tmp(op) != 0);
@@ -2752,7 +2736,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
             ggml_metal_encoder_set_buffer(enc, bid_tmp, 7);
 
             ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
-            ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg, 32, nsg, 1);
+            ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, (ne02 + nhptg - 1)/nhptg, ne03*nwg, 32, nsg, 1);
 
             // sync the 2 kernels
             ggml_metal_op_concurrency_reset(ctx);
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 17e358d1a8..3259213fd6 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -5931,7 +5931,7 @@ template<
     void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &),
     short DK,         // K head size
     short DV,         // V head size
-    short Q  = OP_FLASH_ATTN_EXT_NQPTG, // queries per threadgroup
+    short Q  = OP_FLASH_ATTN_EXT_NQPSG, // queries per threadgroup
     short C  = OP_FLASH_ATTN_EXT_NCPSG> // cache items per threadgroup
 kernel void kernel_flash_attn_ext(
         constant ggml_metal_kargs_flash_attn_ext & args,
@@ -6141,11 +6141,10 @@ template<
     void (*deq_v_t4)(device const vd4_t *, short, thread v4_t &),
     short DK,       // K head size
     short DV,       // V head size
-    short NE,       // head elements per thread
-    short Q,        // queries per threadgroup
-    short C,        // cache items per threadgroup
-    short NSG>      // number of simd groups
-void kernel_flash_attn_ext_vec_impl(
+    short NE = 4,   // head elements per thread
+    short Q  = OP_FLASH_ATTN_EXT_VEC_NQPSG,  // queries per threadgroup
+    short C  = OP_FLASH_ATTN_EXT_VEC_NCPSG>  // cache items per threadgroup
+kernel void kernel_flash_attn_ext_vec(
         constant ggml_metal_kargs_flash_attn_ext_vec & args,
         device const char * q,
         device const char * k,
@@ -6162,6 +6161,7 @@ void kernel_flash_attn_ext_vec_impl(
     static_assert(DV % 32 == 0, "DV must be divisible by 32");
 
 #define NWG  (FC_flash_attn_ext_vec_nwg)
+#define NSG  (FC_flash_attn_ext_vec_nsg)
 
 #define NS10 (FC_flash_attn_ext_vec_ns10)
 #define NS20 (FC_flash_attn_ext_vec_ns20)
@@ -6190,12 +6190,12 @@ void kernel_flash_attn_ext_vec_impl(
 
     const short T = PK + NSG*SH; // shared memory size per query in (half)
 
-  //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                    0*PK); // holds the query data
-    threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                    0*PK); // same as above but in q4_t
-    threadgroup s_t   * ss  = (threadgroup s_t   *) (shmem_f16 +   sgitg*SH       + Q*PK); // scratch buffer for attention
-    threadgroup s4_t  * ss4 = (threadgroup s4_t  *) (shmem_f16 +   sgitg*SH       + Q*PK); // same as above but in s4_t
-    threadgroup half  * sm  = (threadgroup half  *) (shmem_f16 +   sgitg*SH + 2*C + Q*PK); // scratch buffer for mask
-    threadgroup o4_t  * so4 = (threadgroup o4_t  *) (shmem_f16 + 2*sgitg*PV       + Q*T);  // scratch buffer for the results
+  //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                      0*PK); // holds the query data
+    threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                      0*PK); // same as above but in q4_t
+    threadgroup s_t   * ss  = (threadgroup s_t   *) (shmem_f16 +   sgitg*SH       + NSG*PK); // scratch buffer for attention
+    threadgroup s4_t  * ss4 = (threadgroup s4_t  *) (shmem_f16 +   sgitg*SH       + NSG*PK); // same as above but in s4_t
+    threadgroup half  * sm  = (threadgroup half  *) (shmem_f16 +   sgitg*SH + 2*C + NSG*PK); // scratch buffer for mask
+    threadgroup o4_t  * so4 = (threadgroup o4_t  *) (shmem_f16 + 2*sgitg*PV       + NSG*PK + NSG*SH); // scratch buffer for the results
 
     // store the result for all queries in shared memory (the O matrix from the paper)
     so4 += tiisg;
@@ -6213,11 +6213,13 @@ void kernel_flash_attn_ext_vec_impl(
     // load heads from Q to shared memory
     device const float4 * q4 = (device const float4 *) ((device const char *) q);
 
-    for (short i = tiisg; i < PK4; i += NW) {
-        if (iq1 < args.ne01 && i < DK4) {
-            sq4[i] = (q4_t) q4[i];
-        } else {
-            sq4[i] = (q4_t) 0.0f;
+    if (iq1 < args.ne01) {
+        for (short i = tiisg; i < PK4; i += NW) {
+            if (i < DK4) {
+                sq4[i] = (q4_t) q4[i];
+            } else {
+                sq4[i] = (q4_t) 0.0f;
+            }
         }
     }
 
@@ -6295,7 +6297,7 @@ void kernel_flash_attn_ext_vec_impl(
             }
 
             // skip -INF blocks
-            if (simd_max(sm[tiisg]) == -INFINITY) {
+            if (simd_max(sm[tiisg]) <= -MAXHALF) {
                 continue;
             }
 
@@ -6569,57 +6571,11 @@ void kernel_flash_attn_ext_vec_impl(
     }
 
 #undef NWG
+#undef NSG
 #undef NS10
 #undef NS20
 }
 
-template<
-    typename q4_t,  // query types in shared memory
-    typename k4_t,  // key types in shared memory
-    typename v4_t,  // value types in shared memory
-    typename qk_t,  // Q*K types
-    typename s_t,   // soft-max types
-    typename s4_t,
-    typename o4_t,  // attention accumulation types
-    typename kd4_t, // key type in device memory
-    short nl_k,
-    void (*deq_k_t4)(device const kd4_t *, short, thread k4_t &),
-    typename vd4_t, // value type in device memory
-    short nl_v,
-    void (*deq_v_t4)(device const vd4_t *, short, thread v4_t &),
-    short DK,       // K head size
-    short DV,       // V head size
-    short NE = 4,   // head elements per thread
-    short Q  = OP_FLASH_ATTN_EXT_VEC_NQPTG,  // queries per threadgroup
-    short C  = OP_FLASH_ATTN_EXT_VEC_NCPSG>  // cache items per threadgroup
-kernel void kernel_flash_attn_ext_vec(
-        constant ggml_metal_kargs_flash_attn_ext_vec & args,
-        device const char * q,
-        device const char * k,
-        device const char * v,
-        device const char * mask,
-        device const char * sinks,
-        device const char * pad,
-        device       char * dst,
-        threadgroup  half * shmem_f16 [[threadgroup(0)]],
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort  tiisg[[thread_index_in_simdgroup]],
-        ushort  sgitg[[simdgroup_index_in_threadgroup]]) {
-#define FWD_TMPL q4_t, k4_t, v4_t, qk_t, s_t, s4_t, o4_t, kd4_t, nl_k, deq_k_t4, vd4_t, nl_v, deq_v_t4, DK, DV, NE, Q, C
-#define FWD_ARGS args, q, k, v, mask, sinks, pad, dst, shmem_f16, tgpig, tiisg, sgitg
-    switch (FC_flash_attn_ext_vec_nsg) {
-      // note: disabled cases to reduce library load time
-        case 1:  kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-        case 2:  kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-        case 4:  kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-      //case 8:  kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-      //case 16: kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-      //case 32: kernel_flash_attn_ext_vec_impl(FWD_ARGS); break;
-    }
-#undef FWD_TMPL
-#undef FWD_ARGS
-}
-
 // note: I think the s_t can be half instead of float, because the Q*K scaling is done before storing to shared mem
 //       in the other (non-vec) kernel, we need s_t to also be float because we scale during the soft_max
 //

From a6fd8ca1fee621addff1695165414c4822fb08bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= 
Date: Tue, 3 Feb 2026 14:20:57 +0100
Subject: [PATCH 108/206] models : remove unnecessary cont in openelm (#19289)

---
 src/models/openelm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/openelm.cpp b/src/models/openelm.cpp
index ee46a3375e..fbf682ec83 100644
--- a/src/models/openelm.cpp
+++ b/src/models/openelm.cpp
@@ -43,7 +43,7 @@ llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_
             ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head);
             cb(Kcur, "Kcur", il);
 
-            ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
+            ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv));
             cb(Vcur, "Vcur", il);
 
             Qcur = build_norm(Qcur,

From 8bece2eb20f0134632ae229849fbde6559882d36 Mon Sep 17 00:00:00 2001
From: Aman Gupta 
Date: Tue, 3 Feb 2026 23:31:23 +0800
Subject: [PATCH 109/206] CUDA: use mmvq for mul-mat-id for small batch sizes
 (#18958)

* CUDA: use mmvq for mul-mat-id for small batch sizes

* add mmvq too

* Fix perf issue on ampere. Use mmvf mm-id only for non-nvidia GPUs

* templatize multi_token_path
---
 ggml/src/ggml-cuda/ggml-cuda.cu |  14 ++-
 ggml/src/ggml-cuda/mmvf.cu      | 194 +++++++++++++++++++++-----------
 ggml/src/ggml-cuda/mmvf.cuh     |   2 +
 ggml/src/ggml-cuda/mmvq.cu      | 135 ++++++++++++++--------
 4 files changed, 224 insertions(+), 121 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 1bcd1ab1f8..eeb8625dbe 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2279,13 +2279,19 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
 
     if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-        if (ne2 == 1) {
+        static_assert(MMVQ_MAX_BATCH_SIZE == MMVF_MAX_BATCH_SIZE);
+        if (ne2 <= MMVQ_MAX_BATCH_SIZE) {
             if (ggml_is_quantized(src0->type)) {
-                ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
+                if (ne2 <= 4) {
+                    ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
+                    return;
+                }
             } else {
-                ggml_cuda_mul_mat_vec_f(ctx, src0, src1, ids, dst);
+                if (GGML_CUDA_CC_IS_AMD(cc)) {
+                    ggml_cuda_mul_mat_vec_f(ctx, src0, src1, ids, dst);
+                    return;
+                }
             }
-            return;
         }
 
         if (ggml_cuda_should_use_mmq(src0->type, cc, ne12, /*n_experts=*/ne02)) {
diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu
index 32948e4d7a..d914720242 100644
--- a/ggml/src/ggml-cuda/mmvf.cu
+++ b/ggml/src/ggml-cuda/mmvf.cu
@@ -4,26 +4,48 @@
 #include "mmvf.cuh"
 #include "convert.cuh"
 
-template 
+template 
 static __global__ void mul_mat_vec_f(
         const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, 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 ncols2, const uint3 nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
         const uint3 channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
-        const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
+        const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
+        const int ids_stride) {
     const int row         = blockIdx.x;
+    // for MUL_MAT_ID - blockIdx.y = n_expert_used, blockIdx.z = ncols_dst (tokens)
     const int channel_dst = blockIdx.y;
-    const int channel_x   = ids ? ids[channel_dst]          : fastdiv((uint32_t) channel_dst, channel_ratio);
-    const int channel_y   = ids ? channel_dst % nchannels_y : channel_dst;
-    const int sample_dst  = blockIdx.z;
+    const int tid         = threadIdx.x;
+
+    int token_idx;
+    int channel_x;
+    int channel_y;
+    int sample_dst;
+
+    if constexpr (is_multi_token_id) {
+        // Multi-token MUL_MAT_ID path, adding these in the normal path causes a perf regression for n_tokens=1 case
+        token_idx  = blockIdx.z;
+        channel_x  = ids[channel_dst + token_idx * ids_stride];
+        channel_y  = fastmodulo(channel_dst, nchannels_y);
+        sample_dst = 0;
+    } else {
+        token_idx  = ids ? blockIdx.z                                          : 0;
+        channel_x  = ids ? ids[blockIdx.y + token_idx * ids_stride]            : fastdiv((uint32_t) channel_dst, channel_ratio);
+        channel_y  = ids ? fastmodulo(blockIdx.y, nchannels_y)                 : channel_dst;
+        sample_dst = ids ? 0                                                   : blockIdx.z;
+    }
+
     const int sample_x    = fastdiv((uint32_t) sample_dst, sample_ratio);
     const int sample_y    = sample_dst;
-    const int tid         = threadIdx.x;
 
     constexpr int warp_size   = ggml_cuda_get_physical_warp_size();
 
     x   += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row*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;
+    if constexpr (is_multi_token_id) {
+        y   += token_idx*stride_col_y2*2;
+        dst += token_idx*stride_col_dst;
+    }
 
     bool use_gate = false;
     bool use_bias = false;
@@ -56,8 +78,10 @@ static __global__ void mul_mat_vec_f(
     if (use_gate) {
         gate_x += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row*stride_row;
     }
+
+    const int channel_bias = ids ? channel_x : channel_dst;
+
     if constexpr (has_fusion) {
-        const int channel_bias = ids ? channel_x : channel_dst;
         if (use_bias) {
             x_bias += int64_t(sample_dst)*stride_sample_dst + channel_bias*stride_channel_dst;
         }
@@ -349,36 +373,36 @@ static __global__ void mul_mat_vec_f(
     }
 }
 
-template
+template
 static void mul_mat_vec_f_switch_fusion(
         const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
-        const int64_t ncols, const int64_t nrows,
+        const int64_t ncols, const uint3 nchannels_y,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
         const uint3 channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
         const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
-        const dim3 & block_dims, const dim3 & block_nums, const int nbytes_shared, const cudaStream_t stream) {
+        const dim3 & block_dims, const dim3 & block_nums, const int nbytes_shared, const int ids_stride, const cudaStream_t stream) {
 
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     if constexpr (ncols_dst == 1) {
         if (has_fusion) {
-            mul_mat_vec_f<<>>
-                (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_f<<>>
+                (x, y, ids, fusion, 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);
+                sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
             return;
        }
     }
 
     GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
 
-    mul_mat_vec_f<<>>
-        (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+    mul_mat_vec_f<<>>
+        (x, y, ids, fusion, 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);
+        sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
 
 }
 
-template 
+template 
 void launch_mul_mat_vec_f_cuda(
         const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
         const int64_t ncols, const int64_t nrows,
@@ -386,12 +410,13 @@ void launch_mul_mat_vec_f_cuda(
         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) {
+        const int64_t nsamples_or_ntokens, const int64_t ids_stride, cudaStream_t stream) {
     GGML_ASSERT(ncols        % 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 uint3 nchannels_y_fd   = ids ? init_fastdiv_values(nchannels_y) : make_uint3(0, 0, 0);
     const uint3 channel_ratio_fd = ids ? make_uint3(0, 0, 0) : init_fastdiv_values(nchannels_dst / nchannels_x);
     const uint3 sample_ratio_fd  = init_fastdiv_values(nsamples_dst  / nsamples_x);
 
@@ -415,56 +440,56 @@ void launch_mul_mat_vec_f_cuda(
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
 
     const int nbytes_shared = warp_size*sizeof(float) + (has_fusion ? warp_size*sizeof(float) : 0);
-    const dim3 block_nums(nrows, nchannels_dst, nsamples_dst);
+    const dim3 block_nums(nrows, nchannels_dst, nsamples_or_ntokens);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case   64: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case   96: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case  128: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case  160: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case  192: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case  224: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         case  256: {
-            mul_mat_vec_f_switch_fusion
-                (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+            mul_mat_vec_f_switch_fusion
+                (x, y, ids, fusion, dst, ncols/2, nchannels_y_fd, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
+                 sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, ids_stride, stream);
         } break;
         default: {
             GGML_ABORT("fatal error");
@@ -480,55 +505,88 @@ static void mul_mat_vec_f_cuda_switch_ncols_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) {
+        const int64_t ids_stride, cudaStream_t stream) {
+
+    const bool has_ids = ids != nullptr;
+
+    if (has_ids && ncols_dst > 1) {
+        // Multi-token MUL_MAT_ID path only - single-token goes through regular path below
+        constexpr int c_ncols_dst = 1;
+        launch_mul_mat_vec_f_cuda
+            (x, y, ids, fusion, 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,
+             ncols_dst, ids_stride, stream);
+        return;
+    }
+
+    if (has_ids) {
+        // Single-token MUL_MAT_ID path
+        constexpr int c_ncols_dst = 1;
+        launch_mul_mat_vec_f_cuda
+            (x, y, ids, fusion, 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,
+             ncols_dst, ids_stride, stream);
+        return;
+    }
+
     switch (ncols_dst) {
         case 1:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 2:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 3:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 4:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 5:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 6:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 7:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         case 8:
             launch_mul_mat_vec_f_cuda
                 (x, y, ids, fusion, 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);
+                 stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 nsamples_dst, ids_stride, stream);
             break;
         default:
             GGML_ABORT("fatal error");
@@ -544,21 +602,21 @@ static void mul_mat_vec_f_cuda(
         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,
-        enum ggml_prec prec, cudaStream_t stream) {
+        const int64_t ids_stride, enum ggml_prec prec, cudaStream_t stream) {
 
     if constexpr(std::is_same_v) {
         if (prec == GGML_PREC_DEFAULT) {
             mul_mat_vec_f_cuda_switch_ncols_dst
                 (x, y, ids, fusion, 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);
+                stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             return;
         }
     }
     mul_mat_vec_f_cuda_switch_ncols_dst
         (x, y, ids, fusion, 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);
+        stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
 }
 
 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,
@@ -573,7 +631,7 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     const size_t ts_src1 = ggml_type_size(src1->type);
     const size_t ts_dst  = ggml_type_size(dst->type);
 
-    GGML_ASSERT(!ids || ne12 == 1); // Implementation is only correct for  batch size 1.
+    GGML_ASSERT(!ids || ne12 <= MMVF_MAX_BATCH_SIZE);
     GGML_ASSERT(ne13 == ne3);
 
     GGML_ASSERT(        nb00       == ts_src0);
@@ -626,29 +684,31 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     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_col_dst     = ids ? s2   : s1;
+    const int64_t stride_col_y       = ids ? s12  : s11;
     const int64_t stride_channel_dst = ids ? s1   : s2;
     const int64_t stride_channel_y   = ids ? s11  : s12;
 
-    GGML_ASSERT(!ids || ncols_dst == 1);
+    const int64_t ids_stride = ids ? ids->nb[1] / ggml_type_size(ids->type) : 0;
 
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, stride_col_y, stride_col_dst,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
+                ne03,              ne3,           s03, s13,              s3,                 ids_stride, prec, ctx.stream());
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, stride_col_y, stride_col_dst,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
+                ne03,              ne3,           s03, s13,              s3,                 ids_stride, prec, ctx.stream());
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, stride_col_y, stride_col_dst,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
+                ne03,              ne3,           s03, s13,              s3,                 ids_stride, prec, ctx.stream());
         } break;
         default:
             GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
@@ -695,19 +755,19 @@ void ggml_cuda_op_mul_mat_vec_f(
             const float * src0_d = (const float *) src0_dd_i;
             mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, 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);
+                nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, 0, prec, stream);
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0_dd_i;
             mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, 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);
+                nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, 0, prec, stream);
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
             mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, 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);
+                nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, 0, prec, stream);
         } break;
         default:
             GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
diff --git a/ggml/src/ggml-cuda/mmvf.cuh b/ggml/src/ggml-cuda/mmvf.cuh
index a09fbdc720..a50f7c0218 100644
--- a/ggml/src/ggml-cuda/mmvf.cuh
+++ b/ggml/src/ggml-cuda/mmvf.cuh
@@ -1,5 +1,7 @@
 #include "common.cuh"
 
+#define MMVF_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVF kernels.
+
 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,
     const ggml_cuda_mm_fusion_args_host * fusion = nullptr);
 
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index d671551c17..ce25ccf427 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -137,15 +137,15 @@ static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int
     return 1;
 }
 
-// tell the compiler to use as many registers as it wants, see nwarps definition below
-template 
+template 
 __launch_bounds__(calc_nwarps(ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
 static __global__ void mul_mat_vec_q(
         const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
         const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
         const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
         const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
-        const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst) {
+        const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst,
+        const uint32_t ids_stride) {
 
     constexpr int qk  = ggml_cuda_type_traits::qk;
     constexpr int qi  = ggml_cuda_type_traits::qi;
@@ -162,11 +162,25 @@ static __global__ void mul_mat_vec_q(
     const     int blocks_per_row_x = ncols_x / qk;
     constexpr int blocks_per_iter = vdr * nwarps*warp_size / qi;
 
-    // The MUL_MAT_ID code path with ids != nullptr is only implemented for ncols_dst == 1.
     const uint32_t channel_dst = blockIdx.y;
-    const uint32_t channel_x   = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
-    const uint32_t channel_y   = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
-    const uint32_t sample_dst  = blockIdx.z;
+
+    uint32_t token_idx = 0;
+    uint32_t channel_x;
+    uint32_t channel_y;
+    uint32_t sample_dst;
+
+    if constexpr (is_multi_token_id) {
+        // Multi-token MUL_MAT_ID path, adding these in the normal path causes a perf regression for n_tokens=1 case
+        token_idx  = blockIdx.z;
+        channel_x  = ids[channel_dst + token_idx * ids_stride];
+        channel_y  = fastmodulo(channel_dst, nchannels_y);
+        sample_dst = 0;
+    } else {
+        channel_x  = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
+        channel_y  = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
+        sample_dst = blockIdx.z;
+    }
+
     const uint32_t sample_x    = fastdiv(sample_dst, sample_ratio);
     const uint32_t sample_y    = sample_dst;
 
@@ -188,11 +202,11 @@ static __global__ void mul_mat_vec_q(
         active_glu    = fusion.glu_op;
     }
 
-    const uint32_t channel_bias = ids ? channel_x : channel_dst;
 
     float x_biases[ncols_dst]    = { 0.0f };
     float gate_biases[ncols_dst] = { 0.0f };
     if constexpr (has_fusion) {
+        const uint32_t channel_bias = ids ? channel_x : channel_dst;
         if (use_bias) {
             x_bias = x_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
             // 1. Hide latency by prefetching bias and gate here
@@ -222,6 +236,9 @@ static __global__ void mul_mat_vec_q(
     float tmp_gate[ncols_dst][rows_per_cuda_block] = {{0.0f}};
 
     const block_q8_1 * y = ((const block_q8_1 *) vy) + sample_y*stride_sample_y + channel_y*stride_channel_y;
+    if constexpr (is_multi_token_id) {
+        y += token_idx*stride_col_y;
+    }
     const int kbx_offset = sample_x*stride_sample_x + channel_x*stride_channel_x + row0*stride_row_x;
 
     for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
@@ -275,6 +292,10 @@ static __global__ void mul_mat_vec_q(
 
     dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst + row0;
 
+    if constexpr (is_multi_token_id) {
+        dst += token_idx*stride_col_dst;
+    }
+
     // sum up partial sums and write back result
 #pragma unroll
     for (int j = 0; j < ncols_dst; ++j) {
@@ -335,40 +356,41 @@ static __global__ void mul_mat_vec_q(
 }
 
 static std::pair calc_launch_params(
-        const int ncols_dst, const int nrows_x, const int nchannels_y, const int nsamples_y,
+        const int ncols_dst, const int nrows_x, const int nchannels_dst, const int nsamples_or_ntokens,
         const int warp_size, const mmvq_parameter_table_id table_id) {
     const int64_t nblocks = (nrows_x + calc_rows_per_block(ncols_dst, table_id) - 1) / calc_rows_per_block(ncols_dst, table_id);
-    const dim3 block_nums(nblocks, nchannels_y, nsamples_y);
+    const dim3 block_nums(nblocks, nchannels_dst, nsamples_or_ntokens);
     const dim3 block_dims(warp_size, calc_nwarps(ncols_dst, table_id), 1);
     return {block_nums, block_dims};
 }
 
-template
+template
 static void mul_mat_vec_q_switch_fusion(
         const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
         const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
         const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
         const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
         const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst,
-        const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared, cudaStream_t stream) {
+        const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared,
+        const uint32_t ids_stride, cudaStream_t stream) {
 
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
     if constexpr (c_ncols_dst == 1) {
         if (has_fusion) {
-            mul_mat_vec_q<<>>
+            mul_mat_vec_q<<>>
                 (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, 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);
+                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
             return;
         }
     }
 
     GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
 
-    mul_mat_vec_q<<>>
+    mul_mat_vec_q<<>>
         (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, 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);
+        sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
 }
 
 template 
@@ -379,7 +401,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
         const int nchannels_x, const int nchannels_y, const int nchannels_dst,
         const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
         const int nsamples_x, const int nsamples_dst, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
-        cudaStream_t stream) {
+        const int ids_stride, cudaStream_t stream) {
 
     GGML_ASSERT(ncols_x % ggml_blck_size(type) == 0);
     GGML_ASSERT(ncols_dst <= MMVQ_MAX_BATCH_SIZE);
@@ -393,8 +415,19 @@ static void mul_mat_vec_q_switch_ncols_dst(
     const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);
 
     const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+    const bool has_ids = ids != nullptr;
+
+    if (has_ids && ncols_dst > 1) {
+        // Multi-token MUL_MAT_ID path only - single-token goes through regular path below
+        constexpr int c_ncols_dst = 1;
+        std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id);
+        mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+             channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
+             sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+             dims.first, dims.second, 0, ids_stride, stream);
+        return;
+    }
 
-    GGML_ASSERT(!ids || ncols_dst == 1);
     switch (ncols_dst) {
         case 1: {
             constexpr int c_ncols_dst = 1;
@@ -402,7 +435,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 2: {
             constexpr int c_ncols_dst = 2;
@@ -410,7 +443,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 3: {
             constexpr int c_ncols_dst = 3;
@@ -418,7 +451,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 4: {
             constexpr int c_ncols_dst = 4;
@@ -426,7 +459,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 5: {
             constexpr int c_ncols_dst = 5;
@@ -434,7 +467,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 6: {
             constexpr int c_ncols_dst = 6;
@@ -442,7 +475,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 7: {
             constexpr int c_ncols_dst = 7;
@@ -450,7 +483,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         case 8: {
             constexpr int c_ncols_dst = 8;
@@ -458,7 +491,7 @@ static void mul_mat_vec_q_switch_ncols_dst(
             mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
                  channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 dims.first, dims.second, 0, stream);
+                 dims.first, dims.second, 0, ids_stride, stream);
         } break;
         default:
             GGML_ABORT("fatal error");
@@ -474,127 +507,127 @@ static void mul_mat_vec_q_switch_type(
         const int nchannels_x, const int nchannels_y, const int nchannels_dst,
         const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
         const int nsamples_x, const int nsamples_dst, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
-        cudaStream_t stream) {
+        const int ids_stride, cudaStream_t stream) {
     switch (type_x) {
         case GGML_TYPE_Q4_0:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q4_1:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q5_0:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q5_1:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q8_0:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_MXFP4:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q2_K:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q3_K:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q4_K:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q5_K:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_Q6_K:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ2_XXS:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ2_XS:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ2_S:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ3_XXS:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ1_S:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ1_M:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ4_NL:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ4_XS:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         case GGML_TYPE_IQ3_S:
             mul_mat_vec_q_switch_ncols_dst
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, 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);
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
             break;
         default:
             GGML_ABORT("fatal error");
@@ -622,7 +655,7 @@ void ggml_cuda_mul_mat_vec_q(
     GGML_ASSERT(        nb0        == ts_dst);
     GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
 
-    GGML_ASSERT(!ids || ne12 == 1); // Implementation is only correct for batch size 1.
+    GGML_ASSERT(!ids || ne12 <= MMVQ_MAX_BATCH_SIZE);
 
     const float   * src1_d =       (const float   *) src1->data;
     const int32_t *  ids_d = ids ? (const int32_t *)  ids->data : nullptr;
@@ -693,11 +726,13 @@ void ggml_cuda_mul_mat_vec_q(
     const int64_t stride_channel_dst = ids ? s1   : s2;
     const int64_t stride_channel_y   = ids ? s11  : s12;
 
+    const int64_t ids_stride = ids ? ids->nb[1] / ggml_type_size(ids->type) : 0;
+
     mul_mat_vec_q_switch_type(
         src0->data, src0->type, src1_q8_1.get(), ids_d, fusion_local, dst_d, ne00,
         ne01,              ncols_dst,     s01, stride_col_y,     stride_col_dst,
         ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
-        ne03,              ne3,           s03, s13,              s3,               stream);
+        ne03,              ne3,           s03, s13,              s3,               ids_stride, stream);
 }
 
 void ggml_cuda_op_mul_mat_vec_q(
@@ -726,7 +761,7 @@ void ggml_cuda_op_mul_mat_vec_q(
     ggml_cuda_mm_fusion_args_device fusion_local{};
     mul_mat_vec_q_switch_type(
         src0_dd_i, src0->type, src1_ddq_i, nullptr, fusion_local, 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);
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, stream);
 
     GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_ncols, src1_padded_row_size);
 }

From 32b17abdb044a17cfeb6cdf26215cfa01756fea4 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam 
Date: Tue, 3 Feb 2026 17:37:32 +0100
Subject: [PATCH 110/206] vulkan: disable coopmat1 fa on Nvidia Turing (#19290)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index a99375c088..cb7fa2c9cb 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -254,6 +254,7 @@ enum vk_device_architecture {
     AMD_RDNA3,
     INTEL_XE2,
     NVIDIA_PRE_TURING,
+    NVIDIA_TURING,
 };
 
 static vk_device_architecture get_device_architecture(const vk::PhysicalDevice& device) {
@@ -336,18 +337,34 @@ static vk_device_architecture get_device_architecture(const vk::PhysicalDevice&
         const std::vector ext_props = device.enumerateDeviceExtensionProperties();
 
         bool cooperative_matrix = false;
+        bool sm_builtins = false;
 
         // Detect "pre-turing" based on lack of coopmat support.
         for (const auto& properties : ext_props) {
             if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0) {
                 cooperative_matrix = true;
-                break;
+            } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) {
+                sm_builtins = true;
             }
         }
 
         if (!cooperative_matrix) {
             return vk_device_architecture::NVIDIA_PRE_TURING;
         }
+
+        if (sm_builtins) {
+            vk::PhysicalDeviceProperties2 props2;
+            vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props;
+
+            props2.pNext = &sm_props;
+
+            device.getProperties2(&props2);
+
+            // Turing has 32, following architectures have 48
+            if (sm_props.shaderWarpsPerSM == 32) {
+                return vk_device_architecture::NVIDIA_TURING;
+            }
+        }
     }
     return vk_device_architecture::OTHER;
 }
@@ -8460,6 +8477,11 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     FaCodePath path = ctx->device->coopmat2 ? FA_COOPMAT2 :
                       ctx->device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR;
 
+    if (path == FA_COOPMAT1 && ctx->device->architecture == vk_device_architecture::NVIDIA_TURING) {
+        // Nvidia compiler bug, see https://github.com/ggml-org/llama.cpp/pull/19075#issuecomment-3820716090
+        path = FA_SCALAR;
+    }
+
     if (path == FA_COOPMAT1) {
         const bool coopmat_shape_supported = (dst->op_params[3] == GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f32acc) ||
                                              (dst->op_params[3] != GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f16acc);

From faa1bc26eed0e56539ac5e7a751607d76f01f10f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Tue, 3 Feb 2026 22:16:16 +0200
Subject: [PATCH 111/206] sampling : delegate input allocation to the scheduler
 (#19266)

* sampling : delegate input allocation to the scheduler

* graph : compute backend samplers only if needed
---
 src/llama-context.cpp  |  6 +---
 src/llama-graph.cpp    | 19 ++++++----
 src/llama-sampling.cpp | 81 ++++++++++--------------------------------
 3 files changed, 33 insertions(+), 73 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 203852d0f1..95b207e9e1 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1027,11 +1027,7 @@ bool llama_context::set_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
         llama_sampler_chain_n(sampler) > 0;
 
     if (sampler && can_offload) {
-        ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(model.dev_output());
-        auto * host_buft = ggml_backend_dev_host_buffer_type(model.dev_output());
-        if (host_buft) {
-            buft = host_buft;
-        }
+        auto * buft = ggml_backend_dev_buffer_type(model.dev_output());
 
         sampler->iface->backend_init(sampler, buft);
 
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 16d42c4ae3..54f4ed2481 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -2419,6 +2419,9 @@ void llm_graph_context::build_sampling() const {
         return;
     }
 
+    std::array outs;
+    outs[0] = res->t_logits;
+
     auto inp_sampling = std::make_unique(samplers);
     res->add_input(std::move(inp_sampling));
 
@@ -2439,14 +2442,14 @@ void llm_graph_context::build_sampling() const {
     // add a dummy row of logits
     // this trick makes the graph static, regardless of which samplers are activated
     // this is important in order to minimize graph reallocations
-    // TODO: use `ggml_build_forward_select()` when available (https://github.com/ggml-org/llama.cpp/pull/18550)
     ggml_tensor * logits_t = ggml_pad(ctx0, res->t_logits, 0, 1, 0, 0);
 
     for (const auto & [seq_id, sampler] : samplers) {
         const auto it = seq_to_logit_row.find(seq_id);
 
         // inactive samplers always work on the first row
-        const auto row_idx = seq_to_logit_row.find(seq_id) != seq_to_logit_row.end() ? it->second : 0;
+        const auto row_idx = it != seq_to_logit_row.end() ? it->second : 0;
+        const int i_out    = it != seq_to_logit_row.end() ? 1          : 0;
 
         ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], row_idx * logits_t->nb[1]);
         ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
@@ -2463,22 +2466,26 @@ void llm_graph_context::build_sampling() const {
 
         if (data.sampled != nullptr) {
             res->t_sampled[seq_id] = data.sampled;
-            ggml_build_forward_expand(gf, data.sampled);
+            outs[1] = data.sampled;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.probs != nullptr) {
             res->t_sampled_probs[seq_id] = data.probs;
-            ggml_build_forward_expand(gf, data.probs);
+            outs[1] = data.probs;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.logits != nullptr) {
             res->t_sampled_logits[seq_id] = data.logits;
-            ggml_build_forward_expand(gf, data.logits);
+            outs[1] = data.logits;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
 
         if (data.candidates != nullptr) {
             res->t_candidates[seq_id] = data.candidates;
-            ggml_build_forward_expand(gf, data.candidates);
+            outs[1] = data.candidates;
+            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
         }
     }
 
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 5dde513065..515d6c163b 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1025,11 +1025,7 @@ struct llama_sampler_dist : public llama_sampler_backend {
 
     std::mt19937 rng;
 
-    // backend input
-    struct ggml_tensor * inp_uniform;
-
-    ggml_context_ptr        inp_ctx;
-    ggml_backend_buffer_ptr inp_buf;
+    ggml_tensor * inp_uniform;
 };
 
 static const char * llama_sampler_dist_name(const struct llama_sampler * smpl) {
@@ -1138,37 +1134,10 @@ static bool llama_sampler_dist_backend_init(
         ggml_backend_buffer_type_t   buft) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
-    // allocate inputs
-    {
-        ggml_init_params params = {
-            /*.mem_size   =*/ ggml_tensor_overhead(),
-            /*.mem_buffer =*/ nullptr,
-            /*.no_alloc   =*/ true,
-        };
-
-        sctx->inp_ctx.reset(ggml_init(params));
-
-        // Create the uniform random scalar input tensor. This will be set by
-        // llama_sampler_dist_backend_set_input after this graph is built.
-        sctx->inp_uniform = ggml_new_tensor_1d(sctx->inp_ctx.get(), GGML_TYPE_F32, 1);
-        ggml_set_name (sctx->inp_uniform, "uniform");
-        ggml_set_input(sctx->inp_uniform);
-
-        // Allocate all tensors from our context to the backend
-        sctx->inp_buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(sctx->inp_ctx.get(), buft));
-
-        ggml_backend_buffer_clear(sctx->inp_buf.get(), 0);
-    }
-
     const bool res = llama_sampler_backend_support(smpl, buft);
 
     sctx->init(res);
 
-    if (!res) {
-        sctx->inp_ctx.reset(nullptr);
-        sctx->inp_buf.reset(nullptr);
-    }
-
     return res;
 }
 
@@ -1178,8 +1147,13 @@ static void llama_sampler_dist_backend_apply(
         struct ggml_cgraph        * gf,
         struct llama_sampler_data * data) {
     GGML_UNUSED(gf);
+
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
+    sctx->inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    ggml_set_name (sctx->inp_uniform, "uniform");
+    ggml_set_input(sctx->inp_uniform);
+
     struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
     ggml_set_name(probs, "dist_probs");
 
@@ -1226,6 +1200,7 @@ static void llama_sampler_dist_backend_apply(
 
 static void llama_sampler_dist_backend_set_input(struct llama_sampler * smpl) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
+
     GGML_ASSERT(sctx->inp_uniform != nullptr);
 
     // We sample in double precision and cast to float to match rnd numbers of
@@ -1262,8 +1237,6 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
             /* .seed_cur    = */ seed_cur,
             /* .rng         = */ std::mt19937(seed_cur),
             /* .inp_uniform = */ nullptr,
-            /* .inp_ctx     = */ nullptr,
-            /* .inp_buf     = */ nullptr,
         }
     );
 }
@@ -3461,9 +3434,6 @@ struct llama_sampler_logit_bias : public llama_sampler_backend {
 
     struct ggml_tensor * inp_logit_bias;
     struct ggml_tensor * inp_logit_idxs;
-
-    ggml_context_ptr        inp_ctx;
-    ggml_backend_buffer_ptr inp_buf;
 };
 
 static const char * llama_sampler_logit_bias_name(const struct llama_sampler * smpl) {
@@ -3526,6 +3496,16 @@ static void llama_sampler_logit_bias_backend_apply(
         return;
     }
 
+    const size_t n = sctx->logit_bias.size();
+
+    sctx->inp_logit_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n);
+    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
+    ggml_set_input(sctx->inp_logit_bias);
+
+    sctx->inp_logit_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n);
+    ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
+    ggml_set_input(sctx->inp_logit_idxs);
+
     ggml_tensor * cur = ggml_fill(ctx, data->logits, 0.0f);
 
     cur = ggml_reshape_2d(ctx, cur, 1, ggml_nelements(cur));
@@ -3562,6 +3542,8 @@ static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * sm
 static bool llama_sampler_logit_bias_backend_init(
         struct llama_sampler       * smpl,
         ggml_backend_buffer_type_t   buft) {
+    GGML_UNUSED(buft);
+
     auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
 
     sctx->init(true);
@@ -3570,29 +3552,6 @@ static bool llama_sampler_logit_bias_backend_init(
         return true;
     }
 
-    ggml_init_params params = {
-        /*.mem_size   =*/ 2*ggml_tensor_overhead(),
-        /*.mem_buffer =*/ nullptr,
-        /*.no_alloc   =*/ true,
-    };
-
-    sctx->inp_ctx.reset(ggml_init(params));
-
-    const size_t n = sctx->logit_bias.size();
-
-    sctx->inp_logit_bias = ggml_new_tensor_2d(sctx->inp_ctx.get(), GGML_TYPE_F32, 1, n);
-    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
-    ggml_set_input(sctx->inp_logit_bias);
-
-    sctx->inp_logit_idxs = ggml_new_tensor_1d(sctx->inp_ctx.get(), GGML_TYPE_I32, n);
-    ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
-    ggml_set_input(sctx->inp_logit_idxs);
-
-    // Allocate all tensors from our context to the backend
-    sctx->inp_buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(sctx->inp_ctx.get(), buft));
-
-    ggml_backend_buffer_clear(sctx->inp_buf.get(), 0);
-
     return true;
 }
 
@@ -3628,8 +3587,6 @@ struct llama_sampler * llama_sampler_init_logit_bias(
             /* .to_search      = */ {},
             /* .inp_logit_bias = */ nullptr,
             /* .inp_logit_idxs = */ nullptr,
-            /* .inp_ctx        = */ nullptr,
-            /* .inp_buf        = */ nullptr,
         }
     );
 }

From 6a9bf2f7882394d74fc54a43764d47316b3f6aef Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Tue, 3 Feb 2026 22:41:20 +0200
Subject: [PATCH 112/206] ci : add sanitizer runs for server (#19291)

---
 .github/workflows/server.yml | 16 ++++++++++++----
 CMakeLists.txt               | 23 -----------------------
 cmake/common.cmake           | 23 +++++++++++++++++++++++
 3 files changed, 35 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 9f1ef48c82..3d342c35f7 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -36,7 +36,7 @@ jobs:
 
     strategy:
       matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow
         build_type: [RelWithDebInfo]
         include:
           - build_type: Release
@@ -45,7 +45,7 @@ jobs:
           - build_type: Release
             sanitizer: ""
             extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+      fail-fast: false
 
     steps:
       - name: Dependencies
@@ -72,7 +72,15 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON -DGGML_SCHED_NO_REALLOC=ON
+          cmake -B build \
+            -DLLAMA_BUILD_BORINGSSL=ON \
+            -DGGML_SCHED_NO_REALLOC=ON \
+            -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \
+            -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
           cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup
@@ -88,7 +96,7 @@ jobs:
 
       - name: Tests
         id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) && matrix.build_type == 'Release' }}
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
         run: |
           cd tools/server/tests
           export ${{ matrix.extra_args }}
diff --git a/CMakeLists.txt b/CMakeLists.txt
index d24fa080ae..6d4ed67020 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -164,29 +164,6 @@ llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
 llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
 llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
-if (NOT MSVC)
-    if (LLAMA_SANITIZE_THREAD)
-        message(STATUS "Using -fsanitize=thread")
-
-        add_compile_options(-fsanitize=thread)
-        link_libraries     (-fsanitize=thread)
-    endif()
-
-    if (LLAMA_SANITIZE_ADDRESS)
-        message(STATUS "Using -fsanitize=address")
-
-        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries     (-fsanitize=address)
-    endif()
-
-    if (LLAMA_SANITIZE_UNDEFINED)
-        message(STATUS "Using -fsanitize=undefined")
-
-        add_compile_options(-fsanitize=undefined)
-        link_libraries     (-fsanitize=undefined)
-    endif()
-endif()
-
 include("cmake/license.cmake")
 license_add_file("llama.cpp" "LICENSE")
 
diff --git a/cmake/common.cmake b/cmake/common.cmake
index a5bb787f15..bcf403e0ee 100644
--- a/cmake/common.cmake
+++ b/cmake/common.cmake
@@ -32,4 +32,27 @@ function(llama_add_compile_flags)
             set(CXX_FLAGS "" PARENT_SCOPE)
         endif()
     endif()
+
+    if (NOT MSVC)
+        if (LLAMA_SANITIZE_THREAD)
+            message(STATUS "Using -fsanitize=thread")
+
+            add_compile_options(-fsanitize=thread)
+            link_libraries     (-fsanitize=thread)
+        endif()
+
+        if (LLAMA_SANITIZE_ADDRESS)
+            message(STATUS "Using -fsanitize=address")
+
+            add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
+            link_libraries     (-fsanitize=address)
+        endif()
+
+        if (LLAMA_SANITIZE_UNDEFINED)
+            message(STATUS "Using -fsanitize=undefined")
+
+            add_compile_options(-fsanitize=undefined)
+            link_libraries     (-fsanitize=undefined)
+        endif()
+    endif()
 endfunction()

From 44008ce8f93a5dc025a40ba3150ab9d119af22f0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Tue, 3 Feb 2026 23:43:14 +0200
Subject: [PATCH 113/206] metal : add solve_tri (#19302)

---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 30 +++++++++
 ggml/src/ggml-metal/ggml-metal-device.h   |  1 +
 ggml/src/ggml-metal/ggml-metal-device.m   |  1 +
 ggml/src/ggml-metal/ggml-metal-impl.h     | 30 ++++++++-
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 61 ++++++++++++++++++
 ggml/src/ggml-metal/ggml-metal-ops.h      |  1 +
 ggml/src/ggml-metal/ggml-metal.metal      | 77 +++++++++++++++++++++++
 7 files changed, 200 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 377b0d3eb8..4cd3d93d81 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -534,6 +534,36 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv(ggml_metal_
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri(ggml_metal_library_t lib, const ggml_tensor * op) {
+    char base[256];
+    char name[256];
+
+    const int nsg = 8;
+    const int n   = op->src[1]->ne[1];
+    const int k   = op->src[1]->ne[0];
+
+    snprintf(base, 256, "kernel_solve_tri_%s", ggml_type_name(op->src[0]->type));
+    snprintf(name, 256, "%s_nsg=%d_n=%d_k=%d", base, nsg, n, k);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, nsg, FC_SOLVE_TRI + 0);
+        ggml_metal_cv_set_int16(cv, n,   FC_SOLVE_TRI + 1);
+        ggml_metal_cv_set_int16(cv, k,   FC_SOLVE_TRI + 2);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
+    }
+
+    res.nsg  = nsg;
+    res.smem = GGML_PAD(GGML_PAD(n, 32)*nsg*sizeof(float), 16);
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext(ggml_metal_library_t lib, ggml_type tsrc0, ggml_type tsrc1, int nsg, int nxpsg, int r1ptg) {
     char base[256];
     char name[256];
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index afb091e725..d898432712 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -121,6 +121,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched  (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan          (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv              (ggml_metal_library_t lib, const struct ggml_tensor * op);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext        (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv            (ggml_metal_library_t lib, const struct ggml_tensor * op);
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 285dd1630e..8a0b85c6e4 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1152,6 +1152,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return has_simdgroup_reduction;
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_RWKV_WKV7:
+        case GGML_OP_SOLVE_TRI:
             return true;
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index e074f2ef3d..640ade8f88 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -78,7 +78,8 @@
 #define FC_MUL_MM                      700
 #define FC_ROPE                        800
 #define FC_SSM_CONV                    900
-#define FC_COUNT_EQUAL                 1000
+#define FC_SOLVE_TRI                   1000
+#define FC_COUNT_EQUAL                 1100
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
@@ -733,6 +734,33 @@ typedef struct {
     uint64_t nb0;
 } ggml_metal_kargs_ssm_scan;
 
+typedef struct {
+    int32_t  ne00;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne10;
+    int32_t  ne11;
+    int32_t  ne12;
+    int32_t  ne13;
+    uint64_t nb10;
+    uint64_t nb11;
+    uint64_t nb12;
+    uint64_t nb13;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
+} ggml_metal_kargs_solve_tri;
+
 typedef struct {
     int32_t  ne00t;
     int32_t  ne00;
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index f97c4435de..753fcec317 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -341,6 +341,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_rwkv(ctx, idx);
             } break;
+        case GGML_OP_SOLVE_TRI:
+            {
+                n_fuse = ggml_metal_op_solve_tri(ctx, idx);
+            } break;
         case GGML_OP_MUL_MAT:
             {
                 n_fuse = ggml_metal_op_mul_mat(ctx, idx);
@@ -1557,6 +1561,63 @@ int ggml_metal_op_rwkv(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
+int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_library_t lib = ctx->lib;
+    ggml_metal_encoder_t enc = ctx->enc;
+
+    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
+    GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
+    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
+
+    ggml_metal_kargs_solve_tri args = {
+        /*.ne00 =*/ ne00,
+        /*.ne01 =*/ ne01,
+        /*.ne02 =*/ ne02,
+        /*.ne03 =*/ ne03,
+        /*.nb00 =*/ nb00,
+        /*.nb01 =*/ nb01,
+        /*.nb02 =*/ nb02,
+        /*.nb03 =*/ nb03,
+        /*.ne10 =*/ ne10,
+        /*.ne11 =*/ ne11,
+        /*.ne12 =*/ ne12,
+        /*.ne13 =*/ ne13,
+        /*.nb10 =*/ nb10,
+        /*.nb11 =*/ nb11,
+        /*.nb12 =*/ nb12,
+        /*.nb13 =*/ nb13,
+        /*.ne0  =*/ ne0,
+        /*.ne1  =*/ ne1,
+        /*.ne2  =*/ ne2,
+        /*.ne3  =*/ ne3,
+        /*.nb0  =*/ nb0,
+        /*.nb1  =*/ nb1,
+        /*.nb2  =*/ nb2,
+        /*.nb3  =*/ nb3,
+    };
+
+    auto pipeline = ggml_metal_library_get_pipeline_solve_tri(lib, op);
+
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         3);
+
+    const int nsg = pipeline.nsg;
+
+    ggml_metal_encoder_set_threadgroup_memory_size(enc, pipeline.smem, 0);
+
+    ggml_metal_encoder_dispatch_threadgroups(enc, (ne10 + nsg - 1)/nsg, ne02, ne03, 32, nsg, 1);
+
+    return 1;
+}
+
 int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h
index 10686a334e..2e4c7d3fa1 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.h
+++ b/ggml/src/ggml-metal/ggml-metal-ops.h
@@ -60,6 +60,7 @@ int ggml_metal_op_soft_max          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_conv          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_scan          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_rwkv              (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_solve_tri         (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_cpy               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_pool_1d           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_pool_2d           (ggml_metal_op_t ctx, int idx);
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 3259213fd6..c09a54e661 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -2737,6 +2737,83 @@ kernel void kernel_rwkv_wkv7_f32(
     }
 }
 
+constant short FC_solve_tri_nsg [[function_constant(FC_SOLVE_TRI + 0)]];
+constant short FC_solve_tri_n   [[function_constant(FC_SOLVE_TRI + 1)]];
+constant short FC_solve_tri_k   [[function_constant(FC_SOLVE_TRI + 2)]];
+
+kernel void kernel_solve_tri_f32(
+        constant ggml_metal_kargs_solve_tri & args,
+        device   const char * src0,
+        device   const char * src1,
+        device         char * dst,
+        threadgroup    char * shmem [[threadgroup(0)]],
+        ushort3 tgpig[[threadgroup_position_in_grid]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    constexpr short NW = N_SIMDWIDTH;
+
+    const short NSG = FC_solve_tri_nsg;
+    const short N   = FC_solve_tri_n;
+    const short K   = FC_solve_tri_k;
+    const short NP  = PAD2(N, NW);
+
+    const int32_t ne02 = args.ne02;
+    const int32_t ne03 = args.ne03;
+
+    const int32_t i03 = tgpig.z;
+    const int32_t i02 = tgpig.y;
+    const int32_t i01 = tgpig.x*NSG + sgitg;
+
+    threadgroup float * sh0 = (threadgroup float *) shmem;
+
+    device const float * src0_ptr = (device const float *)(src0 + i02 * args.nb02 + i03 * args.nb03) + sgitg*N;
+    device const float * src1_ptr = (device const float *)(src1 + i02 * args.nb12 + i03 * args.nb13) + i01;
+    device       float * dst_ptr  = (device       float *)(dst  + i02 * args.nb2  + i03 * args.nb3)  + i01;
+
+    for (short rr = 0; rr < N; rr += NSG) {
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        {
+            threadgroup float * sh0_cur = sh0 + sgitg*NP;
+
+            for (short t = 0; t*NW < N; ++t) {
+                const short idx = t*NW + tiisg;
+                sh0_cur[idx] = src0_ptr[idx];
+            }
+
+            src0_ptr += NSG*N;
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        if (i01 >= args.ne10) {
+            continue;
+        }
+
+        for (short ir = 0; ir < NSG && rr + ir < N; ++ir) {
+            const short r = rr + ir;
+
+            threadgroup float * sh0_cur = sh0 + ir*NP;
+
+            float sum = 0.0f;
+
+            for (short t = 0; t*NW < r; ++t) {
+                const short idx = t*NW + tiisg;
+                sum += sh0_cur[idx] * dst_ptr[idx*K] * (idx < r);
+            }
+
+            sum = simd_sum(sum);
+
+            if (tiisg == 0) {
+                const float diag = sh0_cur[r];
+
+                dst_ptr[r*K] = (src1_ptr[r*K] - sum) / diag;
+            }
+        }
+    }
+}
+
 kernel void kernel_argmax_f32(
         constant ggml_metal_kargs_argmax & args,
         device   const char * src0,

From 2ceda3f6622661af839c19767705f33fb9f6cdd2 Mon Sep 17 00:00:00 2001
From: Aman Gupta 
Date: Wed, 4 Feb 2026 09:43:29 +0800
Subject: [PATCH 114/206] ggml-cpu: use LUT for converting e8->f32 scales on
 x86 (#19288)

* ggml-cpu: use LUT for converting e8->f32 scales on x86

* add dispatch based on macro
---
 ggml/src/ggml-cpu/arch/x86/quants.c | 18 +++++++++---------
 ggml/src/ggml-cpu/ggml-cpu.c        |  8 ++++++++
 ggml/src/ggml-cpu/simd-mappings.h   | 11 +++++++++++
 3 files changed, 28 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-cpu/arch/x86/quants.c b/ggml/src/ggml-cpu/arch/x86/quants.c
index cb49320a67..74d699f633 100644
--- a/ggml/src/ggml-cpu/arch/x86/quants.c
+++ b/ggml/src/ggml-cpu/arch/x86/quants.c
@@ -268,9 +268,9 @@ static inline __m256 quad_fp16_delta_float(const float x0, const float y0, const
                            _mm_set1_ps(GGML_CPU_FP16_TO_FP32(x0) * GGML_CPU_FP16_TO_FP32(y0)));
 }
 
-static inline __m256 quad_mx_delta_float(const int8_t x0, const float y0, const int8_t x1, const float y1) {
-    return _mm256_set_m128(_mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x1) * GGML_CPU_FP16_TO_FP32(y1)),
-                           _mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x0) * GGML_CPU_FP16_TO_FP32(y0)));
+static inline __m256 quad_mx_delta_float(const uint8_t x0, const float y0, const uint8_t x1, const float y1) {
+    return _mm256_set_m128(_mm_set1_ps(GGML_CPU_E8M0_TO_FP32_HALF(x1) * GGML_CPU_FP16_TO_FP32(y1)),
+                           _mm_set1_ps(GGML_CPU_E8M0_TO_FP32_HALF(x0) * GGML_CPU_FP16_TO_FP32(y0)));
 }
 #endif
 #elif defined(__SSSE3__)
@@ -782,6 +782,7 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 
     __m256 accum1 = _mm256_setzero_ps();
     __m256 accum2 = _mm256_setzero_ps();
+
     for (; ib + 1 < nb; ib += 2) {
         const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs);
         const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs);
@@ -795,10 +796,10 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
         const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
         const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
-        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 0].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 0].e)),
-                _mm256_cvtepi32_ps(p_1), accum1);
-        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 1].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 1].e)),
-                _mm256_cvtepi32_ps(p_2), accum2);
+        const __m256 scale0 = _mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 0].d)*GGML_CPU_E8M0_TO_FP32_HALF(x[ib + 0].e));
+        const __m256 scale1 = _mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 1].d)*GGML_CPU_E8M0_TO_FP32_HALF(x[ib + 1].e));
+        accum1 = _mm256_fmadd_ps(scale0, _mm256_cvtepi32_ps(p_1), accum1);
+        accum2 = _mm256_fmadd_ps(scale1, _mm256_cvtepi32_ps(p_2), accum2);
     }
 
     sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
@@ -830,7 +831,7 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 
 #endif
     for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_E8M0_TO_FP32_HALF(x[ib].e);
         int sumi1 = 0;
         int sumi2 = 0;
         for (int j = 0; j < QK_MXFP4/2; ++j) {
@@ -3817,4 +3818,3 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 }
-
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 3e5f01e3fb..b003fe13fd 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -75,6 +75,9 @@
 // precomputed f32 table for f16 (256 KB) (simd-mappings.h)
 float ggml_table_f32_f16[1 << 16];
 
+// precomputed f32 table for e8m0 half (1 KB) (simd-mappings.h)
+float ggml_table_f32_e8m0_half[1 << 8];
+
 #if defined(__ARM_ARCH)
 struct ggml_arm_arch_features_type {
     int sve_cnt;
@@ -3681,6 +3684,11 @@ void ggml_cpu_init(void) {
                 ggml_table_gelu_quick_f16[i] = GGML_CPU_FP32_TO_FP16(ggml_gelu_quick_f32(f));
             }
 
+            // initialize E8M0 half table (256 entries)
+            for (int i = 0; i < (1 << 8); ++i) {
+                ggml_table_f32_e8m0_half[i] = GGML_E8M0_TO_FP32_HALF(i);
+            }
+
             const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
 
             GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h
index e367f110b4..630e506542 100644
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -116,6 +116,17 @@ extern "C" {
 // defined in ggml-cpu.c, initialized in ggml_cpu_init()
 extern float ggml_table_f32_f16[1 << 16];
 
+// precomputed f32 table for e8m0 half (1 KB)
+// defined in ggml-cpu.c, initialized in ggml_cpu_init()
+extern float ggml_table_f32_e8m0_half[1 << 8];
+
+// Use lookup table for E8M0 on x86 (faster than bit manipulation)
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+#define GGML_CPU_E8M0_TO_FP32_HALF(x) ggml_table_f32_e8m0_half[(uint8_t)(x)]
+#else
+#define GGML_CPU_E8M0_TO_FP32_HALF(x) GGML_E8M0_TO_FP32_HALF(x)
+#endif
+
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_CPU_FP16_TO_FP32 and GGML_CPU_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.

From 015deb90485b9ebfff492e10fe7080a0439202b6 Mon Sep 17 00:00:00 2001
From: Kevin Pouget 
Date: Wed, 4 Feb 2026 03:46:18 +0100
Subject: [PATCH 115/206] ggml-virtgpu: make the code thread safe (#19204)

* ggml-virtgpu: regenerate_remoting.py: add the ability to deprecate a function

* ggml-virtgpu: deprecate buffer_type is_host remoting

not necessary

* ggml-virtgpu: stop using static vars as cache

The static init isn't thread safe.

* ggml-virtgpu: protect the use of the shared memory to transfer data

* ggml-virtgpu: make the remote calls thread-safe

* ggml-virtgpu: backend: don't continue if couldn't allocate the tensor memory

* ggml-virtgpu: add a cleanup function for consistency

* ggml-virtgpu: backend: don't crash if buft->iface.get_max_size is missing

* fix style and ordering

* Remove the static variable in apir_device_get_count

* ggml-virtgpu: improve the logging

* fix review minor formatting changes
---
 ggml/include/ggml-virtgpu.h                   |   2 -
 .../ggml-virtgpu/apir_cs_ggml-rpc-front.cpp   |   2 +-
 .../backend/backend-dispatched-backend.cpp    |   4 +-
 .../backend-dispatched-buffer-type.cpp        |  12 +-
 .../backend/backend-dispatched-buffer.cpp     |   6 +-
 .../backend/backend-dispatched-device.cpp     |   2 +-
 .../backend/backend-dispatched.cpp            |   8 +-
 .../backend/backend-dispatched.gen.h          |   5 +-
 .../ggml-virtgpu/backend/backend-dispatched.h |   2 +
 ggml/src/ggml-virtgpu/backend/backend.cpp     |  32 ++--
 .../src/ggml-virtgpu/backend/shared/apir_cs.h |  11 +-
 .../backend/shared/apir_cs_ggml.h             |  14 +-
 .../ggml-virtgpu/ggml-backend-buffer-type.cpp |  29 +---
 ggml/src/ggml-virtgpu/ggml-backend-device.cpp |  61 ++++---
 ggml/src/ggml-virtgpu/ggml-backend-reg.cpp    |  94 ++++++++---
 ggml/src/ggml-virtgpu/ggml-remoting.h         |   5 +-
 .../ggml-virtgpu/ggmlremoting_functions.yaml  |  20 ++-
 ggml/src/ggml-virtgpu/regenerate_remoting.py  |  18 ++-
 .../ggml-virtgpu/virtgpu-forward-backend.cpp  |  14 +-
 .../virtgpu-forward-buffer-type.cpp           |  41 ++---
 .../ggml-virtgpu/virtgpu-forward-buffer.cpp   |  28 +++-
 .../ggml-virtgpu/virtgpu-forward-device.cpp   |  22 +--
 ggml/src/ggml-virtgpu/virtgpu-forward-impl.h  |   6 +-
 ggml/src/ggml-virtgpu/virtgpu-forward.gen.h   |  21 +--
 ggml/src/ggml-virtgpu/virtgpu-shm.cpp         |   3 +-
 ggml/src/ggml-virtgpu/virtgpu.cpp             | 149 ++++++++++++------
 ggml/src/ggml-virtgpu/virtgpu.h               |  23 +++
 27 files changed, 397 insertions(+), 237 deletions(-)

diff --git a/ggml/include/ggml-virtgpu.h b/ggml/include/ggml-virtgpu.h
index 1cb4bd7a03..faaba8f246 100644
--- a/ggml/include/ggml-virtgpu.h
+++ b/ggml/include/ggml-virtgpu.h
@@ -7,8 +7,6 @@
 extern "C" {
 #endif
 
-#define GGML_REMOTING_FRONTEND_NAME "RemotingFrontend"
-
 GGML_BACKEND_API ggml_backend_reg_t ggml_backend_virtgpu_reg();
 
 #ifdef  __cplusplus
diff --git a/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp b/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp
index f60ae3556c..d2e87330a6 100644
--- a/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp
+++ b/ggml/src/ggml-virtgpu/apir_cs_ggml-rpc-front.cpp
@@ -36,7 +36,7 @@ apir_rpc_tensor apir_serialize_tensor(const ggml_tensor * tensor) {
     result.data      = reinterpret_cast(tensor->data);
     if (tensor->data) {
         if (!tensor->buffer) {
-            GGML_ABORT("tensor has data but not buffer");
+            GGML_ABORT("%s: tensor has data but not buffer", __func__);
         }
         // tensor->data is serialized as an offset to the buffer base address
         result.data -= reinterpret_cast(BUFFER_TO_GGML_CONTEXT(tensor->buffer)->base);
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
index 77b4ee71e1..cc879e51d0 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
@@ -27,7 +27,7 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v
 
     const void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
     if (!shmem_data) {
-        GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
         apir_decoder_set_fatal(dec);
         return 1;
     }
@@ -45,7 +45,7 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v
         if (dev->iface.supports_op(dev, op)) {
             continue;
         }
-        GGML_LOG_ERROR("Graph node %d (%s) not supported by the backend\n", idx, ggml_op_desc(op));
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", idx, ggml_op_desc(op));
 
         status = GGML_STATUS_ABORTED;
         apir_encode_ggml_status(enc, &status);
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp
index 8ea1bb4fb4..d55eec2761 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp
@@ -36,18 +36,22 @@ uint32_t backend_buffer_type_get_max_size(apir_encoder * enc, apir_decoder * dec
     ggml_backend_buffer_type_t buft;
     buft = apir_decode_ggml_buffer_type(dec);
 
-    size_t value = buft->iface.get_max_size(buft);
+    size_t value = SIZE_MAX;
+    if (buft->iface.get_max_size) {
+        value = buft->iface.get_max_size(buft);
+    }
+
     apir_encode_size_t(enc, &value);
 
     return 0;
 }
 
+/* APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST is deprecated. Keeping the handler for backward compatibility. */
 uint32_t backend_buffer_type_is_host(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
     GGML_UNUSED(ctx);
-    ggml_backend_buffer_type_t buft;
-    buft = apir_decode_ggml_buffer_type(dec);
+    GGML_UNUSED(dec);
+    const bool is_host = false;
 
-    bool is_host = buft->iface.is_host(buft);
     apir_encode_bool_t(enc, &is_host);
 
     return 0;
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
index cf81888e98..8cc063ff0a 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
@@ -40,7 +40,7 @@ uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl
     void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
 
     if (!shmem_data) {
-        GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
         return 1;
     }
 
@@ -71,7 +71,7 @@ uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl
 
     void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
     if (!shmem_data) {
-        GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
         return 1;
     }
 
@@ -121,7 +121,7 @@ uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virg
     buffer = apir_decode_ggml_buffer(dec);
 
     if (!apir_untrack_backend_buffer(buffer)) {
-        GGML_LOG_WARN("%s: unknown buffer %p\n", __func__, (void *) buffer);
+        GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: unknown buffer %p\n", __func__, (void *) buffer);
         return 1;
     }
 
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp
index 497f737a88..c7acb8b51c 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp
@@ -124,7 +124,7 @@ uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec,
 
     void * shmem_ptr = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
     if (!shmem_ptr) {
-        GGML_LOG_ERROR("Couldn't get the shmem addr from virgl\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
         apir_decoder_set_fatal(dec);
         return 1;
     }
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp
index 51d445725f..64152eef0d 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp
@@ -17,26 +17,26 @@ uint64_t timer_count = 0;
 
 uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) {
     if (reg != NULL) {
-        GGML_LOG_WARN("%s: already initialized\n", __func__);
+        GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: already initialized\n", __func__);
         return APIR_BACKEND_INITIALIZE_ALREADY_INITED;
     }
     ggml_backend_reg_t (*ggml_backend_reg_fct)(void) = (ggml_backend_reg_t (*)()) ggml_backend_reg_fct_p;
 
     reg = ggml_backend_reg_fct();
     if (reg == NULL) {
-        GGML_LOG_ERROR("%s: backend registration failed\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend registration failed\n", __func__);
         return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED;
     }
 
     if (!reg->iface.get_device_count(reg)) {
-        GGML_LOG_ERROR("%s: backend initialization failed: no device found\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device found\n", __func__);
         return APIR_BACKEND_INITIALIZE_NO_DEVICE;
     }
 
     dev = reg->iface.get_device(reg, 0);
 
     if (!dev) {
-        GGML_LOG_ERROR("%s: backend initialization failed: no device received\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device received\n", __func__);
         return APIR_BACKEND_INITIALIZE_NO_DEVICE;
     }
 
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h
index b81fd5039b..481d7f3150 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h
@@ -16,6 +16,7 @@ uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec,
 uint32_t backend_buffer_type_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
 uint32_t backend_buffer_type_get_alignment(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
 uint32_t backend_buffer_type_get_max_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
+/* APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST is deprecated. Keeping the handler for backward compatibility. */
 uint32_t backend_buffer_type_is_host(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
 uint32_t backend_buffer_type_alloc_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
 uint32_t backend_buffer_type_get_alloc_size(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);
@@ -62,7 +63,7 @@ static inline const char * backend_dispatch_command_name(ApirBackendCommandType
         case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE:
             return "backend_buffer_type_get_max_size";
         case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST:
-            return "backend_buffer_type_is_host";
+            return "backend_buffer_type_is_host (DEPRECATED)";
         case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER:
             return "backend_buffer_type_alloc_buffer";
         case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE:
@@ -110,7 +111,7 @@ static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATC
     /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME  = */ backend_buffer_type_get_name,
     /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT  = */ backend_buffer_type_get_alignment,
     /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE  = */ backend_buffer_type_get_max_size,
-    /* APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST  = */ backend_buffer_type_is_host,
+    /* APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST  = */ backend_buffer_type_is_host /* DEPRECATED */,
     /* APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER  = */ backend_buffer_type_alloc_buffer,
     /* APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE  = */ backend_buffer_type_get_alloc_size,
 
diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h
index 6ccbecf078..10311631d4 100644
--- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.h
+++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h
@@ -11,6 +11,8 @@
 #include "shared/apir_cs.h"
 #include "shared/apir_cs_ggml.h"
 
+#define GGML_VIRTGPU_BCK "ggml-virtgpu-backend: "
+
 struct virgl_apir_context {
     uint32_t               ctx_id;
     virgl_apir_callbacks * iface;
diff --git a/ggml/src/ggml-virtgpu/backend/backend.cpp b/ggml/src/ggml-virtgpu/backend/backend.cpp
index 95d602ed60..d93414a078 100644
--- a/ggml/src/ggml-virtgpu/backend/backend.cpp
+++ b/ggml/src/ggml-virtgpu/backend/backend.cpp
@@ -35,14 +35,8 @@ void apir_backend_deinit(uint32_t virgl_ctx_id) {
         buffer->iface.free_buffer(buffer);
     }
 
-    if (dev) {
-        size_t free, total;
-        dev->iface.get_memory(dev, &free, &total);
-        GGML_LOG_INFO("%s: free memory: %ld MB\n", __func__, (size_t) free / 1024 / 1024);
-    }
-
     if (backend_library_handle) {
-        GGML_LOG_INFO("%s: The GGML backend library was loaded. Unloading it.\n", __func__);
+        GGML_LOG_INFO(GGML_VIRTGPU_BCK "The GGML backend library was loaded. Unloading it.\n");
         dlclose(backend_library_handle);
         backend_library_handle = NULL;
     }
@@ -65,7 +59,7 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
         if (apir_logfile) {
             ggml_log_set(log_to_file_callback, apir_logfile);
         } else {
-            GGML_LOG_INFO("Could not open the log file at '%s'\n", apir_log_to_file);
+            GGML_LOG_INFO(GGML_VIRTGPU_BCK "Could not open the log file at '%s'\n", apir_log_to_file);
         }
     }
 
@@ -74,7 +68,10 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
     const char * library_reg = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG;
 
     if (!library_name) {
-        GGML_LOG_ERROR("cannot open the GGML library: env var '%s' not defined\n", APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
+                       "%s: cannot open the GGML library: env var '%s' not defined\n",
+                       __func__, APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV);
+
 
         return APIR_LOAD_LIBRARY_ENV_VAR_MISSING;
     }
@@ -82,13 +79,16 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
     backend_library_handle = dlopen(library_name, RTLD_LAZY);
 
     if (!backend_library_handle) {
-        GGML_LOG_ERROR("cannot open the GGML library: %s\n", dlerror());
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
+                       "%s: cannot open the GGML library: %s\n", __func__, dlerror());
 
         return APIR_LOAD_LIBRARY_CANNOT_OPEN;
     }
 
     if (!library_reg) {
-        GGML_LOG_ERROR("cannot register the GGML library: env var '%s' not defined\n", APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
+                       "%s: cannot register the GGML library: env var '%s' not defined\n",
+                       __func__, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV);
 
         return APIR_LOAD_LIBRARY_ENV_VAR_MISSING;
     }
@@ -96,8 +96,10 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
     void * ggml_backend_reg_fct = dlsym(backend_library_handle, library_reg);
     dlsym_error                 = dlerror();
     if (dlsym_error) {
-        GGML_LOG_ERROR("cannot find the GGML backend registration symbol '%s' (from %s): %s\n", library_reg,
-              APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV, dlsym_error);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
+                       "%s: cannot find the GGML backend registration symbol '%s' (from %s): %s\n",
+                       __func__, library_reg, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV, dlsym_error);
+
 
         return APIR_LOAD_LIBRARY_SYMBOL_MISSING;
     }
@@ -134,7 +136,9 @@ uint32_t apir_backend_dispatcher(uint32_t               virgl_ctx_id,
     };
 
     if (cmd_type >= APIR_BACKEND_DISPATCH_TABLE_COUNT) {
-        GGML_LOG_ERROR("Received an invalid dispatch index (%d >= %d)\n", cmd_type, APIR_BACKEND_DISPATCH_TABLE_COUNT);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
+                       "%s: Received an invalid dispatch index (%d >= %d)\n",
+                        __func__, cmd_type, APIR_BACKEND_DISPATCH_TABLE_COUNT);
         return APIR_BACKEND_FORWARD_INDEX_INVALID;
     }
 
diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h
index 27a61091ff..1bc3a5f685 100644
--- a/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h
+++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h
@@ -86,7 +86,7 @@ static inline bool apir_decoder_peek_internal(apir_decoder * dec,
     assert(val_size <= size);
 
     if (unlikely(size > (size_t) (dec->end - dec->cur))) {
-        GGML_LOG_ERROR("reading too much from the decoder ...\n");
+        GGML_LOG_ERROR("%s: reading too much from the decoder ...\n", __func__);
         apir_decoder_set_fatal(dec);
         memset(val, 0, val_size);
         return false;
@@ -103,7 +103,7 @@ static inline void apir_decoder_peek(apir_decoder * dec, size_t size, void * val
 
 static inline const void * apir_decoder_use_inplace(apir_decoder * dec, size_t size) {
     if (unlikely(size > (size_t) (dec->end - dec->cur))) {
-        GGML_LOG_ERROR("reading too much from the decoder ...\n");
+        GGML_LOG_ERROR("%s: reading too much from the decoder ...\n", __func__);
         apir_decoder_set_fatal(dec);
         return NULL;
     }
@@ -221,7 +221,7 @@ static inline uint64_t apir_decode_array_size(apir_decoder * dec, uint64_t expec
     uint64_t size;
     apir_decode_uint64_t(dec, &size);
     if (size != expected_size) {
-        GGML_LOG_ERROR("Couldn't decode array from the decoder\n");
+        GGML_LOG_ERROR("%s: Couldn't decode array from the decoder\n", __func__);
         apir_decoder_set_fatal(dec);
         size = 0;
     }
@@ -322,7 +322,7 @@ static inline void apir_decode_char_array(apir_decoder * dec, char * val, size_t
     if (size) {
         val[size - 1] = '\0';
     } else {
-        GGML_LOG_ERROR("Couldn't decode the blog array\n");
+        GGML_LOG_ERROR("%s: Couldn't decode the blog array\n", __func__);
         apir_decoder_set_fatal(dec);
     }
 }
@@ -332,7 +332,8 @@ static inline void apir_decode_char_array(apir_decoder * dec, char * val, size_t
 static inline void * apir_decoder_alloc_array(size_t size, size_t count) {
     size_t alloc_size;
     if (unlikely(__builtin_mul_overflow(size, count, &alloc_size))) {
-        GGML_LOG_ERROR("overflow in array allocation of %zu * %zu bytes\n", size, count);
+        GGML_LOG_ERROR("%s: overflow in array allocation of %zu * %zu bytes\n",
+                       __func__, size, count);
         return NULL;
     }
 
diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h
index 070c3b25fb..289f4b77d7 100644
--- a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h
+++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h
@@ -39,11 +39,17 @@ static inline void apir_encode_ggml_tensor(apir_encoder * enc, const ggml_tensor
 
 static inline const ggml_tensor * apir_decode_ggml_tensor(apir_decoder * dec) {
     const apir_rpc_tensor * apir_rpc_tensor = apir_decode_apir_rpc_tensor_inplace(dec);
+
+    if (!apir_rpc_tensor) {
+        return NULL;
+    }
+
     ggml_init_params params{
         /*.mem_size   =*/ ggml_tensor_overhead(),
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
+
     ggml_context * ctx = ggml_init(params);
 
     const ggml_tensor * tensor = apir_deserialize_tensor(ctx, apir_rpc_tensor);
@@ -71,6 +77,10 @@ static inline ggml_backend_buffer_type_t apir_decode_ggml_buffer_type(apir_decod
     return (ggml_backend_buffer_type_t) handle;
 }
 
+static inline void apir_encode_apir_buffer_type_host_handle(apir_encoder * enc, apir_buffer_type_host_handle_t handle) {
+    apir_encoder_write(enc, sizeof(handle), &handle, sizeof(handle));
+}
+
 static inline apir_buffer_type_host_handle_t apir_decode_apir_buffer_type_host_handle(apir_decoder * dec) {
     apir_buffer_type_host_handle_t handle;
 
@@ -154,13 +164,13 @@ static inline void apir_encode_ggml_tensor_inline(apir_encoder * enc, const ggml
     size_t tensor_size = sizeof(*tensor);
 
     if (tensor->extra) {
-        GGML_ABORT("Cannot pass tensors with extra");
+        GGML_ABORT("%s: Cannot pass tensors with extra", __func__);
     }
 
     if (tensor->src[0] && tensor->buffer) {
         static int first = 1;
         if (first) {
-            GGML_LOG_WARN("Cannot pass tensors with src and buffer\n");
+            GGML_LOG_WARN("%s: Cannot pass tensors with src and buffer\n", __func__);
             first = 0;
         }
     }
diff --git a/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp
index 7f650659b8..c493a8e2ae 100644
--- a/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp
+++ b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp
@@ -6,7 +6,7 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml
 
     ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) malloc(sizeof(*context));
     if (!context) {
-        GGML_ABORT("Couldn't allocate the buffer context ...");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the buffer context ...", __func__);
     }
 
     context->gpu = gpu;
@@ -20,7 +20,7 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml
         context->base         = context->apir_context.shmem.mmap_ptr;
         context->is_from_ptr  = true;
     } else {
-        context->apir_context = apir_buffer_type_alloc_buffer(gpu, buft, size);
+        context->apir_context = apir_buffer_type_alloc_buffer(gpu, gpu->cached_buffer_type.host_handle, size);
         context->is_from_ptr  = false;
         context->base         = NULL;
     }
@@ -34,36 +34,19 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml
 static const char * ggml_backend_remoting_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
     virtgpu * gpu = BUFT_TO_GPU(buft);
 
-    return apir_buffer_type_get_name(gpu, buft);
+    return gpu->cached_buffer_type.name;
 }
 
 static size_t ggml_backend_remoting_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     virtgpu * gpu = BUFT_TO_GPU(buft);
 
-    static size_t align = 0;
-
-    if (align == 0) {
-        align = apir_buffer_type_get_alignment(gpu, buft);
-    }
-
-    return align;
+    return gpu->cached_buffer_type.alignment;
 }
 
 static size_t ggml_backend_remoting_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
     virtgpu * gpu = BUFT_TO_GPU(buft);
 
-    static size_t max_size = 0;
-    if (max_size == 0) {
-        max_size = apir_buffer_type_get_max_size(gpu, buft);
-    }
-
-    return max_size;
-}
-
-static bool ggml_backend_remoting_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
-    virtgpu * gpu = BUFT_TO_GPU(buft);
-
-    return apir_buffer_type_is_host(gpu, buft);
+    return gpu->cached_buffer_type.max_size;
 }
 
 static size_t ggml_backend_remoting_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft,
@@ -76,7 +59,7 @@ static size_t ggml_backend_remoting_buffer_type_get_alloc_size(ggml_backend_buff
         return ggml_nbytes(tensor);
     }
 
-    return apir_buffer_type_get_alloc_size(gpu, buft, tensor);
+    return apir_buffer_type_get_alloc_size(gpu, gpu->cached_buffer_type.host_handle, tensor);
 }
 
 const ggml_backend_buffer_type_i ggml_backend_remoting_buffer_type_interface = {
diff --git a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
index 579eb99078..c7d2881058 100644
--- a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
+++ b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
@@ -3,32 +3,27 @@
 static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    return apir_device_get_name(gpu);
+    return gpu->cached_device_info.name;
 }
 
 static const char * ggml_backend_remoting_device_get_description(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    return apir_device_get_description(gpu);
+    // Return the pre-cached description from the virtgpu structure
+    return gpu->cached_device_info.description;
 }
 
 static enum ggml_backend_dev_type ggml_backend_remoting_device_get_type(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    static enum ggml_backend_dev_type type;
-    static bool                       has_type = false;
-    if (!has_type) {
-        has_type = true;
-        type     = (enum ggml_backend_dev_type) apir_device_get_type(gpu);
-    }
-
-    return type;
+    return (enum ggml_backend_dev_type) gpu->cached_device_info.type;
 }
 
 static void ggml_backend_remoting_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    return apir_device_get_memory(gpu, free, total);
+    *free = gpu->cached_device_info.memory_free;
+    *total = gpu->cached_device_info.memory_total;
 }
 
 static bool ggml_backend_remoting_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
@@ -77,13 +72,22 @@ static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_
 ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    apir_buffer_type_host_handle_t ctx = apir_device_get_buffer_type(gpu);
+    static std::atomic initialized = false;
+    static ggml_backend_buffer_type buft;
 
-    static ggml_backend_buffer_type buft{
-        /* .iface    = */ ggml_backend_remoting_buffer_type_interface,
-        /* .device   = */ dev,
-        /* .context  = */ (void *) ctx,
-    };
+    if (!initialized) {
+        static std::mutex           mutex;
+        std::lock_guard lock(mutex);
+
+        if (!initialized) {
+            buft = {
+                /* .iface    = */ ggml_backend_remoting_buffer_type_interface,
+                /* .device   = */ dev,
+                /* .context  = */ (void *) gpu->cached_buffer_type.host_handle,
+            };
+            initialized = true;
+        }
+    }
 
     return &buft;
 }
@@ -91,13 +95,22 @@ ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_bac
 static ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_from_ptr_type(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
-    apir_buffer_type_host_handle_t ctx = apir_device_get_buffer_type(gpu);
+    static std::atomic initialized = false;
+    static ggml_backend_buffer_type buft;
 
-    static ggml_backend_buffer_type buft{
-        /* .iface    = */ ggml_backend_remoting_buffer_from_ptr_type_interface,
-        /* .device   = */ dev,
-        /* .context  = */ (void *) ctx,
-    };
+    if (!initialized) {
+        static std::mutex           mutex;
+        std::lock_guard lock(mutex);
+
+        if (!initialized) {
+            buft = {
+                /* .iface    = */ ggml_backend_remoting_buffer_from_ptr_type_interface,
+                /* .device   = */ dev,
+                /* .context  = */ (void *) gpu->cached_buffer_type.host_handle,
+            };
+            initialized = true;
+        }
+    }
 
     return &buft;
 }
@@ -110,7 +123,7 @@ static ggml_backend_buffer_t ggml_backend_remoting_device_buffer_from_ptr(ggml_b
 
     ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) malloc(sizeof(*context));
     if (!context) {
-        GGML_ABORT("Couldn't allocate the buffer context ...");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the buffer context ...", __func__);
     }
 
     context->gpu          = gpu;
diff --git a/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp
index c46cf51c02..2d02cfec1d 100644
--- a/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp
@@ -4,37 +4,70 @@
 #include 
 #include 
 
+void ggml_virtgpu_cleanup(virtgpu * gpu);
+
 static virtgpu * apir_initialize() {
-    static virtgpu * apir_gpu_instance = NULL;
-    static bool      apir_initialized  = false;
+    static virtgpu *         gpu          = NULL;
+    static std::atomic initialized  = false;
+
+    if (initialized) {
+        // fast track
+        return gpu;
+    }
 
     {
         static std::mutex           mutex;
         std::lock_guard lock(mutex);
 
-        if (apir_initialized) {
-            return apir_gpu_instance;
+        if (initialized) {
+            // thread safe
+            return gpu;
         }
 
-        apir_gpu_instance = create_virtgpu();
-        if (!apir_gpu_instance) {
-            GGML_ABORT("failed to initialize the virtgpu");
+        gpu = create_virtgpu();
+        if (!gpu) {
+            initialized = true;
+            return NULL;
         }
 
-        apir_initialized = true;
+        // Pre-fetch and cache all device information, it will not change
+        gpu->cached_device_info.description  = apir_device_get_description(gpu);
+        if (!gpu->cached_device_info.description) {
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device description", __func__);
+        }
+        gpu->cached_device_info.name         = apir_device_get_name(gpu);
+        if (!gpu->cached_device_info.name) {
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device name", __func__);
+        }
+        gpu->cached_device_info.device_count = apir_device_get_count(gpu);
+        gpu->cached_device_info.type         = apir_device_get_type(gpu);
+
+        apir_device_get_memory(gpu,
+                              &gpu->cached_device_info.memory_free,
+                              &gpu->cached_device_info.memory_total);
+
+        apir_buffer_type_host_handle_t buft_host_handle = apir_device_get_buffer_type(gpu);
+        gpu->cached_buffer_type.host_handle             = buft_host_handle;
+        gpu->cached_buffer_type.name                    = apir_buffer_type_get_name(gpu, buft_host_handle);
+        if (!gpu->cached_buffer_type.name) {
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu buffer type name", __func__);
+        }
+        gpu->cached_buffer_type.alignment               = apir_buffer_type_get_alignment(gpu, buft_host_handle);
+        gpu->cached_buffer_type.max_size                = apir_buffer_type_get_max_size(gpu, buft_host_handle);
+
+        initialized = true;
     }
 
-    return apir_gpu_instance;
+    return gpu;
 }
 
 static int ggml_backend_remoting_get_device_count() {
     virtgpu * gpu = apir_initialize();
     if (!gpu) {
-        GGML_LOG_WARN("apir_initialize failed\n");
         return 0;
     }
 
-    return apir_device_get_count(gpu);
+    return gpu->cached_device_info.device_count;
 }
 
 static size_t ggml_backend_remoting_reg_get_device_count(ggml_backend_reg_t reg) {
@@ -52,17 +85,21 @@ ggml_backend_dev_t ggml_backend_remoting_get_device(size_t device) {
 
 static void ggml_backend_remoting_reg_init_devices(ggml_backend_reg_t reg) {
     if (devices.size() > 0) {
-        GGML_LOG_INFO("%s: already initialized\n", __func__);
+        GGML_LOG_INFO(GGML_VIRTGPU "%s: already initialized\n", __func__);
         return;
     }
 
     virtgpu * gpu = apir_initialize();
     if (!gpu) {
-        GGML_LOG_ERROR("apir_initialize failed\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: apir_initialize failed\n", __func__);
         return;
     }
 
-    static bool initialized = false;
+    static std::atomic initialized = false;
+
+    if (initialized) {
+        return; // fast track
+    }
 
     {
         static std::mutex           mutex;
@@ -70,10 +107,10 @@ static void ggml_backend_remoting_reg_init_devices(ggml_backend_reg_t reg) {
         if (!initialized) {
             for (int i = 0; i < ggml_backend_remoting_get_device_count(); i++) {
                 ggml_backend_remoting_device_context * ctx       = new ggml_backend_remoting_device_context;
-                char                                   desc[256] = "API Remoting device";
+                char                                   desc[256] = "ggml-virtgpu API Remoting device";
 
                 ctx->device      = i;
-                ctx->name        = GGML_REMOTING_FRONTEND_NAME + std::to_string(i);
+                ctx->name        = GGML_VIRTGPU_NAME + std::to_string(i);
                 ctx->description = desc;
                 ctx->gpu         = gpu;
 
@@ -98,7 +135,7 @@ static ggml_backend_dev_t ggml_backend_remoting_reg_get_device(ggml_backend_reg_
 static const char * ggml_backend_remoting_reg_get_name(ggml_backend_reg_t reg) {
     UNUSED(reg);
 
-    return GGML_REMOTING_FRONTEND_NAME;
+    return GGML_VIRTGPU_NAME;
 }
 
 static const ggml_backend_reg_i ggml_backend_remoting_reg_i = {
@@ -111,8 +148,7 @@ static const ggml_backend_reg_i ggml_backend_remoting_reg_i = {
 ggml_backend_reg_t ggml_backend_virtgpu_reg() {
     virtgpu * gpu = apir_initialize();
     if (!gpu) {
-        GGML_LOG_ERROR("virtgpu_apir_initialize failed\n");
-        return NULL;
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: virtgpu_apir_initialize failed\n", __func__);
     }
 
     static ggml_backend_reg reg = {
@@ -129,9 +165,25 @@ ggml_backend_reg_t ggml_backend_virtgpu_reg() {
 
     ggml_backend_remoting_reg_init_devices(®);
 
-    GGML_LOG_INFO("%s: initialized\n", __func__);
-
     return ®
 }
 
+// public function, not exposed in the GGML interface at the moment
+void ggml_virtgpu_cleanup(virtgpu * gpu) {
+    if (gpu->cached_device_info.name) {
+        free(gpu->cached_device_info.name);
+        gpu->cached_device_info.name = NULL;
+    }
+    if (gpu->cached_device_info.description) {
+        free(gpu->cached_device_info.description);
+        gpu->cached_device_info.description = NULL;
+    }
+    if (gpu->cached_buffer_type.name) {
+        free(gpu->cached_buffer_type.name);
+        gpu->cached_buffer_type.name = NULL;
+    }
+
+    mtx_destroy(&gpu->data_shmem_mutex);
+}
+
 GGML_BACKEND_DL_IMPL(ggml_backend_virtgpu_reg)
diff --git a/ggml/src/ggml-virtgpu/ggml-remoting.h b/ggml/src/ggml-virtgpu/ggml-remoting.h
index 36fc6b2a7b..0876640867 100644
--- a/ggml/src/ggml-virtgpu/ggml-remoting.h
+++ b/ggml/src/ggml-virtgpu/ggml-remoting.h
@@ -8,6 +8,9 @@
 #include 
 #include 
 
+#define GGML_VIRTGPU_NAME "ggml-virtgpu"
+#define GGML_VIRTGPU "ggml-virtgpu: "
+
 // USE_ALWAYS_TRUE_SUPPORTS_OP: 1 is fast, 0 avoid micro-benchmark crashes
 
 #define USE_ALWAYS_TRUE_SUPPORTS_OP 1
@@ -62,7 +65,7 @@ static inline apir_buffer_type_host_handle_t ggml_buffer_type_to_apir_handle(ggm
 
 static inline apir_buffer_host_handle_t ggml_buffer_to_apir_handle(ggml_backend_buffer_t buffer) {
     if (!buffer->context) {
-        GGML_ABORT("%s: no context available :/", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: no context available :/", __func__);
     }
     return BUFFER_TO_HOST_HANDLE(buffer);
 }
diff --git a/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml b/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml
index 0b7cccfe9c..14ef2433e4 100644
--- a/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml
+++ b/ggml/src/ggml-virtgpu/ggmlremoting_functions.yaml
@@ -24,10 +24,10 @@ functions:
         frontend_return: "int"
 
       get_name:
-        frontend_return: "const char *"
+        frontend_return: "char *"
 
       get_description:
-        frontend_return: "const char *"
+        frontend_return: "char *"
 
       get_type:
         frontend_return: "uint32_t"
@@ -64,35 +64,33 @@ functions:
     group_description: "buffer-type"
     functions:
       get_name:
-        frontend_return: "const char *"
+        frontend_return: "char *"
         frontend_extra_params:
-        - "ggml_backend_buffer_type_t buft"
+        - "apir_buffer_type_host_handle_t host_handle"
 
       get_alignment:
         frontend_return: "size_t"
         frontend_extra_params:
-        - "ggml_backend_buffer_type_t buft"
+        - "apir_buffer_type_host_handle_t host_handle"
 
       get_max_size:
         frontend_return: "size_t"
         frontend_extra_params:
-        - "ggml_backend_buffer_type_t buft"
+        - "apir_buffer_type_host_handle_t host_handle"
 
       is_host:
-        frontend_return: "bool"
-        frontend_extra_params:
-        - "ggml_backend_buffer_type_t buft"
+        deprecated: true
 
       alloc_buffer:
         frontend_return: "apir_buffer_context_t"
         frontend_extra_params:
-        - "ggml_backend_buffer_type_t buffer_buft"
+        - "apir_buffer_type_host_handle_t host_handle"
         - "size_t size"
 
       get_alloc_size:
         frontend_return: "size_t"
         frontend_extra_params:
-        - "ggml_backend_buffer_type_t buft"
+        - "apir_buffer_type_host_handle_t host_handle"
         - "const ggml_tensor *op"
 
   buffer:
diff --git a/ggml/src/ggml-virtgpu/regenerate_remoting.py b/ggml/src/ggml-virtgpu/regenerate_remoting.py
index 4174a24327..aeb48a4087 100755
--- a/ggml/src/ggml-virtgpu/regenerate_remoting.py
+++ b/ggml/src/ggml-virtgpu/regenerate_remoting.py
@@ -116,7 +116,7 @@ class RemotingCodebaseGenerator:
                         'frontend_return': func_metadata.get('frontend_return', 'void'),
                         'frontend_extra_params': func_metadata.get('frontend_extra_params', []),
                         'group_description': group_description,
-                        'newly_added': func_metadata.get('newly_added', False)
+                        'deprecated': func_metadata.get('deprecated', False),
                     })
                     enum_value += 1
 
@@ -165,6 +165,9 @@ class RemotingCodebaseGenerator:
 
             signature = "uint32_t"
             params = "apir_encoder *enc, apir_decoder *dec, virgl_apir_context *ctx"
+            if func['deprecated']:
+                decl_lines.append(f"/* {func['enum_name']} is deprecated. Keeping the handler for backward compatibility. */")
+
             decl_lines.append(f"{signature} {func['backend_function']}({params});")
 
         # Switch cases
@@ -176,7 +179,9 @@ class RemotingCodebaseGenerator:
                 switch_lines.append(f"  /* {func['group_description']} */")
                 current_group = func['group_name']
 
-            switch_lines.append(f"  case {func['enum_name']}: return \"{func['backend_function']}\";")
+            deprecated = " (DEPRECATED)" if func['deprecated'] else ""
+
+            switch_lines.append(f"  case {func['enum_name']}: return \"{func['backend_function']}{deprecated}\";")
 
         # Dispatch table
         table_lines = []
@@ -188,7 +193,8 @@ class RemotingCodebaseGenerator:
                 table_lines.append("")
                 current_group = func['group_name']
 
-            table_lines.append(f"  /* {func['enum_name']}  = */ {func['backend_function']},")
+            deprecated = " /* DEPRECATED */" if func['deprecated'] else ""
+            table_lines.append(f"  /* {func['enum_name']}  = */ {func['backend_function']}{deprecated},")
 
         header_content = f'''\
 #pragma once
@@ -225,6 +231,10 @@ static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATC
                 decl_lines.append(f"/* {func['group_description']} */")
                 current_group = func['group_name']
 
+            if func['deprecated']:
+                decl_lines.append(f"/* {func['frontend_function']} is deprecated. */")
+                continue
+
             # Build parameter list
             params = [self.naming_patterns['frontend_base_param']]
             params.extend(func['frontend_extra_params'])
@@ -287,7 +297,7 @@ static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATC
         generated_files = [apir_backend_path, backend_dispatched_path, virtgpu_forward_path]
 
         if not self.clang_format_available:
-            logging.warning("\n⚠️clang-format not found in PATH. Generated files will not be formatted."
+            logging.warning("\n⚠️clang-format not found in PATH. Generated files will not be formatted.\n"
                             "   Install clang-format to enable automatic code formatting.")
         else:
             logging.info("\n🎨 Formatting files with clang-format...")
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
index bf3c41011a..07d9a66849 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
@@ -18,12 +18,17 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) {
 
     virtgpu_shmem   temp_shmem;  // Local storage for large buffers
     virtgpu_shmem * shmem = &temp_shmem;
+    bool using_shared_shmem = false;
 
     if (cgraph_size <= gpu->data_shmem.mmap_size) {
-        // prefer the init-time allocated page, if large enough
+        // Lock mutex before using shared data_shmem buffer
+        if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) {
+            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
+        }
+        using_shared_shmem = true;
         shmem = &gpu->data_shmem;
     } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) {
-        GGML_ABORT("Couldn't allocate the guest-host shared buffer");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
     }
 
     apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id);
@@ -42,7 +47,10 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) {
 
     remote_call_finish(gpu, encoder, decoder);
 
-    if (shmem != &gpu->data_shmem) {
+    // Unlock mutex before cleanup
+    if (using_shared_shmem) {
+        mtx_unlock(&gpu->data_shmem_mutex);
+    } else {
         virtgpu_shmem_destroy(gpu, shmem);
     }
 
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp
index 03cb09e064..cab74fd170 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp
@@ -1,20 +1,20 @@
 #include "virtgpu-forward-impl.h"
 
-const char * apir_buffer_type_get_name(virtgpu * gpu, ggml_backend_buffer_type_t buft) {
+char * apir_buffer_type_get_name(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
 
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME);
 
-    apir_encode_ggml_buffer_type(encoder, buft);
+    apir_encode_apir_buffer_type_host_handle(encoder, host_handle);
 
     REMOTE_CALL(gpu, encoder, decoder, ret);
 
     const size_t string_size = apir_decode_array_size_unchecked(decoder);
     char *       string      = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
     if (!string) {
-        GGML_LOG_ERROR("%s: Could not allocate the device name buffer\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__);
         apir_decoder_set_fatal(decoder);
     }
     apir_decode_char_array(decoder, string, string_size);
@@ -24,14 +24,14 @@ const char * apir_buffer_type_get_name(virtgpu * gpu, ggml_backend_buffer_type_t
     return string;
 }
 
-size_t apir_buffer_type_get_alignment(virtgpu * gpu, ggml_backend_buffer_type_t buft) {
+size_t apir_buffer_type_get_alignment(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
 
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT);
 
-    apir_encode_ggml_buffer_type(encoder, buft);
+    apir_encode_apir_buffer_type_host_handle(encoder, host_handle);
 
     REMOTE_CALL(gpu, encoder, decoder, ret);
 
@@ -43,14 +43,14 @@ size_t apir_buffer_type_get_alignment(virtgpu * gpu, ggml_backend_buffer_type_t
     return alignment;
 }
 
-size_t apir_buffer_type_get_max_size(virtgpu * gpu, ggml_backend_buffer_type_t buft) {
+size_t apir_buffer_type_get_max_size(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
 
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE);
 
-    apir_encode_ggml_buffer_type(encoder, buft);
+    apir_encode_apir_buffer_type_host_handle(encoder, host_handle);
 
     REMOTE_CALL(gpu, encoder, decoder, ret);
 
@@ -62,26 +62,7 @@ size_t apir_buffer_type_get_max_size(virtgpu * gpu, ggml_backend_buffer_type_t b
     return max_size;
 }
 
-bool apir_buffer_type_is_host(virtgpu * gpu, ggml_backend_buffer_type_t buft) {
-    apir_encoder *        encoder;
-    apir_decoder *        decoder;
-    ApirForwardReturnCode ret;
-
-    REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST);
-
-    apir_encode_ggml_buffer_type(encoder, buft);
-
-    REMOTE_CALL(gpu, encoder, decoder, ret);
-
-    bool is_host;
-    apir_decode_bool_t(decoder, &is_host);
-
-    remote_call_finish(gpu, encoder, decoder);
-
-    return is_host;
-}
-
-apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, ggml_backend_buffer_type_t buft, size_t size) {
+apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, size_t size) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
@@ -90,7 +71,7 @@ apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, ggml_backend_
 
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER);
 
-    apir_encode_ggml_buffer_type(encoder, buft);
+    apir_encode_apir_buffer_type_host_handle(encoder, host_handle);
 
     apir_encode_size_t(encoder, &size);
 
@@ -103,14 +84,14 @@ apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, ggml_backend_
     return buffer_context;
 }
 
-size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, ggml_backend_buffer_type_t buft, const ggml_tensor * op) {
+size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, const ggml_tensor * op) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
 
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE);
 
-    apir_encode_ggml_buffer_type(encoder, buft);
+    apir_encode_apir_buffer_type_host_handle(encoder, host_handle);
 
     apir_encode_ggml_tensor_inline(encoder, op);
 
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp
index 3181e39440..86eee358cf 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp
@@ -36,13 +36,18 @@ void apir_buffer_set_tensor(virtgpu *               gpu,
 
     virtgpu_shmem   temp_shmem;  // Local storage for large buffers
     virtgpu_shmem * shmem = &temp_shmem;
+    bool using_shared_shmem = false;
 
     if (size <= gpu->data_shmem.mmap_size) {
-        // prefer the init-time allocated page, if large enough
+        // Lock mutex before using shared data_shmem buffer
+        if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) {
+            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
+        }
+        using_shared_shmem = true;
         shmem = &gpu->data_shmem;
 
     } else if (virtgpu_shmem_create(gpu, size, shmem)) {
-        GGML_ABORT("Couldn't allocate the guest-host shared buffer");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
     }
 
     memcpy(shmem->mmap_ptr, data, size);
@@ -55,7 +60,10 @@ void apir_buffer_set_tensor(virtgpu *               gpu,
 
     remote_call_finish(gpu, encoder, decoder);
 
-    if (shmem != &gpu->data_shmem) {
+    // Unlock mutex before cleanup
+    if (using_shared_shmem) {
+        mtx_unlock(&gpu->data_shmem_mutex);
+    } else {
         virtgpu_shmem_destroy(gpu, shmem);
     }
 
@@ -79,13 +87,18 @@ void apir_buffer_get_tensor(virtgpu *               gpu,
 
     virtgpu_shmem   temp_shmem;  // Local storage for large buffers
     virtgpu_shmem * shmem = &temp_shmem;
+    bool using_shared_shmem = false;
 
     if (size <= gpu->data_shmem.mmap_size) {
-        // prefer the init-time allocated page, if large enough
+        // Lock mutex before using shared data_shmem buffer
+        if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) {
+            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
+        }
+        using_shared_shmem = true;
         shmem = &gpu->data_shmem;
 
     } else if (virtgpu_shmem_create(gpu, size, shmem)) {
-        GGML_ABORT("Couldn't allocate the guest-host shared buffer");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
     }
 
     apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id);
@@ -98,7 +111,10 @@ void apir_buffer_get_tensor(virtgpu *               gpu,
 
     remote_call_finish(gpu, encoder, decoder);
 
-    if (shmem != &gpu->data_shmem) {
+    // Unlock mutex before cleanup
+    if (using_shared_shmem) {
+        mtx_unlock(&gpu->data_shmem_mutex);
+    } else {
         virtgpu_shmem_destroy(gpu, shmem);
     }
 }
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
index 3e45e55bdc..4b6b8f527b 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
@@ -2,11 +2,6 @@
 #include "virtgpu-shm.h"
 
 int apir_device_get_count(virtgpu * gpu) {
-    static int32_t dev_count = -1;
-    if (dev_count != -1) {
-        return dev_count;
-    }
-
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
@@ -14,6 +9,7 @@ int apir_device_get_count(virtgpu * gpu) {
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_COUNT);
     REMOTE_CALL(gpu, encoder, decoder, ret);
 
+    int32_t dev_count = -1;
     apir_decode_int32_t(decoder, &dev_count);
 
     remote_call_finish(gpu, encoder, decoder);
@@ -21,11 +17,7 @@ int apir_device_get_count(virtgpu * gpu) {
     return dev_count;
 }
 
-const char * apir_device_get_name(virtgpu * gpu) {
-    static char * string = nullptr;
-    if (string) {
-        return string;
-    }
+char * apir_device_get_name(virtgpu * gpu) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
@@ -34,9 +26,9 @@ const char * apir_device_get_name(virtgpu * gpu) {
     REMOTE_CALL(gpu, encoder, decoder, ret);
 
     const size_t string_size = apir_decode_array_size_unchecked(decoder);
-    string                   = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
+    char            * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
     if (!string) {
-        GGML_LOG_ERROR("%s: Could not allocate the device name buffer\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__);
         return NULL;
     }
     apir_decode_char_array(decoder, string, string_size);
@@ -46,7 +38,7 @@ const char * apir_device_get_name(virtgpu * gpu) {
     return string;
 }
 
-const char * apir_device_get_description(virtgpu * gpu) {
+char * apir_device_get_description(virtgpu * gpu) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
@@ -58,7 +50,7 @@ const char * apir_device_get_description(virtgpu * gpu) {
     const size_t string_size = apir_decode_array_size_unchecked(decoder);
     char *       string      = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
     if (!string) {
-        GGML_LOG_ERROR("%s: Could not allocate the device description buffer\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device description buffer\n", __func__);
 
         return NULL;
     }
@@ -181,7 +173,7 @@ apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, si
     REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR);
 
     if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) {
-        GGML_ABORT("Couldn't allocate the guest-host shared buffer");
+        GGML_ABORT(GGML_VIRTGPU "Couldn't allocate the guest-host shared buffer");
     }
 
     apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id);
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h
index eea3e7e5a9..f23c75bb96 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h
@@ -11,7 +11,7 @@
         int32_t forward_flag = (int32_t) apir_command_type__;                                              \
         encoder_name         = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, forward_flag); \
         if (!encoder_name) {                                                                               \
-            GGML_ABORT("%s: failed to prepare the remote call encoder", __func__);                       \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__);                       \
         }                                                                                                  \
     } while (0)
 
@@ -19,10 +19,10 @@
     do {                                                                                                          \
         ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL);       \
         if (!decoder_name) {                                                                                      \
-            GGML_ABORT("%s: failed to kick the remote call", __func__);                                         \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__);                                         \
         }                                                                                                         \
         if (ret_name < APIR_FORWARD_BASE_INDEX) {                                                                 \
-            GGML_ABORT("%s: failed to forward the API call: %s: code %d", __func__,                             \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__,                             \
                        apir_forward_error(ret_name), ret_name);                                                   \
         }                                                                                                         \
         ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX);                                  \
diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h
index c27c07f086..fe4cae2025 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h
+++ b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h
@@ -3,8 +3,8 @@
 /* device */
 void                           apir_device_get_device_count(struct virtgpu * gpu);
 int                            apir_device_get_count(struct virtgpu * gpu);
-const char *                   apir_device_get_name(struct virtgpu * gpu);
-const char *                   apir_device_get_description(struct virtgpu * gpu);
+char *                         apir_device_get_name(struct virtgpu * gpu);
+char *                         apir_device_get_description(struct virtgpu * gpu);
 uint32_t                       apir_device_get_type(struct virtgpu * gpu);
 void                           apir_device_get_memory(struct virtgpu * gpu, size_t * free, size_t * total);
 bool                           apir_device_supports_op(struct virtgpu * gpu, const ggml_tensor * op);
@@ -17,14 +17,15 @@ void                           apir_device_get_props(struct virtgpu * gpu,
 apir_buffer_context_t          apir_device_buffer_from_ptr(struct virtgpu * gpu, size_t size, size_t max_tensor_size);
 
 /* buffer-type */
-const char *          apir_buffer_type_get_name(struct virtgpu * gpu, ggml_backend_buffer_type_t buft);
-size_t                apir_buffer_type_get_alignment(struct virtgpu * gpu, ggml_backend_buffer_type_t buft);
-size_t                apir_buffer_type_get_max_size(struct virtgpu * gpu, ggml_backend_buffer_type_t buft);
-bool                  apir_buffer_type_is_host(struct virtgpu * gpu, ggml_backend_buffer_type_t buft);
-apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu *           gpu,
-                                                    ggml_backend_buffer_type_t buffer_buft,
-                                                    size_t                     size);
-size_t apir_buffer_type_get_alloc_size(struct virtgpu * gpu, ggml_backend_buffer_type_t buft, const ggml_tensor * op);
+char *                apir_buffer_type_get_name(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
+size_t                apir_buffer_type_get_alignment(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
+size_t                apir_buffer_type_get_max_size(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
+apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu *               gpu,
+                                                    apir_buffer_type_host_handle_t host_handle,
+                                                    size_t                         size);
+size_t                apir_buffer_type_get_alloc_size(struct virtgpu *               gpu,
+                                                      apir_buffer_type_host_handle_t host_handle,
+                                                      const ggml_tensor *            op);
 
 /* buffer */
 void * apir_buffer_get_base(struct virtgpu * gpu, apir_buffer_context_t * buffer_context);
diff --git a/ggml/src/ggml-virtgpu/virtgpu-shm.cpp b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
index 4def405a62..ce6b3b3e60 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
@@ -85,8 +85,7 @@ int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem) {
     void * ptr = virtgpu_ioctl_map(gpu, gem_handle, size);
     if (!ptr) {
         virtgpu_ioctl_gem_close(gpu, gem_handle);
-        GGML_LOG_ERROR("virtgpu_ioctl_map FAILED\n");
-        exit(1);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: virtgpu_ioctl_map failed\n", __func__);
         return 1;
     }
 
diff --git a/ggml/src/ggml-virtgpu/virtgpu.cpp b/ggml/src/ggml-virtgpu/virtgpu.cpp
index 005c8e21db..1e650dc65b 100644
--- a/ggml/src/ggml-virtgpu/virtgpu.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu.cpp
@@ -33,7 +33,7 @@ static int virtgpu_handshake(virtgpu * gpu) {
 
     encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_HANDSHAKE, 0);
     if (!encoder) {
-        GGML_ABORT("%s: failed to prepare the remote call encoder", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__);
         return 1;
     }
 
@@ -52,7 +52,7 @@ static int virtgpu_handshake(virtgpu * gpu) {
     log_call_duration(call_duration_ns, "API Remoting handshake");
 
     if (!decoder) {
-        GGML_ABORT(
+        GGML_ABORT(GGML_VIRTGPU
             "%s: failed to initiate the communication with the virglrenderer library. "
             "Most likely, the wrong virglrenderer library was loaded in the hypervisor.",
             __func__);
@@ -65,7 +65,8 @@ static int virtgpu_handshake(virtgpu * gpu) {
     uint32_t host_minor;
 
     if (ret_magic != APIR_HANDSHAKE_MAGIC) {
-        GGML_ABORT("%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic,
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic,
                    apir_backend_initialize_error(ret_magic));
     } else {
         apir_decode_uint32_t(decoder, &host_major);
@@ -78,13 +79,13 @@ static int virtgpu_handshake(virtgpu * gpu) {
         return 1;
     }
 
-    GGML_LOG_INFO("%s: Guest is running with %u.%u\n", __func__, guest_major, guest_minor);
-    GGML_LOG_INFO("%s: Host is running with %u.%u\n", __func__, host_major, host_minor);
+    GGML_LOG_INFO(GGML_VIRTGPU "%s: Guest is running with %u.%u\n", __func__, guest_major, guest_minor);
+    GGML_LOG_INFO(GGML_VIRTGPU "%s: Host is running with %u.%u\n", __func__, host_major, host_minor);
 
     if (guest_major != host_major) {
-        GGML_LOG_ERROR("Host major (%d) and guest major (%d) version differ\n", host_major, guest_major);
+        GGML_LOG_ERROR(GGML_VIRTGPU "Host major (%d) and guest major (%d) version differ\n", host_major, guest_major);
     } else if (guest_minor != host_minor) {
-        GGML_LOG_WARN("Host minor (%d) and guest minor (%d) version differ\n", host_minor, guest_minor);
+        GGML_LOG_WARN(GGML_VIRTGPU "Host minor (%d) and guest minor (%d) version differ\n", host_minor, guest_minor);
     }
 
     return 0;
@@ -97,7 +98,7 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) {
 
     encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_LOADLIBRARY, 0);
     if (!encoder) {
-        GGML_ABORT("%s: hypercall error: failed to prepare the remote call encoder", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: hypercall error: failed to prepare the API Remoting command encoder", __func__);
         return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR;
     }
 
@@ -108,36 +109,67 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) {
     log_call_duration(call_duration_ns, "API Remoting LoadLibrary");
 
     if (!decoder) {
-        GGML_ABORT("%s: hypercall error: failed to kick the API remoting hypercall.\n", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: hypercall error: failed to trigger the API Remoting hypercall.\n", __func__);
         return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR;
     }
 
     remote_call_finish(gpu, encoder, decoder);
 
     if (ret == APIR_LOAD_LIBRARY_SUCCESS) {
-        GGML_LOG_INFO("%s: The API Remoting backend was successfully loaded and initialized\n", __func__);
+        GGML_LOG_INFO(GGML_VIRTGPU "The API Remoting backend was successfully loaded and initialized\n");
 
         return ret;
     }
 
     // something wrong happened, find out what.
-
     if (ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) {
-        GGML_ABORT("%s: virglrenderer could not load the API Remoting backend library: %s (code %d)", __func__,
-                   apir_load_library_error(ret), ret);
+        if (ret == APIR_LOAD_LIBRARY_ENV_VAR_MISSING) {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: virglrenderer could not open the API Remoting backend library, "
+                       "some environment variables are missing. "
+                       "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
+                       __func__, apir_load_library_error(ret));
+        } else if (ret == APIR_LOAD_LIBRARY_CANNOT_OPEN) {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: virglrenderer could not open the API Remoting backend library. "
+                       "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
+                       __func__, apir_load_library_error(ret));
+        } else if (ret == APIR_LOAD_LIBRARY_ENV_VAR_MISSING) {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: could not load the backend library, some symbols are missing. "
+                       "Make sure virglrenderer is correctly configured by the hypervisor. (%s) ",
+                       __func__, apir_load_library_error(ret));
+        } else {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)", __func__,
+                       apir_load_library_error(ret), ret);
+        }
         return ret;
     }
 
-    GGML_LOG_INFO("%s: virglrenderer successfully loaded the API Remoting backend library", __func__);
+    GGML_LOG_INFO(GGML_VIRTGPU
+                  "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__);
 
     ApirLoadLibraryReturnCode apir_ret = (ApirLoadLibraryReturnCode) (ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX);
 
-    if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) {
-        GGML_ABORT("%s: the API Remoting backend library couldn't load the backend library: apir code=%d | %s)",
+    if (apir_ret == APIR_LOAD_LIBRARY_CANNOT_OPEN) {
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: the API Remoting backend library couldn't load the GGML backend library. "
+                   "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
+                   __func__, apir_load_library_error(apir_ret));
+    } else if (apir_ret == APIR_LOAD_LIBRARY_SYMBOL_MISSING) {
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. "
+                   "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
+                   __func__, apir_load_library_error(apir_ret));
+    } else if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) {
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: the API Remoting backend library couldn't load the GGML backend library: apir code=%d | %s)",
                    __func__, apir_ret, apir_load_library_error(apir_ret));
     } else {
         uint32_t lib_ret = apir_ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX;
-        GGML_ABORT("%s: the API Remoting backend library initialize its backend library: apir code=%d)", __func__,
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: the API Remoting backend library initialize its backend library: apir code=%d)", __func__,
                    lib_ret);
     }
     return ret;
@@ -149,38 +181,58 @@ virtgpu * create_virtgpu() {
     gpu->use_apir_capset = getenv("GGML_REMOTING_USE_APIR_CAPSET") != nullptr;
     util_sparse_array_init(&gpu->shmem_array, sizeof(virtgpu_shmem), 1024);
 
+    // Initialize mutex to protect shared data_shmem buffer
+    if (mtx_init(&gpu->data_shmem_mutex, mtx_plain) != thrd_success) {
+        delete gpu;
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to initialize data_shmem mutex", __func__);
+        return NULL;
+    }
+
     if (virtgpu_open(gpu) != APIR_SUCCESS) {
-        GGML_ABORT("%s: failed to open the virtgpu device", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU
+                       "%s: failed to open the virtgpu device\n", __func__);
         return NULL;
     }
 
     if (virtgpu_init_capset(gpu) != APIR_SUCCESS) {
-        GGML_ABORT("%s: failed to initialize the GPU capset", __func__);
+        if (gpu->use_apir_capset) {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library supports it.", __func__);
+        } else {
+            GGML_ABORT(GGML_VIRTGPU
+                       "%s: failed to initialize the virtgpu Venus capset", __func__);
+        }
         return NULL;
     }
 
     if (virtgpu_init_context(gpu) != APIR_SUCCESS) {
-        GGML_ABORT("%s: failed to initialize the GPU context", __func__);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to initialize the GPU context", __func__);
         return NULL;
     }
 
     if (virtgpu_shmem_create(gpu, SHMEM_REPLY_SIZE, &gpu->reply_shmem)) {
-        GGML_ABORT("%s: failed to create the shared reply memory pages", __func__);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to create the shared reply memory pages", __func__);
         return NULL;
     }
 
     if (virtgpu_shmem_create(gpu, SHMEM_DATA_SIZE, &gpu->data_shmem)) {
-        GGML_ABORT("%s: failed to create the shared data memory pages", __func__);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to create the shared data memory pages", __func__);
         return NULL;
     }
 
     if (virtgpu_handshake(gpu)) {
-        GGML_ABORT("%s: failed to handshake with the virglrenderer library", __func__);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to handshake with the virglrenderer library", __func__);
         return NULL;
     }
 
     if (virtgpu_load_library(gpu) != APIR_LOAD_LIBRARY_SUCCESS) {
-        GGML_ABORT("%s: failed to load the backend library", __func__);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to load the backend library", __func__);
         return NULL;
     }
 
@@ -191,7 +243,8 @@ static virt_gpu_result_t virtgpu_open(virtgpu * gpu) {
     drmDevicePtr devs[8];
     int          count = drmGetDevices2(0, devs, ARRAY_SIZE(devs));
     if (count < 0) {
-        GGML_LOG_ERROR("%s: failed to enumerate DRM devices\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU
+                       "%s: failed to enumerate DRM devices\n", __func__);
         return APIR_ERROR_INITIALIZATION_FAILED;
     }
 
@@ -213,16 +266,19 @@ static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr d
 
     int fd = open(node_path, O_RDWR | O_CLOEXEC);
     if (fd < 0) {
-        GGML_ABORT("failed to open %s", node_path);
+        GGML_ABORT(GGML_VIRTGPU
+                   "%s: failed to open %s", __func__, node_path);
         return APIR_ERROR_INITIALIZATION_FAILED;
     }
 
     drmVersionPtr version = drmGetVersion(fd);
     if (!version || strcmp(version->name, "virtio_gpu") || version->version_major != 0) {
         if (version) {
-            GGML_ABORT("unknown DRM driver %s version %d", version->name, version->version_major);
+            GGML_LOG_ERROR(GGML_VIRTGPU
+                           "%s: unknown DRM driver %s version %d\n", __func__, version->name, version->version_major);
         } else {
-            GGML_ABORT("failed to get DRM driver version");
+            GGML_LOG_ERROR(GGML_VIRTGPU
+                           "%s: failed to get DRM driver version\n", __func__);
         }
 
         if (version) {
@@ -236,7 +292,7 @@ static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr d
 
     drmFreeVersion(version);
 
-    GGML_LOG_INFO("using DRM device %s\n", node_path);
+    GGML_LOG_INFO(GGML_VIRTGPU "using DRM device %s\n", node_path);
 
     return APIR_SUCCESS;
 }
@@ -245,7 +301,7 @@ static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu) {
     assert(!gpu->capset.version);
     const int ret = virtgpu_ioctl_context_init(gpu, gpu->capset.id);
     if (ret) {
-        GGML_LOG_INFO("failed to initialize context: %s\n", strerror(errno));
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to initialize context: %s\n", __func__, strerror(errno));
         return APIR_ERROR_INITIALIZATION_FAILED;
     }
 
@@ -254,10 +310,10 @@ static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu) {
 
 static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) {
     if (gpu->use_apir_capset) {
-        GGML_LOG_INFO("Using the APIR capset\n");
+        GGML_LOG_INFO(GGML_VIRTGPU "Using the APIR capset\n");
         gpu->capset.id = VIRTGPU_DRM_CAPSET_APIR;
     } else {
-        GGML_LOG_INFO("Using the Venus capset\n");
+        GGML_LOG_INFO(GGML_VIRTGPU "Using the Venus capset\n");
         gpu->capset.id = VIRTGPU_DRM_CAPSET_VENUS;
     }
     gpu->capset.version = 0;
@@ -266,7 +322,9 @@ static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) {
         virtgpu_ioctl_get_caps(gpu, gpu->capset.id, gpu->capset.version, &gpu->capset.data, sizeof(gpu->capset.data));
 
     if (ret) {
-        GGML_LOG_INFO("failed to get APIR v%d capset: %s\n", gpu->capset.version, strerror(errno));
+        GGML_LOG_ERROR(GGML_VIRTGPU
+                       "%s: failed to get APIR v%d capset: %s\n",
+                       __func__, gpu->capset.version, strerror(errno));
         return APIR_ERROR_INITIALIZATION_FAILED;
     }
 
@@ -333,9 +391,9 @@ apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type,
      * Prepare the command encoder and its buffer
      */
 
-    static char encoder_buffer[4096];
+    thread_local char encoder_buffer[4096];
 
-    static apir_encoder enc;
+    thread_local apir_encoder enc;
     enc = {
         .cur   = encoder_buffer,
         .start = encoder_buffer,
@@ -369,19 +427,19 @@ void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec) {
     UNUSED(gpu);
 
     if (!enc) {
-        GGML_LOG_ERROR("Invalid (null) encoder\n");
+        GGML_ABORT(GGML_VIRTGPU "%s: Invalid (null) encoder", __func__);
     }
 
     if (!dec) {
-        GGML_LOG_ERROR("Invalid (null) decoder\n");
+        GGML_ABORT(GGML_VIRTGPU "%s: Invalid (null) decoder", __func__);
     }
 
     if (apir_encoder_get_fatal(enc)) {
-        GGML_LOG_ERROR("Failed to encode the output parameters.\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Failed to encode the output parameters.", __func__);
     }
 
     if (apir_decoder_get_fatal(dec)) {
-        GGML_LOG_ERROR("Failed to decode the input parameters.\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Failed to decode the input parameters.", __func__);
     }
 }
 
@@ -423,7 +481,7 @@ uint32_t remote_call(virtgpu *       gpu,
     int ret = drmIoctl(gpu->fd, DRM_IOCTL_VIRTGPU_EXECBUFFER, &args);
 
     if (ret != 0) {
-        GGML_ABORT("%s: the virtgpu EXECBUFFER ioctl failed (%d)", __func__, ret);
+        GGML_ABORT(GGML_VIRTGPU "%s: the virtgpu EXECBUFFER ioctl failed (%d)", __func__, ret);
     }
 
     /*
@@ -467,7 +525,7 @@ uint32_t remote_call(virtgpu *       gpu,
     }
 
     if (max_wait_ms && timedout) {
-        GGML_LOG_ERROR("timed out waiting for the host answer...\n");
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: timed out waiting for the host answer...\n", __func__);
         return APIR_FORWARD_TIMEOUT;
     }
 
@@ -489,10 +547,13 @@ static void log_call_duration(long long call_duration_ns, const char * name) {
     double call_duration_s  = (double) call_duration_ns / 1e9;  // 1 second = 1e9 nanoseconds
 
     if (call_duration_s > 1) {
-        GGML_LOG_INFO("%s: waited %.2fs for the %s host reply...\n", __func__, call_duration_s, name);
+        GGML_LOG_INFO(GGML_VIRTGPU
+                      "waited %.2fs for the %s host reply...\n", call_duration_s, name);
     } else if (call_duration_ms > 1) {
-        GGML_LOG_INFO("%s: waited %.2fms for the %s host reply...\n", __func__, call_duration_ms, name);
+        GGML_LOG_INFO(GGML_VIRTGPU
+                      "waited %.2fms for the %s host reply...\n", call_duration_ms, name);
     } else {
-        GGML_LOG_INFO("%s: waited %lldns for the %s host reply...\n", __func__, call_duration_ns, name);
+        GGML_LOG_INFO(GGML_VIRTGPU
+                      "waited %lldns for the %s host reply...\n", call_duration_ns, name);
     }
 }
diff --git a/ggml/src/ggml-virtgpu/virtgpu.h b/ggml/src/ggml-virtgpu/virtgpu.h
index d4bb42e20b..68e0f3a376 100644
--- a/ggml/src/ggml-virtgpu/virtgpu.h
+++ b/ggml/src/ggml-virtgpu/virtgpu.h
@@ -17,6 +17,8 @@
 
 #include 
 
+#include "ggml-remoting.h"
+
 #define VIRGL_RENDERER_UNSTABLE_APIS 1
 #include "apir_hw.h"
 #include 
@@ -73,6 +75,27 @@ struct virtgpu {
     /* APIR communication pages */
     virtgpu_shmem reply_shmem;
     virtgpu_shmem data_shmem;
+
+    /* Mutex to protect shared data_shmem buffer from concurrent access */
+    mtx_t data_shmem_mutex;
+
+    /* Cached device information to prevent memory leaks and race conditions */
+    struct {
+        char *   description;
+        char *   name;
+        int32_t  device_count;
+        uint32_t type;
+        size_t   memory_free;
+        size_t   memory_total;
+    } cached_device_info;
+
+    /* Cached buffer type information to prevent memory leaks and race conditions */
+    struct {
+        apir_buffer_type_host_handle_t host_handle;
+        char *                         name;
+        size_t                         alignment;
+        size_t                         max_size;
+    } cached_buffer_type;
 };
 
 static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) {

From 25f40ca65f1aa596f8b1702bbac4bc48a45b87d7 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 4 Feb 2026 05:43:28 +0100
Subject: [PATCH 116/206] completion : simplify batch (embd) processing
 (#19286)

* completion : simplify batch (embd) processing

This commit simplifies the processing of embd by removing the for loop
that currently exists which uses params.n_batch as its increment. This
commit also removes the clamping of n_eval as the size of embd is always
at most the size of params.n_batch.

The motivation is to clarify the code as it is currently a little
confusing when looking at this for loop in isolation and thinking that
it can process multiple batches.

* add an assert to verify n_eval is not greater than n_batch
---
 tools/completion/completion.cpp | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp
index f368a2f4c6..977132756f 100644
--- a/tools/completion/completion.cpp
+++ b/tools/completion/completion.cpp
@@ -674,15 +674,12 @@ int main(int argc, char ** argv) {
                 }
             }
 
-            for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
-                int n_eval = (int) embd.size() - i;
-                if (n_eval > params.n_batch) {
-                    n_eval = params.n_batch;
-                }
-
+            if (!embd.empty()) {
+                int n_eval = (int) embd.size();
                 LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
 
-                if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
+                GGML_ASSERT(n_eval <= params.n_batch);
+                if (llama_decode(ctx, llama_batch_get_one(embd.data(), n_eval))) {
                     LOG_ERR("%s : failed to eval\n", __func__);
                     return 1;
                 }
@@ -743,7 +740,7 @@ int main(int argc, char ** argv) {
                 common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
 
                 ++n_consumed;
-                if ((int) embd.size() >= params.n_batch) {
+                if ((int) embd.size() == params.n_batch) {
                     break;
                 }
             }

From d838c22bb33d6b486a73c0ff285346deb575be3f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 4 Feb 2026 10:39:53 +0200
Subject: [PATCH 117/206] spec : fix the check-rate logic of ngram-simple
 (#19261)

* spec : fix the check-rate logic of ngram-simple

* cont : refactor + fix checks
---
 common/ngram-map.cpp   | 15 +++------------
 common/ngram-map.h     | 16 +---------------
 common/speculative.cpp | 20 ++++++++++++++------
 3 files changed, 18 insertions(+), 33 deletions(-)

diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp
index cab231bad7..c5b8fc75ed 100644
--- a/common/ngram-map.cpp
+++ b/common/ngram-map.cpp
@@ -47,21 +47,15 @@ static std::string common_tokens_to_str(const llama_tokens & inp, size_t start,
  * @return Vector of draft tokens, empty if no matching pattern is found
  */
 llama_tokens common_ngram_simple_draft(
-        common_ngram_simple_state & state,
+        const common_ngram_simple_config & config,
         const llama_tokens & tokens, llama_token sampled) {
 
     // Simple implementation of self-speculative decoding without a draft model.
     //
     const size_t cur_len = tokens.size();
-    // Only check every check_rate tokens to save compute
-    // i.e., perform check if (cur_len - idx_last_check) >= check_rate
-    if (state.idx_last_check + state.config.check_rate > cur_len) {
-        llama_tokens draft_tokens;
-        return draft_tokens;
-    }
 
-    size_t n_draft_min = state.config.size_ngram; // size of n-gram to lookup in token history
-    size_t n_draft_max = state.config.size_mgram; // the m-gram following the found n-gram is used for draft
+    const size_t n_draft_min = config.size_ngram; // size of n-gram to lookup in token history
+    const size_t n_draft_max = config.size_mgram; // the m-gram following the found n-gram is used for draft
 
     // vector for tokens we want to verify.
     // return empty vector if there is no match.
@@ -80,9 +74,6 @@ llama_tokens common_ngram_simple_draft(
     }
     pattern.push_back(sampled); // add the last token to the pattern
 
-    // We do a search in the token history.
-    state.idx_last_check = cur_len;
-
     size_t match_pos = 0; // we ignore position 0, position 0 == no match
                           // search backwards, but skip the current match (we are currently there)
     for (size_t j = cur_len - n_draft_min - 1; j > 0; --j) {
diff --git a/common/ngram-map.h b/common/ngram-map.h
index c094d513d5..9668bd5a7c 100644
--- a/common/ngram-map.h
+++ b/common/ngram-map.h
@@ -27,23 +27,9 @@ struct common_ngram_simple_config {
     uint16_t   check_rate;      // check for speculative decoding without draft model for each check_rate token
 };
 
-// current state (and config) of n-gram simple.
-struct common_ngram_simple_state {
-    common_ngram_simple_config config;
-
-    size_t idx_last_check = 0; // index of last check in context history (mutable)
-
-    common_ngram_simple_state(const common_ngram_simple_config & config)
-        : config(config) {}
-};
-
 // Searches for a n-gram in the history and checks whether a draft sequence should be generated.
-// state:              the ngram simple state to search in.
-// inp:                the tokens generated so far.
-// sampled:            the token that was just sampled.
-// draft:              vector to store the draft tokens, initially empty.
 llama_tokens common_ngram_simple_draft(
-        common_ngram_simple_state & state,
+        const common_ngram_simple_config & config,
         const llama_tokens & tokens, llama_token sampled);
 
 
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 80cd31e35f..c99b19dbfd 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -463,12 +463,14 @@ struct common_speculative_state_eagle3 : public common_speculative_state {
 
 // state of self-speculation (simple implementation, not ngram-map)
 struct common_speculative_state_ngram_simple : public common_speculative_state {
-    common_ngram_simple_state state;
+    common_ngram_simple_config config;
+
+    uint16_t check_id = 0; // used to control the frequency of generating drafts
 
     common_speculative_state_ngram_simple(
             enum common_speculative_type type,
-            common_ngram_simple_state state)
-        : common_speculative_state(type), state(state) {}
+            common_ngram_simple_config config)
+        : common_speculative_state(type), config(config) {}
 
     void begin(const llama_tokens & prompt) override {
         GGML_UNUSED(prompt);
@@ -479,7 +481,13 @@ struct common_speculative_state_ngram_simple : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
-        result = common_ngram_simple_draft(state, prompt_tgt, id_last);
+        ++check_id;
+        if (check_id < config.check_rate) {
+            return;
+        }
+        check_id = 0;
+
+        result = common_ngram_simple_draft(config, prompt_tgt, id_last);
         GGML_UNUSED(params);
     }
 
@@ -889,14 +897,14 @@ common_speculative * common_speculative_init(
                 uint16_t mgram_size_value = ngram_map.size_value;
                 uint16_t check_rate       = ngram_map.check_rate;
 
-                auto config_simple = common_ngram_simple_config{
+                auto config_simple = common_ngram_simple_config {
                     /* .size_ngram      = */ ngram_size_key,
                     /* .size_mgram      = */ mgram_size_value,
                     /* .check_rate      = */ check_rate
                 };
                 auto state = std::make_unique(
                     /* .type            = */ config.type,
-                    /* .state           = */ common_ngram_simple_state(config_simple)
+                    /* .state           = */ config_simple
                 );
                 impls.push_back(std::move(state));
                 break;

From 6ab881b7c3abd7c1aac0d7f3a00bde68d9cfd484 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 4 Feb 2026 10:40:53 +0100
Subject: [PATCH 118/206] model-conversion : add tensor-info.py utility
 (#18954)

This commit adds a new python script that can be used to print tensors
information from a tensor in a safetensors model.

The motivation for this is that during model conversion work it can
sometimes be useful to verify the shape of tensors in the original
model. While it is possible to print the tensors when loading the model
this can be slow when working with larger models.
With this script it is possible to quickly query tensor shapes.

Example usage:
```console
(venv) $ ./scripts/utils/tensor-info.py --help
usage: tensor-info.py [-h] [-m MODEL_PATH] [-l] [tensor_name]

Print tensor information from a safetensors model

positional arguments:
  tensor_name           Name of the tensor to inspect

options:
  -h, --help            show this help message and exit
  -m MODEL_PATH, --model-path MODEL_PATH
                        Path to the model directory (default: MODEL_PATH environment variable)
  -l, --list            List unique tensor patterns in the model (layer numbers replaced with #)
```

Listing tensor names:
```console
(venv) $ ./scripts/utils/tensor-info.py -m ~/work/ai/models/google/embeddinggemma-300m -l
embed_tokens.weight
layers.#.input_layernorm.weight
layers.#.mlp.down_proj.weight
layers.#.mlp.gate_proj.weight
layers.#.mlp.up_proj.weight
layers.#.post_attention_layernorm.weight
layers.#.post_feedforward_layernorm.weight
layers.#.pre_feedforward_layernorm.weight
layers.#.self_attn.k_norm.weight
layers.#.self_attn.k_proj.weight
layers.#.self_attn.o_proj.weight
layers.#.self_attn.q_norm.weight
layers.#.self_attn.q_proj.weight
layers.#.self_attn.v_proj.weight
norm.weight
```

Printing a specific tensor's information:
```console
(venv) $ ./scripts/utils/tensor-info.py -m ~/work/ai/models/google/embeddinggemma-300m layers.0.input_layernorm.weight
Tensor: layers.0.input_layernorm.weight
File:   model.safetensors
Shape:  [768]
```
---
 .../scripts/utils/tensor-info.py              | 159 ++++++++++++++++++
 1 file changed, 159 insertions(+)
 create mode 100755 examples/model-conversion/scripts/utils/tensor-info.py

diff --git a/examples/model-conversion/scripts/utils/tensor-info.py b/examples/model-conversion/scripts/utils/tensor-info.py
new file mode 100755
index 0000000000..12a3430b49
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/tensor-info.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Optional
+from safetensors import safe_open
+
+
+MODEL_SAFETENSORS_FILE = "model.safetensors"
+MODEL_SAFETENSORS_INDEX = "model.safetensors.index.json"
+
+
+def get_weight_map(model_path: Path) -> Optional[dict[str, str]]:
+    index_file = model_path / MODEL_SAFETENSORS_INDEX
+
+    if index_file.exists():
+        with open(index_file, 'r') as f:
+            index = json.load(f)
+            return index.get("weight_map", {})
+
+    return None
+
+
+def get_all_tensor_names(model_path: Path) -> list[str]:
+    weight_map = get_weight_map(model_path)
+
+    if weight_map is not None:
+        return list(weight_map.keys())
+
+    single_file = model_path / MODEL_SAFETENSORS_FILE
+    if single_file.exists():
+        try:
+            with safe_open(single_file, framework="pt", device="cpu") as f:
+                return list(f.keys())
+        except Exception as e:
+            print(f"Error reading {single_file}: {e}")
+            sys.exit(1)
+
+    print(f"Error: No safetensors files found in {model_path}")
+    sys.exit(1)
+
+
+def find_tensor_file(model_path: Path, tensor_name: str) -> Optional[str]:
+    weight_map = get_weight_map(model_path)
+
+    if weight_map is not None:
+        return weight_map.get(tensor_name)
+
+    single_file = model_path / MODEL_SAFETENSORS_FILE
+    if single_file.exists():
+        return single_file.name
+
+    return None
+
+
+def normalize_tensor_name(tensor_name: str) -> str:
+    normalized = re.sub(r'\.\d+\.', '.#.', tensor_name)
+    normalized = re.sub(r'\.\d+$', '.#', normalized)
+    return normalized
+
+
+def list_all_tensors(model_path: Path, unique: bool = False):
+    tensor_names = get_all_tensor_names(model_path)
+
+    if unique:
+        seen = set()
+        for tensor_name in sorted(tensor_names):
+            normalized = normalize_tensor_name(tensor_name)
+            if normalized not in seen:
+                seen.add(normalized)
+                print(normalized)
+    else:
+        for tensor_name in sorted(tensor_names):
+            print(tensor_name)
+
+
+def print_tensor_info(model_path: Path, tensor_name: str):
+    tensor_file = find_tensor_file(model_path, tensor_name)
+
+    if tensor_file is None:
+        print(f"Error: Could not find tensor '{tensor_name}' in model index")
+        print(f"Model path: {model_path}")
+        sys.exit(1)
+
+    file_path = model_path / tensor_file
+
+    try:
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            if tensor_name in f.keys():
+                tensor_slice = f.get_slice(tensor_name)
+                shape = tensor_slice.get_shape()
+                print(f"Tensor: {tensor_name}")
+                print(f"File:   {tensor_file}")
+                print(f"Shape:  {shape}")
+            else:
+                print(f"Error: Tensor '{tensor_name}' not found in {tensor_file}")
+                sys.exit(1)
+
+    except FileNotFoundError:
+        print(f"Error: The file '{file_path}' was not found.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        sys.exit(1)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Print tensor information from a safetensors model"
+    )
+    parser.add_argument(
+        "tensor_name",
+        nargs="?",  # optional (if --list is used for example)
+        help="Name of the tensor to inspect"
+    )
+    parser.add_argument(
+        "-m", "--model-path",
+        type=Path,
+        help="Path to the model directory (default: MODEL_PATH environment variable)"
+    )
+    parser.add_argument(
+        "-l", "--list",
+        action="store_true",
+        help="List unique tensor patterns in the model (layer numbers replaced with #)"
+    )
+
+    args = parser.parse_args()
+
+    model_path = args.model_path
+    if model_path is None:
+        model_path_str = os.environ.get("MODEL_PATH")
+        if model_path_str is None:
+            print("Error: --model-path not provided and MODEL_PATH environment variable not set")
+            sys.exit(1)
+        model_path = Path(model_path_str)
+
+    if not model_path.exists():
+        print(f"Error: Model path does not exist: {model_path}")
+        sys.exit(1)
+
+    if not model_path.is_dir():
+        print(f"Error: Model path is not a directory: {model_path}")
+        sys.exit(1)
+
+    if args.list:
+        list_all_tensors(model_path, unique=True)
+    else:
+        if args.tensor_name is None:
+            print("Error: tensor_name is required when not using --list")
+            sys.exit(1)
+        print_tensor_info(model_path, args.tensor_name)
+
+
+if __name__ == "__main__":
+    main()

From eaba92c3dcc980ebe753348855d4a5d75c069997 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 4 Feb 2026 12:45:21 +0200
Subject: [PATCH 119/206] tests : add non-cont, inplace rope tests (#19296)

* tests : add non-cont, inplace rope tests

* cont : exercise dim 3

Co-authored-by: Jeff Bolz 

* cont : more dim3 exercises

---------

Co-authored-by: Jeff Bolz 
---
 tests/test-backend-ops.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 90cc0d7da2..cecdf47038 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8032,6 +8032,8 @@ static std::vector> make_test_cases_eval() {
         for (int mode : {GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX, GGML_ROPE_TYPE_MROPE, GGML_ROPE_TYPE_IMROPE, GGML_ROPE_TYPE_VISION}) {
             for (bool ff : {false, true}) {
                 test_cases.emplace_back(new test_rope(type, {128,  32, 2, 1}, 128, mode, 512, 1.4245f, 0.7465f, 1.4245f, ff, 0, true, true));
+                test_cases.emplace_back(new test_rope(type, {128,  32, 2, 1}, 128, mode, 512, 1.4245f, 0.7465f, 1.4245f, ff, 1, true, true));
+                test_cases.emplace_back(new test_rope(type, {128,  32, 2, 3}, 128, mode, 512, 1.4245f, 0.7465f, 1.4245f, ff, 1, true, true));
             }
         }
     }

From 8abcc70a747eff568198ae64aa1a60b7625a3c36 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen 
Date: Wed, 4 Feb 2026 13:09:58 +0100
Subject: [PATCH 120/206] model: (qwen3next) correct vectorized key_gdiff
 calculation (#19324)

* model: (qwen3next) correct vectorized key_gdiff calculation

* move transpose to outside of loop
---
 src/models/qwen3next.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 57b6659baf..06d946c5fa 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -265,9 +265,15 @@ std::pair llm_build_qwen3next::build_delta_net_chu
     cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
 
     ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp, 
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
     cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
 
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)); 
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
 
     // state to be updated per chunk
     ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
@@ -322,9 +328,9 @@ std::pair llm_build_qwen3next::build_delta_net_chu
             : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
 
         // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
         //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
 
         // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
         ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));

From 423bee462b46323433e5f1e322e3076cedbeec83 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 4 Feb 2026 15:12:03 +0200
Subject: [PATCH 121/206] ci : fix sanitize workflow to enable ggml sanitizers
 too (#19323)

---
 .github/workflows/build.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index fd251ac4c2..8ce679bd9a 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -293,6 +293,7 @@ jobs:
           cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
@@ -303,6 +304,7 @@ jobs:
           cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)

From e0c93af2a03f5c53d052dfaefd86c06ed3784646 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen 
Date: Wed, 4 Feb 2026 17:55:31 +0100
Subject: [PATCH 122/206] debug: make common_debug_print_tensor readable
 (#19331)

* debug: make common_debug_print_tensor readable

* editorconfig
---
 common/debug.cpp         | 34 ++++++++++++++++++----------------
 src/models/qwen3next.cpp |  4 ++--
 2 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/common/debug.cpp b/common/debug.cpp
index fdaddb1443..0df409a79d 100644
--- a/common/debug.cpp
+++ b/common/debug.cpp
@@ -45,6 +45,8 @@ static float common_ggml_get_float_value(const uint8_t * data,
     return v;
 }
 
+#define INDENT "    "
+
 template 
 void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
     GGML_ASSERT(n > 0);
@@ -60,41 +62,41 @@ void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * n
         }
     }
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        LOG_ERR("                                     [\n");
+        LOG(INDENT "[\n");
         for (int64_t i2 = 0; i2 < ne[2]; i2++) {
             if (i2 == n && ne[2] > 2 * n) {
-                LOG_ERR("                                      ..., \n");
+                LOG(INDENT INDENT "..., \n");
                 i2 = ne[2] - n;
             }
-            LOG_ERR("                                      [\n");
+            LOG(INDENT INDENT "[\n");
             for (int64_t i1 = 0; i1 < ne[1]; i1++) {
                 if (i1 == n && ne[1] > 2 * n) {
-                    LOG_ERR("                                       ..., \n");
+                    LOG(INDENT INDENT INDENT "..., \n");
                     i1 = ne[1] - n;
                 }
-                LOG_ERR("                                       [");
+                LOG(INDENT INDENT INDENT "[");
                 for (int64_t i0 = 0; i0 < ne[0]; i0++) {
                     if (i0 == n && ne[0] > 2 * n) {
-                        LOG_ERR("..., ");
+                        LOG("   ..., ");
                         i0 = ne[0] - n;
                     }
                     const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    LOG_ERR("%12.4f", v);
+                    LOG("%12.4f", v);
                     if (i0 < ne[0] - 1) {
-                        LOG_ERR(", ");
+                        LOG(", ");
                     }
                 }
-                LOG_ERR("],\n");
+                LOG("  ],\n");
             }
-            LOG_ERR("                                      ],\n");
+            LOG(INDENT INDENT "],\n");
         }
-        LOG_ERR("                                     ]\n");
-        LOG_ERR("                                     sum = %f\n", sum);
+        LOG(INDENT "]\n");
+        LOG(INDENT "sum = %f\n", sum);
     }
 
     if constexpr (abort) {
         if (std::isnan(sum)) {
-            LOG_ERR("encountered NaN - aborting\n");
+            LOG("encountered NaN - aborting\n");
             exit(0);
         }
     }
@@ -137,9 +139,9 @@ template  bool common_debug_cb_eval(struct ggml_tensor * t, b
     }
 
     if (matches_filter) {
-        LOG_ERR("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
-                ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
-                common_ggml_ne_string(t).c_str());
+        LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
+            ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
+            common_ggml_ne_string(t).c_str());
     }
 
     const bool is_host = ggml_backend_buffer_is_host(t->buffer);
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 06d946c5fa..99b1a76a48 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -265,13 +265,13 @@ std::pair llm_build_qwen3next::build_delta_net_chu
     cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
 
     ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp, 
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
                                                  1, chunk_size, n_chunks, g_diff_exp->ne[3]);
 
     ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
     cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
 
-    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)); 
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
     cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
 
 

From b536eb023368701fe3564210440e2df6151c3e65 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 4 Feb 2026 20:20:40 +0100
Subject: [PATCH 123/206] codeowners : add danbev for examples/debug (#19332)

* codeowners : add danbev for examples/debug

* Add @pwilkin to CODEOWNERS for debug

---------

Co-authored-by: Piotr Wilkin (ilintar) 
---
 CODEOWNERS | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CODEOWNERS b/CODEOWNERS
index e573a3d2e6..9d252c9b8d 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -27,6 +27,7 @@
 /examples/batched.swift/                @ggerganov
 /examples/batched/                      @ggerganov
 /examples/convert-llama2c-to-ggml/      @ggerganov
+/examples/debug/                        @danbev @pwilkin
 /examples/deprecation-warning/          @ggerganov
 /examples/diffusion/                    @am17an
 /examples/embedding/                    @ggerganov

From e6e934c5ea1540522171ec91eee05d37909286d1 Mon Sep 17 00:00:00 2001
From: Aaron Teo 
Date: Thu, 5 Feb 2026 05:15:03 +0800
Subject: [PATCH 124/206] vendor: update cpp-httplib version (#19313)

Signed-off-by: Aaron Teo 
---
 scripts/sync_vendor.py         |   4 +-
 vendor/cpp-httplib/httplib.cpp | 238 ++++++++++++++++++++++++++-------
 vendor/cpp-httplib/httplib.h   | 102 ++++++++++++--
 3 files changed, 285 insertions(+), 59 deletions(-)

diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py
index 0771942d49..1ff6a9a40f 100755
--- a/scripts/sync_vendor.py
+++ b/scripts/sync_vendor.py
@@ -12,8 +12,8 @@ vendor = {
     # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.23/miniaudio.h": "vendor/miniaudio/miniaudio.h",
     "https://github.com/mackron/miniaudio/raw/669ed3e844524fcd883231b13095baee9f6de304/miniaudio.h": "vendor/miniaudio/miniaudio.h",
 
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.1/httplib.h": "vendor/cpp-httplib/httplib.h",
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.1/LICENSE":   "vendor/cpp-httplib/LICENSE",
+    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.2/httplib.h": "vendor/cpp-httplib/httplib.h",
+    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.30.2/LICENSE":   "vendor/cpp-httplib/LICENSE",
 
     "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h",
 }
diff --git a/vendor/cpp-httplib/httplib.cpp b/vendor/cpp-httplib/httplib.cpp
index d707e65fd3..ba5f9c8ff9 100644
--- a/vendor/cpp-httplib/httplib.cpp
+++ b/vendor/cpp-httplib/httplib.cpp
@@ -117,6 +117,8 @@ time_t parse_http_date(const std::string &date_str) {
 
 #ifdef _WIN32
   return _mkgmtime(&tm_buf);
+#elif defined _AIX
+  return mktime(&tm_buf);
 #else
   return timegm(&tm_buf);
 #endif
@@ -1376,7 +1378,7 @@ int getaddrinfo_with_timeout(const char *node, const char *service,
 
   // Allocate on the heap, so the resolver thread can keep using the data.
   auto state = std::make_shared();
-  state->node = node;
+  if (node) { state->node = node; }
   state->service = service;
   state->hints = *hints;
 
@@ -2896,10 +2898,20 @@ bool parse_range_header(const std::string &s, Ranges &ranges) try {
         return;
       }
 
-      const auto first =
-          static_cast(lhs.empty() ? -1 : std::stoll(lhs));
-      const auto last =
-          static_cast(rhs.empty() ? -1 : std::stoll(rhs));
+      ssize_t first = -1;
+      if (!lhs.empty()) {
+        ssize_t v;
+        auto res = detail::from_chars(lhs.data(), lhs.data() + lhs.size(), v);
+        if (res.ec == std::errc{}) { first = v; }
+      }
+
+      ssize_t last = -1;
+      if (!rhs.empty()) {
+        ssize_t v;
+        auto res = detail::from_chars(rhs.data(), rhs.data() + rhs.size(), v);
+        if (res.ec == std::errc{}) { last = v; }
+      }
+
       if ((first == -1 && last == -1) ||
           (first != -1 && last != -1 && first > last)) {
         all_valid_ranges = false;
@@ -2974,25 +2986,17 @@ bool parse_accept_header(const std::string &s,
         return;
       }
 
-#ifdef CPPHTTPLIB_NO_EXCEPTIONS
       {
-        std::istringstream iss(quality_str);
-        iss >> accept_entry.quality;
-
-        // Check if conversion was successful and entire string was consumed
-        if (iss.fail() || !iss.eof()) {
+        double v = 0.0;
+        auto res = detail::from_chars(
+            quality_str.data(), quality_str.data() + quality_str.size(), v);
+        if (res.ec == std::errc{}) {
+          accept_entry.quality = v;
+        } else {
           has_invalid_entry = true;
           return;
         }
       }
-#else
-      try {
-        accept_entry.quality = std::stod(quality_str);
-      } catch (...) {
-        has_invalid_entry = true;
-        return;
-      }
-#endif
       // Check if quality is in valid range [0.0, 1.0]
       if (accept_entry.quality < 0.0 || accept_entry.quality > 1.0) {
         has_invalid_entry = true;
@@ -5570,13 +5574,26 @@ bool Server::read_content(Stream &strm, Request &req, Response &res) {
           strm, req, res,
           // Regular
           [&](const char *buf, size_t n) {
+            // Prevent arithmetic overflow when checking sizes.
+            // Avoid computing (req.body.size() + n) directly because
+            // adding two unsigned `size_t` values can wrap around and
+            // produce a small result instead of indicating overflow.
+            // Instead, check using subtraction: ensure `n` does not
+            // exceed the remaining capacity `max_size() - size()`.
+            if (req.body.size() >= req.body.max_size() ||
+                n > req.body.max_size() - req.body.size()) {
+              return false;
+            }
+
             // Limit decompressed body size to payload_max_length_ to protect
             // against "zip bomb" attacks where a small compressed payload
             // decompresses to a massive size.
-            if (req.body.size() + n > payload_max_length_ ||
-                req.body.size() + n > req.body.max_size()) {
+            if (payload_max_length_ > 0 &&
+                (req.body.size() >= payload_max_length_ ||
+                 n > payload_max_length_ - req.body.size())) {
               return false;
             }
+
             req.body.append(buf, n);
             return true;
           },
@@ -5666,22 +5683,29 @@ bool Server::read_content_core(
   // oversized request and fail early (causing connection close). For SSL
   // builds we cannot reliably peek the decrypted application bytes, so keep
   // the original behaviour.
-#if !defined(CPPHTTPLIB_OPENSSL_SUPPORT) && !defined(_WIN32)
+#if !defined(CPPHTTPLIB_OPENSSL_SUPPORT)
   if (!req.has_header("Content-Length") &&
       !detail::is_chunked_transfer_encoding(req.headers)) {
-    socket_t s = strm.socket();
-    if (s != INVALID_SOCKET) {
-      // Peek up to payload_max_length_ + 1 bytes. If more than
-      // payload_max_length_ bytes are pending, reject the request.
-      size_t to_peek =
-          (payload_max_length_ > 0)
-              ? (std::min)(payload_max_length_ + 1, static_cast(4096))
-              : 1;
-      std::vector peekbuf(to_peek);
-      ssize_t n = ::recv(s, peekbuf.data(), to_peek, MSG_PEEK);
-      if (n > 0 && static_cast(n) > payload_max_length_) {
-        // Indicate failure so connection will be closed.
-        return false;
+    // Only peek if payload_max_length is set to a finite value
+    if (payload_max_length_ > 0 &&
+        payload_max_length_ < (std::numeric_limits::max)()) {
+      socket_t s = strm.socket();
+      if (s != INVALID_SOCKET) {
+        // Peek to check if there is any pending data
+        char peekbuf[1];
+        ssize_t n = ::recv(s, peekbuf, 1, MSG_PEEK);
+        if (n > 0) {
+          // There is data, so read it with payload limit enforcement
+          auto result = detail::read_content_without_length(
+              strm, payload_max_length_, out);
+          if (result == detail::ReadContentResult::PayloadTooLarge) {
+            res.status = StatusCode::PayloadTooLarge_413;
+            return false;
+          } else if (result != detail::ReadContentResult::Success) {
+            return false;
+          }
+          return true;
+        }
       }
     }
     return true;
@@ -6656,7 +6680,8 @@ void ClientImpl::close_socket(Socket &socket) {
 }
 
 bool ClientImpl::read_response_line(Stream &strm, const Request &req,
-                                           Response &res) const {
+                                           Response &res,
+                                           bool skip_100_continue) const {
   std::array buf{};
 
   detail::stream_line_reader line_reader(strm, buf.data(), buf.size());
@@ -6677,8 +6702,8 @@ bool ClientImpl::read_response_line(Stream &strm, const Request &req,
   res.status = std::stoi(std::string(m[2]));
   res.reason = std::string(m[3]);
 
-  // Ignore '100 Continue'
-  while (res.status == StatusCode::Continue_100) {
+  // Ignore '100 Continue' (only when not using Expect: 100-continue explicitly)
+  while (skip_100_continue && res.status == StatusCode::Continue_100) {
     if (!line_reader.getline()) { return false; } // CRLF
     if (!line_reader.getline()) { return false; } // next response line
 
@@ -7463,7 +7488,8 @@ bool ClientImpl::write_content_with_provider(Stream &strm,
 }
 
 bool ClientImpl::write_request(Stream &strm, Request &req,
-                                      bool close_connection, Error &error) {
+                                      bool close_connection, Error &error,
+                                      bool skip_body) {
   // Prepare additional headers
   if (close_connection) {
     if (!req.has_header("Connection")) {
@@ -7582,7 +7608,59 @@ bool ClientImpl::write_request(Stream &strm, Request &req,
     }
   }
 
+  // After sending request line and headers, wait briefly for an early server
+  // response (e.g. 4xx) and avoid sending a potentially large request body
+  // unnecessarily. This workaround is only enabled on Windows because Unix
+  // platforms surface write errors (EPIPE) earlier; on Windows kernel send
+  // buffering can accept large writes even when the peer already responded.
+  // Check the stream first (which covers SSL via `is_readable()`), then
+  // fall back to select on the socket. Only perform the wait for very large
+  // request bodies to avoid interfering with normal small requests and
+  // reduce side-effects. Poll briefly (up to 50ms as default) for an early
+  // response. Skip this check when using Expect: 100-continue, as the protocol
+  // handles early responses properly.
+#if defined(_WIN32)
+  if (!skip_body &&
+      req.body.size() > CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_THRESHOLD &&
+      req.path.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) {
+    auto start = std::chrono::high_resolution_clock::now();
+
+    for (;;) {
+      // Prefer socket-level readiness to avoid SSL_pending() false-positives
+      // from SSL internals. If the underlying socket is readable, assume an
+      // early response may be present.
+      auto sock = strm.socket();
+      if (sock != INVALID_SOCKET && detail::select_read(sock, 0, 0) > 0) {
+        return false;
+      }
+
+      // Fallback to stream-level check for non-socket streams or when the
+      // socket isn't reporting readable. Avoid using `is_readable()` for
+      // SSL, since `SSL_pending()` may report buffered records that do not
+      // indicate a complete application-level response yet.
+      if (!is_ssl() && strm.is_readable()) { return false; }
+
+      auto now = std::chrono::high_resolution_clock::now();
+      auto elapsed =
+          std::chrono::duration_cast(now - start)
+              .count();
+      if (elapsed >= CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_TIMEOUT_MSECOND) {
+        break;
+      }
+
+      std::this_thread::sleep_for(std::chrono::milliseconds(1));
+    }
+  }
+#endif
+
   // Body
+  if (skip_body) { return true; }
+
+  return write_request_body(strm, req, error);
+}
+
+bool ClientImpl::write_request_body(Stream &strm, Request &req,
+                                           Error &error) {
   if (req.body.empty()) {
     return write_content_with_provider(strm, req, error);
   }
@@ -7758,8 +7836,20 @@ void ClientImpl::output_error_log(const Error &err,
 bool ClientImpl::process_request(Stream &strm, Request &req,
                                         Response &res, bool close_connection,
                                         Error &error) {
-  // Send request
-  if (!write_request(strm, req, close_connection, error)) { return false; }
+  // Auto-add Expect: 100-continue for large bodies
+  if (CPPHTTPLIB_EXPECT_100_THRESHOLD > 0 && !req.has_header("Expect")) {
+    auto body_size = req.body.empty() ? req.content_length_ : req.body.size();
+    if (body_size >= CPPHTTPLIB_EXPECT_100_THRESHOLD) {
+      req.set_header("Expect", "100-continue");
+    }
+  }
+
+  // Check for Expect: 100-continue
+  auto expect_100_continue = req.get_header_value("Expect") == "100-continue";
+
+  // Send request (skip body if using Expect: 100-continue)
+  auto write_request_success =
+      write_request(strm, req, close_connection, error, expect_100_continue);
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
   if (is_ssl()) {
@@ -7774,14 +7864,48 @@ bool ClientImpl::process_request(Stream &strm, Request &req,
   }
 #endif
 
+  // Handle Expect: 100-continue with timeout
+  if (expect_100_continue && CPPHTTPLIB_EXPECT_100_TIMEOUT_MSECOND > 0) {
+    time_t sec = CPPHTTPLIB_EXPECT_100_TIMEOUT_MSECOND / 1000;
+    time_t usec = (CPPHTTPLIB_EXPECT_100_TIMEOUT_MSECOND % 1000) * 1000;
+    auto ret = detail::select_read(strm.socket(), sec, usec);
+    if (ret <= 0) {
+      // Timeout or error: send body anyway (server didn't respond in time)
+      if (!write_request_body(strm, req, error)) { return false; }
+      expect_100_continue = false; // Switch to normal response handling
+    }
+  }
+
   // Receive response and headers
-  if (!read_response_line(strm, req, res) ||
+  // When using Expect: 100-continue, don't auto-skip `100 Continue` response
+  if (!read_response_line(strm, req, res, !expect_100_continue) ||
       !detail::read_headers(strm, res.headers)) {
-    error = Error::Read;
+    if (write_request_success) { error = Error::Read; }
     output_error_log(error, &req);
     return false;
   }
 
+  if (!write_request_success) { return false; }
+
+  // Handle Expect: 100-continue response
+  if (expect_100_continue) {
+    if (res.status == StatusCode::Continue_100) {
+      // Server accepted, send the body
+      if (!write_request_body(strm, req, error)) { return false; }
+
+      // Read the actual response
+      res.headers.clear();
+      res.body.clear();
+      if (!read_response_line(strm, req, res) ||
+          !detail::read_headers(strm, res.headers)) {
+        error = Error::Read;
+        output_error_log(error, &req);
+        return false;
+      }
+    }
+    // If not 100 Continue, server returned an error; proceed with that response
+  }
+
   // Body
   if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" &&
       req.method != "CONNECT") {
@@ -9543,7 +9667,7 @@ bool SSLClient::load_certs() {
         last_openssl_error_ = ERR_get_error();
         ret = false;
       }
-    } else {
+    } else if (!ca_cert_store_) {
       auto loaded = false;
 #ifdef _WIN32
       loaded =
@@ -9790,7 +9914,11 @@ bool SSLClient::verify_host_with_common_name(X509 *server_cert) const {
 
 bool SSLClient::check_host_name(const char *pattern,
                                        size_t pattern_len) const {
-  if (host_.size() == pattern_len && host_ == pattern) { return true; }
+  // Exact match (case-insensitive)
+  if (host_.size() == pattern_len &&
+      detail::case_ignore::equal(host_, std::string(pattern, pattern_len))) {
+    return true;
+  }
 
   // Wildcard match
   // https://bugs.launchpad.net/ubuntu/+source/firefox-3.0/+bug/376484
@@ -9805,9 +9933,23 @@ bool SSLClient::check_host_name(const char *pattern,
   auto itr = pattern_components.begin();
   for (const auto &h : host_components_) {
     auto &p = *itr;
-    if (p != h && p != "*") {
-      auto partial_match = (p.size() > 0 && p[p.size() - 1] == '*' &&
-                            !p.compare(0, p.size() - 1, h));
+    if (!httplib::detail::case_ignore::equal(p, h) && p != "*") {
+      bool partial_match = false;
+      if (!p.empty() && p[p.size() - 1] == '*') {
+        const auto prefix_length = p.size() - 1;
+        if (prefix_length == 0) {
+          partial_match = true;
+        } else if (h.size() >= prefix_length) {
+          partial_match =
+              std::equal(p.begin(),
+                         p.begin() + static_cast(
+                                         prefix_length),
+                         h.begin(), [](const char ca, const char cb) {
+                           return httplib::detail::case_ignore::to_lower(ca) ==
+                                  httplib::detail::case_ignore::to_lower(cb);
+                         });
+        }
+      }
       if (!partial_match) { return false; }
     }
     ++itr;
diff --git a/vendor/cpp-httplib/httplib.h b/vendor/cpp-httplib/httplib.h
index 613020d12c..7c7790f41f 100644
--- a/vendor/cpp-httplib/httplib.h
+++ b/vendor/cpp-httplib/httplib.h
@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.30.1"
-#define CPPHTTPLIB_VERSION_NUM "0x001E01"
+#define CPPHTTPLIB_VERSION "0.30.2"
+#define CPPHTTPLIB_VERSION_NUM "0x001E02"
 
 /*
  * Platform compatibility check
@@ -98,6 +98,22 @@
 #define CPPHTTPLIB_CLIENT_MAX_TIMEOUT_MSECOND 0
 #endif
 
+#ifndef CPPHTTPLIB_EXPECT_100_THRESHOLD
+#define CPPHTTPLIB_EXPECT_100_THRESHOLD 1024
+#endif
+
+#ifndef CPPHTTPLIB_EXPECT_100_TIMEOUT_MSECOND
+#define CPPHTTPLIB_EXPECT_100_TIMEOUT_MSECOND 1000
+#endif
+
+#ifndef CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_THRESHOLD
+#define CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_THRESHOLD (1024 * 1024)
+#endif
+
+#ifndef CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_TIMEOUT_MSECOND
+#define CPPHTTPLIB_WAIT_EARLY_SERVER_RESPONSE_TIMEOUT_MSECOND 50
+#endif
+
 #ifndef CPPHTTPLIB_IDLE_INTERVAL_SECOND
 #define CPPHTTPLIB_IDLE_INTERVAL_SECOND 0
 #endif
@@ -286,8 +302,10 @@ using socket_t = int;
 #include 
 #include 
 #include 
+#include 
 #include 
 #include 
+#include 
 #include 
 #include 
 #include 
@@ -305,6 +323,7 @@ using socket_t = int;
 #include 
 #include 
 #include 
+#include 
 #include 
 #include 
 #include 
@@ -494,6 +513,69 @@ private:
   bool execute_on_destruction;
 };
 
+// Simple from_chars implementation for integer and double types (C++17
+// substitute)
+template  struct from_chars_result {
+  const char *ptr;
+  std::errc ec;
+};
+
+template 
+inline from_chars_result from_chars(const char *first, const char *last,
+                                       T &value, int base = 10) {
+  value = 0;
+  const char *p = first;
+  bool negative = false;
+
+  if (p != last && *p == '-') {
+    negative = true;
+    ++p;
+  }
+  if (p == last) { return {first, std::errc::invalid_argument}; }
+
+  T result = 0;
+  for (; p != last; ++p) {
+    char c = *p;
+    int digit = -1;
+    if ('0' <= c && c <= '9') {
+      digit = c - '0';
+    } else if ('a' <= c && c <= 'z') {
+      digit = c - 'a' + 10;
+    } else if ('A' <= c && c <= 'Z') {
+      digit = c - 'A' + 10;
+    } else {
+      break;
+    }
+
+    if (digit < 0 || digit >= base) { break; }
+    if (result > ((std::numeric_limits::max)() - digit) / base) {
+      return {p, std::errc::result_out_of_range};
+    }
+    result = result * base + digit;
+  }
+
+  if (p == first || (negative && p == first + 1)) {
+    return {first, std::errc::invalid_argument};
+  }
+
+  value = negative ? -result : result;
+  return {p, std::errc{}};
+}
+
+// from_chars for double (simple wrapper for strtod)
+inline from_chars_result from_chars(const char *first, const char *last,
+                                            double &value) {
+  std::string s(first, last);
+  char *endptr = nullptr;
+  errno = 0;
+  value = std::strtod(s.c_str(), &endptr);
+  if (endptr == s.c_str()) { return {first, std::errc::invalid_argument}; }
+  if (errno == ERANGE) {
+    return {first + (endptr - s.c_str()), std::errc::result_out_of_range};
+  }
+  return {first + (endptr - s.c_str()), std::errc{}};
+}
+
 } // namespace detail
 
 enum SSLVerifierResponse {
@@ -1848,10 +1930,11 @@ private:
   Result send_(Request &&req);
 
   socket_t create_client_socket(Error &error) const;
-  bool read_response_line(Stream &strm, const Request &req,
-                          Response &res) const;
+  bool read_response_line(Stream &strm, const Request &req, Response &res,
+                          bool skip_100_continue = true) const;
   bool write_request(Stream &strm, Request &req, bool close_connection,
-                     Error &error);
+                     Error &error, bool skip_body = false);
+  bool write_request_body(Stream &strm, Request &req, Error &error);
   void prepare_default_headers(Request &r, bool for_stream,
                                const std::string &ct);
   bool redirect(Request &req, Response &res, Error &error);
@@ -3243,10 +3326,11 @@ private:
       msg.id = value;
     } else if (field == "retry") {
       // Parse retry interval in milliseconds
-      try {
-        retry_ms = std::stoi(value);
-      } catch (...) {
-        // Invalid retry value, ignore
+      {
+        int v = 0;
+        auto res =
+            detail::from_chars(value.data(), value.data() + value.size(), v);
+        if (res.ec == std::errc{}) { retry_ms = v; }
       }
     }
     // Unknown fields are ignored per SSE spec

From 11fb327bf3846f390e3af1cbe929da9287c618da Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= 
Date: Thu, 5 Feb 2026 02:27:38 +0100
Subject: [PATCH 125/206] vendor : add missing llama_add_compile_flags (#19322)

* add missing llama_add_compile_flags

* disable all warnings for ssl, crypto and fipsmodule
---
 vendor/cpp-httplib/CMakeLists.txt | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index 3d938d9f36..18974d64ca 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -3,9 +3,14 @@ license_add_file("cpp-httplib" "LICENSE")
 
 find_package(Threads REQUIRED)
 
+llama_add_compile_flags()
+
 add_library(${TARGET} STATIC httplib.cpp httplib.h)
-if (NOT MSVC)
-    # disable warnings in 3rd party code
+
+# disable warnings in 3rd party code
+if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+    target_compile_options(${TARGET} PRIVATE /w)
+else()
     target_compile_options(${TARGET} PRIVATE -w)
 endif()
 
@@ -146,6 +151,23 @@ elseif (LLAMA_OPENSSL)
     endif()
 endif()
 
+# disable warnings in 3rd party code
+if(LLAMA_BUILD_BORINGSSL OR LLAMA_BUILD_LIBRESSL)
+    if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        target_compile_options(ssl PRIVATE /w)
+        target_compile_options(crypto PRIVATE /w)
+        if(LLAMA_BUILD_BORINGSSL)
+            target_compile_options(fipsmodule PRIVATE /w)
+        endif()
+    else()
+        target_compile_options(ssl PRIVATE -w)
+        target_compile_options(crypto PRIVATE -w)
+        if(LLAMA_BUILD_BORINGSSL)
+            target_compile_options(fipsmodule PRIVATE -w)
+        endif()
+    endif()
+endif()
+
 if (CPPHTTPLIB_OPENSSL_SUPPORT)
     target_compile_definitions(${TARGET} PUBLIC CPPHTTPLIB_OPENSSL_SUPPORT) # used in server.cpp
     if (APPLE AND CMAKE_SYSTEM_NAME STREQUAL "Darwin")

From af252d0758b0d3ed67c57bad2a735abf53d21c55 Mon Sep 17 00:00:00 2001
From: will-lms 
Date: Thu, 5 Feb 2026 01:05:09 -0500
Subject: [PATCH 126/206] metal : add missing includes (#19348)

---
 ggml/src/ggml-metal/ggml-metal.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index a616dcdb46..1c705362fb 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -7,6 +7,9 @@
 #include "ggml-metal-context.h"
 #include "ggml-metal-ops.h"
 
+#include 
+#include 
+
 #define GGML_METAL_NAME "MTL"
 #define GGML_METAL_MAX_DEVICES 16
 

From c342c3b93de358a4571941b41c35dc5ba2081145 Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Thu, 5 Feb 2026 01:38:59 -0600
Subject: [PATCH 127/206] vulkan: fix non-contig rope (#19299)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 32 ++++--
 .../ggml-vulkan/vulkan-shaders/rms_norm.comp  |  5 +-
 .../vulkan-shaders/rope_funcs.glsl            | 99 +++++++------------
 .../vulkan-shaders/rope_multi.comp            | 11 ++-
 .../ggml-vulkan/vulkan-shaders/rope_neox.comp | 11 ++-
 .../ggml-vulkan/vulkan-shaders/rope_norm.comp | 11 ++-
 .../vulkan-shaders/rope_params.glsl           | 15 ++-
 .../vulkan-shaders/rope_vision.comp           | 11 ++-
 8 files changed, 100 insertions(+), 95 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index cb7fa2c9cb..af57685a37 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1263,25 +1263,30 @@ struct vk_op_diag_mask_push_constants {
 
 struct vk_op_rope_push_constants {
     uint32_t rope_mode;
-    uint32_t ncols;
     uint32_t nrows;
     uint32_t n_dims;
     float freq_scale;
-    uint32_t p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
     float corr_dims[2];
     float theta_scale;
     uint32_t has_ff;
-    uint32_t ne02;
-    uint32_t s1;
-    uint32_t s2;
     int32_t sections[4];
     uint32_t is_imrope;
     uint32_t is_back;
     uint32_t set_rows_stride;
+    uint32_t ne00;
+    uint32_t ne01;
+    uint32_t ne02;
+    uint32_t nb01;
+    uint32_t nb02;
+    uint32_t nb03;
+    uint32_t nb11;
+    uint32_t nb12;
+    uint32_t nb13;
 };
+static_assert(sizeof(vk_op_rope_push_constants) <= 128, "sizeof(vk_op_rope_push_constants) must be <= 128");
 
 // For fused rms_norm+mul+rope(+view+set_rows)
 struct vk_op_rms_norm_mul_rope_push_constants {
@@ -10405,12 +10410,22 @@ static vk_op_rope_push_constants ggml_vk_make_rope_constants(const ggml_tensor *
 
     uint32_t nb01 = src0->nb[1] / ggml_type_size(src0->type);
     uint32_t nb02 = src0->nb[2] / ggml_type_size(src0->type);
+    uint32_t nb03 = src0->nb[3] / ggml_type_size(src0->type);
+
+    uint32_t nb11 = dst->nb[1] / ggml_type_size(dst->type);
+    uint32_t nb12 = dst->nb[2] / ggml_type_size(dst->type);
+    uint32_t nb13 = dst->nb[3] / ggml_type_size(dst->type);
 
     vk_op_rope_push_constants rope {
-        (uint32_t)mode, (uint32_t)src0->ne[0], (uint32_t)ggml_nrows(src0), (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,
-        has_ff, (uint32_t)src0->ne[2], nb01, nb02,
+        (uint32_t)mode, (uint32_t)ggml_nrows(src0), (uint32_t)n_dims, freq_scale,
+        freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, has_ff,
         { sections[0], sections[1], sections[2], sections[3] }, is_imrope, backprop, set_rows_stride,
+
+        (uint32_t)src0->ne[0],
+        (uint32_t)src0->ne[1],
+        (uint32_t)src0->ne[2],
+        nb01, nb02, nb03,
+        nb11, nb12, nb13,
     };
 
     return rope;
@@ -14798,6 +14813,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_REPEAT_BACK:
             return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ROPE:
+            return ggml_is_contiguous_rows(op) && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_ROPE_BACK:
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
index 9d6d366542..55b89f19a7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
@@ -112,12 +112,11 @@ void rms_norm(uint num_iters) {
 #if RMS_NORM_ROPE_FUSION
     barrier();
     rope_params rp = p.rope;
-    uint rope_row = (samp*nchannels + channel)*nrows + row;
     for (uint t = 2*tid; t < ncols; t += 2*BLOCK_SIZE) {
         if (rp.rope_mode == GGML_ROPE_TYPE_NEOX) {
-            rope_neox(t, rope_row, rp);
+            rope_neox(t, row, channel, samp, rp);
         } else if (rp.rope_mode == GGML_ROPE_TYPE_NORMAL) {
-            rope_norm(t, rope_row, rp);
+            rope_norm(t, row, channel, samp, rp);
         }
     }
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl
index aacec98469..2e53459909 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl
@@ -4,12 +4,12 @@ float rope_yarn_ramp(const float low, const float high, const uint i0) {
     return 1.0f - min(1.0f, max(0.0f, y));
 }
 
-uint rope_a_coord(const uint i0, const uint i01, const uint i02, rope_params p) {
+uint rope_a_coord(const uint i0, const uint i01, const uint i02, const uint i03, rope_params p) {
 #if RMS_NORM_ROPE_FUSION
     // Per-row offset in shared memory
     const uint ix = i0;
 #else
-    const uint ix = i02*p.nb02 + i01*p.nb01 + i0;
+    const uint ix = i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i0;
 #endif
     return ix;
 }
@@ -34,26 +34,19 @@ void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out
     sin_theta = sin(theta) * mscale;
 }
 
-void rope_norm(const uint i0, const uint i1, rope_params p) {
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-
-    if (i0 >= ne0) {
+void rope_norm(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) {
+    if (i0 >= p.ne00) {
         return;
     }
 
-    // i1 is actually i2*nb2+i1, but the rows are contiguous
-    const uint i01 = i1 % ne1;
-    const uint i02 = i1 / ne1;
-
-    uint idst = i1*ne0 + i0;
-    const uint ix = rope_a_coord(i0, i01, i02, p);
+    uint idst = i0 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13;
+    const uint ix = rope_a_coord(i0, i1, i2, i3, p);
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i.
     if (p.set_rows_stride != 0) {
-        idst = i01*ne0 + i0;
-        idst += rope_data_i[i02].x * p.set_rows_stride;
+        idst = i1*p.nb11 + i0;
+        idst += rope_data_i[i2].x * p.set_rows_stride;
     }
 
     if (i0 >= p.n_dims) {
@@ -63,7 +56,7 @@ void rope_norm(const uint i0, const uint i1, rope_params p) {
         return;
     }
 
-    const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
+    const float theta_base = rope_data_pos[i2] * pow(p.theta_scale, i0/2.0f);
 
     const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
 
@@ -77,25 +70,19 @@ void rope_norm(const uint i0, const uint i1, rope_params p) {
     rope_data_d[idst + 1] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
 }
 
-void rope_neox(const uint i0, const uint i1, rope_params p) {
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-
-    if (i0 >= ne0) {
+void rope_neox(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) {
+    if (i0 >= p.ne00) {
         return;
     }
 
-    const uint i01 = i1 % ne1;
-    const uint i02 = i1 / ne1;
-
-    uint idst = i1*ne0 + i0/2;
-    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+    uint idst = i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13;
+    const uint ix = rope_a_coord(i0/2, i1, i2, i3, p);
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i.
     if (p.set_rows_stride != 0) {
-        idst = i01*ne0 + i0/2;
-        idst += rope_data_i[i02].x * p.set_rows_stride;
+        idst = i1*p.nb11 + i0/2;
+        idst += rope_data_i[i2].x * p.set_rows_stride;
     }
 
     if (i0 >= p.n_dims) {
@@ -105,7 +92,7 @@ void rope_neox(const uint i0, const uint i1, rope_params p) {
         return;
     }
 
-    const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
+    const float theta_base = rope_data_pos[i2] * pow(p.theta_scale, i0/2.0f);
 
     const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
 
@@ -120,26 +107,19 @@ void rope_neox(const uint i0, const uint i1, rope_params p) {
 }
 
 
-void rope_multi(const uint i0, const uint i1, rope_params p) {
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-    uint ne2 = p.ne02;
-
-    if (i0 >= ne0) {
+void rope_multi(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) {
+    if (i0 >= p.ne00) {
         return;
     }
 
-    const uint i01 = i1 % ne1;
-    const uint i02 = i1 / ne1;
-
-    uint idst = i1*ne0 + i0/2;
-    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+    uint idst = i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13;
+    const uint ix = rope_a_coord(i0/2, i1, i2, i3, p);
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i.
     if (p.set_rows_stride != 0) {
-        idst = i01*ne0 + i0/2;
-        idst += rope_data_i[i02].x * p.set_rows_stride;
+        idst = i1*p.nb11 + i0/2;
+        idst += rope_data_i[i2].x * p.set_rows_stride;
     }
 
     if (i0 >= p.n_dims) {
@@ -156,26 +136,26 @@ void rope_multi(const uint i0, const uint i1, rope_params p) {
     float theta_base = 0.0;
     if (p.is_imrope != 0) {
         if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
-            theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 1]*pow(p.theta_scale, i0/2.0f);
         } else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
-            theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 2]*pow(p.theta_scale, i0/2.0f);
         } else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
-            theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2]*pow(p.theta_scale, i0/2.0f);
         } else {
-            theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 3]*pow(p.theta_scale, i0/2.0f);
         }
     } else {
         if (sector < p.sections[0]) {
-            theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2]*pow(p.theta_scale, i0/2.0f);
         }
         else if (sector >= p.sections[0] && sector < sec_w) {
-            theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 1]*pow(p.theta_scale, i0/2.0f);
         }
         else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
-            theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 2]*pow(p.theta_scale, i0/2.0f);
         }
         else if (sector >= sec_w + p.sections[2]) {
-            theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
+            theta_base = rope_data_pos[i2 + p.ne02 * 3]*pow(p.theta_scale, i0/2.0f);
         }
     }
 
@@ -191,20 +171,13 @@ void rope_multi(const uint i0, const uint i1, rope_params p) {
     rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
 }
 
-void rope_vision(const uint i0, const uint i1, rope_params p) {
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-    uint ne2 = p.ne02;
-
-    if (i0 >= ne0) {
+void rope_vision(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) {
+    if (i0 >= p.ne00) {
         return;
     }
 
-    const uint i01 = i1 % ne1;
-    const uint i02 = i1 / ne1;
-
-    const uint idst = i1*ne0 + i0/2;
-    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+    const uint idst = i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13;
+    const uint ix = rope_a_coord(i0/2, i1, i2, i3, p);
 
     const int sect_dims = p.sections[0] + p.sections[1];
     const int sec_w = p.sections[1] + p.sections[0];
@@ -213,11 +186,11 @@ void rope_vision(const uint i0, const uint i1, rope_params p) {
     float theta_base = 0.0;
     if (sector < p.sections[0]) {
         const uint p0 = sector;
-        theta_base = rope_data_pos[i02]*pow(p.theta_scale, p0);
+        theta_base = rope_data_pos[i2]*pow(p.theta_scale, p0);
     }
     else if (sector >= p.sections[0] && sector < sec_w) {
         const uint p0 = sector - p.sections[0];
-        theta_base = rope_data_pos[i02 + ne2]*pow(p.theta_scale, p0);
+        theta_base = rope_data_pos[i2 + p.ne02]*pow(p.theta_scale, p0);
     }
 
     const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp
index f7587468a8..1528fbeeae 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp
@@ -5,10 +5,13 @@
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    // i1 is actually i2*nb2+i1, but the rows are contiguous
-    const uint i1 = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
-    if (i1 >= pc.nrows) {
+    const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
+    if (row >= pc.nrows) {
         return;
     }
-    rope_multi(i0, i1, pc);
+    const uint i3 = row / (pc.ne01*pc.ne02);
+    const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01;
+    const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01);
+
+    rope_multi(i0, i1, i2, i3, pc);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp
index acb8ed7815..ad0896095d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp
@@ -5,10 +5,13 @@
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    // i1 is actually i2*nb2+i1, but the rows are contiguous
-    const uint i1 = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
-    if (i1 >= pc.nrows) {
+    const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
+    if (row >= pc.nrows) {
         return;
     }
-    rope_neox(i0, i1, pc);
+    const uint i3 = row / (pc.ne01*pc.ne02);
+    const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01;
+    const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01);
+
+    rope_neox(i0, i1, i2, i3, pc);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp
index 0033cdb224..11220817df 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp
@@ -5,10 +5,13 @@
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    // i1 is actually i2*nb2+i1, but the rows are contiguous
-    const uint i1 = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
-    if (i1 >= pc.nrows) {
+    const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
+    if (row >= pc.nrows) {
         return;
     }
-    rope_norm(i0, i1, pc);
+    const uint i3 = row / (pc.ne01*pc.ne02);
+    const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01;
+    const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01);
+
+    rope_norm(i0, i1, i2, i3, pc);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl
index 939cf3c51c..ec6ceaca9b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl
@@ -5,24 +5,29 @@
 
 struct rope_params {
     uint rope_mode;
-    uint ncols;
     uint nrows;
     uint n_dims;
     float freq_scale;
-    uint p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
     float corr_dims[2];
     float theta_scale;
     uint has_ff;
-    uint ne02;
-    uint nb01;
-    uint nb02;
     int sections[4];
     uint is_imrope;
     uint is_back;
     uint set_rows_stride;
+
+    uint ne00;
+    uint ne01;
+    uint ne02;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    uint nb11;
+    uint nb12;
+    uint nb13;
 };
 
 #endif // !defined(GGML_ROPE_PARAMS)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp
index d93800b5e7..ca71efb2f5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp
@@ -5,10 +5,13 @@
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    // i1 is actually i2*nb2+i1, but the rows are contiguous
-    const uint i1 = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
-    if (i1 >= pc.nrows) {
+    const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z;
+    if (row >= pc.nrows) {
         return;
     }
-    rope_vision(i0, i1, pc);
+    const uint i3 = row / (pc.ne01*pc.ne02);
+    const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01;
+    const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01);
+
+    rope_vision(i0, i1, i2, i3, pc);
 }

From 3409ab842d988c3e0dc0d3110dd793a5e187d37e Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Thu, 5 Feb 2026 01:48:33 -0600
Subject: [PATCH 128/206] vulkan: Set k_load_shmem to false when K is too large
 (#19301)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index af57685a37..2f6570181a 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -3204,9 +3204,10 @@ static void ggml_vk_load_shaders(vk_device& device) {
         const uint32_t D_lsb = D ^ (D & (D-1));
         uint32_t D_split = std::min(std::min(device->subgroup_size, 8u), D_lsb / 4);
 
-        // Nvidia prefers shared memory use to load large tiles of K
+        // Nvidia prefers shared memory use to load large tiles of K.
+        // Switch to loading from global memory when it would use too much shared memory.
         // AMD prefers loading K directly from global memory
-        const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA ? 1 : 0;
+        const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 ? 1 : 0;
 
         return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem};
     };
@@ -8412,7 +8413,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
     const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br;
     const uint32_t sfsh = Bc * sfshstride * acctype;
 
-    const bool k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA;
+    const bool k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256;
     const uint32_t kshstride = (k_load_shmem ? hsk_pad : MatBr) / 4 + 2;
     const uint32_t vsh_stride = MatBc / 4 * row_split;
     const uint32_t ksh = ((kshstride >= vsh_stride) ? (Bc * kshstride) : (Bc * vsh_stride)) * f16vec4;

From a498c75ad1d7019bc2cc1e5d83e11bb8a062861f Mon Sep 17 00:00:00 2001
From: Oleksandr Kuvshynov <661042+okuvshynov@users.noreply.github.com>
Date: Thu, 5 Feb 2026 03:06:59 -0500
Subject: [PATCH 129/206] vulkan: fix GPU deduplication logic. (#19222)

* vulkan: fix GPU deduplication logic.

As reported in https://github.com/ggml-org/llama.cpp/issues/19221, the
(same uuid, same driver) logic is problematic for windows+intel igpu.

Let's just avoid filtering for MoltenVK which is apple-specific, and
keep the logic the  same as before 88d23ad5 - just dedup based on UUID.

Verified that MacOS + 4xVega still reports 4 GPUs with this version.

* vulkan: only skip dedup when both drivers are moltenVk
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2f6570181a..ff9cb7355c 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5561,9 +5561,9 @@ static void ggml_vk_instance_init() {
                 // Check if there are two physical devices corresponding to the same GPU
                 // This handles the case where the same GPU appears with different drivers (e.g., RADV + AMDVLK on Linux),
                 // see https://github.com/ggml-org/llama.cpp/pull/7582 for original deduplication.
-                // However, for MoltenVK on macOS, multiple GPUs on the same card may report the same UUID,
-                // see https://github.com/KhronosGroup/MoltenVK/issues/2683. Until this is fixed, we'll only deduplicate
-                // when drivers differ (same driver + same UUID = likely different GPUs)
+                // MoltenVK on macOS may report the same UUID for distinct GPUs on multi-GPU cards,
+                // see https://github.com/KhronosGroup/MoltenVK/issues/2683. Skip when both old/new
+                // driver is MoltenVK
                 auto old_device = std::find_if(
                     vk_instance.device_indices.begin(),
                     vk_instance.device_indices.end(),
@@ -5580,11 +5580,9 @@ static void ggml_vk_instance_init() {
                             old_id.deviceLUIDValid && new_id.deviceLUIDValid &&
                             std::equal(std::begin(old_id.deviceLUID), std::end(old_id.deviceLUID), std::begin(new_id.deviceLUID))
                         );
+                        bool both_molten_vk = (new_driver.driverID == vk::DriverId::eMoltenvk && old_driver.driverID == vk::DriverId::eMoltenvk);
 
-                        // Only deduplicate if same UUID AND different drivers
-                        // (same driver + same UUID on MoltenVK = likely different GPUs on multi-GPU card)
-                        bool different_driver = (old_driver.driverID != new_driver.driverID);
-                        return same_uuid && different_driver;
+                        return same_uuid && !both_molten_vk;
                     }
                 );
                 if (old_device == vk_instance.device_indices.end()) {

From 7a4f97d19694ca4ffa643567c6c353dea976455b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Thu, 5 Feb 2026 10:08:45 +0200
Subject: [PATCH 130/206] metal : add diag (#19330)

---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 20 ++++++++++
 ggml/src/ggml-metal/ggml-metal-device.h   |  1 +
 ggml/src/ggml-metal/ggml-metal-device.m   |  4 +-
 ggml/src/ggml-metal/ggml-metal-impl.h     | 19 ++++++++++
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 46 +++++++++++++++++++++++
 ggml/src/ggml-metal/ggml-metal-ops.h      |  1 +
 ggml/src/ggml-metal/ggml-metal.metal      | 20 ++++++++++
 7 files changed, 110 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 4cd3d93d81..6af0dd88d5 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -176,6 +176,26 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_me
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag(ggml_metal_library_t lib, const ggml_tensor * op) {
+    char base[256];
+    char name[256];
+
+    const int n = op->src[0]->ne[0];
+
+    snprintf(base, 256, "kernel_diag_%s", ggml_type_name(op->src[0]->type));
+    snprintf(name, 256, "%s_n=%d", base, n);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+    }
+
+    res.nsg  = 1;
+    res.smem = 0;
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat(ggml_metal_library_t lib, ggml_type tsrc) {
     char base[256];
     char name[256];
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index d898432712..84dcec3083 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -108,6 +108,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_1d
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_2d           (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows          (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows          (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag              (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat            (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary             (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_glu               (ggml_metal_library_t lib, const struct ggml_tensor * op);
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 8a0b85c6e4..c8e737d418 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1152,8 +1152,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return has_simdgroup_reduction;
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_RWKV_WKV7:
-        case GGML_OP_SOLVE_TRI:
             return true;
+        case GGML_OP_SOLVE_TRI:
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             return has_simdgroup_reduction;
@@ -1235,6 +1235,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                         return false;
                 };
             }
+        case GGML_OP_DIAG:
+            return true;
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return has_simdgroup_reduction;
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 640ade8f88..7f73cb97bb 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -792,6 +792,25 @@ typedef struct {
     uint64_t nb3;
 } ggml_metal_kargs_set_rows;
 
+typedef struct {
+    int32_t  ne00;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
+} ggml_metal_kargs_diag;
+
 typedef struct {
     int64_t  ne00;
     int64_t  ne01;
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 753fcec317..e0ed6c7805 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -361,6 +361,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_set_rows(ctx, idx);
             } break;
+        case GGML_OP_DIAG:
+            {
+                n_fuse = ggml_metal_op_diag(ctx, idx);
+            } break;
         case GGML_OP_L2_NORM:
             {
                 n_fuse = ggml_metal_op_l2_norm(ctx, idx);
@@ -1259,6 +1263,48 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
+int ggml_metal_op_diag(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_library_t lib = ctx->lib;
+    ggml_metal_encoder_t enc = ctx->enc;
+
+    GGML_TENSOR_LOCALS(int32_t,  ne0, op->src[0], ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
+    GGML_TENSOR_LOCALS(int32_t,  ne, op, ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
+
+    ggml_metal_kargs_diag args = {
+        /*.ne00 =*/ne00,
+        /*.ne01 =*/ne01,
+        /*.ne02 =*/ne02,
+        /*.ne03 =*/ne03,
+        /*.nb00 =*/nb00,
+        /*.nb01 =*/nb01,
+        /*.nb02 =*/nb02,
+        /*.nb03 =*/nb03,
+        /*.ne0  =*/ne0,
+        /*.ne1  =*/ne1,
+        /*.ne2  =*/ne2,
+        /*.ne3  =*/ne3,
+        /*.nb0  =*/nb0,
+        /*.nb1  =*/nb1,
+        /*.nb2  =*/nb2,
+        /*.nb3  =*/nb3,
+    };
+
+    auto pipeline = ggml_metal_library_get_pipeline_diag(lib, op);
+
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),         2);
+
+    ggml_metal_encoder_dispatch_threadgroups(enc, ne1, ne2, ne3, 32, 1, 1);
+
+    return 1;
+}
+
 int ggml_metal_op_soft_max(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h
index 2e4c7d3fa1..3c64e4f600 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.h
+++ b/ggml/src/ggml-metal/ggml-metal-ops.h
@@ -56,6 +56,7 @@ int ggml_metal_op_sum_rows          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_cumsum            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_get_rows          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_set_rows          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_diag              (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_soft_max          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_conv          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_scan          (ggml_metal_op_t ctx, int idx);
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index c09a54e661..e54cdab39d 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -8815,6 +8815,26 @@ kernel void kernel_set_rows_f(
     }
 }
 
+kernel void kernel_diag_f32(
+        constant ggml_metal_kargs_diag & args,
+        device   const char * src0,
+        device         char * dst,
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        ushort tiitg[[thread_index_in_threadgroup]]) {
+    constexpr short NW = N_SIMDWIDTH;
+
+    const int32_t i3 = tgpig.z;
+    const int32_t i2 = tgpig.y;
+    const int32_t i1 = tgpig.x;
+
+    device const float * src0_ptr = (device const float *)(src0 +                i2*args.nb02 + i3*args.nb03);
+    device       float * dst_ptr  = (device       float *)(dst  + i1*args.nb01 + i2*args.nb2  + i3*args.nb3);
+
+    for (int i0 = tiitg; i0 < args.ne0; i0 += NW) {
+        dst_ptr[i0] = i0 == i1 ? src0_ptr[i0] : 0.0f;
+    }
+}
+
 constant bool FC_mul_mm_bc_inp [[function_constant(FC_MUL_MM + 0)]];
 constant bool FC_mul_mm_bc_out [[function_constant(FC_MUL_MM + 1)]];
 

From a4ea7a188f3f777da665d73fe297fb7bb716e526 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= 
Date: Thu, 5 Feb 2026 09:53:35 +0100
Subject: [PATCH 131/206] vendor : update BoringSSL to 0.20260204.0 (#19333)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët 
---
 vendor/cpp-httplib/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index 18974d64ca..a8a59e02f4 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -39,7 +39,7 @@ if (LLAMA_BUILD_BORINGSSL)
     set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
 
     set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
-    set(BORINGSSL_VERSION "0.20251002.0" CACHE STRING "BoringSSL version")
+    set(BORINGSSL_VERSION "0.20260204.0" CACHE STRING "BoringSSL version")
 
     message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
 

From b828e18c75137e29fbfd3f3daa38281172d6a636 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= 
Date: Thu, 5 Feb 2026 11:10:39 +0100
Subject: [PATCH 132/206] docker : fix vulkan build (#19352)

---
 .devops/vulkan.Dockerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index 9797c5e0f3..5d6c87ed6b 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -54,6 +54,7 @@ RUN apt-get update \
     build-essential \
     git \
     python3 \
+    python3-dev \
     python3-pip \
     python3-wheel \
     && pip install --break-system-packages --upgrade setuptools \

From 3795cc1e89e16fbc145f8a5457ea30abd86e0d1d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Thu, 5 Feb 2026 14:34:07 +0200
Subject: [PATCH 133/206] benches : update models + numbers (#19359)

* bench : update script

* benches : update numbers
---
 benches/dgx-spark/dgx-spark.md       | 371 +++++++++++++++------------
 benches/mac-m2-ultra/mac-m2-ultra.md | 298 +++++++++++++++++++++
 scripts/bench-models.sh              |  60 +++--
 3 files changed, 541 insertions(+), 188 deletions(-)
 create mode 100644 benches/mac-m2-ultra/mac-m2-ultra.md
 mode change 100644 => 100755 scripts/bench-models.sh

diff --git a/benches/dgx-spark/dgx-spark.md b/benches/dgx-spark/dgx-spark.md
index ec6c20d8a0..fd5c4e2c78 100644
--- a/benches/dgx-spark/dgx-spark.md
+++ b/benches/dgx-spark/dgx-spark.md
@@ -8,7 +8,7 @@ g++ --version
 g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
 
 nvidia-smi
-Sun Nov  2 10:43:25 2025
+Thu Feb  5 13:49:40 2026
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
 +-----------------------------------------+------------------------+----------------------+
@@ -17,7 +17,7 @@ Sun Nov  2 10:43:25 2025
 |                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  NVIDIA GB10                    On  |   0000000F:01:00.0 Off |                  N/A |
-| N/A   35C    P8              4W /  N/A  | Not Supported          |      0%      Default |
+| N/A   47C    P0             13W /  N/A  | Not Supported          |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 ```
@@ -29,46 +29,46 @@ Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.374 |  1369.01 |    0.383 |    83.64 |    0.757 |   719.01 |
-|   512 |     32 |    2 |   1088 |    0.274 |  3741.35 |    0.659 |    97.14 |    0.933 |  1166.66 |
-|   512 |     32 |    4 |   2176 |    0.526 |  3896.47 |    0.817 |   156.73 |    1.342 |  1621.08 |
-|   512 |     32 |    8 |   4352 |    1.044 |  3925.10 |    0.987 |   259.44 |    2.030 |  2143.56 |
-|   512 |     32 |   16 |   8704 |    2.076 |  3945.84 |    1.248 |   410.32 |    3.324 |  2618.60 |
-|   512 |     32 |   32 |  17408 |    4.170 |  3929.28 |    1.630 |   628.40 |    5.799 |  3001.76 |
-|  4096 |     32 |    1 |   4128 |    1.083 |  3782.66 |    0.394 |    81.21 |    1.477 |  2795.13 |
-|  4096 |     32 |    2 |   8256 |    2.166 |  3782.72 |    0.725 |    88.28 |    2.891 |  2856.14 |
-|  4096 |     32 |    4 |  16512 |    4.333 |  3780.88 |    0.896 |   142.82 |    5.230 |  3157.38 |
-|  4096 |     32 |    8 |  33024 |    8.618 |  3802.14 |    1.155 |   221.69 |    9.773 |  3379.08 |
-|  4096 |     32 |   16 |  66048 |   17.330 |  3781.73 |    1.598 |   320.34 |   18.928 |  3489.45 |
-|  4096 |     32 |   32 | 132096 |   34.671 |  3780.48 |    2.336 |   438.35 |   37.007 |  3569.51 |
-|  8192 |     32 |    1 |   8224 |    2.233 |  3668.56 |    0.438 |    72.98 |    2.671 |  3078.44 |
-|  8192 |     32 |    2 |  16448 |    4.425 |  3702.95 |    0.756 |    84.66 |    5.181 |  3174.95 |
-|  8192 |     32 |    4 |  32896 |    8.859 |  3698.64 |    0.967 |   132.38 |    9.826 |  3347.72 |
-|  8192 |     32 |    8 |  65792 |   17.714 |  3699.57 |    1.277 |   200.52 |   18.991 |  3464.35 |
-|  8192 |     32 |   16 | 131584 |   35.494 |  3692.84 |    1.841 |   278.12 |   37.335 |  3524.46 |
-|  8192 |     32 |   32 | 263168 |   70.949 |  3694.82 |    2.798 |   365.99 |   73.747 |  3568.53 |
+|   512 |     32 |    1 |    544 |    0.270 |  1895.57 |    0.399 |    80.13 |    0.669 |   812.60 |
+|   512 |     32 |    2 |   1088 |    0.230 |  4451.23 |    0.583 |   109.71 |    0.813 |  1337.56 |
+|   512 |     32 |    4 |   2176 |    0.437 |  4688.87 |    0.820 |   156.03 |    1.257 |  1730.91 |
+|   512 |     32 |    8 |   4352 |    0.863 |  4744.23 |    0.942 |   271.79 |    1.805 |  2410.73 |
+|   512 |     32 |   16 |   8704 |    1.725 |  4748.19 |    1.173 |   436.38 |    2.899 |  3002.85 |
+|   512 |     32 |   32 |  17408 |    3.437 |  4767.38 |    1.503 |   681.49 |    4.939 |  3524.40 |
+|  4096 |     32 |    1 |   4128 |    0.907 |  4513.91 |    0.407 |    78.54 |    1.315 |  3139.56 |
+|  4096 |     32 |    2 |   8256 |    1.796 |  4560.42 |    0.625 |   102.37 |    2.422 |  3409.45 |
+|  4096 |     32 |    4 |  16512 |    3.596 |  4555.66 |    0.888 |   144.11 |    4.485 |  3681.93 |
+|  4096 |     32 |    8 |  33024 |    7.184 |  4561.44 |    1.098 |   233.11 |    8.282 |  3987.51 |
+|  4096 |     32 |   16 |  66048 |   14.369 |  4560.82 |    1.503 |   340.74 |   15.872 |  4161.30 |
+|  4096 |     32 |   32 | 132096 |   28.760 |  4557.52 |    2.162 |   473.59 |   30.922 |  4271.95 |
+|  8192 |     32 |    1 |   8224 |    1.859 |  4405.59 |    0.430 |    74.36 |    2.290 |  3591.61 |
+|  8192 |     32 |    2 |  16448 |    3.698 |  4430.02 |    0.656 |    97.59 |    4.354 |  3777.47 |
+|  8192 |     32 |    4 |  32896 |    7.403 |  4426.10 |    0.957 |   133.82 |    8.360 |  3934.97 |
+|  8192 |     32 |    8 |  65792 |   14.802 |  4427.63 |    1.222 |   209.44 |   16.024 |  4105.87 |
+|  8192 |     32 |   16 | 131584 |   29.596 |  4428.67 |    1.741 |   294.13 |   31.337 |  4199.00 |
+|  8192 |     32 |   32 | 263168 |   59.169 |  4430.42 |    2.619 |   390.92 |   61.789 |  4259.17 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      3714.25 ± 20.36 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         86.58 ± 0.43 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |      3445.17 ± 17.85 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         81.72 ± 0.53 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      3218.78 ± 11.34 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.86 ± 0.64 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       2732.83 ± 7.17 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         71.57 ± 0.51 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      2119.75 ± 12.81 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         62.33 ± 0.24 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      4505.82 ± 12.90 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         83.43 ± 0.59 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |      4158.34 ± 18.84 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         79.22 ± 0.60 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      3993.81 ± 17.55 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         75.22 ± 1.05 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |      3449.98 ± 12.13 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         70.36 ± 0.37 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      2689.42 ± 18.89 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         61.65 ± 0.30 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/gpt-oss-120b-GGUF
 
@@ -77,46 +77,46 @@ Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.571 |   897.18 |    0.543 |    58.96 |    1.113 |   488.60 |
-|   512 |     32 |    2 |   1088 |    0.593 |  1725.37 |    1.041 |    61.45 |    1.635 |   665.48 |
-|   512 |     32 |    4 |   2176 |    1.043 |  1963.15 |    1.334 |    95.95 |    2.377 |   915.36 |
-|   512 |     32 |    8 |   4352 |    2.099 |  1951.63 |    1.717 |   149.07 |    3.816 |  1140.45 |
-|   512 |     32 |   16 |   8704 |    4.207 |  1947.12 |    2.311 |   221.56 |    6.518 |  1335.35 |
-|   512 |     32 |   32 |  17408 |    8.422 |  1945.36 |    3.298 |   310.46 |   11.720 |  1485.27 |
-|  4096 |     32 |    1 |   4128 |    2.138 |  1915.88 |    0.571 |    56.09 |    2.708 |  1524.12 |
-|  4096 |     32 |    2 |   8256 |    4.266 |  1920.25 |    1.137 |    56.27 |    5.404 |  1527.90 |
-|  4096 |     32 |    4 |  16512 |    8.564 |  1913.02 |    1.471 |    86.99 |   10.036 |  1645.29 |
-|  4096 |     32 |    8 |  33024 |   17.092 |  1917.19 |    1.979 |   129.33 |   19.071 |  1731.63 |
-|  4096 |     32 |   16 |  66048 |   34.211 |  1915.65 |    2.850 |   179.66 |   37.061 |  1782.15 |
-|  4096 |     32 |   32 | 132096 |   68.394 |  1916.44 |    4.381 |   233.72 |   72.775 |  1815.13 |
-|  8192 |     32 |    1 |   8224 |    4.349 |  1883.45 |    0.620 |    51.65 |    4.969 |  1655.04 |
-|  8192 |     32 |    2 |  16448 |    8.674 |  1888.83 |    1.178 |    54.33 |    9.852 |  1669.48 |
-|  8192 |     32 |    4 |  32896 |   17.351 |  1888.55 |    1.580 |    81.01 |   18.931 |  1737.68 |
-|  8192 |     32 |    8 |  65792 |   34.743 |  1886.31 |    2.173 |   117.80 |   36.916 |  1782.20 |
-|  8192 |     32 |   16 | 131584 |   69.413 |  1888.29 |    3.297 |   155.28 |   72.710 |  1809.70 |
-|  8192 |     32 |   32 | 263168 |  138.903 |  1887.24 |    5.004 |   204.63 |  143.907 |  1828.73 |
+|   512 |     32 |    1 |    544 |    0.445 |  1151.80 |    0.560 |    57.14 |    1.005 |   541.53 |
+|   512 |     32 |    2 |   1088 |    0.472 |  2169.85 |    0.874 |    73.27 |    1.345 |   808.65 |
+|   512 |     32 |    4 |   2176 |    0.826 |  2480.33 |    1.299 |    98.51 |    2.125 |  1023.94 |
+|   512 |     32 |    8 |   4352 |    1.644 |  2491.67 |    1.608 |   159.18 |    3.252 |  1338.20 |
+|   512 |     32 |   16 |   8704 |    3.292 |  2488.35 |    2.117 |   241.85 |    5.409 |  1609.13 |
+|   512 |     32 |   32 |  17408 |    6.604 |  2481.07 |    2.898 |   353.31 |    9.502 |  1832.04 |
+|  4096 |     32 |    1 |   4128 |    1.698 |  2412.65 |    0.580 |    55.21 |    2.277 |  1812.66 |
+|  4096 |     32 |    2 |   8256 |    3.399 |  2409.88 |    0.934 |    68.53 |    4.333 |  1905.27 |
+|  4096 |     32 |    4 |  16512 |    6.823 |  2401.21 |    1.411 |    90.72 |    8.234 |  2005.30 |
+|  4096 |     32 |    8 |  33024 |   13.574 |  2413.97 |    1.841 |   139.07 |   15.415 |  2142.31 |
+|  4096 |     32 |   16 |  66048 |   27.176 |  2411.52 |    2.609 |   196.26 |   29.785 |  2217.49 |
+|  4096 |     32 |   32 | 132096 |   54.359 |  2411.23 |    3.905 |   262.20 |   58.264 |  2267.19 |
+|  8192 |     32 |    1 |   8224 |    3.491 |  2346.81 |    0.613 |    52.23 |    4.103 |  2004.21 |
+|  8192 |     32 |    2 |  16448 |    6.939 |  2361.03 |    0.981 |    65.21 |    7.921 |  2076.56 |
+|  8192 |     32 |    4 |  32896 |   13.888 |  2359.40 |    1.511 |    84.71 |   15.399 |  2136.21 |
+|  8192 |     32 |    8 |  65792 |   27.756 |  2361.18 |    2.034 |   125.86 |   29.790 |  2208.56 |
+|  8192 |     32 |   16 | 131584 |   55.554 |  2359.34 |    3.021 |   169.49 |   58.575 |  2246.41 |
+|  8192 |     32 |   32 | 263168 |  111.036 |  2360.89 |    4.537 |   225.72 |  115.573 |  2277.08 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       1919.36 ± 5.01 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         60.40 ± 0.30 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       1825.30 ± 6.37 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         56.94 ± 0.29 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1739.19 ± 6.00 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         52.51 ± 0.42 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1536.75 ± 4.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         49.33 ± 0.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1255.85 ± 3.26 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         42.99 ± 0.18 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |       2443.91 ± 7.47 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         58.72 ± 0.20 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2309.84 ± 3.63 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         55.67 ± 0.35 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      2216.68 ± 10.16 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         52.87 ± 0.43 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1956.31 ± 6.39 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         49.45 ± 0.20 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      1567.08 ± 11.79 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         42.76 ± 0.14 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
 
@@ -125,46 +125,46 @@ Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.398 |  1285.90 |    0.530 |    60.41 |    0.928 |   586.27 |
-|   512 |     32 |    2 |   1088 |    0.386 |  2651.65 |    0.948 |    67.50 |    1.334 |   815.38 |
-|   512 |     32 |    4 |   2176 |    0.666 |  3076.37 |    1.209 |   105.87 |    1.875 |  1160.71 |
-|   512 |     32 |    8 |   4352 |    1.325 |  3091.39 |    1.610 |   158.98 |    2.935 |  1482.65 |
-|   512 |     32 |   16 |   8704 |    2.664 |  3075.58 |    2.150 |   238.19 |    4.813 |  1808.39 |
-|   512 |     32 |   32 |  17408 |    5.336 |  3070.31 |    2.904 |   352.59 |    8.240 |  2112.50 |
-|  4096 |     32 |    1 |   4128 |    1.444 |  2836.81 |    0.581 |    55.09 |    2.025 |  2038.81 |
-|  4096 |     32 |    2 |   8256 |    2.872 |  2852.14 |    1.084 |    59.06 |    3.956 |  2086.99 |
-|  4096 |     32 |    4 |  16512 |    5.744 |  2852.32 |    1.440 |    88.90 |    7.184 |  2298.47 |
-|  4096 |     32 |    8 |  33024 |   11.463 |  2858.68 |    2.068 |   123.78 |   13.531 |  2440.65 |
-|  4096 |     32 |   16 |  66048 |   22.915 |  2859.95 |    3.018 |   169.67 |   25.933 |  2546.90 |
-|  4096 |     32 |   32 | 132096 |   45.956 |  2852.10 |    4.609 |   222.18 |   50.565 |  2612.39 |
-|  8192 |     32 |    1 |   8224 |    3.063 |  2674.72 |    0.693 |    46.20 |    3.755 |  2189.92 |
-|  8192 |     32 |    2 |  16448 |    6.109 |  2681.87 |    1.214 |    52.71 |    7.323 |  2245.98 |
-|  8192 |     32 |    4 |  32896 |   12.197 |  2686.63 |    1.682 |    76.11 |   13.878 |  2370.30 |
-|  8192 |     32 |    8 |  65792 |   24.409 |  2684.94 |    2.556 |   100.17 |   26.965 |  2439.95 |
-|  8192 |     32 |   16 | 131584 |   48.753 |  2688.50 |    3.994 |   128.20 |   52.747 |  2494.64 |
-|  8192 |     32 |   32 | 263168 |   97.508 |  2688.42 |    6.528 |   156.86 |  104.037 |  2529.57 |
+|   512 |     32 |    1 |    544 |    0.393 |  1303.73 |    0.548 |    58.36 |    0.941 |   578.10 |
+|   512 |     32 |    2 |   1088 |    0.387 |  2648.68 |    0.910 |    70.35 |    1.296 |   839.27 |
+|   512 |     32 |    4 |   2176 |    0.659 |  3107.63 |    1.302 |    98.33 |    1.961 |  1109.77 |
+|   512 |     32 |    8 |   4352 |    1.322 |  3099.35 |    1.669 |   153.42 |    2.990 |  1455.43 |
+|   512 |     32 |   16 |   8704 |    2.639 |  3104.63 |    2.212 |   231.44 |    4.851 |  1794.32 |
+|   512 |     32 |   32 |  17408 |    5.284 |  3100.80 |    2.955 |   346.53 |    8.239 |  2112.93 |
+|  4096 |     32 |    1 |   4128 |    1.417 |  2890.36 |    0.598 |    53.51 |    2.015 |  2048.45 |
+|  4096 |     32 |    2 |   8256 |    2.829 |  2895.62 |    1.019 |    62.82 |    3.848 |  2145.60 |
+|  4096 |     32 |    4 |  16512 |    5.656 |  2896.96 |    1.528 |    83.79 |    7.183 |  2298.71 |
+|  4096 |     32 |    8 |  33024 |   11.338 |  2890.02 |    2.127 |   120.36 |   13.465 |  2452.53 |
+|  4096 |     32 |   16 |  66048 |   22.709 |  2885.96 |    3.104 |   164.97 |   25.812 |  2558.79 |
+|  4096 |     32 |   32 | 132096 |   45.301 |  2893.35 |    4.723 |   216.80 |   50.024 |  2640.63 |
+|  8192 |     32 |    1 |   8224 |    3.022 |  2711.09 |    0.678 |    47.20 |    3.700 |  2222.89 |
+|  8192 |     32 |    2 |  16448 |    6.039 |  2713.01 |    1.149 |    55.70 |    7.188 |  2288.21 |
+|  8192 |     32 |    4 |  32896 |   12.050 |  2719.35 |    1.785 |    71.69 |   13.835 |  2377.67 |
+|  8192 |     32 |    8 |  65792 |   24.113 |  2717.90 |    2.629 |    97.39 |   26.741 |  2460.31 |
+|  8192 |     32 |   16 | 131584 |   48.178 |  2720.58 |    4.099 |   124.91 |   52.277 |  2517.06 |
+|  8192 |     32 |   32 | 263168 |   96.401 |  2719.31 |    6.696 |   152.93 |  103.097 |  2552.63 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2925.55 ± 4.25 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         62.80 ± 0.27 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2531.01 ± 6.79 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         55.86 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       2244.39 ± 5.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         45.95 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1783.17 ± 3.68 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         39.07 ± 0.10 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1241.90 ± 3.13 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         29.92 ± 0.06 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      2986.97 ± 18.87 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         61.06 ± 0.23 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2633.45 ± 6.26 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         54.77 ± 0.28 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |       2354.14 ± 3.84 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         48.02 ± 0.40 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1908.86 ± 4.25 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         40.23 ± 0.10 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |       1348.17 ± 2.00 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         30.21 ± 0.04 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
 
@@ -173,46 +173,46 @@ Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.211 |  2421.57 |    1.055 |    30.33 |    1.266 |   429.57 |
-|   512 |     32 |    2 |   1088 |    0.419 |  2441.34 |    1.130 |    56.65 |    1.549 |   702.32 |
-|   512 |     32 |    4 |   2176 |    0.873 |  2345.54 |    1.174 |   108.99 |    2.048 |  1062.74 |
-|   512 |     32 |    8 |   4352 |    1.727 |  2371.85 |    1.254 |   204.22 |    2.980 |  1460.19 |
-|   512 |     32 |   16 |   8704 |    3.452 |  2373.22 |    1.492 |   343.16 |    4.944 |  1760.56 |
-|   512 |     32 |   32 |  17408 |    6.916 |  2368.93 |    1.675 |   611.51 |    8.591 |  2026.36 |
-|  4096 |     32 |    1 |   4128 |    1.799 |  2277.26 |    1.084 |    29.51 |    2.883 |  1431.91 |
-|  4096 |     32 |    2 |   8256 |    3.577 |  2290.01 |    1.196 |    53.50 |    4.774 |  1729.51 |
-|  4096 |     32 |    4 |  16512 |    7.172 |  2284.36 |    1.313 |    97.50 |    8.485 |  1946.00 |
-|  4096 |     32 |    8 |  33024 |   14.341 |  2284.96 |    1.520 |   168.46 |   15.860 |  2082.18 |
-|  4096 |     32 |   16 |  66048 |   28.675 |  2285.44 |    1.983 |   258.21 |   30.658 |  2154.33 |
-|  4096 |     32 |   32 | 132096 |   57.354 |  2285.32 |    2.640 |   387.87 |   59.994 |  2201.82 |
-|  8192 |     32 |    1 |   8224 |    3.701 |  2213.75 |    1.119 |    28.59 |    4.820 |  1706.34 |
-|  8192 |     32 |    2 |  16448 |    7.410 |  2211.19 |    1.272 |    50.31 |    8.682 |  1894.56 |
-|  8192 |     32 |    4 |  32896 |   14.802 |  2213.83 |    1.460 |    87.68 |   16.261 |  2022.96 |
-|  8192 |     32 |    8 |  65792 |   29.609 |  2213.35 |    1.781 |   143.74 |   31.390 |  2095.93 |
-|  8192 |     32 |   16 | 131584 |   59.229 |  2212.96 |    2.495 |   205.17 |   61.725 |  2131.79 |
-|  8192 |     32 |   32 | 263168 |  118.449 |  2213.15 |    3.714 |   275.75 |  122.162 |  2154.25 |
+|   512 |     32 |    1 |    544 |    0.212 |  2420.12 |    1.100 |    29.10 |    1.311 |   414.85 |
+|   512 |     32 |    2 |   1088 |    0.428 |  2393.89 |    1.185 |    54.00 |    1.613 |   674.56 |
+|   512 |     32 |    4 |   2176 |    0.894 |  2290.41 |    1.229 |   104.17 |    2.123 |  1025.02 |
+|   512 |     32 |    8 |   4352 |    1.758 |  2330.36 |    1.319 |   194.15 |    3.076 |  1414.70 |
+|   512 |     32 |   16 |   8704 |    3.508 |  2335.21 |    1.543 |   331.90 |    5.051 |  1723.33 |
+|   512 |     32 |   32 |  17408 |    7.035 |  2328.93 |    1.738 |   589.21 |    8.773 |  1984.29 |
+|  4096 |     32 |    1 |   4128 |    1.831 |  2237.25 |    1.125 |    28.44 |    2.956 |  1396.42 |
+|  4096 |     32 |    2 |   8256 |    3.642 |  2249.48 |    1.253 |    51.07 |    4.895 |  1686.64 |
+|  4096 |     32 |    4 |  16512 |    7.274 |  2252.26 |    1.380 |    92.72 |    8.655 |  1907.81 |
+|  4096 |     32 |    8 |  33024 |   14.576 |  2248.09 |    1.617 |   158.29 |   16.193 |  2039.37 |
+|  4096 |     32 |   16 |  66048 |   29.138 |  2249.17 |    2.081 |   246.01 |   31.219 |  2115.63 |
+|  4096 |     32 |   32 | 132096 |   58.275 |  2249.19 |    2.814 |   363.87 |   61.089 |  2162.34 |
+|  8192 |     32 |    1 |   8224 |    3.757 |  2180.26 |    1.184 |    27.03 |    4.941 |  1664.37 |
+|  8192 |     32 |    2 |  16448 |    7.522 |  2178.05 |    1.341 |    47.73 |    8.863 |  1855.77 |
+|  8192 |     32 |    4 |  32896 |   15.043 |  2178.25 |    1.548 |    82.69 |   16.591 |  1982.74 |
+|  8192 |     32 |    8 |  65792 |   30.111 |  2176.49 |    1.937 |   132.13 |   32.048 |  2052.90 |
+|  8192 |     32 |   16 | 131584 |   60.405 |  2169.90 |    2.706 |   189.21 |   63.111 |  2084.97 |
+|  8192 |     32 |   32 | 263168 |  120.439 |  2176.58 |    3.993 |   256.46 |  124.432 |  2114.96 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2272.74 ± 4.68 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         30.66 ± 0.02 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2107.80 ± 9.55 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         29.71 ± 0.05 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1937.80 ± 6.75 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         28.86 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1641.12 ± 1.78 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         27.24 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1296.02 ± 2.67 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         23.78 ± 0.03 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |       2250.28 ± 6.41 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         29.43 ± 0.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2100.19 ± 8.96 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         28.61 ± 0.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |       2007.56 ± 4.16 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         27.38 ± 0.09 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1779.11 ± 6.42 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         25.72 ± 0.03 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |       1471.23 ± 1.71 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         22.51 ± 0.02 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/gemma-3-4b-it-qat-GGUF
 
@@ -221,44 +221,91 @@ Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.094 |  5434.73 |    0.394 |    81.21 |    0.488 |  1114.15 |
-|   512 |     32 |    2 |   1088 |    0.168 |  6091.68 |    0.498 |   128.52 |    0.666 |  1633.41 |
-|   512 |     32 |    4 |   2176 |    0.341 |  6010.68 |    0.542 |   236.37 |    0.882 |  2466.43 |
-|   512 |     32 |    8 |   4352 |    0.665 |  6161.46 |    0.678 |   377.74 |    1.342 |  3241.72 |
-|   512 |     32 |   16 |   8704 |    1.323 |  6193.19 |    0.902 |   567.41 |    2.225 |  3911.74 |
-|   512 |     32 |   32 |  17408 |    2.642 |  6202.03 |    1.231 |   832.03 |    3.872 |  4495.36 |
-|  4096 |     32 |    1 |   4128 |    0.701 |  5840.49 |    0.439 |    72.95 |    1.140 |  3621.23 |
-|  4096 |     32 |    2 |   8256 |    1.387 |  5906.82 |    0.574 |   111.48 |    1.961 |  4210.12 |
-|  4096 |     32 |    4 |  16512 |    2.758 |  5940.33 |    0.651 |   196.58 |    3.409 |  4843.33 |
-|  4096 |     32 |    8 |  33024 |    5.491 |  5967.56 |    0.876 |   292.40 |    6.367 |  5187.12 |
-|  4096 |     32 |   16 |  66048 |   10.978 |  5969.58 |    1.275 |   401.69 |   12.253 |  5390.38 |
-|  4096 |     32 |   32 | 132096 |   21.944 |  5972.93 |    1.992 |   514.16 |   23.936 |  5518.73 |
-|  8192 |     32 |    1 |   8224 |    1.402 |  5841.91 |    0.452 |    70.73 |    1.855 |  4434.12 |
-|  8192 |     32 |    2 |  16448 |    2.793 |  5865.34 |    0.637 |   100.55 |    3.430 |  4795.51 |
-|  8192 |     32 |    4 |  32896 |    5.564 |  5889.64 |    0.770 |   166.26 |    6.334 |  5193.95 |
-|  8192 |     32 |    8 |  65792 |   11.114 |  5896.44 |    1.122 |   228.07 |   12.237 |  5376.51 |
-|  8192 |     32 |   16 | 131584 |   22.210 |  5901.38 |    1.789 |   286.15 |   24.000 |  5482.74 |
-|  8192 |     32 |   32 | 263168 |   44.382 |  5906.56 |    3.044 |   336.38 |   47.426 |  5549.02 |
+|   512 |     32 |    1 |    544 |    0.092 |  5566.97 |    0.412 |    77.63 |    0.504 |  1078.95 |
+|   512 |     32 |    2 |   1088 |    0.161 |  6345.67 |    0.522 |   122.70 |    0.683 |  1593.06 |
+|   512 |     32 |    4 |   2176 |    0.325 |  6309.87 |    0.562 |   227.68 |    0.887 |  2453.87 |
+|   512 |     32 |    8 |   4352 |    0.643 |  6374.42 |    0.685 |   373.67 |    1.328 |  3277.94 |
+|   512 |     32 |   16 |   8704 |    1.277 |  6413.64 |    0.915 |   559.47 |    2.192 |  3970.01 |
+|   512 |     32 |   32 |  17408 |    2.518 |  6506.57 |    1.249 |   819.61 |    3.767 |  4620.64 |
+|  4096 |     32 |    1 |   4128 |    0.674 |  6079.68 |    0.453 |    70.60 |    1.127 |  3662.88 |
+|  4096 |     32 |    2 |   8256 |    1.335 |  6137.82 |    0.627 |   102.03 |    1.962 |  4208.11 |
+|  4096 |     32 |    4 |  16512 |    2.657 |  6167.35 |    0.749 |   170.92 |    3.405 |  4848.71 |
+|  4096 |     32 |    8 |  33024 |    5.307 |  6173.91 |    0.974 |   262.89 |    6.281 |  5257.53 |
+|  4096 |     32 |   16 |  66048 |   10.610 |  6176.96 |    1.379 |   371.42 |   11.988 |  5509.40 |
+|  4096 |     32 |   32 | 132096 |   21.213 |  6178.89 |    2.122 |   482.50 |   23.335 |  5660.82 |
+|  8192 |     32 |    1 |   8224 |    1.359 |  6027.34 |    0.467 |    68.52 |    1.826 |  4503.48 |
+|  8192 |     32 |    2 |  16448 |    2.699 |  6069.68 |    0.653 |    98.03 |    3.352 |  4906.68 |
+|  8192 |     32 |    4 |  32896 |    5.366 |  6106.74 |    0.818 |   156.55 |    6.184 |  5319.96 |
+|  8192 |     32 |    8 |  65792 |   10.755 |  6093.50 |    1.174 |   218.04 |   11.929 |  5515.22 |
+|  8192 |     32 |   16 | 131584 |   21.484 |  6100.82 |    1.829 |   279.90 |   23.314 |  5644.11 |
+|  8192 |     32 |   32 | 263168 |   42.950 |  6103.40 |    3.058 |   334.91 |   46.008 |  5720.05 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      5810.04 ± 21.71 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         84.54 ± 0.18 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       5288.04 ± 3.54 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         78.82 ± 1.37 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      4960.43 ± 16.64 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.13 ± 0.30 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |      4495.92 ± 31.11 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         72.37 ± 0.29 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      3746.90 ± 40.01 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         63.02 ± 0.20 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      5948.74 ± 10.61 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         81.05 ± 0.20 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |      5652.69 ± 34.29 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         76.37 ± 0.58 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      5509.57 ± 40.69 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         71.61 ± 0.80 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |      5340.86 ± 36.92 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         70.89 ± 0.34 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      5023.30 ± 13.52 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         62.28 ± 0.30 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
+## ggml-org/GLM-4.7-Flash-GGUF
+
+Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.433 |  1181.83 |    0.693 |    46.16 |    1.126 |   482.94 |
+|   512 |     32 |    2 |   1088 |    0.439 |  2334.46 |    1.034 |    61.89 |    1.473 |   738.75 |
+|   512 |     32 |    4 |   2176 |    0.772 |  2654.46 |    1.459 |    87.76 |    2.230 |   975.77 |
+|   512 |     32 |    8 |   4352 |    1.541 |  2658.78 |    2.043 |   125.31 |    3.583 |  1214.47 |
+|   512 |     32 |   16 |   8704 |    3.083 |  2656.91 |    2.675 |   191.42 |    5.758 |  1511.62 |
+|   512 |     32 |   32 |  17408 |    6.159 |  2660.12 |    3.615 |   283.24 |    9.774 |  1780.98 |
+|  4096 |     32 |    1 |   4128 |    1.915 |  2139.30 |    0.725 |    44.14 |    2.640 |  1563.83 |
+|  4096 |     32 |    2 |   8256 |    3.834 |  2136.40 |    1.119 |    57.21 |    4.953 |  1666.81 |
+|  4096 |     32 |    4 |  16512 |    7.636 |  2145.72 |    1.631 |    78.49 |    9.266 |  1781.93 |
+|  4096 |     32 |    8 |  33024 |   15.295 |  2142.40 |    2.344 |   109.21 |   17.639 |  1872.20 |
+|  4096 |     32 |   16 |  66048 |   30.573 |  2143.62 |    3.773 |   135.70 |   34.346 |  1923.04 |
+|  4096 |     32 |   32 | 132096 |   61.282 |  2138.82 |    5.795 |   176.71 |   67.077 |  1969.31 |
+|  8192 |     32 |    1 |   8224 |    4.510 |  1816.24 |    0.760 |    42.11 |    5.270 |  1560.44 |
+|  8192 |     32 |    2 |  16448 |    9.036 |  1813.19 |    1.206 |    53.06 |   10.242 |  1605.91 |
+|  8192 |     32 |    4 |  32896 |   18.070 |  1813.43 |    1.783 |    71.80 |   19.852 |  1657.03 |
+|  8192 |     32 |    8 |  65792 |   36.125 |  1814.15 |    2.635 |    97.14 |   38.760 |  1697.41 |
+|  8192 |     32 |   16 | 131584 |   72.367 |  1811.20 |    4.954 |   103.34 |   77.322 |  1701.77 |
+|  8192 |     32 |   32 | 263168 |  144.501 |  1814.13 |    8.103 |   126.37 |  152.604 |  1724.51 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | --: | --------------: | -------------------: |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |          pp2048 |      2364.18 ± 11.43 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |            tg32 |         48.68 ± 0.12 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |  pp2048 @ d4096 |       1684.13 ± 1.24 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |    tg32 @ d4096 |         44.62 ± 0.22 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |  pp2048 @ d8192 |       1314.68 ± 1.41 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |    tg32 @ d8192 |         42.59 ± 0.11 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 | pp2048 @ d16384 |        914.05 ± 3.32 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |   tg32 @ d16384 |         38.72 ± 0.13 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 | pp2048 @ d32768 |        567.20 ± 0.90 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |   tg32 @ d32768 |         32.65 ± 0.09 |
+
+build: 11fb327bf (7941)
diff --git a/benches/mac-m2-ultra/mac-m2-ultra.md b/benches/mac-m2-ultra/mac-m2-ultra.md
new file mode 100644
index 0000000000..cf8a953388
--- /dev/null
+++ b/benches/mac-m2-ultra/mac-m2-ultra.md
@@ -0,0 +1,298 @@
+## System info
+
+```bash
+uname -a
+Darwin gg-studio 25.2.0 Darwin Kernel Version 25.2.0: Tue Nov 18 21:07:05 PST 2025; root:xnu-12377.61.12~1/RELEASE_ARM64_T6020 arm64
+
+g++ --version
+Apple clang version 17.0.0 (clang-1700.3.19.1)
+Target: arm64-apple-darwin25.2.0
+```
+
+## ggml-org/gpt-oss-20b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.215 |  2381.35 |    0.245 |   130.45 |    0.460 |  1181.81 |
+|   512 |     32 |    2 |   1088 |    0.379 |  2701.43 |    0.382 |   167.56 |    0.761 |  1429.67 |
+|   512 |     32 |    4 |   2176 |    0.721 |  2839.27 |    0.604 |   211.76 |    1.326 |  1641.32 |
+|   512 |     32 |    8 |   4352 |    1.433 |  2858.30 |    1.033 |   247.75 |    2.466 |  1764.57 |
+|   512 |     32 |   16 |   8704 |    2.853 |  2871.12 |    1.570 |   326.11 |    4.423 |  1967.77 |
+|   512 |     32 |   32 |  17408 |    5.699 |  2874.95 |    1.910 |   536.15 |    7.609 |  2287.88 |
+|  4096 |     32 |    1 |   4128 |    1.552 |  2638.56 |    0.334 |    95.72 |    1.887 |  2188.00 |
+|  4096 |     32 |    2 |   8256 |    3.084 |  2655.88 |    0.404 |   158.54 |    3.488 |  2366.86 |
+|  4096 |     32 |    4 |  16512 |    6.151 |  2663.78 |    0.652 |   196.39 |    6.802 |  2427.37 |
+|  4096 |     32 |    8 |  33024 |   12.288 |  2666.77 |    1.135 |   225.47 |   13.423 |  2460.27 |
+|  4096 |     32 |   16 |  66048 |   24.563 |  2668.12 |    1.762 |   290.55 |   26.325 |  2508.97 |
+|  4096 |     32 |   32 | 132096 |   49.114 |  2668.73 |    2.398 |   426.94 |   51.512 |  2564.35 |
+|  8192 |     32 |    1 |   8224 |    3.345 |  2448.78 |    0.275 |   116.46 |    3.620 |  2271.76 |
+|  8192 |     32 |    2 |  16448 |    6.665 |  2458.11 |    0.425 |   150.71 |    7.090 |  2319.91 |
+|  8192 |     32 |    4 |  32896 |   13.315 |  2460.92 |    0.691 |   185.21 |   14.006 |  2348.63 |
+|  8192 |     32 |    8 |  65792 |   26.611 |  2462.73 |    1.212 |   211.16 |   27.823 |  2364.62 |
+|  8192 |     32 |   16 | 131584 |   53.232 |  2462.27 |    1.919 |   266.83 |   55.151 |  2385.88 |
+|  8192 |     32 |   32 | 263168 |  110.455 |  2373.30 |    2.752 |   372.03 |  113.208 |  2324.64 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2713.40 ± 3.56 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |        129.97 ± 3.90 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       2324.59 ± 3.01 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |        123.38 ± 0.17 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |      1989.82 ± 30.11 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |        117.39 ± 0.33 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       1556.54 ± 6.22 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |        109.75 ± 0.42 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |       1122.63 ± 1.45 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         98.25 ± 0.08 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/gpt-oss-120b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.426 |  1200.92 |    0.361 |    88.56 |    0.788 |   690.64 |
+|   512 |     32 |    2 |   1088 |    0.683 |  1500.14 |    0.545 |   117.35 |    1.228 |   886.02 |
+|   512 |     32 |    4 |   2176 |    1.204 |  1701.56 |    0.847 |   151.19 |    2.050 |  1061.34 |
+|   512 |     32 |    8 |   4352 |    2.402 |  1705.20 |    1.455 |   176.00 |    3.857 |  1128.45 |
+|   512 |     32 |   16 |   8704 |    4.802 |  1705.90 |    2.349 |   217.93 |    7.152 |  1217.08 |
+|   512 |     32 |   32 |  17408 |    9.593 |  1707.85 |    3.665 |   279.42 |   13.258 |  1313.01 |
+|  4096 |     32 |    1 |   4128 |    2.581 |  1587.08 |    0.390 |    82.12 |    2.970 |  1389.67 |
+|  4096 |     32 |    2 |   8256 |    5.124 |  1598.79 |    0.589 |   108.62 |    5.713 |  1445.10 |
+|  4096 |     32 |    4 |  16512 |   10.231 |  1601.47 |    0.928 |   137.98 |   11.158 |  1479.80 |
+|  4096 |     32 |    8 |  33024 |   20.468 |  1600.94 |    1.606 |   159.38 |   22.074 |  1496.04 |
+|  4096 |     32 |   16 |  66048 |   40.924 |  1601.42 |    2.639 |   193.99 |   43.563 |  1516.15 |
+|  4096 |     32 |   32 | 132096 |   81.819 |  1601.98 |    4.466 |   229.29 |   86.284 |  1530.94 |
+|  8192 |     32 |    1 |   8224 |    5.517 |  1484.74 |    0.409 |    78.16 |    5.927 |  1387.58 |
+|  8192 |     32 |    2 |  16448 |   11.008 |  1488.43 |    0.622 |   102.92 |   11.629 |  1414.34 |
+|  8192 |     32 |    4 |  32896 |   22.002 |  1489.29 |    0.987 |   129.66 |   22.990 |  1430.90 |
+|  8192 |     32 |    8 |  65792 |   46.051 |  1423.11 |    1.858 |   137.79 |   47.909 |  1373.27 |
+|  8192 |     32 |   16 | 131584 |   97.680 |  1341.85 |    2.872 |   178.28 |  100.552 |  1308.62 |
+|  8192 |     32 |   32 | 263168 |  176.407 |  1486.02 |    5.048 |   202.85 |  181.455 |  1450.32 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1648.69 ± 1.80 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         85.60 ± 0.52 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1429.86 ± 1.01 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         82.03 ± 0.12 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1257.90 ± 1.81 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         78.23 ± 0.33 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       1013.49 ± 0.70 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         73.20 ± 0.28 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        721.11 ± 0.58 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         65.52 ± 0.10 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.243 |  2109.23 |    0.419 |    76.34 |    0.662 |   821.84 |
+|   512 |     32 |    2 |   1088 |    0.406 |  2521.40 |    0.575 |   111.36 |    0.981 |  1109.27 |
+|   512 |     32 |    4 |   2176 |    0.744 |  2751.65 |    0.841 |   152.22 |    1.585 |  1372.71 |
+|   512 |     32 |    8 |   4352 |    1.479 |  2770.20 |    1.330 |   192.48 |    2.809 |  1549.53 |
+|   512 |     32 |   16 |   8704 |    2.951 |  2776.20 |    2.572 |   199.05 |    5.523 |  1575.93 |
+|   512 |     32 |   32 |  17408 |    5.899 |  2777.64 |    2.603 |   393.34 |    8.502 |  2047.54 |
+|  4096 |     32 |    1 |   4128 |    1.901 |  2154.15 |    0.474 |    67.58 |    2.375 |  1738.14 |
+|  4096 |     32 |    2 |   8256 |    3.788 |  2162.89 |    0.652 |    98.17 |    4.439 |  1859.69 |
+|  4096 |     32 |    4 |  16512 |    7.564 |  2166.18 |    0.990 |   129.24 |    8.554 |  1930.34 |
+|  4096 |     32 |    8 |  33024 |   15.121 |  2166.98 |    1.632 |   156.82 |   16.754 |  1971.12 |
+|  4096 |     32 |   16 |  66048 |   30.241 |  2167.09 |    3.166 |   161.72 |   33.407 |  1977.04 |
+|  4096 |     32 |   32 | 132096 |   60.474 |  2167.42 |    3.780 |   270.93 |   64.254 |  2055.86 |
+|  8192 |     32 |    1 |   8224 |    4.733 |  1730.92 |    0.483 |    66.29 |    5.215 |  1576.85 |
+|  8192 |     32 |    2 |  16448 |    9.459 |  1732.09 |    0.722 |    88.58 |   10.182 |  1615.46 |
+|  8192 |     32 |    4 |  32896 |   18.912 |  1732.65 |    1.120 |   114.26 |   20.032 |  1642.14 |
+|  8192 |     32 |    8 |  65792 |   37.797 |  1733.91 |    1.873 |   136.67 |   39.670 |  1658.49 |
+|  8192 |     32 |   16 | 131584 |   84.133 |  1557.92 |    3.718 |   137.72 |   87.850 |  1497.82 |
+|  8192 |     32 |   32 | 263168 |  157.550 |  1663.88 |    4.854 |   210.98 |  162.403 |  1620.46 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2453.11 ± 1.70 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         78.97 ± 0.46 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1569.46 ± 1.97 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         71.18 ± 0.37 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1145.51 ± 1.16 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         65.11 ± 0.36 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        741.04 ± 0.74 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         56.87 ± 0.14 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        431.31 ± 0.31 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         45.26 ± 0.11 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.339 |  1509.22 |    0.409 |    78.17 |    0.749 |   726.67 |
+|   512 |     32 |    2 |   1088 |    0.646 |  1584.93 |    0.483 |   132.45 |    1.129 |   963.45 |
+|   512 |     32 |    4 |   2176 |    1.258 |  1627.50 |    0.585 |   218.67 |    1.844 |  1180.21 |
+|   512 |     32 |    8 |   4352 |    2.506 |  1634.41 |    1.005 |   254.83 |    3.511 |  1239.64 |
+|   512 |     32 |   16 |   8704 |    5.007 |  1635.99 |    1.595 |   321.07 |    6.602 |  1318.38 |
+|   512 |     32 |   32 |  17408 |   10.007 |  1637.19 |    1.676 |   611.12 |   11.683 |  1490.03 |
+|  4096 |     32 |    1 |   4128 |    2.730 |  1500.46 |    0.431 |    74.31 |    3.160 |  1306.12 |
+|  4096 |     32 |    2 |   8256 |    5.446 |  1504.33 |    0.524 |   122.04 |    5.970 |  1382.91 |
+|  4096 |     32 |    4 |  16512 |   10.875 |  1506.59 |    0.662 |   193.45 |   11.537 |  1431.28 |
+|  4096 |     32 |    8 |  33024 |   21.749 |  1506.61 |    1.158 |   221.11 |   22.907 |  1441.64 |
+|  4096 |     32 |   16 |  66048 |   43.477 |  1507.36 |    1.901 |   269.32 |   45.378 |  1455.49 |
+|  4096 |     32 |   32 | 132096 |   86.954 |  1507.37 |    2.325 |   440.42 |   89.279 |  1479.59 |
+|  8192 |     32 |    1 |   8224 |    5.940 |  1379.21 |    0.449 |    71.20 |    6.389 |  1287.20 |
+|  8192 |     32 |    2 |  16448 |   11.865 |  1380.84 |    0.559 |   114.59 |   12.424 |  1323.92 |
+|  8192 |     32 |    4 |  32896 |   23.723 |  1381.25 |    0.728 |   175.80 |   24.452 |  1345.35 |
+|  8192 |     32 |    8 |  65792 |   47.434 |  1381.63 |    1.279 |   200.09 |   48.713 |  1350.60 |
+|  8192 |     32 |   16 | 131584 |   94.864 |  1381.69 |    2.198 |   232.97 |   97.061 |  1355.68 |
+|  8192 |     32 |   32 | 263168 |  189.743 |  1381.57 |    3.052 |   335.50 |  192.795 |  1365.01 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1565.91 ± 0.86 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         79.68 ± 0.39 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1317.41 ± 1.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         74.70 ± 0.04 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1134.65 ± 0.76 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         71.31 ± 0.12 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        886.46 ± 0.78 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         65.93 ± 0.06 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        612.21 ± 0.30 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         56.83 ± 0.02 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/gemma-3-4b-it-qat-GGUF
+
+Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.186 |  2748.06 |    0.235 |   136.28 |    0.421 |  1291.78 |
+|   512 |     32 |    2 |   1088 |    0.342 |  2990.95 |    0.312 |   204.99 |    0.655 |  1662.15 |
+|   512 |     32 |    4 |   2176 |    0.662 |  3092.69 |    0.404 |   316.97 |    1.066 |  2041.21 |
+|   512 |     32 |    8 |   4352 |    1.317 |  3110.41 |    0.579 |   441.80 |    1.896 |  2294.97 |
+|   512 |     32 |   16 |   8704 |    2.625 |  3120.23 |    1.207 |   424.08 |    3.833 |  2270.93 |
+|   512 |     32 |   32 |  17408 |    5.242 |  3125.34 |    1.299 |   788.23 |    6.541 |  2661.19 |
+|  4096 |     32 |    1 |   4128 |    1.408 |  2909.90 |    0.296 |   108.07 |    1.704 |  2422.95 |
+|  4096 |     32 |    2 |   8256 |    2.793 |  2933.40 |    0.325 |   197.00 |    3.118 |  2648.25 |
+|  4096 |     32 |    4 |  16512 |    5.567 |  2943.22 |    0.440 |   291.07 |    6.006 |  2749.05 |
+|  4096 |     32 |    8 |  33024 |   11.114 |  2948.23 |    0.640 |   400.26 |   11.754 |  2809.59 |
+|  4096 |     32 |   16 |  66048 |   22.217 |  2949.76 |    1.327 |   385.83 |   23.544 |  2805.26 |
+|  4096 |     32 |   32 | 132096 |   44.420 |  2950.77 |    1.553 |   659.30 |   45.973 |  2873.36 |
+|  8192 |     32 |    1 |   8224 |    2.860 |  2864.58 |    0.250 |   127.90 |    3.110 |  2644.42 |
+|  8192 |     32 |    2 |  16448 |    5.702 |  2873.63 |    0.335 |   191.07 |    6.036 |  2724.77 |
+|  8192 |     32 |    4 |  32896 |   11.383 |  2878.69 |    0.456 |   280.72 |   11.839 |  2778.63 |
+|  8192 |     32 |    8 |  65792 |   22.750 |  2880.75 |    0.671 |   381.48 |   23.421 |  2809.14 |
+|  8192 |     32 |   16 | 131584 |   45.484 |  2881.74 |    1.406 |   364.04 |   46.890 |  2806.22 |
+|  8192 |     32 |   32 | 263168 |   90.956 |  2882.10 |    1.793 |   570.98 |   92.749 |  2837.41 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2923.59 ± 3.10 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |        134.28 ± 1.29 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       2748.21 ± 3.05 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |        133.11 ± 0.08 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       2641.45 ± 2.31 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |        125.85 ± 0.35 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       2446.20 ± 2.94 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |        125.00 ± 0.12 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |       2129.18 ± 7.43 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |        113.14 ± 0.10 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/GLM-4.7-Flash-GGUF
+
+Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.326 |  1568.69 |    0.522 |    61.28 |    0.849 |   641.09 |
+|   512 |     32 |    2 |   1088 |    0.528 |  1939.42 |    0.744 |    86.07 |    1.272 |   855.63 |
+|   512 |     32 |    4 |   2176 |    0.968 |  2114.85 |    1.105 |   115.85 |    2.073 |  1049.56 |
+|   512 |     32 |    8 |   4352 |    1.928 |  2124.62 |    1.684 |   151.99 |    3.612 |  1204.82 |
+|   512 |     32 |   16 |   8704 |    3.844 |  2131.34 |    3.141 |   162.99 |    6.985 |  1246.11 |
+|   512 |     32 |   32 |  17408 |    7.683 |  2132.38 |    3.924 |   260.95 |   11.608 |  1499.71 |
+|  4096 |     32 |    1 |   4128 |    3.280 |  1248.75 |    0.723 |    44.29 |    4.003 |  1031.33 |
+|  4096 |     32 |    2 |   8256 |    6.545 |  1251.63 |    0.930 |    68.85 |    7.475 |  1104.53 |
+|  4096 |     32 |    4 |  16512 |   13.080 |  1252.64 |    1.454 |    88.03 |   14.534 |  1136.12 |
+|  4096 |     32 |    8 |  33024 |   26.154 |  1252.90 |    2.388 |   107.20 |   28.542 |  1157.04 |
+|  4096 |     32 |   16 |  66048 |   52.297 |  1253.14 |    4.724 |   108.37 |   57.022 |  1158.30 |
+|  4096 |     32 |   32 | 132096 |  104.578 |  1253.34 |    7.266 |   140.93 |  111.844 |  1181.08 |
+|  8192 |     32 |    1 |   8224 |    9.623 |   851.31 |    0.767 |    41.72 |   10.390 |   791.54 |
+|  8192 |     32 |    2 |  16448 |   20.916 |   783.32 |    1.148 |    55.74 |   22.064 |   745.45 |
+|  8192 |     32 |    4 |  32896 |   43.509 |   753.14 |    1.833 |    69.82 |   45.342 |   725.51 |
+|  8192 |     32 |    8 |  65792 |   79.621 |   823.10 |    3.180 |    80.50 |   82.801 |   794.58 |
+|  8192 |     32 |   16 | 131584 |  153.770 |   852.39 |    6.502 |    78.74 |  160.272 |   821.00 |
+|  8192 |     32 |   32 | 263168 |  307.539 |   852.39 |   10.839 |    94.48 |  318.378 |   826.59 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1629.33 ± 0.27 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         59.58 ± 0.13 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |        732.67 ± 0.42 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         47.44 ± 0.15 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |        474.33 ± 0.33 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         40.20 ± 0.20 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        277.46 ± 0.09 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         31.50 ± 0.93 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        151.44 ± 0.05 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         21.81 ± 0.01 |
+
+build: b828e18c7 (7948)
diff --git a/scripts/bench-models.sh b/scripts/bench-models.sh
old mode 100644
new mode 100755
index 744b0de359..c241013040
--- a/scripts/bench-models.sh
+++ b/scripts/bench-models.sh
@@ -7,47 +7,54 @@ ARGS_BB="-c 270336 -npp 512,4096,8192 -npl 1,2,4,8,16,32 -ntg 32"
 ARGS_B="-d 0,4096,8192,16384,32768 -p 2048 -n 32"
 
 QUICK=0
+DIO=0
 while (( "$#" )); do
-  case "$1" in
-    --quick) QUICK=1; shift ;;
-    *) shift ;;
-  esac
+    case "$1" in
+        --quick) QUICK=1; shift ;;
+        --dio) DIO=1; shift ;;
+        *) shift ;;
+    esac
 done
 
 if (( QUICK )); then
-  ARGS_BB="-c 20480 -npp 512,4096 -npl 1,2,4 -ntg 32"
-  ARGS_B="-d 0 -p 2048 -n 32"
+    ARGS_BB="-c 20480 -npp 512,4096 -npl 1,2,4 -ntg 32"
+    ARGS_B="-d 0 -p 2048 -n 32"
+fi
+
+if (( DIO )); then
+    ARGS_BB="${ARGS_BB} --no-mmap --direct-io"
+    ARGS_B="${ARGS_B} -mmp 0 -dio 1"
 fi
 
 run_model() {
-  local HFR=$1
-  local HFF=$2
+    local HFR=$1
+    local HFF=$2
 
-  printf "## ${HFR}\n" | tee -a "$RESULTS"
-  printf "\n" | tee -a "$RESULTS"
-  printf "Model: https://huggingface.co/${HFR}\n" | tee -a "$RESULTS"
-  printf "\n" | tee -a "$RESULTS"
+    printf "## ${HFR}\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
+    printf "Model: https://huggingface.co/${HFR}\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
 
-  printf -- "- \`llama-batched-bench\`\n" | tee -a "$RESULTS"
-  printf "\n" | tee -a "$RESULTS"
+    printf -- "- \`llama-batched-bench\`\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
 
-  ./bin/llama-batched-bench \
-    -hfr "${HFR}" -hff "${HFF}" \
-    -m "${HFF}" -fa 1 -ub 2048 --no-mmap \
-    ${ARGS_BB} | tee -a "$RESULTS"
+    ./bin/llama-batched-bench \
+        -hfr "${HFR}" -hff "${HFF}" \
+        -m "${HFF}" -fa 1 -ub 2048 \
+        ${ARGS_BB} | tee -a "$RESULTS"
 
-  printf "\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
 
-  printf -- "- \`llama-bench\`\n" | tee -a "$RESULTS"
-  printf "\n" | tee -a "$RESULTS"
+    printf -- "- \`llama-bench\`\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
 
-  ./bin/llama-bench \
-    -m "${HFF}" -fa 1 -ub 2048 -mmp 0 \
-    ${ARGS_B} | tee -a "$RESULTS"
+    ./bin/llama-bench \
+        -m "${HFF}" -fa 1 -ub 2048 \
+        ${ARGS_B} | tee -a "$RESULTS"
 
-  printf "\n" | tee -a "$RESULTS"
+    printf "\n" | tee -a "$RESULTS"
 
-  printf "\n"
+    printf "\n"
 }
 
 run_model "ggml-org/gpt-oss-20b-GGUF"                       "gpt-oss-20b-mxfp4.gguf"
@@ -55,6 +62,7 @@ run_model "ggml-org/gpt-oss-120b-GGUF"                      "gpt-oss-120b-mxfp4-
 run_model "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF" "qwen3-coder-30b-a3b-instruct-q8_0.gguf"
 run_model "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"             "qwen2.5-coder-7b-q8_0.gguf"
 run_model "ggml-org/gemma-3-4b-it-qat-GGUF"                 "gemma-3-4b-it-qat-Q4_0.gguf"
+run_model "ggml-org/GLM-4.7-Flash-GGUF"                     "GLM-4.7-Flash-Q8_0.gguf"
 
 if [[ -f models-extra.txt ]]; then
     while read -r HFR HFF; do

From 449ec2ab0751fc713fe338da2ced153125b5c674 Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Thu, 5 Feb 2026 09:26:38 -0600
Subject: [PATCH 134/206] vulkan: Preprocess FA mask to detect all-neg-inf and
 all-zero. (#19281)

Write out a 2-bit code per block and avoid loading the mask when it
matches these two common cases.

Apply this optimization when the mask is relatively large (i.e. prompt
processing).
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 109 +++++++++++---
 .../vulkan-shaders/flash_attn.comp            |  39 ++---
 .../vulkan-shaders/flash_attn_base.glsl       |   6 +
 .../vulkan-shaders/flash_attn_cm1.comp        | 112 +++++++-------
 .../vulkan-shaders/flash_attn_cm2.comp        |  63 ++++----
 .../vulkan-shaders/flash_attn_mask_opt.comp   | 142 ++++++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |   2 +
 tests/test-backend-ops.cpp                    |  10 +-
 8 files changed, 360 insertions(+), 123 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ff9cb7355c..4357da24d4 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -402,18 +402,19 @@ enum FaCodePath {
 };
 
 struct vk_fa_pipeline_state {
-    vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc)
-        : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc) {}
+    vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc, bool use_mask_opt)
+        : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc), use_mask_opt(use_mask_opt) {}
 
     uint32_t HSK, HSV;
     bool small_rows, small_cache;
     FaCodePath path;
     bool aligned;
     bool f32acc;
+    bool use_mask_opt;
 
     bool operator<(const vk_fa_pipeline_state &b) const {
-        return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc) <
-               std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc);
+        return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, use_mask_opt) <
+               std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc, b.use_mask_opt);
     }
 };
 
@@ -820,6 +821,8 @@ struct vk_device_struct {
 
     std::map pipeline_flash_attn_f32_f16[GGML_TYPE_COUNT];
 
+    std::map, vk_pipeline> pipeline_fa_mask_opt;
+
     vk_pipeline pipeline_flash_attn_split_k_reduce;
     vk_pipeline pipeline_count_experts;
 
@@ -1549,6 +1552,18 @@ struct vk_op_flash_attn_split_k_reduce_push_constants {
     uint32_t sinks;
 };
 
+struct vk_op_flash_attn_mask_opt_push_constants {
+    uint32_t nem0;
+    uint32_t nem1;
+    uint32_t nem2;
+    uint32_t nbm1;
+    uint32_t nbm2;
+    uint32_t nbm3;
+    uint32_t nbd1;
+    uint32_t nbd2;
+    uint32_t nbd3;
+};
+
 // 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) {}
@@ -1757,6 +1772,7 @@ class vk_perf_logger {
                 " k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " <<
                 " v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " <<
                 " m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")";
+            *n_flops = 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3];
             return name.str();
         }
         if (node->op == GGML_OP_TOP_K) {
@@ -3177,7 +3193,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache)[0], 1, 1};
     };
 
-    auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) -> std::vector {
+    auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache, bool use_mask_opt) -> std::vector {
         // For large number of rows, 128 invocations seems to work best.
         // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we
         // can't use 256 for D==80.
@@ -3209,7 +3225,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // AMD prefers loading K directly from global memory
         const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 ? 1 : 0;
 
-        return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem};
+        return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem, use_mask_opt};
     };
 
 #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
@@ -3221,18 +3237,19 @@ static void ggml_vk_load_shaders(vk_device& device) {
             FaCodePath path = fa.first.path; \
             bool aligned = fa.first.aligned; \
             bool f32acc = fa.first.f32acc; \
+            bool use_mask_opt = fa.first.use_mask_opt; \
             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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,use_mask_opt), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,use_mask_opt), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,use_mask_opt), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,use_mask_opt), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
                     } \
                 } \
             } \
@@ -4028,6 +4045,11 @@ 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, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
 
+    for (auto &it : device->pipeline_fa_mask_opt) {
+        auto BrBc = it.first;
+        ggml_vk_create_pipeline(device, it.second, "fa_mask_opt", fa_mask_opt_len, fa_mask_opt_data, "main", 2, sizeof(vk_op_flash_attn_mask_opt_push_constants), {1, 1, 1}, {128, 128 / device->subgroup_size, BrBc.first, BrBc.second}, 1, true, true, device->subgroup_size);
+    }
+
     if (device->subgroup_clustered && device->subgroup_require_full_support) {
         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, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true);
     } else {
@@ -8400,8 +8422,6 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
     const uint32_t acctype = f32acc ? 4 : 2;
     const uint32_t f16vec4 = 8;
 
-    const uint32_t tmpsh = (Bc / MatBc) * sizeof(float);
-
     const uint32_t qstride = hsk_pad / 4 + 2;
     const uint32_t Qf = Br * qstride * f16vec4;
 
@@ -8418,7 +8438,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
 
     const uint32_t slope = Br * acctype;
 
-    const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + slope;
+    const uint32_t total_size = Qf + Psh + sfsh + ksh + slope;
     const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
 
     VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", kv_type=" << kv_type << ", total_size=" << total_size << ", supported=" << supported);
@@ -8445,6 +8465,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
     GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
 
+    const uint32_t nem0 = mask ? mask->ne[0] : 0;
     const uint32_t nem1 = mask ? mask->ne[1] : 0;
     const uint32_t nem2 = mask ? mask->ne[2] : 0;
     const uint32_t nem3 = mask ? mask->ne[3] : 0;
@@ -8574,7 +8595,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
 
-    vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc);
+    // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
+    bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768;
+
+    vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, use_mask_opt);
 
     vk_pipeline pipeline = nullptr;
 
@@ -8625,10 +8649,32 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         ggml_vk_preallocate_buffers(ctx, subctx);
     }
 
-    {
-        // Request descriptor sets
-        if (split_k > 1) {
-            ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
+    auto rows_cols = fa_rows_cols(path, HSK, HSV, !aligned, k->type, small_rows, small_cache);
+    const uint32_t Br = rows_cols[0];
+    const uint32_t Bc = rows_cols[1];
+
+    const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc);
+    const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3;
+
+    vk_pipeline pipeline_fa_mask_opt = nullptr;
+    if (use_mask_opt) {
+        std::lock_guard guard(ctx->device->mutex);
+        auto &pipelines = ctx->device->pipeline_fa_mask_opt;
+        auto it = pipelines.find({Br, Bc});
+        if (it != pipelines.end()) {
+            pipeline_fa_mask_opt = it->second;
+        } else {
+            pipelines[{Br, Bc}] = pipeline_fa_mask_opt = std::make_shared();
+        }
+        assert(pipeline_fa_mask_opt);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline_fa_mask_opt, 1);
+
+        if (ctx->prealloc_size_y < mask_opt_size) {
+            ctx->prealloc_size_y = mask_opt_size;
+            ggml_vk_preallocate_buffers(ctx, subctx);
+        }
+        if (ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
         }
     }
 
@@ -8655,9 +8701,30 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
     vk_subbuffer mask_buf = mask ? ggml_vk_tensor_subbuffer(ctx, mask) : q_buf;
     vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf;
+    vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf;
 
     uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2;
 
+    if (use_mask_opt)
+    {
+        const vk_op_flash_attn_mask_opt_push_constants opt_pc = {
+            nem0,
+            nem1,
+            nem2,
+            (uint32_t)(mask->nb[1] / sizeof(ggml_fp16_t)),
+            (uint32_t)(mask->nb[2] / sizeof(ggml_fp16_t)),
+            (uint32_t)(mask->nb[3] / sizeof(ggml_fp16_t)),
+            mask_opt_num_dwords,
+            mask_opt_num_dwords * CEIL_DIV(nem1, Br),
+            mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2,
+        };
+
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline_fa_mask_opt,
+                                  { mask_buf, mask_opt_buf }, opt_pc,
+                                  { mask_opt_num_dwords, CEIL_DIV(nem1, Br), nem2 * nem3 });
+        ggml_vk_sync_buffers(ctx, subctx);
+    }
+
     const vk_flash_attn_push_constants pc = { N, KV,
                                               (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
                                               (uint32_t)neq2, (uint32_t)neq3,
@@ -8672,13 +8739,15 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
                                               gqa_ratio, split_kv, split_k };
 
     if (split_k > 1) {
+        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
+
         if (ctx->prealloc_split_k_need_sync) {
             ggml_vk_sync_buffers(ctx, subctx);
         }
         workgroups_x *= pipeline->wg_denoms[0];
         vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
-                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf},
+                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf},
                                     // We only use split_k when group query attention is enabled, which means
                                     // there's no more than one tile of rows (i.e. workgroups_x would have been
                                     // one). We reuse workgroups_x to mean the number of splits, so we need to
@@ -8697,7 +8766,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
             workgroups_x *= pipeline->wg_denoms[0];
         }
         ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
-                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf},
+                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf, mask_opt_buf},
                                     pc, { workgroups_x, workgroups_y, workgroups_z });
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 3ce8d07be8..49a3c530cb 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -94,6 +94,10 @@ void main() {
         }
     }
 
+    const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
+    // mo_offset will point to the tile starting at row i*Br and col 0
+    uint32_t mo_offset = mo_stride * i;
+
 #if BLOCK_SIZE > 1
     uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
     uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
@@ -104,15 +108,28 @@ void main() {
     uint32_t m_offset = gqa_iq1*KV;
     if (p.nem2 != 1 || p.nem3 != 1) {
         m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
+        mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
     }
 
+    uint32_t mask_opt = 0;
+    uint32_t mask_opt_idx = ~0;
+
     [[dont_unroll]]
     for (uint32_t j = start_j; j < end_j; ++j) {
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if (USE_MASK_OPT && mask_opt_idx != j / 16) {
+            mask_opt_idx = j / 16;
+            mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
+        }
+        uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
+        if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
+            // skip this block
+            continue;
+        }
+        // Only load if the block is not all zeros
+        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0 && mask_opt_bits != MASK_OPT_ALL_ZERO) {
             bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
 
-            float max_mask = NEG_FLT_MAX_OVER_2;
             [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
                 uint32_t c = (idx + tid) % Bc;
                 uint32_t r = (idx + tid) / Bc;
@@ -120,25 +137,12 @@ void main() {
                     if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) {
                         float m = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]);
                         masksh[c][r] = m;
-                        max_mask = max(max_mask, m);
                     } else {
                         masksh[c][r] = float(0);
                     }
                 }
             }
-            // skip the block if the mask is entirely -inf
-            bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
             barrier();
-            if (gl_SubgroupInvocationID == 0) {
-                tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
-            }
-            barrier();
-            [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
-                max_mask = max(max_mask, tmpsh[s]);
-            }
-            if (max_mask <= NEG_FLT_MAX_OVER_2) {
-                continue;
-            }
         }
 
         float Sf[Br][cols_per_thread];
@@ -185,7 +189,7 @@ void main() {
             }
         }
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0 && mask_opt_bits != MASK_OPT_ALL_ZERO) {
             [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
                 [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
                     float mvf = masksh[c * cols_per_iter + col_tid][r];
@@ -256,9 +260,6 @@ void main() {
         barrier();
     }
 
-    // prevent race on tmpsh
-    barrier();
-
     // reduce across threads
 
     [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
index 23a4d2c005..252451101a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
@@ -10,6 +10,7 @@ layout (constant_id = 5) const uint32_t Clamp = 0;
 layout (constant_id = 6) const uint32_t D_split = 16;
 layout (constant_id = 7) const uint32_t SubGroupSize = 32;
 layout (constant_id = 8) const uint32_t K_LOAD_SHMEM = 0;
+layout (constant_id = 9) const bool     USE_MASK_OPT = false;
 
 // Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths
 const uint32_t HSK_pad = (HSK + 15) & ~15;
@@ -66,6 +67,11 @@ layout (binding = 4) readonly buffer S {float data_s[];};
 
 layout (binding = 5) writeonly buffer O {D_TYPE data_o[];};
 
+layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];};
+
+#define MASK_OPT_ALL_NEG_INF 1
+#define MASK_OPT_ALL_ZERO 2
+
 #define BINDING_IDX_K 0
 #define BINDING_IDX_V 1
 #if defined(DATA_A_F32)
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 83d52d19d6..89af3697e1 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -42,8 +42,6 @@ D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TY
     return elem;
 }
 
-shared float tmpsh[row_split];
-
 const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4
 shared f16vec4 Qf[Br * qstride];
 
@@ -134,6 +132,10 @@ void main() {
         }
     }
 
+    const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
+    // mo_offset will point to the tile starting at row i*Br and col 0
+    uint32_t mo_offset = mo_stride * i;
+
 #if BLOCK_SIZE > 1
     uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
     uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
@@ -144,66 +146,74 @@ void main() {
     uint32_t m_offset = gqa_iq1*KV;
     if (p.nem2 != 1 || p.nem3 != 1) {
         m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
+        mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
     }
 
+    uint32_t mask_opt = 0;
+    uint32_t mask_opt_idx = ~0;
+
     [[dont_unroll]]
     for (uint32_t j = start_j; j < end_j; ++j) {
 
         f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize];
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
-            bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
+        [[unroll]] for (uint32_t idx = 0; idx < mask_cache.length(); ++idx) {
+            mask_cache[idx] = f16vec4(0);
+        }
 
-            float max_mask = NEG_FLT_MAX_OVER_2;
-            [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
-                uint32_t c = (idx + tid) / (Br / 4);
-                uint32_t r = (idx + tid) % (Br / 4);
-                if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) {
-                    if ((!KV_bounds_check || j * Bc + c < KV)) {
-                        f16vec4 m;
-                        if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) {
-                            m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]);
-                            max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3]));
-                        } else if (i * Br + r * 4 + 2 < p.nem1) {
-                            m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
-                                        0.0);
-                            max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2]));
-                        } else if (i * Br + r * 4 + 1 < p.nem1) {
-                            m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
-                                        data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
-                                        0.0,
-                                        0.0);
-                            max_mask = max(max(max_mask, float(m[0])), float(m[1]));
-                        } else if (i * Br + r * 4 < p.nem1) {
-                            m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
-                                        0.0,
-                                        0.0,
-                                        0.0);
-                            max_mask = max(max_mask, float(m[0]));
-                        } else {
-                            m = f16vec4(0.0);
+        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+
+            if (USE_MASK_OPT && mask_opt_idx != j / 16) {
+                mask_opt_idx = j / 16;
+                mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
+            }
+            uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
+            if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
+                // skip this block
+                continue;
+            }
+            // Only load if the block is not all zeros
+            if (mask_opt_bits != MASK_OPT_ALL_ZERO) {
+                bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
+
+                float max_mask = NEG_FLT_MAX_OVER_2;
+                [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
+                    uint32_t c = (idx + tid) / (Br / 4);
+                    uint32_t r = (idx + tid) % (Br / 4);
+                    if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) {
+                        if ((!KV_bounds_check || j * Bc + c < KV)) {
+                            f16vec4 m;
+                            if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) {
+                                m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]);
+                                max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3]));
+                            } else if (i * Br + r * 4 + 2 < p.nem1) {
+                                m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
+                                            0.0);
+                                max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2]));
+                            } else if (i * Br + r * 4 + 1 < p.nem1) {
+                                m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
+                                            data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
+                                            0.0,
+                                            0.0);
+                                max_mask = max(max(max_mask, float(m[0])), float(m[1]));
+                            } else if (i * Br + r * 4 < p.nem1) {
+                                m = f16vec4(data_m[m_offset + (i * Br + r * 4    ) * m_stride + (j * Bc + c)],
+                                            0.0,
+                                            0.0,
+                                            0.0);
+                                max_mask = max(max_mask, float(m[0]));
+                            } else {
+                                m = f16vec4(0.0);
+                            }
+                            mask_cache[idx / WorkGroupSize] = m;
                         }
-                        mask_cache[idx / WorkGroupSize] = m;
                     }
                 }
             }
-            // skip the block if the mask is entirely -inf
-            bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
-            barrier();
-            if (gl_SubgroupInvocationID == 0) {
-                tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
-            }
-            barrier();
-            [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
-                max_mask = max(max_mask, tmpsh[s]);
-            }
-            if (max_mask <= NEG_FLT_MAX_OVER_2) {
-                continue;
-            }
         }
 
         if (K_LOAD_SHMEM != 0) {
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 54f1b0b622..47b110621b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -138,48 +138,53 @@ void main() {
         coopMatPerElementNV(slopeMat, slopeMat, perElemOpComputeSlope, iq2);
     }
 
+    const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
+    // mo_offset will point to the tile starting at row i*Br and col 0
+    uint32_t mo_offset = mo_stride * i;
+
     uint32_t m_offset = gqa_iq1*KV * 2 /*sizeof(float16_t)*/;
     if (p.nem2 != 1 || p.nem3 != 1) {
         m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV * 2 /*sizeof(float16_t)*/;
+        mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
     }
 
+    uint32_t mask_opt = 0;
+    uint32_t mask_opt_idx = ~0;
+
     [[dont_unroll]]
     for (uint32_t j = start_j; j < end_j; ++j) {
 
-        coopmat mv;
+        coopmat mv = coopmat(0);
         if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
-            bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
 
-            if (nem1_bounds_check) {
-                tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
-                tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
-                tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
-                tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
+            if (USE_MASK_OPT && mask_opt_idx != j / 16) {
+                mask_opt_idx = j / 16;
+                mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
+            }
+            uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
+            if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
+                // skip this block
+                continue;
+            }
+            // Only load if the block is not all zeros
+            if (mask_opt_bits != MASK_OPT_ALL_ZERO) {
+                bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
 
-                coopmat mvmax;
+                if (nem1_bounds_check) {
+                    tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
+                    tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
+                    tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
+                    tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
 
-                coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
+                    coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
+                } else {
+                    tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
+                    // Don't clamp against nem1 when GQA is enabled
+                    uint32_t m_height = p.gqa_ratio > 1 ? ~0 : p.nem1;
+                    tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV);
+                    tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
 
-                // skip the block if the mask is entirely -inf
-                coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
-                if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
-                    continue;
-                }
-            } else {
-                tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
-                // Don't clamp against nem1 when GQA is enabled
-                uint32_t m_height = p.gqa_ratio > 1 ? ~0 : p.nem1;
-                tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV);
-                tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
-
-                coopmat mvmax;
-
-                coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
-
-                // skip the block if the mask is entirely -inf
-                coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
-                if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
-                    continue;
+                    coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
                 }
             }
         }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp
new file mode 100644
index 0000000000..8c92c1adcd
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp
@@ -0,0 +1,142 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : enable
+#extension GL_KHR_shader_subgroup_arithmetic : enable
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 128;
+layout (constant_id = 1) const uint NUM_SUBGROUPS = 4;
+layout (constant_id = 2) const uint Br = 32;
+layout (constant_id = 3) const uint Bc = 32;
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float16_t data_a[];};
+layout (binding = 0) readonly buffer Av4 {f16vec4 data_av4[];};
+layout (binding = 1) writeonly buffer D {uint data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint nem0;
+    uint nem1;
+    uint nem2;
+    uint nbm1;
+    uint nbm2;
+    uint nbm3;
+    uint nbd1;
+    uint nbd2;
+    uint nbd3;
+};
+
+#define MASK_OPT_ALL_NEG_INF 1
+#define MASK_OPT_ALL_ZERO 2
+
+shared float minsh[NUM_SUBGROUPS];
+shared float maxsh[NUM_SUBGROUPS];
+
+// For each Br x Bc block of the mask (input) buffer, read all values and check
+// if it's all -inf or all zero. Write out a two-bit code indicating which it is
+// (or zero for neither). Each workgroup processes 16 tiles and writes out a
+// 32-bit result mask.
+//
+// TODO: This is a lot of work per workgroup, might make sense to split this into
+// more workgroups in the future.
+void main() {
+    // Each workgroup handles a row
+    const uint tid = gl_LocalInvocationIndex;
+    const uint i0 = gl_WorkGroupID.x;
+    const uint i1 = gl_WorkGroupID.y;
+    const uint i2 = gl_WorkGroupID.z % nem2;
+    const uint i3 = gl_WorkGroupID.z / nem2;
+
+    float FLT_MAX_OVER_2 = uintBitsToFloat(0x7EFFFFFF);
+
+    uint result = 0;
+
+    // Fast path for fully in-bounds blocks where we can do f16vec4 loads
+    if ((nem0 % Bc) == 0 && (nem1 % Br) == 0 &&
+        ((Br * Bc) % (BLOCK_SIZE * 4)) == 0) {
+        [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) {
+            float min_v = FLT_MAX_OVER_2;
+            float max_v = -FLT_MAX_OVER_2;
+            [[unroll]] for (uint i = 0; i < Br * Bc / 4; i += BLOCK_SIZE) {
+                uint j0 = (i + tid) % (Bc / 4);
+                uint j1 = (i + tid) / (Bc / 4);
+
+                j0 *= 4;
+                j0 += (i0 * 16 + block_x) * Bc;
+                j1 += i1 * Br;
+
+                vec4 f = vec4(data_av4[(j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3) / 4]);
+                [[unroll]] for (int c = 0; c < 4; ++c) {
+                    min_v = min(min_v, f[c]);
+                    max_v = max(max_v, f[c]);
+                }
+            }
+            min_v = subgroupMin(min_v);
+            max_v = subgroupMax(max_v);
+            if (gl_SubgroupInvocationID == 0) {
+                minsh[gl_SubgroupID] = min_v;
+                maxsh[gl_SubgroupID] = max_v;
+            }
+            barrier();
+            if (tid == 0) {
+                [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) {
+                    min_v = min(min_v, minsh[i]);
+                    max_v = max(max_v, maxsh[i]);
+                }
+                if (max_v <= -FLT_MAX_OVER_2) {
+                    result |= 1 << (2*block_x);
+                }
+                if (min_v == 0.0f && max_v == 0.0f) {
+                    result |= 2 << (2*block_x);
+                }
+            }
+            barrier();
+        }
+    } else {
+        [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) {
+            float min_v = FLT_MAX_OVER_2;
+            float max_v = -FLT_MAX_OVER_2;
+            [[unroll]] for (uint i = 0; i < Br * Bc; i += BLOCK_SIZE) {
+                if ((Br * Bc % BLOCK_SIZE) != 0 && i + tid >= Br * Bc) {
+                    continue;
+                }
+                uint j0 = (i + tid) % Bc;
+                uint j1 = (i + tid) / Bc;
+
+                j0 += (i0 * 16 + block_x) * Bc;
+                j1 += i1 * Br;
+
+                if (j0 < nem0 && j1 < nem1) {
+                    float f = float(data_a[j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3]);
+                    min_v = min(min_v, f);
+                    max_v = max(max_v, f);
+                }
+            }
+            min_v = subgroupMin(min_v);
+            max_v = subgroupMax(max_v);
+            if (gl_SubgroupInvocationID == 0) {
+                minsh[gl_SubgroupID] = min_v;
+                maxsh[gl_SubgroupID] = max_v;
+            }
+            barrier();
+            if (tid == 0) {
+                [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) {
+                    min_v = min(min_v, minsh[i]);
+                    max_v = max(max_v, maxsh[i]);
+                }
+                if (max_v <= -FLT_MAX_OVER_2) {
+                    result |= 1 << (2*block_x);
+                }
+                if (min_v == 0.0f && max_v == 0.0f) {
+                    result |= 2 << (2*block_x);
+                }
+            }
+            barrier();
+        }
+    }
+
+    if (tid == 0) {
+        data_d[i0 + i1 * nbd1 + i2 * nbd2 + i3 * nbd3] = result;
+    }
+}
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 ca486a288a..42ebc21e2a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -790,6 +790,8 @@ 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("fa_mask_opt", "flash_attn_mask_opt.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"}});
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index cecdf47038..fbe23037cc 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -169,20 +169,22 @@ static void init_tensor_kq_mask(ggml_tensor * tensor, float min = -1.0f, float m
     const int blck0 = 128;
     const int blck1 = 64;
 
-    // number of INF blocks
-    const int n_inf_blocks = 0.1*(ne0*ne1*ne2*ne3)/(blck0*blck1);
+    // number of INF/zero blocks
+    const int n_inf_zero_blocks = 0.2*(ne0*ne1*ne2*ne3)/(blck0*blck1);
 
-    for (int b = 0; b < n_inf_blocks; b++) {
+    for (int b = 0; b < n_inf_zero_blocks; b++) {
         const int p3 = (rd() % ne3);
         const int p2 = (rd() % ne2);
         const int p1 = (rd() % ne1);
         const int p0 = (rd() % ne0);
 
+        bool inf = rd() & 1;
+
         for (int i1 = 0; i1 < blck1 && p1 + i1 < ne1; i1++) {
             const int idx = p3*ne2*ne1*ne0 + p2*ne1*ne0 + (p1 + i1)*ne0 + p0;
 
             for (int i0 = 0; i0 < blck0 && p0 + i0 < ne0; i0++) {
-                data_f32[idx + i0] = -INFINITY;
+                data_f32[idx + i0] = inf ? -INFINITY : 0.0f;
             }
         }
     }

From 22cae832188a1f08d18bd0a707a4ba5cd03c7349 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Thu, 5 Feb 2026 19:07:22 +0200
Subject: [PATCH 135/206] metal : adaptive CPU/GPU interleave based on number
 of nodes (#19369)

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

diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m
index a412d70aed..c7e8ebd3f3 100644
--- a/ggml/src/ggml-metal/ggml-metal-context.m
+++ b/ggml/src/ggml-metal/ggml-metal-context.m
@@ -415,7 +415,7 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
 
 enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) {
     // number of nodes encoded by the main thread (empirically determined)
-    const int n_main = 64;
+    const int n_main = MAX(64, 0.1*gf->n_nodes);
 
     // number of threads in addition to the main thread
     const int n_cb = ctx->n_cb;

From 3e2164766603eca8531f7a06af284811e7457788 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Fri, 6 Feb 2026 07:55:06 +0200
Subject: [PATCH 136/206] cuda : cuda graphs now compare all node params
 (#19383)

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

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index eeb8625dbe..9e77c231c8 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2979,8 +2979,7 @@ static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_
         }
     }
 
-    if ((node->op == GGML_OP_SCALE || node->op == GGML_OP_GLU) &&
-        memcmp(props->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
+    if (memcmp(props->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
         return false;
     }
 

From e696cfc0168ba9616a8bd7d09d6284a1b0fec82b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Fri, 6 Feb 2026 07:26:54 +0100
Subject: [PATCH 137/206] llama : rename llama-sampling to llama-sampler
 (#19363)

This commit addresses the TODO in llama-sampling.h to rename that header
and the implementation to llama-sampler.
---
 src/CMakeLists.txt                            | 2 +-
 src/llama-grammar.cpp                         | 2 +-
 src/{llama-sampling.cpp => llama-sampler.cpp} | 2 +-
 src/{llama-sampling.h => llama-sampler.h}     | 2 --
 4 files changed, 3 insertions(+), 5 deletions(-)
 rename src/{llama-sampling.cpp => llama-sampler.cpp} (99%)
 rename src/{llama-sampling.h => llama-sampler.h} (92%)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index f337afd6b3..bedfa1bc3d 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -31,7 +31,7 @@ add_library(llama
             llama-model-saver.cpp
             llama-model.cpp
             llama-quant.cpp
-            llama-sampling.cpp
+            llama-sampler.cpp
             llama-vocab.cpp
             unicode-data.cpp
             unicode.cpp
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index 64ea2fd00a..2d55070cec 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -2,7 +2,7 @@
 
 #include "llama-impl.h"
 #include "llama-vocab.h"
-#include "llama-sampling.h"
+#include "llama-sampler.h"
 
 #include 
 #include 
diff --git a/src/llama-sampling.cpp b/src/llama-sampler.cpp
similarity index 99%
rename from src/llama-sampling.cpp
rename to src/llama-sampler.cpp
index 515d6c163b..9bbc5dbde2 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampler.cpp
@@ -1,4 +1,4 @@
-#include "llama-sampling.h"
+#include "llama-sampler.h"
 
 #include "llama-impl.h"
 #include "llama-vocab.h"
diff --git a/src/llama-sampling.h b/src/llama-sampler.h
similarity index 92%
rename from src/llama-sampling.h
rename to src/llama-sampler.h
index 6a963c0bb7..b9bfc20d25 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampler.h
@@ -1,7 +1,5 @@
 #pragma once
 
-// TODO: rename llama-sampling.h/.cpp to llama-sampler.h/.cpp ?
-
 #include "llama.h"
 
 #include 

From 7fcf1ef45d37f7af07f23407e1979be679532959 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Fri, 6 Feb 2026 09:25:11 +0200
Subject: [PATCH 138/206] metal : skip loading all-zero mask (#19337)

* metal : skip loading all-zero mask

* cont : minor
---
 ggml/src/ggml-metal/ggml-metal.metal | 63 +++++++++++++++++-----------
 1 file changed, 39 insertions(+), 24 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index e54cdab39d..612a42a1ea 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -5285,6 +5285,7 @@ constant int32_t FC_flash_attn_ext_blk_ncpsg [[function_constant(FC_FLASH_ATTN_E
 // scan the blocks of the mask that are not masked
 // 0 -     masked (i.e. full of -INF, skip)
 // 1 - not masked (i.e. at least one element of the mask is not -INF)
+// 2 - all zero
 kernel void kernel_flash_attn_ext_blk(
         constant ggml_metal_kargs_flash_attn_ext_blk & args,
         device const char * mask,
@@ -5306,27 +5307,29 @@ kernel void kernel_flash_attn_ext_blk(
 
     device const half * mask_src = (device const half *) (mask + (i1*Q)*args.nb31 + i2*args.nb32 + i3*args.nb33) + i0*C + tiisg;
 
-    // fast route
-    if (res == 0) {
-        if (simd_max(*mask_src) > -MAXHALF/2) {
-            res = 1;
-        }
-    }
-
     // detailed check of the elements of the block
     if ((C > NW || Q > 1) && res == 0) {
-        half m = -MAXHALF;
+        half mmin =  MAXHALF;
+        half mmax = -MAXHALF;
 
         FOR_UNROLL (short j = 0; j < Q; ++j) {
             FOR_UNROLL (short ii = 0; ii < C/NW; ++ii) {
-                m = max(m, mask_src[ii*NW]);
+                mmin = min(mmin, mask_src[ii*NW]);
+                mmax = max(mmax, mask_src[ii*NW]);
             }
 
             mask_src += args.nb31/2;
         }
 
-        if (simd_max(m) > -MAXHALF/2) {
-            res = 1;
+        mmin = simd_min(mmin);
+        mmax = simd_max(mmax);
+
+        if (mmax > -MAXHALF) {
+            if (mmin == 0.0 && mmax == 0.0) {
+                res = 2;
+            } else {
+                res = 1;
+            }
         }
     }
 
@@ -5568,9 +5571,13 @@ void kernel_flash_attn_ext_impl(
                 ic = 0;
             }
 
+            char blk_cur = 1;
+
             // read the mask into shared mem
             if (FC_flash_attn_ext_has_mask) {
-                if (blk[ic0] == 0) {
+                blk_cur = blk[ic0];
+
+                if (blk_cur == 0) {
                     FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
                         pm2[jj] += NW;
                     }
@@ -5578,16 +5585,22 @@ void kernel_flash_attn_ext_impl(
                     continue;
                 }
 
-                FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
-                    const short j = jj*NSG + sgitg;
+                if (blk_cur == 1) {
+                    FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
+                        const short j = jj*NSG + sgitg;
 
-                    if (FC_flash_attn_ext_bc_mask) {
-                        sm2[j*SH + tiisg] = (iq1 + j) < args.ne31 ? pm2[jj][tiisg] : half2(-MAXHALF, -MAXHALF);
-                    } else {
-                        sm2[j*SH + tiisg] = pm2[jj][tiisg];
+                        if (FC_flash_attn_ext_bc_mask) {
+                            sm2[j*SH + tiisg] = (iq1 + j) < args.ne31 ? pm2[jj][tiisg] : half2(-MAXHALF, -MAXHALF);
+                        } else {
+                            sm2[j*SH + tiisg] = pm2[jj][tiisg];
+                        }
+
+                        pm2[jj] += NW;
+                    }
+                } else if (blk_cur == 2) {
+                    FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
+                        pm2[jj] += NW;
                     }
-
-                    pm2[jj] += NW;
                 }
 
 #if 0
@@ -5752,10 +5765,12 @@ void kernel_flash_attn_ext_impl(
                 }
 
                 // mqk = mqk + slope*mask
-                if (FC_flash_attn_ext_has_bias) {
-                    s2 += s2_t(sm2[j*SH + tiisg])*slope;
-                } else {
-                    s2 += s2_t(sm2[j*SH + tiisg]);
+                if (blk_cur != 2) {
+                    if (FC_flash_attn_ext_has_bias) {
+                        s2 += s2_t(sm2[j*SH + tiisg])*slope;
+                    } else {
+                        s2 += s2_t(sm2[j*SH + tiisg]);
+                    }
                 }
 
                 M[jj] = simd_max(max(M[jj], max(s2[0], s2[1])));

From f9bd518a6bac615e1060dcc44f3f302f9e7ae0e8 Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Fri, 6 Feb 2026 01:49:58 -0600
Subject: [PATCH 139/206] vulkan: make FA mask/softcap enables spec constants
 (#19309)

* vulkan: make FA mask/softcap enables spec constants

* don't specialize for sinks

* bump timeout a little bit
---
 .github/workflows/build.yml                   |  2 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 56 ++++++++++---------
 .../vulkan-shaders/flash_attn.comp            |  6 +-
 .../vulkan-shaders/flash_attn_base.glsl       |  7 ++-
 .../vulkan-shaders/flash_attn_cm1.comp        |  6 +-
 .../vulkan-shaders/flash_attn_cm2.comp        |  6 +-
 6 files changed, 45 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 8ce679bd9a..51a3dc76e9 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -468,7 +468,7 @@ jobs:
           export GGML_VK_VISIBLE_DEVICES=0
           export GGML_VK_DISABLE_F16=1
           # This is using llvmpipe and runs slower than other backends
-          ctest -L main --verbose --timeout 4200
+          ctest -L main --verbose --timeout 4800
 
   ubuntu-24-cmake-webgpu:
     runs-on: ubuntu-24.04
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 4357da24d4..72097ffd0f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -402,19 +402,19 @@ enum FaCodePath {
 };
 
 struct vk_fa_pipeline_state {
-    vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc, bool use_mask_opt)
-        : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc), use_mask_opt(use_mask_opt) {}
+    vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc, uint32_t flags)
+        : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc), flags(flags) {}
 
     uint32_t HSK, HSV;
     bool small_rows, small_cache;
     FaCodePath path;
     bool aligned;
     bool f32acc;
-    bool use_mask_opt;
+    uint32_t flags;
 
     bool operator<(const vk_fa_pipeline_state &b) const {
-        return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, use_mask_opt) <
-               std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc, b.use_mask_opt);
+        return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags) <
+               std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc, b.flags);
     }
 };
 
@@ -3193,7 +3193,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache)[0], 1, 1};
     };
 
-    auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache, bool use_mask_opt) -> std::vector {
+    auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache, uint32_t flags) -> std::vector {
         // For large number of rows, 128 invocations seems to work best.
         // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we
         // can't use 256 for D==80.
@@ -3225,7 +3225,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // AMD prefers loading K directly from global memory
         const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 ? 1 : 0;
 
-        return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem, use_mask_opt};
+        return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem, flags};
     };
 
 #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
@@ -3237,19 +3237,19 @@ static void ggml_vk_load_shaders(vk_device& device) {
             FaCodePath path = fa.first.path; \
             bool aligned = fa.first.aligned; \
             bool f32acc = fa.first.f32acc; \
-            bool use_mask_opt = fa.first.use_mask_opt; \
+            uint32_t flags = fa.first.flags; \
             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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,use_mask_opt), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,use_mask_opt), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,use_mask_opt), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,use_mask_opt), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
+                        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", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1,                                        true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0));     \
                     } \
                 } \
             } \
@@ -8595,10 +8595,26 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
 
+    float scale         = 1.0f;
+    float max_bias      = 0.0f;
+    float logit_softcap = 0.0f;
+
+    memcpy(&scale,         (const float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias,      (const float *) dst->op_params + 1, sizeof(float));
+    memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
+
+    if (logit_softcap != 0) {
+        scale /= logit_softcap;
+    }
+
     // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
     bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768;
 
-    vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, use_mask_opt);
+    uint32_t flags = (use_mask_opt       ? 1 : 0) |
+                     (mask != nullptr    ? 2 : 0) |
+                     (logit_softcap != 0 ? 4 : 0);
+
+    vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags);
 
     vk_pipeline pipeline = nullptr;
 
@@ -8678,18 +8694,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         }
     }
 
-    float scale         = 1.0f;
-    float max_bias      = 0.0f;
-    float logit_softcap = 0.0f;
-
-    memcpy(&scale,         (const float *) dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias,      (const float *) dst->op_params + 1, sizeof(float));
-    memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
-
-    if (logit_softcap != 0) {
-        scale /= logit_softcap;
-    }
-
     const uint32_t n_head_kv   = neq2;
     const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
@@ -8703,7 +8707,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf;
     vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf;
 
-    uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2;
+    uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | n_head_log2;
 
     if (use_mask_opt)
     {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 49a3c530cb..914f131c96 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -127,7 +127,7 @@ void main() {
             continue;
         }
         // Only load if the block is not all zeros
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0 && mask_opt_bits != MASK_OPT_ALL_ZERO) {
+        if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) {
             bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
 
             [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
@@ -181,7 +181,7 @@ void main() {
             }
         }
 
-        if (p.logit_softcap != 0.0f) {
+        if (LOGIT_SOFTCAP) {
             [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
                 [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
                     Sf[r][c] = p.logit_softcap * tanh(Sf[r][c]);
@@ -189,7 +189,7 @@ void main() {
             }
         }
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0 && mask_opt_bits != MASK_OPT_ALL_ZERO) {
+        if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) {
             [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
                 [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
                     float mvf = masksh[c * cols_per_iter + col_tid][r];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
index 252451101a..74005cffb3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
@@ -10,7 +10,11 @@ layout (constant_id = 5) const uint32_t Clamp = 0;
 layout (constant_id = 6) const uint32_t D_split = 16;
 layout (constant_id = 7) const uint32_t SubGroupSize = 32;
 layout (constant_id = 8) const uint32_t K_LOAD_SHMEM = 0;
-layout (constant_id = 9) const bool     USE_MASK_OPT = false;
+layout (constant_id = 9) const uint32_t Flags = 0;
+
+const bool USE_MASK_OPT  = (Flags & 1) != 0;
+const bool MASK_ENABLE   = (Flags & 2) != 0;
+const bool LOGIT_SOFTCAP = (Flags & 4) != 0;
 
 // Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths
 const uint32_t HSK_pad = (HSK + 15) & ~15;
@@ -60,7 +64,6 @@ layout (push_constant) uniform parameter {
 } p;
 
 #define SINK_ENABLE_BIT (1<<24)
-#define MASK_ENABLE_BIT (1<<16)
 #define N_LOG2_MASK 0xFFFF
 
 layout (binding = 4) readonly buffer S {float data_s[];};
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 89af3697e1..b317773823 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -160,7 +160,7 @@ void main() {
             mask_cache[idx] = f16vec4(0);
         }
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if (MASK_ENABLE) {
 
             if (USE_MASK_OPT && mask_opt_idx != j / 16) {
                 mask_opt_idx = j / 16;
@@ -303,7 +303,7 @@ void main() {
         coopMatStore(SfMat, sfsh, coord, sfshstride, gl_CooperativeMatrixLayoutRowMajor);
         barrier();
 
-        if (p.logit_softcap != 0.0f) {
+        if (LOGIT_SOFTCAP) {
             [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
                 uint32_t c = (idx + tid) / (Br / 4);
                 uint32_t r = (idx + tid) % (Br / 4);
@@ -314,7 +314,7 @@ void main() {
             barrier();
         }
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if (MASK_ENABLE) {
             [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
                 uint32_t c = (idx + tid) / (Br / 4);
                 uint32_t r = (idx + tid) % (Br / 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 47b110621b..b07c21f6e5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -155,7 +155,7 @@ void main() {
     for (uint32_t j = start_j; j < end_j; ++j) {
 
         coopmat mv = coopmat(0);
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if (MASK_ENABLE) {
 
             if (USE_MASK_OPT && mask_opt_idx != j / 16) {
                 mask_opt_idx = j / 16;
@@ -197,14 +197,14 @@ void main() {
         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) {
+        if (LOGIT_SOFTCAP) {
             [[unroll]]
             for (int k = 0; k < S.length(); ++k) {
                 S[k] = ACC_TYPE(p.logit_softcap)*tanh(S[k]);
             }
         }
 
-        if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
+        if (MASK_ENABLE) {
             S += slopeMat*coopmat(mv);
         }
 

From 1946e46f4c29da7b9294d702756969839e922bb8 Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Fri, 6 Feb 2026 02:15:13 -0600
Subject: [PATCH 140/206] vulkan: For coopmat2 FA, use fp16 accumulators for
 the final result (#19376)

The cpu and cuda backends use fp16 for the VKQ accumulator type, this change
does the same for vulkan. This helps particularly with large head sizes which
are very register-limited.

I tried this for the coopmat1 path and it slowed down a bit. I didn't try for
scalar.

I applied the softmax bias that the cuda backend uses to avoid overflow,
although I was not able to reproduce the original bug without it.
---
 .../vulkan-shaders/flash_attn_base.glsl       |  4 ++++
 .../vulkan-shaders/flash_attn_cm2.comp        | 20 +++++++++----------
 2 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
index 74005cffb3..4142c1e6ea 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
@@ -240,3 +240,7 @@ void init_indices()
     // and breaking the alignment detection.
     m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV;
 }
+
+// Bias applied to softmax to stay in fp16 range.
+// Based on ggml-cuda issue https://github.com/ggml-org/llama.cpp/issues/18606
+const float FATTN_KQ_MAX_OFFSET = 3.0f*0.6931f;
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 b07c21f6e5..39f0c4d23b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -117,7 +117,7 @@ void main() {
     Qf16 = coopmat(Q);
     Qf16 *= float16_t(p.scale);
 
-    coopmat O = coopmat(0);
+    coopmat O = coopmat(0);
 
     coopmat L, M;
 
@@ -223,6 +223,8 @@ void main() {
 
         coopMatReduceNV(rowmax, S, gl_CooperativeMatrixReduceRowNV, maxReduce);
 
+        rowmax += coopmat(FATTN_KQ_MAX_OFFSET);
+
         coopmat Mold = M;
 
         // M = max(rowmax, Mold)
@@ -265,11 +267,8 @@ void main() {
         // resize eM by using smear/reduce
         coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce);
 
-        // multiply with fp16 accumulation, then add to O.
-        coopmat PV = coopmat(0);
-        PV = coopMatMulAdd(P_A, V, PV);
-
-        O = eMdiag * O + coopmat(PV);
+        O *= coopmat(eMdiag);
+        O = coopMatMulAdd(P_A, V, O);
     }
 
     // If there is split_k, then the split_k resolve shader does the final
@@ -311,7 +310,7 @@ void main() {
             if (sink > Mr[i]) {
                 ms = exp(Mr[i] - sink);
 
-                O[i] *= ms;
+                O[i] *= float16_t(ms);
             } else {
                 vs = exp(sink - Mr[i]);
             }
@@ -325,15 +324,16 @@ void main() {
         Ldiag[k] = (Ldiag[k] == 0.0) ? ACC_TYPE(0.0) : (ACC_TYPE(1.0) / Ldiag[k]);
     }
 
-    O = Ldiag*O;
+    coopmat O_D = coopmat(O);
+
+    O_D = coopmat(Ldiag)*O_D;
 
 #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); }
+    [[unroll]] for (uint i = 0; i < O_D.length(); ++i) { O_D[i] = clamp(O_D[i], D_TYPE(-ACC_TYPE_MAX), D_TYPE(ACC_TYPE_MAX)); }
 #endif
 
     uint32_t o_offset = gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV;
 
-    coopmat O_D = coopmat(O);
     if (p.gqa_ratio > 1) {
         coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
     } else {

From 3688c4f504f8e336663157bcc6e0af78d617420c Mon Sep 17 00:00:00 2001
From: ymcki <84055651+ymcki@users.noreply.github.com>
Date: Fri, 6 Feb 2026 18:39:58 +0800
Subject: [PATCH 141/206] Kimi-Linear support (backend agnostic + MLA KV cache)
 (#18755)

* kimi linear model implementation

* kimi linear convert_hf_to_gguf

* kimi linear constants.py tensor_mapping.py

* Kimi Linear ggml.h

* kimi linear ggml-cpu

* Kimi Linear ggml-cuda

* Kimi Linear ggml.c

* kimi linear src/llama

* remove "const int64_t n_seq_tokens = q->ne[2];" to get rid of unused variable warning

* remove type mismatch warning

* read MoE params

* removed some hard coded code

* removed all hard code

* use DeepseekV2 tokenizer

* removed unnecessary internal methods called by the old set_vocab of KimiLinear

* rewrite get_vocab for KimiLinear. Removed all kda_scan code

* removed all traces of kda_scan

* reduce OP count by 1 due to removal of kda_scan

* Move KIMI_LINEAR to llm_arch_is_hybrid to enable KV cache

* set n_embd_head_k/v to ensure kv cache works

* don't quantize conv1d of Kimi Linear

* Kimi Linear backend agnostic

* removed LOG_INFO

* naive chunking form implemented

* fixed some comments

* add Kimi-K2 specific tokens to be recognized as EOG

* build_kda_autoregressive is implemented to replace build_kda_recurrent for faster inference. sync'd to b7682

* replaced Akk and Aqk with mul_mat and clamp

* no clamp version

* Moved Aqk computation out of the loop

* fixed typo and split wkv_b into wk_b and wv_b

* MLA KV cache support

* fix trailing spaces

* moved const llama_model & model; around to follow qwen3next format and see if it cna pass the -Wunused-private-field error

* fix trailing whitespace

* removed traling whitespaces in empty line + make sure indentation is multiple of 4

* try to make lint happy

* remove blank lines to make lint happy

* removed at least blank line containing white space

* fixed flake8 complaints locally

* return ggml_tensor * pair in kda_autoregressive and kda_chunking as in ngxson's Qwen3Next improvement

* removed Kimi-Linear specific change that causes failure at server-windows

* removed private: from kimi_linear to make build checks happy

* removed unnecessary ggml_cont before ggml_reshape

* created static function causal_conv1d to abtract similar code for q/k/v

* merged dt_bias to SSM_DT. Do -exp(log_A) in convert_hf_to_gguf.py.

* reverted to original

* fixed find_hparam calls. Fixed e_score_correction_bias to use bias instead of weight. Removed all ssm_conv bias terms.

* remove DT_B from constants.py. remove one comment line in llama-model.cpp

* new class llm_graph_input_mem_hybrid_k to get around the new MLA change. switch the concat order of ggml_concat calls in kimi-linear.cpp to accommodate MLA changes. Removed support for exp_probs_b.weight

* remove ssm_o_norm_b

* remove ssm_o_norm_b

* changed hparams.kda_head_dim to hparams.n_embd_head_kda. added TODO comment for class llama_graph_mem_hybrid_k

* removed all ggml_cont b4 ggml_reshape_4d

* Whitespace

* replaced all hparams.get with find_hparams

* added new names for n_experts, n_experts_used and score_func in TextModel and removed their code in KimiLinear in convert_hf_to_gguf.py. Removed unnecessary ggml_cont and GGML_ASSERT in kimi-linear.cpp

* use is_mla to switch between different mem_hybrid types

* fixed logical errors in convert_hf_to_gguf.py pointed out by CISC

* removed if else for required parameters kv_lora_rank and qk_rope_head_dim

* add back ggml_cont for Vcur

* minor changes

* removed extra line in llama-vocab.cpp. Added back the comment in llama-graph.cpp

* f16 gguf cannot run without context length

* made a mistake of adding back n_ctx parsing

---------

Co-authored-by: Piotr Wilkin (ilintar) 
---
 convert_hf_to_gguf.py          | 225 +++++++++-
 gguf-py/gguf/constants.py      |  65 +++
 gguf-py/gguf/gguf_writer.py    |   3 +
 gguf-py/gguf/tensor_mapping.py |  32 ++
 src/CMakeLists.txt             |   1 +
 src/llama-arch.cpp             |  70 +++
 src/llama-arch.h               |  12 +
 src/llama-context.cpp          |   2 +-
 src/llama-graph.cpp            |  55 +++
 src/llama-graph.h              |  29 ++
 src/llama-hparams.cpp          |  14 +
 src/llama-hparams.h            |   3 +
 src/llama-model.cpp            | 172 ++++++++
 src/llama-model.h              |  13 +
 src/llama-quant.cpp            |   4 +-
 src/llama-vocab.cpp            |  45 +-
 src/models/kimi-linear.cpp     | 772 +++++++++++++++++++++++++++++++++
 src/models/models.h            |  27 ++
 18 files changed, 1521 insertions(+), 23 deletions(-)
 create mode 100644 src/models/kimi-linear.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index eb43520f98..c167de8a46 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -586,6 +586,10 @@ class ModelBase:
                             gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
                             gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
                             gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
+                            # Kimi KDA conv weights should be F32
+                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
                         )
                     )
                     or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
@@ -903,10 +907,10 @@ class TextModel(ModelBase):
         if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
             self.gguf_writer.add_layer_norm_eps(f_norm_eps)
             logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
-        if (n_experts := self.hparams.get("num_local_experts")) is not None:
+        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
             self.gguf_writer.add_expert_count(n_experts)
             logger.info(f"gguf: expert count = {n_experts}")
-        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)) is not None:
             self.gguf_writer.add_expert_used_count(n_experts_used)
             logger.info(f"gguf: experts used count = {n_experts_used}")
         if (n_expert_groups := self.hparams.get("n_group")) is not None:
@@ -916,7 +920,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_expert_group_used_count(n_group_used)
             logger.info(f"gguf: expert groups used count = {n_group_used}")
 
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func"], optional=True)) is not None:
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation_func"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -5013,6 +5017,221 @@ class CodeShellModel(TextModel):
         self.gguf_writer.add_rope_scaling_factor(1.0)
 
 
+@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
+class KimiLinearModel(TextModel):
+    """Kimi-Linear model with hybrid MLA+KDA architecture"""
+    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+            return
+        except Exception:
+            pass
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        if tokpre == "kimi-k2":
+            # Build merges list using the approach similar to HunYuanMoE
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.model._mergeable_ranks
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+            # Build token list
+            vocab_size = self.hparams["vocab_size"]
+            special_tokens = tokenizer.special_tokens
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            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)
+            self.gguf_writer.add_token_merges(merges)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # override eos id in config.json with tiktoken eos id
+            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)
+        else:
+            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
+
+    def set_gguf_parameters(self):
+        # note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
+        self.hparams["num_key_value_heads"] = 1
+
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        # KDA & MLA params
+        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
+        linear_attn_config = self.hparams["linear_attn_config"]
+        # n_head == 0 for KDA layers, n_head > 0 for MLA layers
+        # full_attention_layers list will be used to distingush layer type
+        _num_kv_heads = list()
+        _full_attn_layers = linear_attn_config["full_attn_layers"]
+        for il in range(self.hparams["num_hidden_layers"]):
+            if il + 1 in _full_attn_layers:
+                _num_kv_heads.append(self.hparams["num_key_value_heads"])
+            else:
+                _num_kv_heads.append(0)
+        assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_head_count_kv(_num_kv_heads)
+
+        if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
+            self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
+        if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
+            self.gguf_writer.add_kda_head_dim(kda_head_dim)
+
+        # MLA params - use add_* methods that handle arch substitution
+        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
+        if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
+            self.gguf_writer.add_q_lora_rank(q_lora_rank)
+        # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
+        kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
+        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+
+        # MLA head dimensions
+        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
+        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
+        # Rotation - use qk_rope_head_dim for Kimi
+        qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
+        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
+        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
+        v_head_dim = self.hparams.get("v_head_dim")
+
+        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+        if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+        elif qk_nope_head_dim is not None:
+            n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+
+        # n_embd_head_v_mla = v_head_dim
+        if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
+            self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
+        elif v_head_dim is not None:
+            self.gguf_writer.add_value_length_mla(v_head_dim)
+
+        # moe_intermediate_size (1024 for Kimi)
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        # num_shared_experts (1 for Kimi)
+        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
+
+        # Handle KDA conv1d weights
+        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
+        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
+        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+        # Memory layouts match: both have conv_step (d_conv) changing fastest
+        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
+            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+            if data_torch.ndim == 2:
+                d_inner, d_conv = data_torch.shape
+                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+            elif data_torch.ndim == 3:
+                # Already 3D [d_inner, 1, d_conv] from unsqueeze
+                d_inner, _, d_conv = data_torch.shape
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+
+        # Kimi specific bias
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # Handle A_log: iHF stores as [1, 1, num_heads, 1]
+        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
+        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        if name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+            logger.info("Changed dt_bias to dt_proj.bias")
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=False)
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                # w1: gate, w2: down, w3: up
+                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
+                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
+                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
+                    datas: list[Tensor] = []
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+                    data_torch = torch.stack(datas, dim=0)
+                    new_name = self.format_tensor_name(tname, bid)
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+            return
+
+        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
+        if name.endswith("kv_b_proj.weight"):
+            name_kb = name.replace("kv_b_proj", "k_b_proj")
+            name_vb = name.replace("kv_b_proj", "v_b_proj")
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+            logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+            yield from super().modify_tensors(k_b, name_kb, bid)
+            yield from super().modify_tensors(v_b, name_vb, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("InternLM2ForCausalLM")
 class InternLM2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.INTERNLM2
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 6f56d36c59..3ddbc73d1c 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -207,6 +207,9 @@ class Keys:
         GROUP_COUNT    = "{arch}.ssm.group_count"
         DT_B_C_RMS     = "{arch}.ssm.dt_b_c_rms"
 
+    class KDA:
+        HEAD_DIM = "{arch}.kda.head_dim"
+
     class WKV:
         HEAD_SIZE = "{arch}.wkv.head_size"
 
@@ -461,6 +464,7 @@ class MODEL_ARCH(IntEnum):
     MIMO2            = auto()
     LLAMA_EMBED      = auto()
     MAINCODER        = auto()
+    KIMI_LINEAR      = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -551,6 +555,14 @@ class MODEL_TENSOR(IntEnum):
     SSM_NORM             = auto()
     SSM_OUT              = auto()
     SSM_BETA_ALPHA       = auto() # qwen3next
+    SSM_CONV1D_Q         = auto() # Kimi Linear
+    SSM_CONV1D_K         = auto() # Kimi Linear
+    SSM_CONV1D_V         = auto() # Kimi Linear
+    SSM_F_A              = auto() # Kimi Linear
+    SSM_F_B              = auto() # Kimi Linear
+    SSM_BETA             = auto() # Kimi Linear
+    SSM_G_A              = auto() # Kimi Linear
+    SSM_G_B              = auto() # Kimi Linear
     TIME_MIX_W0          = auto()
     TIME_MIX_W1          = auto()
     TIME_MIX_W2          = auto()
@@ -882,6 +894,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.MIMO2:            "mimo2",
     MODEL_ARCH.LLAMA_EMBED:      "llama-embed",
     MODEL_ARCH.MAINCODER:        "maincoder",
+    MODEL_ARCH.KIMI_LINEAR:      "kimi-linear",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -969,6 +982,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.SSM_NORM:                  "blk.{bid}.ssm_norm",
     MODEL_TENSOR.SSM_OUT:                   "blk.{bid}.ssm_out",
     MODEL_TENSOR.SSM_BETA_ALPHA:            "blk.{bid}.ssm_ba",
+    MODEL_TENSOR.SSM_CONV1D_Q:              "blk.{bid}.ssm_conv1d_q",         # Kimi Linear
+    MODEL_TENSOR.SSM_CONV1D_K:              "blk.{bid}.ssm_conv1d_k",         # Kimi Linear
+    MODEL_TENSOR.SSM_CONV1D_V:              "blk.{bid}.ssm_conv1d_v",         # Kimi Linear
+    MODEL_TENSOR.SSM_F_A:                   "blk.{bid}.ssm_f_a",              # Kimi Linear
+    MODEL_TENSOR.SSM_F_B:                   "blk.{bid}.ssm_f_b",              # Kimi Linear
+    MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear
+    MODEL_TENSOR.SSM_G_A:                   "blk.{bid}.ssm_g_a",              # Kimi Linear
+    MODEL_TENSOR.SSM_G_B:                   "blk.{bid}.ssm_g_b",              # Kimi Linear
     MODEL_TENSOR.TIME_MIX_W0:               "blk.{bid}.time_mix_w0",
     MODEL_TENSOR.TIME_MIX_W1:               "blk.{bid}.time_mix_w1",
     MODEL_TENSOR.TIME_MIX_W2:               "blk.{bid}.time_mix_w2",
@@ -3379,6 +3400,47 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.KIMI_LINEAR: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.SSM_CONV1D_Q,
+        MODEL_TENSOR.SSM_CONV1D_K,
+        MODEL_TENSOR.SSM_CONV1D_V,
+        MODEL_TENSOR.SSM_F_A,
+        MODEL_TENSOR.SSM_F_B,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_G_A,
+        MODEL_TENSOR.SSM_G_B,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     # TODO
 }
 
@@ -3706,6 +3768,9 @@ KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK
 KEY_SSM_GROUP_COUNT    = Keys.SSM.GROUP_COUNT
 KEY_SSM_DT_B_C_RMS     = Keys.SSM.DT_B_C_RMS
 
+# KDA
+KEY_KDA_HEAD_DIM       = Keys.KDA.HEAD_DIM
+
 # tokenization
 KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
 KEY_TOKENIZER_PRE        = Keys.Tokenizer.PRE
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 0b9c650161..f720aa2d54 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -980,6 +980,9 @@ class GGUFWriter:
     def add_ssm_dt_b_c_rms(self, value: bool) -> None:
         self.add_bool(Keys.SSM.DT_B_C_RMS.format(arch=self.arch), value)
 
+    def add_kda_head_dim(self, value: int) -> None:
+        self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value)
+
     def add_tokenizer_model(self, model: str) -> None:
         self.add_string(Keys.Tokenizer.MODEL, model)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 84aa868809..e16c06c2a3 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -438,6 +438,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.e_score_correction",       # minimax-m2
             "backbone.layers.{bid}.mixer.gate.e_score_correction",          # nemotron-h-moe
             "model.layers.{bid}.mlp.e_score_correction",                    # exaone-moe
+            "model.layers.{bid}.block_sparse_moe.gate.e_score_correction",  # kimi
         ),
 
         # Feed-forward up
@@ -502,6 +503,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.up_proj",             # hunyuan
             "layers.{bid}.shared_experts.w3",                        # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
+            "model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi
         ),
 
         MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -549,6 +551,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4
             "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
             "layers.{bid}.shared_experts.w1",                          # mistral-large
+            "model.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi
         ),
 
         MODEL_TENSOR.FFN_GATE_CHEXP: (
@@ -613,6 +616,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.down_proj",             # hunyuan
             "layers.{bid}.shared_experts.w2",                          # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
+            "model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi
         ),
 
         MODEL_TENSOR.FFN_DOWN_CHEXP: (
@@ -759,6 +763,7 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mixer.dt_proj",  # plamo2
             "model.layers.{bid}.linear_attn.dt_proj",   # qwen3next
             "backbone.layers.{bid}.mixer.dt",           # nemotron-h-moe
+            "model.layers.{bid}.self_attn.dt_proj",     # kimi
         ),
 
         MODEL_TENSOR.SSM_DT_NORM: (
@@ -772,6 +777,7 @@ class TensorNameMap:
             "model.layers.{bid}.mamba.A_log",         # jamba falcon-h1 granite-hybrid
             "model.layers.layers.{bid}.mixer.A_log",  # plamo2
             "model.layers.{bid}.linear_attn.A_log",   # qwen3next
+            "model.layers.{bid}.self_attn.A_log",     # kimi
         ),
 
         MODEL_TENSOR.SSM_B_NORM: (
@@ -797,6 +803,7 @@ class TensorNameMap:
             "model.layers.{bid}.mamba.norm",        # falcon-h1 granite-hybrid
             "model.layers.{bid}.linear_attn.norm",  # qwen3next
             "backbone.layers.{bid}.mixer.norm",     # mamba2
+            "model.layers.{bid}.self_attn.o_norm",  # kimi
         ),
 
         MODEL_TENSOR.SSM_OUT: (
@@ -811,6 +818,31 @@ class TensorNameMap:
             "model.layers.{bid}.linear_attn.in_proj_ba",  # qwen3next
         ),
 
+        # Kimi Linear KDA (using SSM_ prefix for consistency)
+        MODEL_TENSOR.SSM_CONV1D_Q: (
+            "model.layers.{bid}.self_attn.q_conv1d",
+        ),
+        MODEL_TENSOR.SSM_CONV1D_K: (
+            "model.layers.{bid}.self_attn.k_conv1d",
+        ),
+        MODEL_TENSOR.SSM_CONV1D_V: (
+            "model.layers.{bid}.self_attn.v_conv1d",
+        ),
+        MODEL_TENSOR.SSM_F_A: (
+            "model.layers.{bid}.self_attn.f_a_proj",
+        ),
+        MODEL_TENSOR.SSM_F_B: (
+            "model.layers.{bid}.self_attn.f_b_proj",
+        ),
+        MODEL_TENSOR.SSM_BETA: (
+            "model.layers.{bid}.self_attn.b_proj",
+        ),
+        MODEL_TENSOR.SSM_G_A: (
+            "model.layers.{bid}.self_attn.g_a_proj",
+        ),
+        MODEL_TENSOR.SSM_G_B: (
+            "model.layers.{bid}.self_attn.g_b_proj",
+        ),
         MODEL_TENSOR.TIME_MIX_W0: (
             "model.layers.{bid}.attention.w0",            # rwkv7
         ),
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index bedfa1bc3d..5238a5e934 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -84,6 +84,7 @@ add_library(llama
             models/internlm2.cpp
             models/jais.cpp
             models/jamba.cpp
+            models/kimi-linear.cpp
             models/lfm2.cpp
             models/llada-moe.cpp
             models/llada.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index a54bc1956a..a8bf1c9b80 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -120,6 +120,7 @@ static const std::map LLM_ARCH_NAMES = {
     { LLM_ARCH_MIMO2,            "mimo2"           },
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
+    { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -246,6 +247,8 @@ static const std::map LLM_KV_NAMES = {
     { LLM_KV_SSM_GROUP_COUNT,    "%s.ssm.group_count"    },
     { LLM_KV_SSM_DT_B_C_RMS,     "%s.ssm.dt_b_c_rms"     },
 
+    { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" },
+
     { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
 
     { LLM_KV_POSNET_EMBEDDING_LENGTH, "%s.posnet.embedding_length" },
@@ -371,6 +374,15 @@ static const std::map LLM_TENSOR_NAMES = {
     { LLM_TENSOR_SSM_DT_NORM,                            "blk.%d.ssm_dt_norm" },
     { LLM_TENSOR_SSM_B_NORM,                             "blk.%d.ssm_b_norm" },
     { LLM_TENSOR_SSM_C_NORM,                             "blk.%d.ssm_c_norm" },
+    { LLM_TENSOR_SSM_CONV1D_Q,                           "blk.%d.ssm_conv1d_q" },
+    { LLM_TENSOR_SSM_CONV1D_K,                           "blk.%d.ssm_conv1d_k" },
+    { LLM_TENSOR_SSM_CONV1D_V,                           "blk.%d.ssm_conv1d_v" },
+    { LLM_TENSOR_SSM_F_A,                                "blk.%d.ssm_f_a" },
+    { LLM_TENSOR_SSM_F_B,                                "blk.%d.ssm_f_b" },
+    { LLM_TENSOR_SSM_BETA,                               "blk.%d.ssm_beta" },
+    { LLM_TENSOR_SSM_G_A,                                "blk.%d.ssm_g_a" },
+    { LLM_TENSOR_SSM_G_B,                                "blk.%d.ssm_g_b" },
+    { LLM_TENSOR_SSM_NORM,                               "blk.%d.ssm_norm" },
     { LLM_TENSOR_ATTN_Q_A_NORM,                          "blk.%d.attn_q_a_norm" },
     { LLM_TENSOR_ATTN_KV_A_NORM,                         "blk.%d.attn_kv_a_norm" },
     { LLM_TENSOR_ATTN_Q_A,                               "blk.%d.attn_q_a" },
@@ -2289,6 +2301,54 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_DOWN,
                 LLM_TENSOR_FFN_UP,
             };
+        case LLM_ARCH_KIMI_LINEAR:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                // Dense FFN (layer 0 only)
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                // MoE FFN (layers 1+)
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+                // Shared experts
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                // KDA (using SSM_ enum prefix, keeping GGUF names for backward compat)
+                LLM_TENSOR_SSM_CONV1D_Q,
+                LLM_TENSOR_SSM_CONV1D_K,
+                LLM_TENSOR_SSM_CONV1D_V,
+                LLM_TENSOR_SSM_F_A,
+                LLM_TENSOR_SSM_F_B,
+                LLM_TENSOR_SSM_BETA,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_G_A,
+                LLM_TENSOR_SSM_G_B,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_NORM,
+                // MLA
+                LLM_TENSOR_ATTN_Q_A,
+                LLM_TENSOR_ATTN_Q_B,
+                LLM_TENSOR_ATTN_Q_A_NORM,
+                LLM_TENSOR_ATTN_KV_A_MQA,
+                LLM_TENSOR_ATTN_KV_B,
+                LLM_TENSOR_ATTN_K_B,
+                LLM_TENSOR_ATTN_V_B,
+                LLM_TENSOR_ATTN_KV_A_NORM,
+            };
         default:
             GGML_ABORT("unknown architecture for tensor mapping");
     }
@@ -2392,6 +2452,15 @@ static const std::map LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SSM_C_NORM,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_SSM_D,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_SSM_NORM,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    // Kimi KDA - Conv tensors are 4D [d_conv, 1, d_inner, 1], reshaped to 2D at runtime
+    {LLM_TENSOR_SSM_CONV1D_Q,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_CONV1D_K,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_CONV1D_V,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_F_A,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_F_B,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_BETA,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_G_A,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_G_B,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_LERP_X,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_TIME_MIX_LN,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_CHANNEL_MIX_LERP_K,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -2573,6 +2642,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_KIMI_LINEAR:
             return true;
         default:
             return false;
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 270d28b16a..f092f72834 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -124,6 +124,7 @@ enum llm_arch {
     LLM_ARCH_MIMO2,
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
+    LLM_ARCH_KIMI_LINEAR,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -250,6 +251,8 @@ enum llm_kv {
     LLM_KV_SSM_GROUP_COUNT,
     LLM_KV_SSM_DT_B_C_RMS,
 
+    LLM_KV_KDA_HEAD_DIM,
+
     LLM_KV_WKV_HEAD_SIZE,
 
     LLM_KV_TOKENIZER_MODEL,
@@ -398,6 +401,15 @@ enum llm_tensor {
     LLM_TENSOR_SSM_NORM,
     LLM_TENSOR_SSM_OUT,
     LLM_TENSOR_SSM_BETA_ALPHA,      // qwen3next
+    // Kimi Linear KDA (using SSM_ prefix for consistency)
+    LLM_TENSOR_SSM_CONV1D_Q,        // kimi: Q conv1d weight
+    LLM_TENSOR_SSM_CONV1D_K,        // kimi: K conv1d weight
+    LLM_TENSOR_SSM_CONV1D_V,        // kimi: V conv1d weight
+    LLM_TENSOR_SSM_F_A,             // kimi: forget gate projection A
+    LLM_TENSOR_SSM_F_B,             // kimi: forget gate projection B
+    LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient
+    LLM_TENSOR_SSM_G_A,             // kimi: output gate projection A
+    LLM_TENSOR_SSM_G_B,             // kimi: output gate projection B
     LLM_TENSOR_TIME_MIX_W0,
     LLM_TENSOR_TIME_MIX_W1,
     LLM_TENSOR_TIME_MIX_W2,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 95b207e9e1..a6df893a31 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
         return std::max(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max(1024u, 8u*model.n_tensors());
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 54f4ed2481..165cbc0a7d 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -533,6 +533,50 @@ bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
     return res;
 }
 
+// TODO: Hybrid input classes are a bit redundant.
+// Instead of creating a hybrid input, the graph can simply create 2 separate inputs.
+// Refactoring is required in the future.
+void llm_graph_input_mem_hybrid_k::set_input(const llama_ubatch * ubatch) {
+    mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
+
+    mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
+
+    const int64_t n_rs = mctx->get_recr()->get_n_rs();
+
+    if (inp_rs->s_copy) {
+        GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
+        int32_t * data = (int32_t *) inp_rs->s_copy->data;
+
+        // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
+        for (uint32_t i = 0; i < n_rs; ++i) {
+            data[i] = mctx->get_recr()->s_copy(i);
+        }
+    }
+}
+
+bool llm_graph_input_mem_hybrid_k::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
+
+    res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
+    res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
+
+    res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
+
+    res &= inp_rs->s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= inp_rs->head == mctx->get_recr()->get_head();
+    res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
+
+    return res;
+}
+
 void llm_graph_input_mem_hybrid_iswa::set_input(const llama_ubatch * ubatch) {
     const auto * attn_ctx = mctx->get_attn();
 
@@ -2268,6 +2312,17 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
 
+llm_graph_input_mem_hybrid_k * llm_graph_context::build_inp_mem_hybrid_k() const {
+    const auto * mctx_cur = static_cast(mctx);
+
+    auto inp_rs   = build_rs_inp_impl     (ctx0, ubatch, mctx_cur->get_recr());
+    auto inp_attn = build_attn_inp_k_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
+
+    auto inp = std::make_unique(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
+
+    return (llm_graph_input_mem_hybrid_k *) res->add_input(std::move(inp));
+}
+
 llm_graph_input_mem_hybrid_iswa * llm_graph_context::build_inp_mem_hybrid_iswa() const {
     const auto * mctx_cur = static_cast(mctx);
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 4090d8116c..1d69ff1a6f 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -433,6 +433,34 @@ public:
     const llama_memory_hybrid_context * mctx;
 };
 
+class llm_graph_input_mem_hybrid_k : public llm_graph_input_i {
+public:
+    llm_graph_input_mem_hybrid_k(
+            const llama_cparams & cparams,
+            std::unique_ptr inp_attn,
+            std::unique_ptr      inp_rs,
+            const llama_memory_hybrid_context *      mctx) :
+        inp_attn(std::move(inp_attn)),
+        inp_rs(std::move(inp_rs)),
+        cparams(cparams),
+        mctx(mctx) { }
+    virtual ~llm_graph_input_mem_hybrid_k() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    bool can_reuse(const llm_graph_params & params) override;
+
+    std::unique_ptr inp_attn;
+    std::unique_ptr      inp_rs;
+
+    llm_graph_input_attn_k * get_attn() const { return inp_attn.get(); }
+    llm_graph_input_rs      * get_recr() const { return inp_rs.get(); }
+
+    const llama_cparams cparams;
+
+    const llama_memory_hybrid_context * mctx;
+};
+
 class llm_graph_input_mem_hybrid_iswa : public llm_graph_input_i {
 public:
     llm_graph_input_mem_hybrid_iswa(
@@ -960,6 +988,7 @@ struct llm_graph_context {
     //
 
     llm_graph_input_mem_hybrid * build_inp_mem_hybrid() const;
+    llm_graph_input_mem_hybrid_k * build_inp_mem_hybrid_k() const;
 
     llm_graph_input_mem_hybrid_iswa * build_inp_mem_hybrid_iswa() const;
 
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 392f9160ce..756dda1a7a 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -139,6 +139,13 @@ uint32_t llama_hparams::n_embd_r() const {
         return n_embd * (n_shortconv_l_cache - 1);
     }
 
+    if (n_embd_head_kda != 0) {
+        // for Kimi KDA layers
+        // Conv state for Q, K, V: 3 * (d_conv - 1) * n_head * head_dim
+        const uint32_t d_inner = n_head() * n_embd_head_kda;  // 32 * 128 = 4096
+        return 3 * (ssm_d_conv > 0 ? ssm_d_conv - 1 : 3) * d_inner;
+    }
+
     // TODO: maybe support other convolution strides than 1
     // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
     // Corresponds to Mamba's conv_states size
@@ -151,6 +158,13 @@ uint32_t llama_hparams::n_embd_s() const {
         return n_embd * wkv_head_size;
     }
 
+    if (n_embd_head_kda != 0) {
+        // for Kimi KDA layers
+        // Full recurrent state: head_dim * head_dim * n_head
+        // h tensor shape for delta attention: [head_dim, head_dim, n_head]
+        return n_embd_head_kda * n_embd_head_kda * n_head();  // 128 * 128 * 32 = 524288
+    }
+
     // corresponds to Mamba's ssm_states size
     return ssm_d_state * ssm_d_inner;
 }
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index dfbc7d95e9..a435043cfe 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -137,6 +137,9 @@ struct llama_hparams {
     uint32_t ssm_dt_rank = 0;
     uint32_t ssm_n_group = 0;
 
+    // for Kimi Linear KDA
+    uint32_t n_embd_head_kda = 0;
+
     // for hybrid state space models
     std::array recurrent_layer_arr;
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 72490a89b5..765e4de2e4 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -125,6 +125,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
         case LLM_TYPE_31B_A3_5B:     return "31B.A3.5B";
+        case LLM_TYPE_48B_A3B:       return "48B.A3B";
         case LLM_TYPE_80B_A3B:       return "80B.A3B";
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_102B_A12B:     return "102B.A12B";
@@ -2450,6 +2451,37 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_KIMI_LINEAR:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
+                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+                ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT,        hparams.n_rot);
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,             hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_KDA_HEAD_DIM,                hparams.n_embd_head_kda);
+
+                // MLA qk_rope_head_dim (for reference)
+                // qk_rope_head_dim = 64, qk_nope_head_dim = 128, qk_head_dim = 192
+
+                // Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
+                // Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
+                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                    hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;  // KDA layers are recurrent
+                }
+
+                // MoE parameters - Kimi uses moe_intermediate_size = 1024
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+
+                switch (hparams.n_layer) {
+                    case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -6752,6 +6784,141 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
                     }
                 } break;
+            case LLM_ARCH_KIMI_LINEAR:
+                {
+                    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}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        // Check for KDA specific tensors to determine layer type or if it's a mixed model
+                        // Assuming KDA layer if KDA tensors are present
+
+                        // KDA uses head_dim = 128 (from linear_attn_config.head_dim)
+                        const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda;
+                        const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda;
+                        const int64_t ssm_d_conv = hparams.ssm_d_conv;
+
+                        // Try loading KDA specific tensors (using SSM_ prefix)
+                        // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1)
+                        // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner]
+                        layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+                        if (!layer.ssm_q_conv) {
+                            layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, TENSOR_NOT_REQUIRED);
+                        }
+
+                        if (layer.ssm_q_conv) {
+                             // KDA Layer - Conv1d weights may be 3D or 4D
+                             layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+                             if (!layer.ssm_k_conv) {
+                                 layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
+                             }
+                             layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+                             if (!layer.ssm_v_conv) {
+                                 layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head}, 0);
+                             }
+
+                             // q, k, v projections
+                             // Python: q_proj, k_proj, v_proj
+                             layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k_kda * n_head}, 0);
+                             layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_head_k_kda * n_head}, 0);
+                             layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_head_v_kda * n_head}, 0);
+
+                             // KDA specific projections
+                             // f_a_proj, f_b_proj
+                             layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); // head_dim
+                             layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0); // projection_size
+
+                             // b_proj (beta mixing coefficient)
+                             layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
+
+                             // A_log - Shape in GGUF: [1, num_heads, 1, 1] (4D) or [1, num_heads] (2D after quantization) Note: -exp(A_log) is applied in convert_hf_to_gguf.py
+                             layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED);
+                             if (!layer.ssm_a) {
+                                 layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
+                             }
+
+                             // dt_bias - shape [n_embd_head_k_kda * n_head] = [4096]
+                             layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_embd_head_k_kda * n_head}, 0);
+
+                             // g_a_proj, g_b_proj (output gate)
+                             layer.ssm_g_a = create_tensor(tn(LLM_TENSOR_SSM_G_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0);
+                             layer.ssm_g_b = create_tensor(tn(LLM_TENSOR_SSM_G_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0);
+
+                             // o_norm (reusing SSM_NORM)
+                             layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); // FusedRMSNormGated
+
+                             // o_proj
+                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0);
+
+                        } else {
+                             // MLA Layer - use MLA-specific head dimensions
+                             const int64_t q_lora_rank  = hparams.n_lora_q;
+                             const int64_t kv_lora_rank = hparams.n_lora_kv;
+                             const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
+                             const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
+
+                             layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED);
+                             layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+                             if (layer.attn_q_a_norm) {
+                                 layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+                                 layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
+                             } else {
+                                 // Kimi MLA without Q compression: wq = [n_embd, n_head * n_embd_head_k_mla]
+                                 layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
+                             }
+
+                             // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA)
+                             // Note: hparams.n_rot may be 72 (from conversion) but actual is 64
+                             const int64_t qk_rope_head_dim = hparams.n_rot;  // From config: qk_rope_head_dim
+                             layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
+                             // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled)
+                             layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED);
+                             if (!layer.wkv_b) { // MLA KV cache enabled
+                                 layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0);
+                                 layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
+                             }
+                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
+                        }
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        // MoE intermediate size (different from dense FFN)
+                        const int64_t n_ff_exp = hparams.n_ff_exp;
+
+                        // Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE
+                        // first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            // Dense FFN layer - use normal n_ff
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "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);
+                        } else {
+                            // MoE layer - use n_ff_exp (1024) instead of n_ff (9216)
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+
+                            // Shared experts use moe_intermediate_size * num_shared_experts
+                            // Kimi: shared_expert_intermediate_size = 1024 * 1 = 1024
+                            // Tensors are 2D: [n_embd, n_ff_shexp] or [n_ff_shexp, n_embd]
+                            const int64_t n_ff_shexp_actual = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp_actual, n_embd}, TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
+
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+                        }
+                    }
+                } break;
             case LLM_ARCH_COGVLM:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -8086,6 +8253,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique(*this, params);
             } break;
+        case LLM_ARCH_KIMI_LINEAR:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -8235,6 +8406,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_WAVTOKENIZER_DEC:
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
+        case LLM_ARCH_KIMI_LINEAR:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
diff --git a/src/llama-model.h b/src/llama-model.h
index d1de16e3f2..5b408bcea2 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -118,6 +118,7 @@ enum llm_type {
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
     LLM_TYPE_31B_A3_5B,
+    LLM_TYPE_48B_A3B, // Kimi Linear
     LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
     LLM_TYPE_102B_A12B, // Solar-Open
@@ -411,6 +412,18 @@ struct llama_layer {
     struct ggml_tensor * ffn_act_beta    = nullptr;
     struct ggml_tensor * ffn_act_eps     = nullptr;
 
+    // Kimi Linear KDA (using ssm_ prefix for consistency)
+    // Note: ssm_dt_b already exists above (mamba bias), reused for Kimi dt_bias
+    struct ggml_tensor * ssm_q_conv = nullptr;
+    struct ggml_tensor * ssm_k_conv = nullptr;
+    struct ggml_tensor * ssm_v_conv = nullptr;
+    struct ggml_tensor * ssm_f_a    = nullptr;
+    struct ggml_tensor * ssm_f_b    = nullptr;
+    struct ggml_tensor * ssm_beta   = nullptr;
+    struct ggml_tensor * ssm_g_a    = nullptr;
+    struct ggml_tensor * ssm_g_b    = nullptr;
+    struct ggml_tensor * ssm_o_norm = nullptr;
+
     struct llama_layer_posnet posnet;
 
     struct llama_layer_convnext convnext;
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 776222cb6f..a7891647c3 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -787,9 +787,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
         quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
 
-        // do not quantize Mamba's small yet 2D weights
+        // do not quantize Mamba /Kimi's small conv1d weights
         // NOTE: can't use LLM_TN here because the layer number is not known
-        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+        quantize &= name.find("ssm_conv1d") == std::string::npos;
         quantize &= name.find("shortconv.conv.weight") == std::string::npos;
 
         // do not quantize RWKV's small yet 2D weights
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 38d03a8c39..6d6bdfa090 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1752,26 +1752,33 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             // read bpe merges and populate bpe ranks
             const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+            // Kimi-K2 uses custom tokenization without traditional BPE merges
+            const bool is_kimi_k2 = (tokenizer_pre == "kimi-k2");
+
             if (merges_keyidx == -1) {
-                throw std::runtime_error("cannot find tokenizer merges in model file\n");
-            }
-
-            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
-            for (int i = 0; i < n_merges; i++) {
-                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
-                //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
-
-                std::string first;
-                std::string second;
-
-                const size_t pos = word.find(' ', 1);
-
-                if (pos != std::string::npos) {
-                    first  = word.substr(0, pos);
-                    second = word.substr(pos + 1);
+                if (!is_kimi_k2) {
+                    throw std::runtime_error("cannot find tokenizer merges in model file\n");
                 }
+                // Kimi-K2 doesn't need merges, skip
+                LLAMA_LOG_INFO("%s: Kimi-K2 tokenizer detected, skipping BPE merges\n", __func__);
+            } else {
+                const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+                for (int i = 0; i < n_merges; i++) {
+                    const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+                    //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
-                bpe_ranks.emplace(std::make_pair(first, second), i);
+                    std::string first;
+                    std::string second;
+
+                    const size_t pos = word.find(' ', 1);
+
+                    if (pos != std::string::npos) {
+                        first  = word.substr(0, pos);
+                        second = word.substr(pos + 1);
+                    }
+
+                    bpe_ranks.emplace(std::make_pair(first, second), i);
+                }
             }
 
             // default special tokens
@@ -2226,6 +2233,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|end_of_text|>" // granite
                         || t.first == ""
                         || t.first == "_"
+                        || t.first == "[EOT]" // Kimi-K2
                         || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
                         || t.first == "" // smoldocling
                    ) {
@@ -2322,6 +2330,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == ""
                         || t.first == ""   // Granite
                         || t.first == ""
+                        || t.first == "[PAD]" // Kimi-K2
                         ) {
                     special_fim_pad_id = t.second;
                     if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2424,6 +2433,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     || t.first == "<|eom_id|>"
                     || t.first == ""
                     || t.first == "_"
+                    || t.first == "[EOT]" // Kimi-K2
+                    || t.first == "[EOS]" // Kimi-K2
                     || t.first == "<|end_of_text|>"
                     || t.first == "" // smoldocling
                ) {
diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp
new file mode 100644
index 0000000000..0f037d1a39
--- /dev/null
+++ b/src/models/kimi-linear.cpp
@@ -0,0 +1,772 @@
+#include "models.h"
+#include "ggml.h"
+
+#define CHUNK_SIZE 64
+
+// Causal Conv1d function for Q,K,V
+// When qkv is 0, it is Q, 1 is K, 2 is V
+static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) {
+    const int64_t d_inner = head_dim * n_head;
+    const int64_t conv_state_size = (d_conv - 1) * d_inner;
+    const int64_t n_embd_r_total = 3 * conv_state_size;  // Q + K + V
+
+    // conv_state_all is [n_embd_r_total, n_seqs], split into Q, K, V
+    // Each conv state is [(d_conv-1) * d_inner] per sequence, need to reshape to [d_conv-1, d_inner, n_seqs]
+    // Memory layout: for each seq, Q state is first conv_state_size elements, then K, then V
+    // conv_state_all has stride: nb[0] = element_size, nb[1] = n_embd_r_total * element_size
+    // View Q conv state: offset 0, size conv_state_size per seq
+    // conv_state_all is [n_embd_r_total, n_seqs] with memory layout:
+    //   state[i + seq * n_embd_r_total] where i = conv_step + channel * (d_conv-1) + {0, conv_state_size, 2*conv_state_size} for Q/K/V
+    // We want [d_conv-1, d_inner, n_seqs] view:
+    //   nb1 = (d_conv-1) * element_size (stride between channels)
+    //   nb2 = n_embd_r_total * element_size (stride between seqs)
+    ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs,
+        (d_conv - 1) * ggml_element_size(conv_state_all),  // nb1: stride between channels
+        n_embd_r_total * ggml_element_size(conv_state_all),  // nb2: stride between seqs
+        qkv * conv_state_size * ggml_element_size(conv_state_all));
+
+// Causal Conv1d function for Q,K,V
+// When qkv is 0, it is Q, 1 is K, 2 is V
+    // Step 1: Q, K, V projections -> [d_inner, n_tokens]
+    ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x);
+
+    // Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs}
+    ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
+
+    // Concat Q conv state and current input: {d_conv-1 + n_seq_tokens, d_inner, n_seqs}
+    ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0);
+
+    // Save last (d_conv-1) columns back to Q conv state
+    ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
+        conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
+    ggml_build_forward_expand(gf,
+        ggml_cpy(ctx0, last_conv_x,
+            ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
+                (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+    // Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner]
+    // GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv]
+    // vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step]
+    // ggml_ssm_conv computes: c[conv_step + channel * d_conv]
+    // GGUF layout: [d_conv, 1, d_inner] or [d_conv, 1, d_inner, 1] -> reshape to [d_conv, d_inner]
+    // Reshape conv weight from [d_conv, 1, d_inner, 1] to [d_conv, d_inner] for ggml_ssm_conv
+    ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner);
+
+    // Apply conv1d
+    // ggml_ssm_conv output: {d_inner, n_seq_tokens, n_seqs}
+    ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight);
+    // Reshape to 2D for bias add: {d_inner, n_tokens}
+    Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens);
+    Xcur = ggml_silu(ctx0, Xcur);
+
+    return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);
+}
+
+llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params), model(model) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "model.embed_tokens", -1);
+
+    // Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
+    // So we don't need inp_pos
+
+    auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr;
+    auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr;
+    auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr();
+    auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr;
+    auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr;
+
+    // Output ids for selecting which tokens to output
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * chunked_causal_mask =
+        ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * chunked_identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * chunked_diag_mask = ggml_add(ctx0, chunked_causal_mask, chunked_identity);
+
+    ggml_build_forward_expand(gf, chunked_causal_mask);
+    ggml_build_forward_expand(gf, chunked_identity);
+    ggml_build_forward_expand(gf, chunked_diag_mask);
+
+    // Kimi dimension constants
+    const int64_t n_head = hparams.n_head();
+    const int64_t head_dim = hparams.n_embd_head_kda;
+    const int64_t d_conv = hparams.ssm_d_conv;
+    const int64_t d_inner = n_head * head_dim;  // 32 * 128 = 4096
+    const int64_t n_seqs = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    // Verify batch consistency for recurrent layers
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    // MLA params
+    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
+    const int64_t kv_lora_rank = hparams.n_lora_kv;
+    // qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot
+    // Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim]
+    const int64_t n_embd_head_qk_rope = hparams.n_rot;  // config.qk_rope_head_dim
+    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;  // 192 - 64 = 128
+    // Attention scale for MLA
+    const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla);
+
+    for (int il = 0; il < n_layer; ++il) {
+        const auto & layer = model.layers[il];
+        ggml_tensor * inpSA = inpL;
+
+        // Attention Norm
+        cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Check layer type by checking which tensors exist
+        // KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor
+        bool is_kda = (layer.ssm_a != nullptr);
+        bool is_mla = (layer.wkv_a_mqa != nullptr);
+
+        if (is_kda) {
+            // === KDA Layer (Kimi Delta Attention) with Recurrent State ===
+            // Reference: vLLM kda.py
+            const auto * mctx_cur = inp_rs->mctx;
+            const auto kv_head = mctx_cur->get_head();
+
+            // Get conv states from r_l tensor (Q, K, V each have separate state)
+            ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+            cb(conv_states_all, "conv_states_all", il);
+            ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
+            ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+            ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+            ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
+
+            // g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias)
+            ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
+            ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a);
+            cb(g1, "g1 f_b(f_a(cur))", il);
+            g1 = ggml_add(ctx0, g1, layer.ssm_dt_b);
+            g1 = ggml_softplus(ctx0, g1);
+            g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens);
+
+            // A_log shape is [1, n_head] or [1, n_head, 1, 1], need to broadcast to [head_dim, n_head, n_tokens]. No need to -exp(a_log) because it was done in convert_hf_to_gguf.py
+            // Reshape to [1, n_head, 1] for broadcasting with g1 [head_dim, n_head, n_tokens]
+            ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1);
+            g1 = ggml_mul(ctx0, g1, A);
+            cb(g1, "kda_g1", il);
+
+            // Compute beta (mixing coefficient)
+            ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur);
+            beta = ggml_reshape_4d(ctx0, beta, n_head, 1, n_seq_tokens, n_seqs);
+            cb(beta, "kda_beta", il);
+
+            // Reshape for KDA recurrence
+            // {n_embd, n_tokens} -> {n_embd, n_seq_tokens, n_seqs}
+            cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+            g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs);
+
+            // Get SSM state and compute KDA recurrence using ggml_kda_scan
+            ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
+            ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs);
+            state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs);
+            // Choose between build_kda_chunking and build_kda_recurrent based on n_tokens
+            std::pair attn_out = n_seq_tokens == 1 ?
+                build_kda_autoregressive(Qcur, Kcur, Vcur, g1, beta, state, il) :
+                build_kda_chunking(Qcur, Kcur, Vcur, g1, beta, state, chunked_causal_mask, chunked_identity, chunked_diag_mask, il);
+
+            ggml_tensor * output = attn_out.first;
+            ggml_tensor * new_state = attn_out.second;
+            cb(output, "attn_output", il);
+            cb(new_state, "new_state", il);
+
+            // Update the recurrent states
+            ggml_build_forward_expand(gf,
+                                     ggml_cpy(ctx0, new_state,
+                                              ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                           kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+            // Output gating g2 = g_b(g_a(x))
+            ggml_tensor * cur_2d = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+            ggml_tensor * g_a = ggml_mul_mat(ctx0, layer.ssm_g_a, cur_2d);
+            ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.ssm_g_b, g_a);
+            cb(g2, "g2 g_b(g_a(cur_2d))", il);
+            g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_seq_tokens * n_seqs);
+
+            // Apply o_norm with sigmoid gating
+            // Note: Kimi model uses sigmoid gating, not SiLU (despite FusedRMSNormGated default being swish)
+            // Formula: output = RMSNorm(x) * sigmoid(g)
+            ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head,  n_seq_tokens * n_seqs);
+            ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il);
+            cb(normed, "kda_normed", il);
+            ggml_tensor * gate = ggml_sigmoid(ctx0, g2);
+            ggml_tensor * gated = ggml_mul(ctx0, normed, gate);
+
+            // Output projection
+            gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens);
+            cur = ggml_mul_mat(ctx0, layer.wo, gated);
+            cb(cur, "kda_out", il);
+
+        } else if (is_mla) {
+            // === MLA Layer (Multi-head Latent Attention) without KV Cache ===
+            // Reference: vLLM mla.py
+            // Step 1: Q projection and reshape
+            // vLLM Kimi: q = q_proj(hidden_states), then view as [n_tokens, n_head, qk_head_dim]
+            // Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
+            ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq, cur);
+
+            // Step 2: KV compression
+            // kv_cmpr_pe = kv_a_proj_with_mqa(hidden_states) -> [kv_lora_rank + qk_rope_head_dim, n_tokens]
+            ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
+
+            // Split: kv_cmpr = kv_lora[:kv_lora_rank], k_pe = kv_lora[kv_lora_rank:]
+            ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+                ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+            ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+                ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+            // Note: Kimi MLA does NOT apply RoPE (rotary_emb=None in vLLM)
+            // k_pe is used directly without RoPE
+            // Normalize kv_c
+            kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+
+            if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled
+                // extract q_nope
+                ggml_tensor * q_nope =
+                    ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
+                                 ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_embd_head_qk_rope, n_head, n_tokens}
+                ggml_tensor * q_pe = ggml_view_3d(
+                    ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
+                    ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, ggml_row_size(Qcur->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd_head_qk_nope, n_tokens, n_head}
+                q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+                cb(q_nope, "q_nope_perm", il);
+
+                // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
+                ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
+                cb(q_nope_absorbed, "q_nope_absorbed", il);
+
+                // {kv_lora_rank, n_head, n_tokens}
+                q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+                cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
+                // note: rope must go first for in-place context shifting in build_rope_shift()
+                Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+                cb(Qcur, "Qcur", il);
+
+                kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+                cb(kv_cmpr, "kv_cmpr_reshape", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
+                cb(Kcur, "Kcur", il);
+
+                // {kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Vcur = kv_cmpr;
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn_k, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il);
+                cb(cur, "mla_out", il);
+            } else { // MLA KV cache disabled. Fall back to MHA KV cache.
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens);
+                cb(Qcur, "mla_Q", il);
+                // KV decompression: kv = kv_b_proj(kv_c_normed)
+                ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr);
+                const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
+
+                // Split kv into k_nope and v
+                ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                    ggml_row_size(kv->type, kv_per_head),
+                    ggml_row_size(kv->type, kv_per_head * n_head), 0);
+                ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens,
+                    ggml_row_size(kv->type, kv_per_head),
+                    ggml_row_size(kv->type, kv_per_head * n_head),
+                    ggml_row_size(kv->type, n_embd_head_qk_nope));
+                Vcur = ggml_cont(ctx0, Vcur);
+                cb(Vcur, "mla_V", il);
+
+                // Concatenate k_nope + k_pe (broadcast k_pe to all heads)
+                // K = [k_nope, k_pe] where k_nope is [qk_nope_head_dim, n_head, n_tokens]
+                // and k_pe is [qk_rope_head_dim, 1, n_tokens] broadcast to all heads
+                // Need to broadcast k_pe from [qk_rope, 1, n_tokens] to [qk_rope, n_head, n_tokens]
+                ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens);
+                ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target);
+                ggml_tensor * Kcur = ggml_concat(ctx0, k_pe_repeated, k_nope, 0);
+                cb(Kcur, "mla_K", il);
+
+                // Direct softmax attention (with MHA KV cache)
+                // Use build_attn with inp_attn for proper mask handling
+                cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
+                cb(cur, "mla_out", il);
+            }
+        } else {
+            // Unknown layer type - this should not happen
+            GGML_ABORT("Kimi layer is neither KDA nor MLA - missing required tensors");
+        }
+
+        // On last layer, select only the output tokens
+        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);
+        }
+
+        // Residual
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FFN Norm
+        cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            // Dense FFN layer
+            cur = build_ffn(cur,
+                layer.ffn_up, NULL, NULL,
+                layer.ffn_gate, NULL, NULL,
+                layer.ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE layer
+            // Kimi uses moe_renormalize=True and routed_scaling_factor (stored as expert_weights_scale) = 2.446
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                layer.ffn_gate_inp,
+                layer.ffn_up_exps,
+                layer.ffn_gate_exps,
+                layer.ffn_down_exps,
+                layer.ffn_exp_probs_b,
+                hparams.n_expert,
+                hparams.n_expert_used,
+                LLM_FFN_SILU, true,
+                true, hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // Shared expert
+            {
+                ggml_tensor * ffn_shexp = build_ffn(cur,
+                        layer.ffn_up_shexp, NULL, NULL,
+                        layer.ffn_gate_shexp, NULL, NULL,
+                        layer.ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+        // Residual
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // Final Norm
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // Output
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+/*
+    This is a ggml implementation of the naive_chunk_kda function of
+    https://github.com/fla-org/flash-linear-attention/blob/main/fla/ops/kda/naive.py
+*/
+std::pair llm_build_kimi_linear::build_kda_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * gk,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    GGML_ASSERT(ggml_is_contiguous(state));
+
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(gk->ne[0] == S_v && gk->ne[1] == H_v && gk->ne[2] == n_tokens && gk->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    // TODO: can this ever be false?
+    const bool use_qk_l2norm = true;
+
+    if (use_qk_l2norm) {
+        const float eps_norm = hparams.f_norm_rms_eps;
+
+        q = ggml_l2_norm(ctx0, q, eps_norm);
+        k = ggml_l2_norm(ctx0, k, eps_norm);
+    }
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(gk, "gk_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(gk, "gk_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    gk = ggml_pad(ctx0, gk, 0, pad, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(gk, "gk_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    const int64_t HB = H_k * n_seqs;
+
+    q      = ggml_cont_4d(ctx0, q,      S_k, chunk_size, n_chunks, HB);
+    k      = ggml_cont_4d(ctx0, k,      S_k, chunk_size, n_chunks, HB);
+    k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, HB);
+    v      = ggml_cont_4d(ctx0, v,      S_v, chunk_size, n_chunks, HB);
+    v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, HB);
+
+    gk    = ggml_cont_4d(ctx0, gk, S_k, chunk_size, n_chunks, HB);
+    beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, HB);
+
+    // switch for cumsum
+    gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 1, 0, 2, 3), chunk_size, S_k, n_chunks, HB);
+    cb(gk, "gk", il);
+    ggml_tensor * gk_cumsum = ggml_cumsum(ctx0, gk);
+    cb(gk_cumsum, "gk_cumsum", il);
+
+/*
+    Compute Akk and Aqk loop together
+    Akk loop:
+    for i in range(BT):
+        k_i = k[..., i, :] # k_i [B,H,NT,S]
+        g_i = g[..., i:i+1, :] # g_i [B,H,NT,1,S]
+        A[..., i] = torch.einsum('... c d, ... d -> ... c', k * (g - g_i).exp(), k_i)
+    Aqk loop:
+    for j in range(BT):
+        k_j = k[:, :, i, j]
+        g_j = g[:, :, i, j:j+1, :]
+        A[..., j] = torch.einsum('... c d, ... d -> ... c', q_i * (g_i - g_j).exp(), k_j)
+*/
+    const int64_t CHB = n_chunks * H_k * n_seqs;
+    ggml_tensor * gkcs_i = ggml_reshape_4d(ctx0, gk_cumsum, chunk_size, 1, S_k, CHB);  // [chunk_size, 1, S_k, CHB]
+    ggml_tensor * gkcs_j = ggml_reshape_4d(ctx0, gkcs_i, 1, chunk_size, S_k, CHB);  // [1, chunk_size, S_k, CHB]
+
+    ggml_tensor * gkcs_j_bc = ggml_repeat_4d(ctx0, gkcs_j, chunk_size, chunk_size, S_k, CHB);  // [1, chunk_size, S_k, CHB] -> [chunk_size, chunk_size, S_k, CHB]
+    // decay_mask [chunk_size,chunk_size,S_k,CHB]
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gkcs_j_bc, gkcs_i);
+    cb(decay_mask, "decay_mask", il);
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    cb(decay_mask, "decay_masked", il);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    // decay_mask [S_k,BT_j,BT_i,CHB] *Note* second and third chunk_sizes are switched
+    decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
+
+    ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, chunk_size, 1, CHB);
+    ggml_tensor * k_j = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
+    ggml_tensor * q_i = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
+
+    ggml_tensor * decay_k_i = ggml_mul(ctx0, decay_mask, k_i);
+    ggml_tensor * decay_q_i = ggml_mul(ctx0, decay_mask, q_i);
+
+    // decay_k_i [S.BT,BT,CHB] @ k_j [S,1,BT,CHB] = Akk [BT,1,BT,CHB]
+    ggml_tensor * Akk = ggml_mul_mat(ctx0, decay_k_i, k_j);
+    ggml_tensor * Aqk = ggml_mul_mat(ctx0, decay_q_i, k_j);
+    Akk = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, Akk, chunk_size, chunk_size, n_chunks, HB)));
+    Aqk = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, Aqk, chunk_size, chunk_size, n_chunks, HB)));
+    cb(Akk, "Akk", il);
+    cb(Aqk, "Aqk", il);
+
+    Akk = ggml_mul(ctx0, Akk, beta);
+    Akk = ggml_neg(ctx0, ggml_mul(ctx0, Akk, causal_mask));
+    cb(Akk, "attn_pre_solve", il);
+
+    Aqk = ggml_mul(ctx0, Aqk, diag_mask);
+    Aqk = ggml_scale(ctx0, Aqk, scale); // scale q
+    cb(Aqk, "Aqk_masked", il);
+
+    // for i in range(1, chunk_size):
+    //          row = attn[..., i, :i].clone()
+    //          sub = attn[..., :i, :i].clone()
+    //          attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
+    // attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
+    //
+    // We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
+    ggml_tensor * attn_lower = ggml_mul(ctx0, Akk, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, Akk, true, true, false);
+    Akk                      = ggml_mul(ctx0, lin_solve, causal_mask);
+    Akk                      = ggml_add(ctx0, Akk, identity);
+
+    cb(Akk, "attn_solved", il);
+
+    // switch back for downstream
+    gk_cumsum = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk_cumsum, 1, 0, 2, 3), S_k, chunk_size, n_chunks, HB);
+    ggml_tensor * gkexp      = ggml_exp(ctx0, gk_cumsum);
+    cb(gk_cumsum, "gk_cumsum", il);
+
+    // u = (A*beta[..., None, :]) @ v  aka U_[t]
+    ggml_tensor * vb = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), Akk);
+
+    ggml_tensor * kbeta_gkexp = ggml_mul(ctx0, k_beta, gkexp);
+    cb(kbeta_gkexp, "kbeta_gkexp", il);
+
+    ggml_tensor * k_cumdecay = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gkexp)), Akk);
+    cb(k_cumdecay, "k_cumdecay", il);
+
+    ggml_tensor * core_attn_out = nullptr;
+    ggml_tensor * new_state = ggml_dup(ctx0, state);
+
+    cb(new_state, "new_state", il);
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+// extract one chunk worth of data
+        auto chunkify = [=](ggml_tensor * t) {
+                    return ggml_cont(ctx0, ggml_view_4d(ctx0, t, t->ne[0], chunk_size, 1, t->ne[3],
+                t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
+        };
+        auto chunkify_A = [=](ggml_tensor * t) {
+                    return ggml_cont(ctx0, ggml_view_4d(ctx0, t, chunk_size, chunk_size, 1, t->ne[3],
+                t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
+        };
+
+
+// k [S,BT,NT,H*B] => k_chunk [S,BT,1,H*B]
+        ggml_tensor * k_chunk = chunkify(k);
+        ggml_tensor * q_chunk = chunkify(q);
+        ggml_tensor * vb_chunk = chunkify(vb);
+
+// gk_cumsum [S,BT,NT,H*B] => gk_cs_chunk [S,BT,1,H*B]
+        ggml_tensor * gk_cs_chunk = chunkify(gk_cumsum);
+        ggml_tensor * k_cumdecay_chunk = chunkify(k_cumdecay);
+        ggml_tensor * gkexp_chunk = ggml_exp(ctx0, gk_cs_chunk);
+        ggml_tensor * Aqk_chunk = chunkify_A(Aqk);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // new_state [S,S,1,H*B] k_cumdecay_chunk [S,BT,1,H*B]
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state or W_[t] @ S_[t]
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+
+        // v_new = v_i - v_prime or U_[t] - W_[t]*S_[t]
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, vb_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+
+        // q_chunk [S,BT,1,H*B] gkexp_chunk [S,BT,1,H*B]
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        // or Gamma_[t]*Q_]t] @ S
+        ggml_tensor * q_gk_exp   = ggml_mul(ctx0, q_chunk, gkexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_gk_exp);
+        attn_inter = ggml_scale(ctx0, attn_inter, scale); // scale q
+
+        // v_new_t [S,BT,1,H*B] Aqk [BT,BT,1,H*B]
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new or A' @ (U_[t] - W_[t]*S_[t])
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, Aqk_chunk);
+
+        // o[:, :, i] = (q_i * g_i.exp()) @ S + A @ v_i
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+
+        core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 1);
+
+        ggml_tensor * gk_cum_last =
+            ggml_cont(ctx0, ggml_view_4d(ctx0, gk_cs_chunk, gk_cs_chunk->ne[0], 1, gk_cs_chunk->ne[2], gk_cs_chunk->ne[3],
+                                        gk_cs_chunk->nb[1], gk_cs_chunk->nb[2], gk_cs_chunk->nb[3],
+                                        gk_cs_chunk->nb[1] * (gk_cs_chunk->ne[1] - 1)));
+
+        ggml_tensor * gkexp_last = ggml_exp(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, gk_cum_last)));
+
+        ggml_tensor * gk_diff = ggml_neg(ctx0, ggml_sub(ctx0, gk_cs_chunk, gk_cum_last));
+
+        ggml_tensor * gk_diff_exp = ggml_exp(ctx0, gk_diff);
+
+        ggml_tensor * key_gkdiff = ggml_mul(ctx0, k_chunk, gk_diff_exp);
+
+        // rearrange((g_i[:,:,-1:] - g_i).exp()*k_i, 'b h c k -> b h k c') @ (U_[t] - W_[t] @ S)
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gkdiff)));
+
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gkexp_last, gkexp_last->ne[0], gkexp_last->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    core_attn_out = ggml_cont_4d(ctx0, core_attn_out, S_v, chunk_size * n_chunks, H_v, n_seqs);
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    cb(new_state, "output_state", il);
+
+    return {output_tokens, new_state};
+}
+
+std::pair llm_build_kimi_linear::build_kda_autoregressive(
+    ggml_tensor * q,
+    ggml_tensor * k,
+    ggml_tensor * v,
+    ggml_tensor * gk,
+    ggml_tensor * beta,
+    ggml_tensor * state,
+    int il) {
+    GGML_ASSERT(ggml_is_contiguous(v));
+    GGML_ASSERT(ggml_is_contiguous(gk));
+
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(gk->ne[0] == S_k && gk->ne[1] == H_k && gk->ne[2] == n_tokens && gk->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_k && state->ne[2] == H_v && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(gk, "gk_in", il);
+
+// g [H,1,B,1] g_t [1,H,B,1] => [1,1,H,B]
+// gk [S,H,1,B] => [S,1,H,B] gk_t [1,S,H,B]
+// beta [H,1,1,B] beta_t [1,H,1,B] => [1,1,H,B]
+    gk = ggml_reshape_4d(ctx0, gk, S_k, 1, H_k, n_seqs);
+    ggml_tensor * gk_t = ggml_cont(ctx0, ggml_transpose(ctx0, gk));
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to gk_t
+    gk_t = ggml_exp(ctx0, gk_t);
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * gk_t
+    // S = S * g_i[..., None].exp()
+    state = ggml_mul(ctx0, state, gk_t);
+
+    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+
+// state [S,S,H,B] k [S,1,H,B] k_state [S_v,1,H,B]
+    k = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
+    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k);
+
+    // v_i - (k_i[..., None] * S).sum(-2)
+    v = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    ggml_tensor * v_diff = ggml_sub(ctx0, v, k_state);
+
+    // b_i[..., None] * k_i
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta_t);
+
+    // S = S + torch.einsum('b h k, b h v -> b h k v', b_i[..., None] * k_i, v_i - (k_i[..., None] * S).sum(-2))
+    // v_diff_t [1,S_v,H,B] k_beta_t [1,S_k,H,B] state [S_v,S_k,H,B]
+    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
+
+    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
+    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
diff --git a/src/models/models.h b/src/models/models.h
index 3a44f7f140..71c1fe8108 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -288,6 +288,33 @@ struct llm_build_jamba : public llm_graph_context_mamba {
     llm_build_jamba(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_kimi_linear : public llm_graph_context_mamba {
+    llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params);
+
+    std::pair build_kda_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * gk,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                        int   il);
+
+    std::pair build_kda_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * gk,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    const llama_model & model;
+};
+
 struct llm_build_lfm2 : public llm_graph_context {
     const llama_model & model;
 

From 06bf3796f48ddd88d984218acee306ccb8638a3e Mon Sep 17 00:00:00 2001
From: Lasse Lauwerys <65569591+Iemand005@users.noreply.github.com>
Date: Fri, 6 Feb 2026 14:56:13 +0100
Subject: [PATCH 142/206] unicode : MSVC regex fix (#19340)

* Fix model loading regex error

* Change comments

* Use const_iterator and remove specializations

---------

Co-authored-by: Alde Rojas 
---
 src/unicode.cpp | 49 +++++++++++++------------------------------------
 1 file changed, 13 insertions(+), 36 deletions(-)

diff --git a/src/unicode.cpp b/src/unicode.cpp
index b47dcbe619..adfc489d1f 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -497,49 +497,26 @@ static std::vector unicode_regex_split_custom_llama3(const std::string &
     return bpe_offsets;
 }
 
-// use std::wregex to split the text
-static std::vector unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector & offsets) {
-    std::wregex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
+template 
+static std::vector unicode_regex_split_stl(const std::basic_string & text, const std::basic_string & regex, const std::vector & offsets) {
+    using BidirIt = typename std::basic_string::const_iterator;
+#ifdef _MSC_VER
+    // Bypass bug in MSVC: https://github.com/ggml-org/llama.cpp/issues/17830
+    constexpr auto regex_flags = std::regex_constants::ECMAScript;
+#else
+    constexpr auto regex_flags = std::regex_constants::optimize | std::regex_constants::nosubs;
+#endif
+    std::basic_regex expr(regex, regex_flags);
     std::vector bpe_offsets; // store the offset of each word
     bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
     size_t start = 0;
     for (auto offset : offsets) {
-        std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr);
-        std::wcregex_iterator end;
+        std::regex_iterator it(text.begin() + start, text.begin() + start + offset, expr);
+        std::regex_iterator end;
 
         int64_t start_idx = 0;
         while (it != end) {
-            std::wcmatch match = *it;
-            if (match.position() > start_idx) {
-                bpe_offsets.emplace_back(match.position() - start_idx);
-            }
-            bpe_offsets.emplace_back(match.length());
-            start_idx = match.position() + match.length();
-            ++it;
-        }
-
-        if (start_idx < (int64_t) offset) {
-            bpe_offsets.emplace_back(offset - start_idx);
-        }
-        start += offset;
-    }
-
-    return bpe_offsets;
-}
-
-// use std::regex to split the text
-static std::vector unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector & offsets) {
-    std::regex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
-    std::vector bpe_offsets; // store the offset of each word
-    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
-    size_t start = 0;
-    for (auto offset : offsets) {
-        std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr);
-        std::cregex_iterator end;
-
-        int64_t start_idx = 0;
-        while (it != end) {
-            std::cmatch match = *it;
+            std::match_results match = *it;
             if (match.position() > start_idx) {
                 bpe_offsets.emplace_back(match.position() - start_idx);
             }

From dfde5993eaed8c2e7b609ab21f7e24d137d40b79 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Fri, 6 Feb 2026 16:47:22 +0200
Subject: [PATCH 143/206] common : add common_speculative_is_compat() (#19270)

* llama : add llama_memory_can_rm_suffix()

* Revert "llama : add llama_memory_can_rm_suffix()"

This reverts commit d30e59b62a15ef4266a6503e3f4eba770aec001b.

* spec : check if the target context is compatible for spec decoding
---
 common/speculative.cpp          | 36 +++++++++++++++++++++++++++++++++
 common/speculative.h            |  4 ++++
 tools/server/server-context.cpp |  7 ++++++-
 3 files changed, 46 insertions(+), 1 deletion(-)

diff --git a/common/speculative.cpp b/common/speculative.cpp
index c99b19dbfd..84d2556ceb 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -805,6 +805,42 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
     return it->second;
 }
 
+bool common_speculative_is_compat(llama_context * ctx_tgt) {
+    auto * mem = llama_get_memory(ctx_tgt);
+    if (mem == nullptr) {
+        return false;
+    }
+
+    bool res = true;
+
+    llama_memory_clear(mem, true);
+
+    // eval 2 tokens to check if the context is compatible
+    std::vector tmp;
+    tmp.push_back(0);
+    tmp.push_back(0);
+
+    int ret = llama_decode(ctx_tgt, llama_batch_get_one(tmp.data(), tmp.size()));
+    if (ret != 0) {
+        LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
+        res = false;
+        goto done;
+    }
+
+    // try to remove the last tokens
+    if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
+        LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
+        res = false;
+        goto done;
+    }
+
+done:
+    llama_memory_clear(mem, true);
+    llama_synchronize(ctx_tgt);
+
+    return res;
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(
diff --git a/common/speculative.h b/common/speculative.h
index 76fe6bb7bc..876cde3d18 100644
--- a/common/speculative.h
+++ b/common/speculative.h
@@ -14,6 +14,10 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
 // convert type to string
 std::string common_speculative_type_to_str(enum common_speculative_type type);
 
+// check if the llama_context is compatible for speculative decoding
+// note: clears the memory of the context
+bool common_speculative_is_compat(llama_context * ctx_tgt);
+
 common_speculative * common_speculative_init(
         common_params_speculative & params,
         llama_context             * ctx_tgt);
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 7f9c3c566b..b71d496eeb 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -740,6 +740,11 @@ private:
 
         slots.clear();
 
+        const bool can_spec = common_speculative_is_compat(ctx);
+        if (!can_spec) {
+            SRV_WRN("%s", "speculative decoding not supported by this context\n");
+        }
+
         // initialize slots
         for (int i = 0; i < params_base.n_parallel; i++) {
             server_slot slot;
@@ -752,7 +757,7 @@ private:
             slot.prompt.tokens.has_mtmd = mctx != nullptr;
 
             // try speculative decoding
-            {
+            if (can_spec) {
                 slot.spec = common_speculative_init(params_base.speculative, slot.ctx);
                 if (slot.spec) {
                     if (mctx) {

From db6adb3c88a96845b7d6863f451a54484a9f5a7e Mon Sep 17 00:00:00 2001
From: Jeff Bolz 
Date: Fri, 6 Feb 2026 08:50:30 -0600
Subject: [PATCH 144/206] tests: reduce number of FA test permutations (#19381)

Only test non-F16 for head size 64 and 72 (one a multiple of QK, one not).
---
 tests/test-backend-ops.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index fbe23037cc..6fe1780f3b 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8231,6 +8231,7 @@ static std::vector> make_test_cases_eval() {
                                                 for (ggml_prec prec : {GGML_PREC_F32, GGML_PREC_DEFAULT}) {
                                                     if (hsk != 128 && prec == GGML_PREC_DEFAULT) continue;
                                                     for (ggml_type type_KV : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
+                                                        if (type_KV != GGML_TYPE_F16 && hsk != 64 && hsk != 72) continue;
                                                         test_cases.emplace_back(new test_flash_attn_ext(
                                                                     hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV));
                                                         // run fewer test cases permuted

From 537eadb1b9e664aa23bf19f7215c1876fc8e5fb9 Mon Sep 17 00:00:00 2001
From: Nechama Krashinski 
Date: Fri, 6 Feb 2026 17:13:44 +0200
Subject: [PATCH 145/206] sycl: add F16 support for GGML_OP_CEIL (#19306)

* Fix SYCL CEIL operator

* sycl: implement GGML_OP_CEIL
---
 docs/ops.md                         |  2 +-
 docs/ops/SYCL.csv                   |  8 ++++----
 ggml/src/ggml-sycl/element_wise.cpp | 13 +++----------
 ggml/src/ggml-sycl/ggml-sycl.cpp    |  2 +-
 4 files changed, 9 insertions(+), 16 deletions(-)

diff --git a/docs/ops.md b/docs/ops.md
index ef1ebff8b0..5754b0a96c 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index 2aa51304b3..c1622cc6f0 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -77,8 +77,8 @@
 "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
@@ -161,8 +161,8 @@
 "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index 651b875b63..00d54b83f8 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -836,16 +836,9 @@ static inline void ggml_sycl_op_floor(ggml_backend_sycl_context & ctx, ggml_tens
 }
 
 static inline void ggml_sycl_op_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
-        [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
-            const int num_blocks = ceil_div(k_elements, 256);
-            stream->parallel_for(
-                sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
-                                  sycl::range<1>(256)),
-                [=](sycl::nd_item<1> item_ct1) {
-                    unary_op_ceil_kernel(src, dst_ptr, k_elements, item_ct1);
-                });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) {
+        return op_ceil(x);
+    });
 }
 
 static inline void ggml_sycl_op_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index a03d26d7f2..0614d7e8f3 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4591,9 +4591,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_CEIL:
                     return true;
                 case GGML_UNARY_OP_FLOOR:
-                case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_ROUND:
                 case GGML_UNARY_OP_TRUNC:
 #if defined (GGML_SYCL_F16)

From 7fbd36c50c1a439a485486729faf20b47a0e6d8c Mon Sep 17 00:00:00 2001
From: Abhijit Ramesh 
Date: Fri, 6 Feb 2026 10:33:30 -0800
Subject: [PATCH 146/206] ggml-webgpu: JIT compile binary operators and handle
 binding overlaps (#19310)

* ggml webgpu: port binary operators to use pre-wgsl

* Add binary.wgsl: unified shader with conditionals for all 4 ops

* Add gen_binary_shaders.cpp: build tool for using pre_wgsl preprocessor

* Remove bin_op.tmpl.wgsl and binary.wgsl (Python template)

* Update CMake to generate binary operator shaders at build time

* ggml-webgpu: migrate binary ops to JIT compilation with overlap handling

* port binary operators from AOT to pre-wgsl JIT compilation

* add src1=dst overlap handling for binary ops

* use compile-time workgroup size defines instead of runtime overrides

* ggml-webgpu: complete overlap handling for binary ops

* add support for inplace & overlap case in binding setup

* restructure conditional logic to handle all overlap cases

* ensure all buffer bindings are correctly assigned for edge cases

* ggml-webgpu: remove unused binary overlap cases

Remove src0==src1 binary overlap case that never occurs in practice.

* keep INPLACE (src0==dst), OVERLAP (src1==dst), DEFAULT

* remove unused src0==src1 and all-same variant

* refactor wgsl to eliminate duplication
---
 .../ggml-webgpu/ggml-webgpu-shader-lib.hpp    |  69 +++++++
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          | 178 ++++++++---------
 .../ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl | 188 ------------------
 ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl | 107 ++++++++++
 .../ggml-webgpu/wgsl-shaders/binary_head.tmpl |  45 -----
 5 files changed, 257 insertions(+), 330 deletions(-)
 delete mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl
 create mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
 delete mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index 84d88e81d4..6997f6bdd3 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -465,4 +465,73 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
     return result;
 }
 
+/** Binary **/
+
+struct ggml_webgpu_binary_pipeline_key {
+    int  type;
+    int  op;
+    bool inplace;
+    bool overlap;
+
+    bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
+        return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap;
+    }
+};
+
+struct ggml_webgpu_binary_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        ggml_webgpu_hash_combine(seed, key.overlap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_binary_shader_lib_context {
+    ggml_webgpu_binary_pipeline_key key;
+    uint32_t                        max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
+    pre_wgsl::Preprocessor &                      preprocessor,
+    const char *                                  shader_src,
+    const ggml_webgpu_binary_shader_lib_context & context) {
+    std::vector defines;
+    std::string              op_name = ggml_op_name((ggml_op) context.key.op);
+    std::string              variant = op_name;
+
+    defines.push_back(std::string("OP_") + op_name);
+
+    switch (context.key.type) {
+        case GGML_TYPE_F32:
+            defines.push_back("TYPE_F32");
+            variant += "_f32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("TYPE_F16");
+            variant += "_f16";
+            break;
+        default:
+            GGML_ABORT("Unsupported type for binary shader");
+    }
+
+    if (context.key.inplace) {
+        defines.push_back("INPLACE");
+        variant += "_inplace";
+    } else if (context.key.overlap) {
+        defines.push_back("OVERLAP");
+        variant += "_overlap";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
+    result.variant                                   = variant;
+    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
+    decisions->wg_size                               = context.max_wg_size;
+    result.decisions                                 = decisions;
+    return result;
+}
 #endif  // GGML_WEBGPU_SHADER_LIB_HPP
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index 4ef50e365e..f7ceca1121 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -348,13 +348,12 @@ struct webgpu_context_struct {
 
     std::unordered_map
                                                   set_rows_pipelines;
-    std::map> get_rows_pipelines;                 // src_type, vectorized
+    std::map> get_rows_pipelines;  // src_type, vectorized
 
-    std::map> cpy_pipelines;                      // src_type, dst_type
-    std::map> add_pipelines;                      // type, inplace
-    std::map> sub_pipelines;                      // type, inplace
-    std::map> mul_pipelines;                      // type, inplace
-    std::map> div_pipelines;                      // type, inplace
+    std::map> cpy_pipelines;       // src_type, dst_type
+
+    std::unordered_map
+        binary_pipelines;
 
     std::map                               rms_norm_pipelines;  // inplace
     std::map>> rope_pipelines;      // type, ff, inplace
@@ -823,6 +822,28 @@ static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
            (ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
 }
 
+// Used to determine if two tensors share the same buffer and their byte ranges overlap,
+static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
+    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
+           ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
+           ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
+}
+
+struct binary_overlap_flags {
+    bool inplace;  // src0 == dst
+    bool overlap;  // src1 == dst
+};
+
+static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
+                                                              ggml_tensor * src1,
+                                                              ggml_tensor * dst) {
+    binary_overlap_flags flags = {};
+    flags.inplace              = ggml_webgpu_tensor_equal(src0, dst);
+    flags.overlap              = ggml_webgpu_tensor_overlap(src1, dst);
+
+    return flags;
+}
+
 static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1375,14 +1396,42 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
-static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
-                                            ggml_tensor *     src0,
-                                            ggml_tensor *     src1,
-                                            ggml_tensor *     dst,
-                                            webgpu_pipeline & pipeline,
-                                            bool              inplace) {
+static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    dst) {
+    binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
+
+    ggml_webgpu_binary_pipeline_key pipeline_key = {
+        .type    = dst->type,
+        .op      = dst->op,
+        .inplace = flags.inplace,
+        .overlap = flags.overlap,
+    };
+    ggml_webgpu_binary_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->binary_pipelines.find(pipeline_key);
+    if (it != ctx->binary_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_binary_shader(ctx->p, wgsl_binary, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->binary_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    ggml_webgpu_generic_shader_decisions decisions =
+        *static_cast(pipeline.context);
+
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
     std::vector params = {
-        (uint32_t) ggml_nelements(dst),
+        ne,
         (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)),
@@ -1399,24 +1448,30 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
         (uint32_t) src1->ne[3],
     };
 
-    std::vector entries = {
-        { .binding = 0,
-         .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_webgpu_tensor_buf(src1),
-         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
-         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) }
-    };
-    if (!inplace) {
+    std::vector entries;
+
+    entries.push_back({
+        .binding = 0,
+        .buffer  = ggml_webgpu_tensor_buf(src0),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src0),
+    });
+
+    entries.push_back({
+        .binding = 1,
+        .buffer  = ggml_webgpu_tensor_buf(src1),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src1),
+    });
+
+    if (!flags.inplace && !flags.overlap) {
         entries.push_back({ .binding = 2,
                             .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 = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -2038,25 +2093,10 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx,
             return std::nullopt;
 #endif
         case GGML_OP_ADD:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_SUB:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_MUL:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_DIV:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace);
-            }
+            return ggml_webgpu_binary_op(ctx, src0, src1, node);
         case GGML_OP_RMS_NORM:
             return ggml_webgpu_rms_norm(ctx, src0, node);
         case GGML_OP_ROPE:
@@ -2665,58 +2705,6 @@ static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
         ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants);
 }
 
-static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants);
-}
-
 static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) {
     std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
 
@@ -3018,10 +3006,6 @@ static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) {
     ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
     ggml_webgpu_init_get_rows_pipeline(webgpu_ctx);
     ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
-    ggml_webgpu_init_add_pipeline(webgpu_ctx);
-    ggml_webgpu_init_sub_pipeline(webgpu_ctx);
-    ggml_webgpu_init_mul_pipeline(webgpu_ctx);
-    ggml_webgpu_init_div_pipeline(webgpu_ctx);
     ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx);
     ggml_webgpu_init_rope_pipeline(webgpu_ctx);
     ggml_webgpu_init_glu_pipeline(webgpu_ctx);
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl
deleted file mode 100644
index 1ce4d83fa8..0000000000
--- a/ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl
+++ /dev/null
@@ -1,188 +0,0 @@
-#define(VARIANTS)
-
-[
-  {
-    "SHADER_NAME": "add_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  }
-]
-
-#end(VARIANTS)
-
-#define(DECLS)
-
-#decl(NOT_INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    dst[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var dst: array<{{TYPE}}>;
-
-@group(0) @binding(3)
-var params: Params;
-
-#enddecl(NOT_INPLACE)
-
-#decl(INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    src0[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var params: Params;
-
-#enddecl(INPLACE)
-
-#end(DECLS)
-
-
-#define(SHADER)
-
-enable f16;
-
-#include "binary_head.tmpl"
-
-@group(0) @binding(0)
-var src0: array<{{TYPE}}>;
-
-@group(0) @binding(1)
-var src1: array<{{TYPE}}>;
-
-DECLS
-
-override wg_size: u32;
-@compute @workgroup_size(wg_size)
-fn main(@builtin(global_invocation_id) gid: vec3) {
-    if (gid.x < params.ne) {
-        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
-    }
-}
-
-#end(SHADER)
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
new file mode 100644
index 0000000000..55dd66408a
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
@@ -0,0 +1,107 @@
+enable f16;
+
+struct Params {
+    ne: u32,
+
+    // offsets in elements
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+
+    stride_src1_0: u32,
+    stride_src1_1: u32,
+    stride_src1_2: u32,
+    stride_src1_3: u32,
+
+    a_ne0: u32,
+    a_ne1: u32,
+    a_ne2: u32,
+
+    b_ne0: u32,
+    b_ne1: u32,
+    b_ne2: u32,
+    b_ne3: u32,
+};
+
+fn src1_index(_i: u32) -> u32 {
+    var i = _i;
+    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    let a_i2 = i / (params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne1 * params.a_ne0);
+    let a_i1 = i / params.a_ne0;
+    let a_i0 = i % params.a_ne0;
+
+    // handle repetition of b
+    // index loops back to the beginning and repeats after elements are exhausted = modulo
+    let b_i0 = a_i0 % params.b_ne0;
+    let b_i1 = a_i1 % params.b_ne1;
+    let b_i2 = a_i2 % params.b_ne2;
+    let b_i3 = a_i3 % params.b_ne3;
+
+    // compute index for position in b's flat array
+    return b_i0 * params.stride_src1_0 +
+           b_i1 * params.stride_src1_1 +
+           b_i2 * params.stride_src1_2 +
+           b_i3 * params.stride_src1_3;
+}
+
+#ifdef TYPE_F32
+#define DataType f32
+#endif
+#ifdef TYPE_F16
+#define DataType f16
+#endif
+
+@group(0) @binding(0)
+var src0: array;
+
+@group(0) @binding(1)
+var src1 : array;
+
+#ifdef INPLACE
+@group(0) @binding(2)
+var params: Params;
+
+#elif defined(OVERLAP)
+@group(0) @binding(2)
+var params: Params;
+
+#else
+@group(0) @binding(2)
+var dst: array;
+
+@group(0) @binding(3)
+var params: Params;
+#endif
+
+fn op(a: DataType, b: DataType) -> DataType {
+#ifdef OP_ADD
+    return a + b;
+#elif defined(OP_SUB)
+    return a - b;
+#elif defined(OP_MUL)
+    return a * b;
+#elif defined(OP_DIV)
+    return a / b;
+#endif
+}
+
+fn update(dst_i: u32, src0_i: u32, src1_i: u32){
+    let result = op(src0[src0_i], src1[src1_i]);
+
+#ifdef INPLACE
+    src0[dst_i] = result;
+#elif defined(OVERLAP)
+    src1[dst_i] = result;
+#else
+    dst[dst_i] = result;
+#endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3) {
+    if (gid.x < params.ne) {
+        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
+    }
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl
deleted file mode 100644
index 4b254f468d..0000000000
--- a/ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl
+++ /dev/null
@@ -1,45 +0,0 @@
-struct Params {
-    ne: u32,
-
-    // offsets in elements
-    offset_src0: u32,
-    offset_src1: u32,
-    offset_dst: u32,
-
-    stride_src1_0: u32,
-    stride_src1_1: u32,
-    stride_src1_2: u32,
-    stride_src1_3: u32,
-
-    a_ne0: u32,
-    a_ne1: u32,
-    a_ne2: u32,
-
-    b_ne0: u32,
-    b_ne1: u32,
-    b_ne2: u32,
-    b_ne3: u32,
-};
-
-fn src1_index(_i: u32) -> u32 {
-    var i = _i;
-    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    let a_i2 = i / (params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne1 * params.a_ne0);
-    let a_i1 = i / params.a_ne0;
-    let a_i0 = i % params.a_ne0;
-
-    // handle repetition of b
-    // index loops back to the beginning and repeats after elements are exhausted = modulo
-    let b_i0 = a_i0 % params.b_ne0;
-    let b_i1 = a_i1 % params.b_ne1;
-    let b_i2 = a_i2 % params.b_ne2;
-    let b_i3 = a_i3 % params.b_ne3;
-
-    // compute index for position in b's flat array
-    return b_i0 * params.stride_src1_0 +
-           b_i1 * params.stride_src1_1 +
-           b_i2 * params.stride_src1_2 +
-           b_i3 * params.stride_src1_3;
-}

From 3228e7728789e0456d0458ce38d20d0b1d60a9aa Mon Sep 17 00:00:00 2001
From: Alex Trotta <44127594+Ahajha@users.noreply.github.com>
Date: Fri, 6 Feb 2026 15:05:19 -0500
Subject: [PATCH 147/206] gguf-py : bump sentencepiece version (#19319)

* gguf-py: Bump sentencepiece version

There's a new version that's been out for a while that addresses the issues mentioned in https://github.com/ggml-org/llama.cpp/pull/14200. There's a long chain of reasons I would like this change, but the short version is that it allows people who use both `sentencepiece` and `gguf` to take advantage of these fixes. On conda-forge, currently, it locks the version (since there is no notion of optional dependencies).

Regardless, I don't think this should be too controversial.

* review feedback
---
 gguf-py/pyproject.toml                             | 2 +-
 pyproject.toml                                     | 2 +-
 requirements/requirements-convert_legacy_llama.txt | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index f6c4cd14e7..48693ae3e3 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -23,7 +23,7 @@ numpy = ">=1.17"
 tqdm = ">=4.27"
 pyyaml = ">=5.1"
 requests = ">=2.25"
-sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true }
+sentencepiece = { version = ">=0.1.98,<0.3.0", optional = true }
 PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
 
 [tool.poetry.dev-dependencies]
diff --git a/pyproject.toml b/pyproject.toml
index 3d71b055a8..422f53c7c7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,7 +17,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.9"
 numpy = "^1.25.0"
-sentencepiece = ">=0.1.98,<=0.2.0"
+sentencepiece = ">=0.1.98,<0.3.0"
 transformers = ">=4.35.2,<5.0.0"
 protobuf = ">=4.21.0,<5.0.0"
 gguf = { path = "./gguf-py" }
diff --git a/requirements/requirements-convert_legacy_llama.txt b/requirements/requirements-convert_legacy_llama.txt
index dbab3b9508..4898bf7ee2 100644
--- a/requirements/requirements-convert_legacy_llama.txt
+++ b/requirements/requirements-convert_legacy_llama.txt
@@ -1,5 +1,5 @@
 numpy~=1.26.4
-sentencepiece~=0.2.0
+sentencepiece>=0.1.98,<0.3.0
 
 transformers>=4.57.1,<5.0.0
 

From b83111815e9a79949257e9d4b087206b320a3063 Mon Sep 17 00:00:00 2001
From: forforever73 <63285796+forforever73@users.noreply.github.com>
Date: Sat, 7 Feb 2026 04:06:14 +0800
Subject: [PATCH 148/206] model : support Step3.5-Flash (#19283)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Support Step3.5-Flash

* fix: norm.weight + 1 (HF zero_centered=true)

* step35: simplify GGUF conversion + drop redundant rope KVs

* Address review feedback

* rename limits -> clamp

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret 

* Apply suggestion from @CISC

Co-authored-by: Sigbjørn Skjæret 

* rename swiglu limits -> swiglu clamp in LLM_KV

* avoid CI fail

* Apply suggestions from code review

* Apply suggestions from code review

* disabled KV shifting for LLM_ARCH_STEP35

* Apply suggestions from code review

* mistakenly removed cmath

* add model size && apply missed suggestion

* assert partial_rotary_factors

* fix CI errors:

* load freq_base_swa

---------

Co-authored-by: lvyichen 
Co-authored-by: Sigbjørn Skjæret 
---
 convert_hf_to_gguf.py          | 131 ++++++++++++++++++++++++-
 gguf-py/gguf/constants.py      |  70 ++++++++++----
 gguf-py/gguf/gguf_writer.py    |   6 ++
 gguf-py/gguf/tensor_mapping.py |   9 ++
 src/CMakeLists.txt             |   1 +
 src/llama-arch.cpp             |  64 +++++++++----
 src/llama-arch.h               |   3 +
 src/llama-graph.cpp            |  41 ++++++++
 src/llama-hparams.h            |   5 +
 src/llama-kv-cache-iswa.cpp    |   4 +-
 src/llama-kv-cache.cpp         |   4 +
 src/llama-model.cpp            | 103 ++++++++++++++++++++
 src/llama-model.h              |   1 +
 src/models/models.h            |   4 +
 src/models/step35-iswa.cpp     | 168 +++++++++++++++++++++++++++++++++
 15 files changed, 576 insertions(+), 38 deletions(-)
 create mode 100644 src/models/step35-iswa.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c167de8a46..843c00a896 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -920,7 +920,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_expert_group_used_count(n_group_used)
             logger.info(f"gguf: expert groups used count = {n_group_used}")
 
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation_func"], optional=True)) is not None:
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -7912,6 +7912,135 @@ class MimoV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Step3p5ForCausalLM")
+class Step35Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STEP35
+
+    def set_gguf_parameters(self):
+        rope_theta = self.hparams.get("rope_theta")
+        if isinstance(rope_theta, list):
+            self.hparams["rope_theta"] = float(rope_theta[0])
+            self.hparams["local_rope_theta"] = float(rope_theta[1])
+            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
+            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
+
+        super().set_gguf_parameters()
+
+        layer_types = self.hparams.get("layer_types") or []
+        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
+        attn_other = self.hparams.get("attention_other_setting") or {}
+
+        n_head_base = self.hparams["num_attention_heads"]
+        n_kv_base = self.hparams["num_attention_groups"]
+
+        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
+        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
+
+        layer_types = layer_types[: self.block_count]
+        partial_rotary_factors = partial_rotary_factors[: self.block_count]
+        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
+        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
+        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
+        swa_pat = [lt == "sliding_attention" for lt in layer_types]
+
+        self.gguf_writer.add_head_count(head_arr)
+        self.gguf_writer.add_head_count_kv(kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(swa_pat)
+
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        # MoE params
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
+
+        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
+        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
+
+        # leading dense blocks
+        leading_dense = 0
+        moe_layers_enum = self.hparams.get("moe_layers_enum")
+        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
+            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
+            if moe_layers:
+                leading_dense = max(0, moe_layers[0])
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
+
+        # Optional per-layer SwiGLU clamps.
+        if (limits := self.hparams.get("swiglu_limits")) is not None:
+            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
+        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
+            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # remove mtp layers
+        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
+            il = int(m.group(1))
+            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
+            if il >= n_main:
+                return
+        if name.endswith("norm.weight"):
+            data_torch += 1.0
+        # Map router bias (expert selection bias) to a GGUF bias tensor
+        if name.endswith(".moe.router_bias"):
+            name += ".bias"
+
+        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
+            data_torch = data_torch.squeeze().contiguous()
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
+        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        rope_type = rope_params.get("rope_type") or ""
+        if rope_type.lower() != "llama3":
+            return
+
+        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
+        rope_theta = self.hparams.get("rope_theta", 10000.0)
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[0]
+        base = float(rope_theta)
+        if (dim := self.hparams.get("head_dim")) is None:
+            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        dim = int(dim)
+
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        factor = float(rope_params.get("factor", 8.0))
+        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
+        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        rope_factors: list[float] = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / float(freq)
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1.0)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
 @ModelBase.register("PanguEmbeddedForCausalLM")
 class PanguEmbeddedModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PANGU_EMBED
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 3ddbc73d1c..3af4fffe95 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -146,6 +146,8 @@ class Keys:
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
         EMBD_LENGTH_PER_LAYER_INP         = "{arch}.embedding_length_per_layer_input"
+        SWIGLU_CLAMP_EXP                  = "{arch}.swiglu_clamp_exp"
+        SWIGLU_CLAMP_SHEXP                = "{arch}.swiglu_clamp_shexp"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
 
@@ -179,20 +181,20 @@ class Keys:
         TEMPERATURE_SCALE            = "{arch}.attention.temperature_scale"
 
     class Rope:
-        DIMENSION_COUNT          = "{arch}.rope.dimension_count"
-        DIMENSION_SECTIONS       = "{arch}.rope.dimension_sections"
-        FREQ_BASE                = "{arch}.rope.freq_base"
-        FREQ_BASE_SWA            = "{arch}.rope.freq_base_swa"
-        SCALING_TYPE             = "{arch}.rope.scaling.type"
-        SCALING_FACTOR           = "{arch}.rope.scaling.factor"
-        SCALING_ATTN_FACTOR      = "{arch}.rope.scaling.attn_factor"
-        SCALING_ORIG_CTX_LEN     = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED        = "{arch}.rope.scaling.finetuned"
-        SCALING_YARN_LOG_MUL     = "{arch}.rope.scaling.yarn_log_multiplier"
-        SCALING_YARN_EXT_FACTOR  = "{arch}.rope.scaling.yarn_ext_factor"
-        SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
-        SCALING_YARN_BETA_FAST   = "{arch}.rope.scaling.yarn_beta_fast"
-        SCALING_YARN_BETA_SLOW   = "{arch}.rope.scaling.yarn_beta_slow"
+        DIMENSION_COUNT           = "{arch}.rope.dimension_count"
+        DIMENSION_SECTIONS        = "{arch}.rope.dimension_sections"
+        FREQ_BASE                 = "{arch}.rope.freq_base"
+        FREQ_BASE_SWA             = "{arch}.rope.freq_base_swa"
+        SCALING_TYPE              = "{arch}.rope.scaling.type"
+        SCALING_FACTOR            = "{arch}.rope.scaling.factor"
+        SCALING_ATTN_FACTOR       = "{arch}.rope.scaling.attn_factor"
+        SCALING_ORIG_CTX_LEN      = "{arch}.rope.scaling.original_context_length"
+        SCALING_FINETUNED         = "{arch}.rope.scaling.finetuned"
+        SCALING_YARN_LOG_MUL      = "{arch}.rope.scaling.yarn_log_multiplier"
+        SCALING_YARN_EXT_FACTOR   = "{arch}.rope.scaling.yarn_ext_factor"
+        SCALING_YARN_ATTN_FACTOR  = "{arch}.rope.scaling.yarn_attn_factor"
+        SCALING_YARN_BETA_FAST    = "{arch}.rope.scaling.yarn_beta_fast"
+        SCALING_YARN_BETA_SLOW    = "{arch}.rope.scaling.yarn_beta_slow"
 
     class Split:
         LLM_KV_SPLIT_NO            = "split.no"
@@ -462,6 +464,7 @@ class MODEL_ARCH(IntEnum):
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
     MIMO2            = auto()
+    STEP35           = auto()
     LLAMA_EMBED      = auto()
     MAINCODER        = auto()
     KIMI_LINEAR      = auto()
@@ -892,6 +895,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
     MODEL_ARCH.MIMO2:            "mimo2",
+    MODEL_ARCH.STEP35:           "step35",
     MODEL_ARCH.LLAMA_EMBED:      "llama-embed",
     MODEL_ARCH.MAINCODER:        "maincoder",
     MODEL_ARCH.KIMI_LINEAR:      "kimi-linear",
@@ -3364,6 +3368,32 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
     ],
+    MODEL_ARCH.STEP35: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_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_GATE,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.LLAMA_EMBED: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3753,12 +3783,12 @@ KEY_ATTENTION_LAYERNORM_EPS     = Keys.Attention.LAYERNORM_EPS
 KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
 
 # RoPE
-KEY_ROPE_DIMENSION_COUNT      = Keys.Rope.DIMENSION_COUNT
-KEY_ROPE_FREQ_BASE            = Keys.Rope.FREQ_BASE
-KEY_ROPE_SCALING_TYPE         = Keys.Rope.SCALING_TYPE
-KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
-KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
-KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
+KEY_ROPE_DIMENSION_COUNT           = Keys.Rope.DIMENSION_COUNT
+KEY_ROPE_FREQ_BASE                 = Keys.Rope.FREQ_BASE
+KEY_ROPE_SCALING_TYPE              = Keys.Rope.SCALING_TYPE
+KEY_ROPE_SCALING_FACTOR            = Keys.Rope.SCALING_FACTOR
+KEY_ROPE_SCALING_ORIG_CTX_LEN      = Keys.Rope.SCALING_ORIG_CTX_LEN
+KEY_ROPE_SCALING_FINETUNED         = Keys.Rope.SCALING_FINETUNED
 
 # SSM
 KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index f720aa2d54..62172b24c3 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -824,6 +824,12 @@ class GGUFWriter:
     def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
         self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
 
+    def add_swiglu_clamp_exp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_EXP.format(arch=self.arch), values)
+
+    def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values)
+
     def add_expert_group_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index e16c06c2a3..167ade7803 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -359,6 +359,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.ATTN_GATE: (
             "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
 
         # Feed-forward norm
@@ -423,6 +424,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.router.gate",               # afmoe
             "layers.{bid}.gate",                                # mistral-large
             "backbone.layers.{bid}.mixer.gate",                 # nemotron-h-moe
+            "model.layers.{bid}.moe.gate",                      # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -439,6 +441,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.gate.e_score_correction",          # nemotron-h-moe
             "model.layers.{bid}.mlp.e_score_correction",                    # exaone-moe
             "model.layers.{bid}.block_sparse_moe.gate.e_score_correction",  # kimi
+            "model.layers.{bid}.moe.router_bias",                           # step3.5 expert selection bias
         ),
 
         # Feed-forward up
@@ -493,6 +496,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.up_proj",      # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w1",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker
+            "model.layers.{bid}.moe.up_proj",                       # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -504,6 +508,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w3",                        # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi
+            "model.layers.{bid}.share_expert.up_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -543,6 +548,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.experts.w1",           # phimoe (merged)
             "model.layers.{bid}.feed_forward.experts.gate_proj",        # llama4
             "model.layers.{bid}.block_sparse_moe.experts.gate",         # smallthinker
+            "model.layers.{bid}.moe.gate_proj",                         # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
@@ -552,6 +558,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
             "layers.{bid}.shared_experts.w1",                          # mistral-large
             "model.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi
+            "model.layers.{bid}.share_expert.gate_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_CHEXP: (
@@ -606,6 +613,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.down_proj",    # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w2",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.down",     # smallthinker
+            "model.layers.{bid}.moe.down_proj",                     # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
@@ -617,6 +625,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w2",                          # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi
+            "model.layers.{bid}.share_expert.down_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_CHEXP: (
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 5238a5e934..2115fc4255 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -135,6 +135,7 @@ add_library(llama
             models/stablelm.cpp
             models/starcoder.cpp
             models/starcoder2.cpp
+            models/step35-iswa.cpp
             models/t5-dec.cpp
             models/t5-enc.cpp
             models/wavtokenizer-dec.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index a8bf1c9b80..bd78f1e556 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -117,7 +117,8 @@ static const std::map LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
-    { LLM_ARCH_MIMO2,            "mimo2"           },
+    { LLM_ARCH_MIMO2,            "mimo2"            },
+    { LLM_ARCH_STEP35,           "step35"           },
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
@@ -162,6 +163,8 @@ static const std::map LLM_KV_NAMES = {
     { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
     { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
     { LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  "%s.expert_chunk_feed_forward_length"  },
+    { LLM_KV_SWIGLU_CLAMP_EXP,                  "%s.swiglu_clamp_exp"                  },
+    { LLM_KV_SWIGLU_CLAMP_SHEXP,                "%s.swiglu_clamp_shexp"                },
     { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
     { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
     { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
@@ -220,21 +223,21 @@ static const std::map LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
 
-    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
-    { LLM_KV_ROPE_DIMENSION_SECTIONS,       "%s.rope.dimension_sections"              },
-    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
-    { LLM_KV_ROPE_FREQ_BASE_SWA,            "%s.rope.freq_base_swa"                   },
-    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
-    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
-    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
-    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
-    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
-    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
-    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
-    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  "%s.rope.scaling.yarn_ext_factor"         },
-    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor"        },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   "%s.rope.scaling.yarn_beta_fast"          },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   "%s.rope.scaling.yarn_beta_slow"          },
+    { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_SECTIONS,        "%s.rope.dimension_sections"              },
+    { LLM_KV_ROPE_FREQ_BASE,                 "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_FREQ_BASE_SWA,             "%s.rope.freq_base_swa"                   },
+    { LLM_KV_ROPE_SCALE_LINEAR,              "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,              "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,            "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,       "%s.rope.scaling.attn_factor"             },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,      "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,         "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,      "%s.rope.scaling.yarn_log_multiplier"     },
+    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,   "%s.rope.scaling.yarn_ext_factor"         },
+    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,  "%s.rope.scaling.yarn_attn_factor"        },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,    "%s.rope.scaling.yarn_beta_fast"          },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,    "%s.rope.scaling.yarn_beta_slow"          },
 
     { LLM_KV_SPLIT_NO,            "split.no"            },
     { LLM_KV_SPLIT_COUNT,         "split.count"         },
@@ -2279,6 +2282,35 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_UP_EXPS,
                 LLM_TENSOR_FFN_EXP_PROBS_B,
             };
+        case LLM_ARCH_STEP35:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
         case LLM_ARCH_GPTJ:
         case LLM_ARCH_UNKNOWN:
             return {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index f092f72834..e8263369b8 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -122,6 +122,7 @@ enum llm_arch {
     LLM_ARCH_PANGU_EMBED,
     LLM_ARCH_MISTRAL3,
     LLM_ARCH_MIMO2,
+    LLM_ARCH_STEP35,
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
     LLM_ARCH_KIMI_LINEAR,
@@ -166,6 +167,8 @@ enum llm_kv {
     LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,
+    LLM_KV_SWIGLU_CLAMP_EXP,
+    LLM_KV_SWIGLU_CLAMP_SHEXP,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 165cbc0a7d..bba747d37b 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -13,6 +13,8 @@
 #include 
 #include 
 #include 
+#include 
+#include 
 #include 
 
 void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
@@ -1014,6 +1016,26 @@ ggml_tensor * llm_graph_context::build_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate && type_gate == LLM_FFN_PAR) {
+                // Step35: HF clamps gate (after SiLU) and up before multiplication
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_shexp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_silu_clamped", il);
+
+                        tmp = ggml_clamp(ctx0, tmp, -limit, limit);
+                        cb(tmp, "ffn_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, tmp);
+                        cb(cur, "ffn_swiglu_limited", il);
+                        type_gate = LLM_FFN_SEQ;
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, tmp);
                 cb(cur, "ffn_swiglu", il);
                 type_gate = LLM_FFN_SEQ;
@@ -1316,6 +1338,25 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate_exps) {
+                // Step35: per-layer clamp for routed experts
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_exp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_moe_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_moe_silu_clamped", il);
+
+                        up = ggml_clamp(ctx0, up, -limit, limit);
+                        cb(up, "ffn_moe_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, up);
+                        cb(cur, "ffn_moe_swiglu_limited", il);
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, up);
                 cb(cur, "ffn_moe_swiglu", il);
             } else {
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index a435043cfe..6c695bdbf6 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -206,6 +206,11 @@ struct llama_hparams {
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
+
+    // Step35: optional per-layer clamps for (Swi)GLU
+    std::array swiglu_clamp_exp; // clamping for expert FFN
+    std::array swiglu_clamp_shexp; // shared expert
+
     // this value n_pattern means that every nth layer is dense (i.e. non-SWA)
     // dense_first means whether the pattern is start with a dense layer
     // note that if n_pattern == 0, all layers are SWA
diff --git a/src/llama-kv-cache-iswa.cpp b/src/llama-kv-cache-iswa.cpp
index 3a34102a23..26e2cb4270 100644
--- a/src/llama-kv-cache-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -218,7 +218,9 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_update(llama_context * lctx,
 }
 
 bool llama_kv_cache_iswa::get_can_shift() const {
-    return kv_base->get_size() == kv_swa->get_size();
+    return kv_base->get_can_shift() &&
+           kv_swa->get_can_shift() &&
+           kv_base->get_size() == kv_swa->get_size();
 }
 
 void llama_kv_cache_iswa::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.cpp b/src/llama-kv-cache.cpp
index c35cd6761b..cb702b2a59 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -974,6 +974,10 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch &
 }
 
 bool llama_kv_cache::get_can_shift() const {
+    // Step35 uses per-layer RoPE dims; K-shift assumes a single global n_rot.
+    if (model.arch == LLM_ARCH_STEP35) {
+        return false;
+    }
     return true;
 }
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 765e4de2e4..674d06c891 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -130,6 +130,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_102B_A12B:     return "102B.A12B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
+        case LLM_TYPE_196B_A11B:     return "196B.A11B";
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
@@ -560,6 +561,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
     std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
     std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
+    std::fill(hparams.swiglu_clamp_exp.begin(),   hparams.swiglu_clamp_exp.end(),   0.0f);
+    std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
 
     ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
     ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
@@ -2482,6 +2485,35 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+                // MoE + SWA parameters
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+
+                // Step35 uses sigmoid gating by default (if not set in GGUF)
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+                }
+
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
+                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa);
+                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
+
+                switch (hparams.n_layer) {
+                    case 45: type = LLM_TYPE_196B_A11B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -7107,6 +7139,72 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_STEP35:
+                {
+                    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}, 0);
+
+                    // STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
+                    // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
+                    uint32_t n_rot_max = 0;
+                    for (int i = 0; i < n_layer; ++i) {
+                        n_rot_max = std::max(n_rot_max, hparams.n_rot);
+                    }
+                    if (n_rot_max == 0) {
+                        n_rot_max = n_rot;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        const uint32_t n_head_l      = hparams.n_head(i);
+                        const uint32_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+                        const uint32_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+
+                        // optional rope factors (llama3) / longrope tensors
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        } else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head_l}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
+
+                        // head-wise attention gate (Step35 self_attn.g_proj)
+                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        // dense MLP (leading dense blocks)
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+
+                        // MoE routed experts + selection bias (router_bias)
+                        const int64_t n_ff_exp = hparams.n_ff_exp;
+                        layer.ffn_gate_inp      = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_gate_exps     = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_exps       = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_exp_probs_b   = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                        // shared expert MLP
+                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             case LLM_ARCH_MAINCODER:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -8257,6 +8355,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique(*this, params);
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -8502,6 +8604,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_MIMO2:
+        case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:
diff --git a/src/llama-model.h b/src/llama-model.h
index 5b408bcea2..7b580043b3 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -123,6 +123,7 @@ enum llm_type {
     LLM_TYPE_100B_A6B,
     LLM_TYPE_102B_A12B, // Solar-Open
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
+    LLM_TYPE_196B_A11B, // Step3.5-Flash
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_235B_A22B,
     LLM_TYPE_300B_A47B, // Ernie MoE big
diff --git a/src/models/models.h b/src/models/models.h
index 71c1fe8108..cfcbb9aaa5 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -583,6 +583,10 @@ struct llm_build_starcoder : public llm_graph_context {
     llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_step35_iswa : public llm_graph_context {
+    llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_t5_dec : public llm_graph_context {
     llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
 };
diff --git a/src/models/step35-iswa.cpp b/src/models/step35-iswa.cpp
new file mode 100644
index 0000000000..f8737815a6
--- /dev/null
+++ b/src/models/step35-iswa.cpp
@@ -0,0 +1,168 @@
+#include "models.h"
+
+llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    ggml_tensor * inp_pos     = build_inp_pos();
+    auto        * inp_attn    = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        const uint32_t n_head_l    = hparams.n_head(il);
+        const uint32_t n_head_kv_l = hparams.n_head_kv(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);
+
+        cur = inpL;
+
+        // dump pre-attn RMSNorm input to pinpoint layer boundary issues
+        cb(cur, "attn_norm_in", il);
+
+        // self-attention
+        {
+            cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+
+            // Q/K per-head RMSNorm (Step35 q_norm / k_norm)
+            if (model.layers[il].attn_q_norm) {
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+            }
+            if (model.layers[il].attn_k_norm) {
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+            }
+
+            // RoPE (partial rotary factors per layer)
+            const bool is_swa = hparams.is_swa(il);
+            ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
+            const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, rope_factors,
+                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+            );
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, rope_factors,
+                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+            );
+            cb(Qcur, "Qcur_pos", il);
+            cb(Kcur, "Kcur_pos", il);
+
+            const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
+            ggml_tensor * attn_out = build_attn(inp_attn,
+                    nullptr, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(attn_out, "attn_out", il);
+            // head-wise attention gate: sigmoid(g_proj(x)) in torch
+            if (model.layers[il].wqkv_gate) {
+                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
+                cb(gate, "attn_gate", il);
+
+                gate = ggml_sigmoid(ctx0, gate);
+                cb(gate, "attn_gate_sigmoid", il);
+
+                // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
+                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
+                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,       1,          n_head_l, n_tokens);
+                cb(gate_3d, "attn_gate_3d", il);
+
+                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
+                cb(attn_3d, "attn_gated_3d", il);
+
+                attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
+                cb(attn_out, "attn_gated", il);
+            }
+
+            // output projection
+            cur = build_lora_mm(model.layers[il].wo, attn_out);
+            cb(cur, "attn_proj", 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);
+
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense MLP
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE routed experts
+            const bool  norm_w  = hparams.expert_weights_norm;
+            const float w_scale = hparams.expert_weights_scale;
+            const bool  scale_w = w_scale != 0.0f;
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU,
+                    norm_w, scale_w, w_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // shared expert MLP (always added on MoE layers in Step35)
+            ggml_tensor * sh_out = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   nullptr, nullptr,
+                    model.layers[il].ffn_gate_shexp, nullptr, nullptr,
+                    model.layers[il].ffn_down_shexp, nullptr, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(sh_out, "ffn_shared_out", il);
+
+            cur = ggml_add(ctx0, moe_out, sh_out);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

From 34ba7b5a2f5cd88f99629a3bd68d003fbd5bc2cf Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Sat, 7 Feb 2026 07:37:15 +0200
Subject: [PATCH 149/206] metal : fix event synchronization in cpy_tensor_async
 (#19402)

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

diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m
index c7e8ebd3f3..5d3a8ce412 100644
--- a/ggml/src/ggml-metal/ggml-metal-context.m
+++ b/ggml/src/ggml-metal/ggml-metal-context.m
@@ -394,7 +394,7 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
         [encoder endEncoding];
 
         ggml_metal_event_t ev_cpy = ggml_metal_get_ev_cpy(ctx_src);
-        ggml_metal_event_record(ctx_src, ev_cpy);
+        ggml_metal_event_encode_signal(ev_cpy, cmd_buf);
 
         [cmd_buf commit];
 

From 8872ad2125336d209a9911a82101f80095a9831d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Sat, 7 Feb 2026 10:35:56 +0200
Subject: [PATCH 150/206] metal : consolidate bin kernels (#19390)

* metal : refactor bin kernels

* cont

* cont : fix cv
---
 ggml/src/ggml-metal/ggml-metal-device.cpp |  80 ++++-
 ggml/src/ggml-metal/ggml-metal-device.h   |   6 +-
 ggml/src/ggml-metal/ggml-metal-device.m   |  10 +-
 ggml/src/ggml-metal/ggml-metal-impl.h     |   1 +
 ggml/src/ggml-metal/ggml-metal-ops.cpp    |  33 +-
 ggml/src/ggml-metal/ggml-metal.metal      | 389 ++++++++--------------
 6 files changed, 224 insertions(+), 295 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 6af0dd88d5..4c4c3ce36c 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -1392,34 +1392,78 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_attn_ext_v
     GGML_UNUSED(op);
 }
 
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(
-        ggml_metal_library_t lib,
-        ggml_op op,
-        int32_t n_fuse,
-        bool row) {
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(ggml_metal_library_t lib, const ggml_tensor * op, int32_t n_fuse) {
     char base[256];
     char name[256];
 
-    const char * op_str = "undefined";
-    switch (op) {
-        case GGML_OP_ADD:   op_str = "add";   break;
-        case GGML_OP_SUB:   op_str = "sub";   break;
-        case GGML_OP_MUL:   op_str = "mul";   break;
-        case GGML_OP_DIV:   op_str = "div";   break;
+    int op_num = -1;
+
+    switch (op->op) {
+        case GGML_OP_ADD: op_num = 0; break;
+        case GGML_OP_SUB: op_num = 1; break;
+        case GGML_OP_MUL: op_num = 2; break;
+        case GGML_OP_DIV: op_num = 3; break;
         default: GGML_ABORT("fatal error");
     };
 
-    if (row) {
-        snprintf(base, 256, "kernel_%s_row_c4_fuse_%d", op_str, n_fuse);
-    } else {
-        snprintf(base, 256, "kernel_%s_fuse_%d", op_str, n_fuse);
-    }
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t1_str = ggml_type_name(op->src[1]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = (op->src[0]->ne[0] % 4 == 0) && (op->src[1]->ne[0] % 4 == 0);
+
+    const bool is_rb = ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && (ggml_nrows(op->src[1]) == 1) && ggml_nelements(op) < 65536;
+
+    snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s%s", t0_str, t1_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d_nf=%d_rb=%d", base, op_num, n_fuse, is_rb);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
+        ggml_metal_cv_set_int16(cv, n_fuse, FC_BIN + 1);
+        ggml_metal_cv_set_bool (cv, is_rb,  FC_BIN + 2);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
+    }
+
+    res.c4  = is_c4;
+    res.cnt = is_rb;
+
+    return res;
+}
+
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_metal_library_t lib, ggml_op op) {
+    char base[256];
+    char name[256];
+
+    int op_num = -1;
+
+    switch (op) {
+        case GGML_OP_ADD: op_num = 0; break;
+        case GGML_OP_SUB: op_num = 1; break;
+        case GGML_OP_MUL: op_num = 2; break;
+        case GGML_OP_DIV: op_num = 3; break;
+        default: GGML_ABORT("fatal error");
+    };
+
+    snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s", "f32", "f32", "f32");
+    snprintf(name, 256, "%s_op=%d_nf=%d", base, op_num, 1);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
+        ggml_metal_cv_set_int16(cv, 1,      FC_BIN + 1);
+        ggml_metal_cv_set_bool (cv, false,  FC_BIN + 2);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
     return res;
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index 84dcec3083..93d7f6a216 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -53,6 +53,9 @@ struct ggml_metal_pipeline_with_params {
     int nr1;
 
     size_t smem;
+
+    bool c4;
+    bool cnt;
 };
 
 int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_with_params pipeline);
@@ -134,7 +137,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge     (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k             (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge       (ggml_metal_library_t lib, const struct ggml_tensor * op);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse );
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one           (ggml_metal_library_t lib, enum ggml_op op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm              (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index c8e737d418..891d70c85a 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -346,10 +346,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
 
     struct ggml_metal_pipeline_with_params res = {
         /*.pipeline =*/ nil,
+        /*.nsg      =*/ 0,
         /*.nr0      =*/ 0,
         /*.nr1      =*/ 0,
-        /*.nsg      =*/ 0,
         /*.smem     =*/ 0,
+        /*.c4       =*/ false,
+        /*.cnt      =*/ false,
     };
 
     res.pipeline = ggml_metal_pipelines_get(lib->pipelines, name);
@@ -362,10 +364,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
 struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_metal_library_t lib, const char * base, const char * name, ggml_metal_cv_t cv) {
     struct ggml_metal_pipeline_with_params res = {
         /*.pipeline =*/ nil,
+        /*.nsg      =*/ 0,
         /*.nr0      =*/ 0,
         /*.nr1      =*/ 0,
-        /*.nsg      =*/ 0,
         /*.smem     =*/ 0,
+        /*.c4       =*/ false,
+        /*.cnt      =*/ false,
     };
 
     [lib->lock lock];
@@ -1054,7 +1058,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_ADD_ID:
-            return op->src[0]->type == GGML_TYPE_F32;
+            return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ACC:
         case GGML_OP_REPEAT:
         case GGML_OP_SCALE:
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 7f73cb97bb..77bb403c15 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -80,6 +80,7 @@
 #define FC_SSM_CONV                    900
 #define FC_SOLVE_TRI                   1000
 #define FC_COUNT_EQUAL                 1100
+#define FC_BIN                         1200
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index e0ed6c7805..dbf25433c2 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -707,7 +707,7 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
         /*.o1   =*/ { 0 },
     };
 
-    auto pipeline = ggml_metal_library_get_pipeline_bin(lib, GGML_OP_ADD, 1, false);
+    auto pipeline = ggml_metal_library_get_pipeline_bin_one(lib, GGML_OP_ADD);
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
@@ -2895,8 +2895,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[1]));
 
-    bool bcast_row = false;
-
     ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
     ggml_metal_buffer_id bid_src1 = ggml_metal_get_buffer_id(op->src[1]);
     ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
@@ -2990,18 +2988,7 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
 
     struct ggml_metal_pipeline_with_params pipeline;
 
-    if (ggml_nelements(op->src[1]) == ne10 && ggml_is_contiguous(op->src[1]) && ne00 % 4 == 0 && ne10 % 4 == 0) {
-        GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-
-        // src1 is a row
-        GGML_ASSERT(ne11 == 1);
-
-        pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, true);
-
-        bcast_row = true;
-    } else {
-        pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, false);
-    }
+    pipeline = ggml_metal_library_get_pipeline_bin(lib, op, n_fuse);
 
     if (n_fuse > 1) {
         bid_dst = ggml_metal_get_buffer_id(ctx->node(idx + n_fuse - 1));
@@ -3015,20 +3002,28 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
         }
     }
 
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne10 = ne10/4;
+        args.ne0  = ne0/4;
+    }
+
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
     ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
     ggml_metal_encoder_set_buffer  (enc, bid_src1, 2);
     ggml_metal_encoder_set_buffer  (enc, bid_dst,  3);
 
-    if (bcast_row) {
-        const int64_t n = ggml_nelements(op)/4;
+    if (pipeline.cnt) {
+        const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op);
 
         ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
     } else {
-        int nth = 32;
+        const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
 
-        while (16*nth < ne0 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+        int nth = 1;
+
+        while (2*nth < args.ne0 && nth < nth_max) {
             nth *= 2;
         }
 
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 612a42a1ea..35cc3bbdfd 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -895,11 +895,13 @@ enum ggml_sort_order {
     GGML_SORT_ORDER_DESC,
 };
 
-// general-purpose kernel for addition, subtraction, multiplication and division of two tensors
-// pros: works for non-contiguous tensors, supports broadcast across all dims
-// cons: not very efficient
-template 
-kernel void kernel_add_fuse_impl(
+// OP: 0 - add, 1 - sub, 2 - mul, 3 - div
+constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
+constant short FC_bin_f  [[function_constant(FC_BIN + 1)]];
+constant bool  FC_bin_rb [[function_constant(FC_BIN + 2)]];
+
+template 
+kernel void kernel_bin_fuse_impl(
         constant ggml_metal_kargs_bin & args,
         device const char * src0,
         device const char * src1,
@@ -907,138 +909,152 @@ kernel void kernel_add_fuse_impl(
         uint3   tgpig[[threadgroup_position_in_grid]],
         ushort3 tpitg[[thread_position_in_threadgroup]],
         ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+#define FC_OP FC_bin_op
+#define FC_F  FC_bin_f
+#define FC_RB FC_bin_rb
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
+    if (FC_RB) {
+        // row broadcast
+        const uint i0 = tgpig.x;
+        const uint i1 = i0%args.ne10;
 
-    device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
-    device       float * dst_ptr  = (device       float *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs);
+        device const T0 * src0_row = (device const T0 *) (src0);
+        device       T  * dst_row  = (device       T  *) (dst);
 
-    device const float * src1_ptr[F];
-    for (short j = 0; j < F; ++j) {
-        src1_ptr[j] = (device const float *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
-    }
+        if (FC_F == 1) {
+            device const T1 * src1_row = (device const T1 *) (src1 + args.o1[0]);
 
-    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
-        const int i10 = i0%args.ne10;
+            if (FC_OP == 0) {
+                dst_row[i0] = src0_row[i0] + src1_row[i1];
+            }
 
-        float res = src0_ptr[i0];
+            if (FC_OP == 1) {
+                dst_row[i0] = src0_row[i0] - src1_row[i1];
+            }
 
-#pragma unroll
-        for (short j = 0; j < F; ++j) {
-            res += src1_ptr[j][i10];
-        }
+            if (FC_OP == 2) {
+                dst_row[i0] = src0_row[i0] * src1_row[i1];
+            }
 
-        dst_ptr[i0] = res;
-    }
-}
+            if (FC_OP == 3) {
+                dst_row[i0] = src0_row[i0] / src1_row[i1];
+            }
+        } else {
+            T0 res = src0_row[i0];
 
-typedef decltype(kernel_add_fuse_impl<2>) kernel_add_fuse_t;
+            if (FC_OP == 0) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res += ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-template [[host_name("kernel_add_fuse_1")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<1>;
-template [[host_name("kernel_add_fuse_2")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<2>;
-template [[host_name("kernel_add_fuse_3")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<3>;
-template [[host_name("kernel_add_fuse_4")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<4>;
-template [[host_name("kernel_add_fuse_5")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<5>;
-template [[host_name("kernel_add_fuse_6")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<6>;
-template [[host_name("kernel_add_fuse_7")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<7>;
-template [[host_name("kernel_add_fuse_8")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<8>;
+            if (FC_OP == 1) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res -= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-kernel void kernel_sub_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+            if (FC_OP == 2) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res *= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
+            if (FC_OP == 3) {
+                FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                    res /= ((device const T1 *) (src1 + args.o1[j]))[i1];
+                }
+            }
 
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    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));
-    }
-}
-
-kernel void kernel_mul_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
-
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
-
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    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;
-
-    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;
+            dst_row[i0] = res;
         }
     } 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));
+        const int i03 = tgpig.z;
+        const int i02 = tgpig.y;
+        const int i01 = tgpig.x;
+
+        if (i01 >= args.ne01) {
+            return;
+        }
+
+        const int i13 = i03%args.ne13;
+        const int i12 = i02%args.ne12;
+        const int i11 = i01%args.ne11;
+
+        device const T0 * src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
+        device       T  * dst_ptr  = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1  + args.offs);
+
+        if (FC_F == 1) {
+            device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
+
+            for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+                const int i10 = i0%args.ne10;
+
+                if (FC_OP == 0) {
+                    dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10];
+                }
+
+                if (FC_OP == 1) {
+                    dst_ptr[i0] = src0_ptr[i0] - src1_ptr[i10];
+                }
+
+                if (FC_OP == 2) {
+                    dst_ptr[i0] = src0_ptr[i0] * src1_ptr[i10];
+                }
+
+                if (FC_OP == 3) {
+                    dst_ptr[i0] = src0_ptr[i0] / src1_ptr[i10];
+                }
+            }
+        } else {
+            device const T1 * src1_ptr[8];
+            FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                src1_ptr[j] = (device const T1 *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
+            }
+
+            for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+                const int i10 = i0%args.ne10;
+
+                T res = src0_ptr[i0];
+
+                if (FC_OP == 0) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res += src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 1) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res -= src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 2) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res *= src1_ptr[j][i10];
+                    }
+                }
+
+                if (FC_OP == 3) {
+                    FOR_UNROLL (short j = 0; j < FC_F; ++j) {
+                        res /= src1_ptr[j][i10];
+                    }
+                }
+
+                dst_ptr[i0] = res;
+            }
         }
     }
+
+#undef FC_OP
+#undef FC_F
+#undef FC_RB
 }
 
-kernel void kernel_div_fuse_1(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    const int i03 = tgpig.z;
-    const int i02 = tgpig.y;
-    const int i01 = tgpig.x;
+typedef decltype(kernel_bin_fuse_impl) kernel_bin_fuse_t;
 
-    const int i13 = i03%args.ne13;
-    const int i12 = i02%args.ne12;
-    const int i11 = i01%args.ne11;
-
-    device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
-    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;
-
-    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));
-        }
-    }
-}
+template [[host_name("kernel_bin_fuse_f32_f32_f32")]]   kernel kernel_bin_fuse_t kernel_bin_fuse_impl;
+template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl;
 
 kernel void kernel_add_id(
         constant ggml_metal_kargs_add_id & args,
@@ -1057,7 +1073,7 @@ kernel void kernel_add_id(
     const size_t nb1 = args.ne0 * sizeof(float);
     const size_t nb2 = args.ne1 * nb1;
 
-    device       float * dst_row  = (device       float *)((device char *)dst + i1*nb1 + i2*nb2);
+    device       float * dst_row  = (device       float *)((device char *)dst  +  i1*nb1       + i2*nb2);
     device const float * src0_row = (device const float *)((device char *)src0 +  i1*args.nb01 + i2*args.nb02);
     device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
 
@@ -1098,141 +1114,6 @@ template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat
 template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat;
 template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat;
 
-// assumption: src1 is a row
-// broadcast src1 into src0
-template 
-kernel void kernel_add_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res += ((device const float4 *) (src1 + args.o1[j]))[i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_add_row_c4_fuse_impl<1>) kernel_add_row_c4_fuse_t;
-
-template [[host_name("kernel_add_row_c4_fuse_1")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<1>;
-template [[host_name("kernel_add_row_c4_fuse_2")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<2>;
-template [[host_name("kernel_add_row_c4_fuse_3")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<3>;
-template [[host_name("kernel_add_row_c4_fuse_4")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<4>;
-template [[host_name("kernel_add_row_c4_fuse_5")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<5>;
-template [[host_name("kernel_add_row_c4_fuse_6")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<6>;
-template [[host_name("kernel_add_row_c4_fuse_7")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<7>;
-template [[host_name("kernel_add_row_c4_fuse_8")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<8>;
-
-template 
-kernel void kernel_sub_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res -= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_sub_row_c4_fuse_impl<1>) kernel_sub_row_c4_fuse_t;
-
-template [[host_name("kernel_sub_row_c4_fuse_1")]] kernel kernel_sub_row_c4_fuse_t kernel_sub_row_c4_fuse_impl<1>;
-
-template 
-kernel void kernel_mul_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res *= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_mul_row_c4_fuse_impl<1>) kernel_mul_row_c4_fuse_t;
-
-template [[host_name("kernel_mul_row_c4_fuse_1")]] kernel kernel_mul_row_c4_fuse_t kernel_mul_row_c4_fuse_impl<1>;
-
-template 
-kernel void kernel_div_row_c4_fuse_impl(
-        constant ggml_metal_kargs_bin & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint tpig[[thread_position_in_grid]]) {
-
-    const uint nb = args.ne00/4;
-    const uint i  = tpig % nb;
-
-    device const float4 * src0_row = (device const float4 *) (src0);
-    device       float4 *  dst_row = (device       float4 *) (dst);
-
-    device const float4 * src1_row[F];
-    for (short j = 0; j < F; ++j) {
-        src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
-    }
-
-    float4 res = src0_row[tpig];
-
-#pragma unroll(F)
-    for (short j = 0; j < F; ++j) {
-        res /= src1_row[j][i];
-    }
-
-    dst_row[tpig] = res;
-}
-
-typedef decltype(kernel_div_row_c4_fuse_impl<1>) kernel_div_row_c4_fuse_t;
-
-template [[host_name("kernel_div_row_c4_fuse_1")]] kernel kernel_div_row_c4_fuse_t kernel_div_row_c4_fuse_impl<1>;
-
 kernel void kernel_scale_f32(
         constant ggml_metal_kargs_scale & args,
         device const float * src0,

From 96441c955ea45cfa2b6834cad419b849aa144463 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Sun, 8 Feb 2026 00:50:47 +0200
Subject: [PATCH 151/206] ci : use -j param correctly when building with
 sanitizers (#19411)

* ci : use less jobs when building with sanitizers

* cont : fix nproc

* cont : fix the fix

* cont : simplify
---
 .github/workflows/build.yml  | 2 ++
 .github/workflows/server.yml | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 51a3dc76e9..6c7ab71143 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -295,6 +295,7 @@ jobs:
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Build (no OpenMP)
@@ -307,6 +308,7 @@ jobs:
             -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Test
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 3d342c35f7..f44a9e739c 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -81,7 +81,7 @@ jobs:
             -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
             -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
             -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
-          cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
       - name: Python setup
         id: setup_python

From 9a5f57795c01c6e67a53eeedeae67ed63aaf7f8e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= 
Date: Sun, 8 Feb 2026 01:20:00 +0100
Subject: [PATCH 152/206] ci : remove server job from webui and move slow test
 (#19424)

* remove server job from webui and move slow test

* use pip-install option
---
 .github/workflows/server-webui.yml | 120 -----------------------------
 .github/workflows/server.yml       |  20 ++---
 2 files changed, 10 insertions(+), 130 deletions(-)

diff --git a/.github/workflows/server-webui.yml b/.github/workflows/server-webui.yml
index 6d1b617371..94899c9376 100644
--- a/.github/workflows/server-webui.yml
+++ b/.github/workflows/server-webui.yml
@@ -8,10 +8,6 @@ on:
         description: 'Commit SHA1 to build'
         required: false
         type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
   push:
     branches:
       - master
@@ -101,119 +97,3 @@ jobs:
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
         working-directory: tools/server/webui
-
-  server-build:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
-
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
-
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        env:
-          GITHUB_ACTIONS: "true"
-        run: |
-          cd tools/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd tools/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          SLOW_TESTS=1 ./tests.sh
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index f44a9e739c..99d05226ba 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -88,11 +88,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests
@@ -102,6 +98,14 @@ jobs:
           export ${{ matrix.extra_args }}
           pytest -v -x -m "not slow"
 
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd tools/server/tests
+          export ${{ matrix.extra_args }}
+          SLOW_TESTS=1 pytest -v -x
+
   server-windows:
     runs-on: windows-2022
 
@@ -124,11 +128,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests

From 5999b50eb00972732b69c519121dda1361f56eb3 Mon Sep 17 00:00:00 2001
From: ddh0 
Date: Sun, 8 Feb 2026 01:22:38 -0600
Subject: [PATCH 153/206] llama-quantize : cleanup `--help` output (#19317)

* cleanup `llama-quantize --help` output

some much needed TLC

* remove future argument

oops, spoiler

* cleanup of cleanup
---
 tools/quantize/quantize.cpp | 59 +++++++++++++++++++++++++------------
 1 file changed, 40 insertions(+), 19 deletions(-)

diff --git a/tools/quantize/quantize.cpp b/tools/quantize/quantize.cpp
index 0709e0bda0..c0f49279ee 100644
--- a/tools/quantize/quantize.cpp
+++ b/tools/quantize/quantize.cpp
@@ -119,27 +119,48 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 [[noreturn]]
 static void usage(const char * executable) {
     printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable);
-    printf("       [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file] [--prune-layers] [--keep-split] [--override-kv]\n");
+    printf("       [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file]\n");
+    printf("       [--prune-layers] [--keep-split] [--override-kv]\n");
     printf("       model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
-    printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
-    printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
-    printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
-    printf("  --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
-    printf("  --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
-    printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
-    printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
-    printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
-    printf("  --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
-    printf("      Advanced option to selectively quantize tensors. May be specified multiple times.\n");
-    printf("  --tensor-type-file tensor_type.txt: list of tensors to quantize to specific ggml_type. example: --tensor-type-file tensor_type_list.txt\n");
-    printf("      Advanced option to selectively quantize a long list of tensors. Format to be tensor_name=ggml_type, separated by spaces/newline.\n");
-    printf("  --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n");
-    printf("      Advanced option to remove all tensors from the given layers\n");
-    printf("  --keep-split: will generate quantized model in the same shards as input\n");
+    printf("  --allow-requantize\n");
+    printf("                                      allow requantizing tensors that have already been quantized\n");
+    printf("                                      WARNING: this can severely reduce quality compared to quantizing\n");
+    printf("                                               from 16bit or 32bit!\n");
+    printf("  --leave-output-tensor\n");
+    printf("                                      leave output.weight un(re)quantized\n");
+    printf("                                      increases model size but may also increase quality, especially when requantizing\n");
+    printf("  --pure\n");
+    printf("                                      disable k-quant mixtures and quantize all tensors to the same type\n");
+    printf("  --imatrix file_name\n");
+    printf("                                      use data in file_name as importance matrix for quant optimizations\n");
+    printf("  --include-weights tensor_name\n");
+    printf("                                      use importance matrix for this/these tensor(s)\n");
+    printf("  --exclude-weights tensor_name\n");
+    printf("                                      do not use importance matrix for this/these tensor(s)\n");
+    printf("  --output-tensor-type ggml_type\n");
+    printf("                                      use this ggml_type for the output.weight tensor\n");
+    printf("  --token-embedding-type ggml_type\n");
+    printf("                                      use this ggml_type for the token embeddings tensor\n");
+    printf("  --tensor-type tensor_name=ggml_type\n");
+    printf("                                      quantize this tensor to this ggml_type\n");
+    printf("                                      this is an advanced option to selectively quantize tensors. may be specified multiple times.\n");
+    printf("                                      example: --tensor-type attn_q=q8_0\n");
+    printf("  --tensor-type-file tensor_types.txt\n");
+    printf("                                      list of tensors to quantize to a specific ggml_type\n");
+    printf("                                      this is an advanced option to selectively quantize a long list of tensors.\n");
+    printf("                                      the file should use the same format as above, separated by spaces or newlines.\n");
+    printf("  --prune-layers L0,L1,L2...\n");
+    printf("                                      comma-separated list of layer numbers to prune from the model\n");
+    printf("                                      WARNING: this is an advanced option, use with care.\n");
+    printf("  --keep-split\n");
+    printf("                                      generate quantized model in the same shards as input\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
-    printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
-    printf("Note: --include-weights and --exclude-weights cannot be used together\n");
-    printf("\nAllowed quantization types:\n");
+    printf("                                      override model metadata by key in the quantized model. may be specified multiple times.\n");
+    printf("                                      WARNING: this is an advanced option, use with care.\n\n");
+    printf("note: --include-weights and --exclude-weights cannot be used together\n\n");
+    printf("-----------------------------------------------------------------------------\n");
+    printf(" allowed quantization types\n");
+    printf("-----------------------------------------------------------------------------\n\n");
     for (const auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
             printf("  %2d  or  ", it.ftype);

From eb449cdfa4319d8fd9066e4633b49f4c867dd11c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Sun, 8 Feb 2026 09:40:04 +0200
Subject: [PATCH 154/206] server : improve context checkpoint logic (#19408)

---
 tools/server/server-context.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index b71d496eeb..8ec8451339 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -2507,7 +2507,8 @@ private:
                         slot.n_prompt_tokens_processed++;
 
                         // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
-                        if (do_checkpoint && slot.task->n_tokens() - slot.prompt.n_tokens() == 64) {
+                        const int n_last = std::min(n_batch, 512);
+                        if (do_checkpoint && slot.task->n_tokens() == slot.prompt.n_tokens() + n_last) {
                             break;
                         }
                     }

From 5fa1c190d9fc86c02698b730a2cb933195e19d96 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= 
Date: Sun, 8 Feb 2026 09:06:45 +0100
Subject: [PATCH 155/206] rpc : update from common.cpp (#19400)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët 
---
 tools/rpc/rpc-server.cpp | 64 ++++++++++++++++++++++++++++++----------
 1 file changed, 49 insertions(+), 15 deletions(-)

diff --git a/tools/rpc/rpc-server.cpp b/tools/rpc/rpc-server.cpp
index 58b93c7468..521f79622d 100644
--- a/tools/rpc/rpc-server.cpp
+++ b/tools/rpc/rpc-server.cpp
@@ -1,12 +1,7 @@
-#if defined(_MSC_VER)
-#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
-#endif
-
 #include "ggml-rpc.h"
 #ifdef _WIN32
 #  define NOMINMAX
 #  define DIRECTORY_SEPARATOR '\\'
-#  include 
 #  include 
 #  include 
 #  include 
@@ -15,23 +10,43 @@
 #  include 
 #  include 
 #endif
-#include 
 #include 
 #include 
 #include 
-#include 
 #include 
 #include 
 #include 
 
-namespace fs = std::filesystem;
+#if defined(__linux__)
+#include 
+#include 
+#endif
+
+// NOTE: this is copied from common.cpp to avoid linking with libcommon
+#ifdef _WIN32
+static std::wstring utf8_to_wstring(const std::string & str) {
+    if (str.empty()) {
+        return std::wstring();
+    }
+
+    int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
+
+    if (size <= 0) {
+        return std::wstring();
+    }
+
+    std::wstring wstr(size, 0);
+    MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
+
+    return wstr;
+}
+#endif
 
 // NOTE: this is copied from common.cpp to avoid linking with libcommon
 // returns true if successful, false otherwise
 static bool fs_create_directory_with_parents(const std::string & path) {
 #ifdef _WIN32
-    std::wstring_convert> converter;
-    std::wstring wpath = converter.from_bytes(path);
+    std::wstring wpath = utf8_to_wstring(path);
 
     // if the path already exists, check whether it's a directory
     const DWORD attributes = GetFileAttributesW(wpath.c_str());
@@ -44,9 +59,16 @@ static bool fs_create_directory_with_parents(const std::string & path) {
     // process path from front to back, procedurally creating directories
     while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
         const std::wstring subpath = wpath.substr(0, pos_slash);
-        const wchar_t * test = subpath.c_str();
 
-        const bool success = CreateDirectoryW(test, NULL);
+        pos_slash += 1;
+
+        // skip the drive letter, in some systems it can return an access denied error
+        if (subpath.length() == 2 && subpath[1] == ':') {
+            continue;
+        }
+
+        const bool success = CreateDirectoryW(subpath.c_str(), NULL);
+
         if (!success) {
             const DWORD error = GetLastError();
 
@@ -60,8 +82,6 @@ static bool fs_create_directory_with_parents(const std::string & path) {
                 return false;
             }
         }
-
-        pos_slash += 1;
     }
 
     return true;
@@ -115,13 +135,27 @@ static std::string fs_get_cache_directory() {
 #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
-        } else {
+        } else if (std::getenv("HOME")) {
             cache_directory = std::getenv("HOME") + std::string("/.cache/");
+        } else {
+#if defined(__linux__)
+            /* no $HOME is defined, fallback to getpwuid */
+            struct passwd *pw = getpwuid(getuid());
+            if ((!pw) || (!pw->pw_dir)) {
+                throw std::runtime_error("Failed to find $HOME directory");
+            }
+
+            cache_directory = std::string(pw->pw_dir) + std::string("/.cache/");
+#else /* defined(__linux__) */
+            throw std::runtime_error("Failed to find $HOME directory");
+#endif /* defined(__linux__) */
         }
 #elif defined(__APPLE__)
         cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
 #elif defined(_WIN32)
         cache_directory = std::getenv("LOCALAPPDATA");
+#elif defined(__EMSCRIPTEN__)
+        GGML_ABORT("not implemented on this platform");
 #else
 #  error Unknown architecture
 #endif

From e06088da0fa86aa444409f38dff274904931c507 Mon Sep 17 00:00:00 2001
From: Oliver Simons 
Date: Sun, 8 Feb 2026 14:12:51 +0100
Subject: [PATCH 156/206] CUDA: Fix non-contig rope (#19338)

* Rename variables + fix rope_neox

Seems memory layout is shared with Vulkan so we can port fix from
https://github.com/ggml-org/llama.cpp/pull/19299

* Fix rope_multi

* Fix rope_vision

* Fix rope_norm

* Rename ne* to ne0* for consistent variable naming

* cont : consistent stride names

---------

Co-authored-by: Georgi Gerganov 
---
 ggml/src/ggml-cuda/rope.cu | 364 +++++++++++++++++++++++--------------
 1 file changed, 232 insertions(+), 132 deletions(-)

diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu
index 88ed79111a..45a49a5dc2 100644
--- a/ggml/src/ggml-cuda/rope.cu
+++ b/ggml/src/ggml-cuda/rope.cu
@@ -43,10 +43,15 @@ static __device__ void rope_yarn(
 template 
 static __global__ void rope_norm(const T *            x,
                                  D *                  dst,
-                                 const int            ne0,
-                                 const int            ne1,
+                                 const int            ne00,
+                                 const int            ne01,
+                                 const int            ne02,
+                                 const int            s01,
+                                 const int            s02,
+                                 const int            s03,
                                  const int            s1,
                                  const int            s2,
+                                 const int            s3,
                                  const int            n_dims,
                                  const int32_t *      pos,
                                  const float          freq_scale,
@@ -59,23 +64,23 @@ static __global__ void rope_norm(const T *            x,
                                  const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
-
-    int       idst = row_dst * ne0 + i0;
-    const int ix   = channel_x*s2 + row_x*s1 + i0;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
+    int       idst = i0 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 + i1 * s01 + i2 * s02 + i3 * s03;
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
     if (set_rows_stride != 0) {
-        idst = row_x * ne0 + i0;
-        idst += row_indices[channel_x] * set_rows_stride;
+        idst = i1 * s1 + i0;
+        idst += row_indices[i2] * set_rows_stride;
     }
 
     const auto & store_coaelsced = [&](float x0, float x1) {
@@ -92,7 +97,7 @@ static __global__ void rope_norm(const T *            x,
         return;
     }
 
-    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
@@ -110,10 +115,15 @@ static __global__ void rope_norm(const T *            x,
 template 
 static __global__ void rope_neox(const T *            x,
                                  D *                  dst,
-                                 const int            ne0,
-                                 const int            ne1,
+                                 const int            ne00,
+                                 const int            ne01,
+                                 const int            ne02,
+                                 const int            s01,
+                                 const int            s02,
+                                 const int            s03,
                                  const int            s1,
                                  const int            s2,
+                                 const int            s3,
                                  const int            n_dims,
                                  const int32_t *      pos,
                                  const float          freq_scale,
@@ -126,23 +136,24 @@ static __global__ void rope_neox(const T *            x,
                                  const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    int       idst = row_dst * ne0 + i0 / 2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     // Fusion optimization: ROPE + VIEW + SET_ROWS.
     // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
     if (set_rows_stride != 0) {
-        idst = row_x * ne0 + i0 / 2;
-        idst += row_indices[channel_x] * set_rows_stride;
+        idst = i1 * s1 + i0 / 2;
+        idst += row_indices[i2] * set_rows_stride;
     }
 
     if (i0 >= n_dims) {
@@ -152,7 +163,7 @@ static __global__ void rope_neox(const T *            x,
         return;
     }
 
-    const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
@@ -168,24 +179,42 @@ static __global__ void rope_neox(const T *            x,
     dst[idst + n_dims / 2] = ggml_cuda_cast(x0 * sin_theta + x1 * cos_theta);
 }
 
-template
-static __global__ void rope_multi(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
-        const int n_dims, const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, const mrope_sections sections, const bool is_imrope) {
-    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+template 
+static __global__ void rope_multi(const T *            x,
+                                  T *                  dst,
+                                  const int            ne00,
+                                  const int            ne01,
+                                  const int            ne02,
+                                  const int            s01,
+                                  const int            s02,
+                                  const int            s03,
+                                  const int            s1,
+                                  const int            s2,
+                                  const int            s3,
+                                  const int            n_dims,
+                                  const int32_t *      pos,
+                                  const float          freq_scale,
+                                  const float          ext_factor,
+                                  const float          attn_factor,
+                                  const rope_corr_dims corr_dims,
+                                  const float          theta_scale,
+                                  const float *        freq_factors,
+                                  const mrope_sections sections,
+                                  const bool           is_imrope) {
+    const int i0 = 2 * (blockDim.y * blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    const int idst = row_dst*ne0 + i0/2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     if (i0 >= n_dims) {
         dst[idst + i0/2 + 0] = x[ix + i0/2 + 0];
@@ -200,27 +229,24 @@ static __global__ void rope_multi(
 
     float theta_base = 0.0;
     if (is_imrope) {
-        if (sector % 3 == 1 && sector < 3 * sections.v[1]) { // h
-            theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
-        } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { // w
-            theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
-        } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { // t
-            theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
+        if (sector % 3 == 1 && sector < 3 * sections.v[1]) {         // h
+            theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) {  // w
+            theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) {  // t
+            theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
         } else {
-            theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
+            theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
         }
     } else {
         if (sector < sections.v[0]) {
-            theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sections.v[0] && sector < sec_w) {
-            theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
-            theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w + sections.v[2]) {
-            theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
+            theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sections.v[0] && sector < sec_w) {
+            theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
+            theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
+        } else if (sector >= sec_w + sections.v[2]) {
+            theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
         }
     }
 
@@ -238,37 +264,53 @@ static __global__ void rope_multi(
     dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
-template
-static __global__ void rope_vision(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
-        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
-        const float theta_scale, const float * freq_factors, const mrope_sections sections) {
+template 
+static __global__ void rope_vision(const T *            x,
+                                   T *                  dst,
+                                   const int            ne00,
+                                   const int            ne01,
+                                   const int            ne02,
+                                   const int            s01,
+                                   const int            s02,
+                                   const int            s03,
+                                   const int            s1,
+                                   const int            s2,
+                                   const int            s3,
+                                   const int            n_dims,
+                                   const int32_t *      pos,
+                                   const float          freq_scale,
+                                   const float          ext_factor,
+                                   const float          attn_factor,
+                                   const rope_corr_dims corr_dims,
+                                   const float          theta_scale,
+                                   const float *        freq_factors,
+                                   const mrope_sections sections) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
-    if (i0 >= ne0) {
+    if (i0 >= ne00) {
         return;
     }
 
     const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const int row_x     = row_dst % ne1;
-    const int channel_x = row_dst / ne1;
+    const uint32_t i3 = row_dst / (ne01 * ne02);
+    const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
+    const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
 
-    const int idst = row_dst*ne0 + i0/2;
-    const int ix   = channel_x*s2 + row_x*s1 + i0/2;
+    int       idst = i0 / 2 + i1 * s1  + i2 * s2  + i3 * s3;
+    const int ix   = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
 
     const int sect_dims = sections.v[0] + sections.v[1];
-    const int sec_w = sections.v[1] + sections.v[0];
-    const int sector = (i0 / 2) % sect_dims;
+    const int sec_w     = sections.v[1] + sections.v[0];
+    const int sector    = (i0 / 2) % sect_dims;
 
     float theta_base = 0.0;
     if (sector < sections.v[0]) {
         const int p = sector;
-        theta_base = pos[channel_x]*powf(theta_scale, p);
-    }
-    else if (sector >= sections.v[0] && sector < sec_w) {
+        theta_base  = pos[i2] * powf(theta_scale, p);
+    } else if (sector >= sections.v[0] && sector < sec_w) {
         const int p = sector - sections.v[0];
-        theta_base = pos[channel_x + ne2]*powf(theta_scale, p);
+        theta_base  = pos[i2 + ne02] * powf(theta_scale, p);
     }
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
@@ -288,10 +330,15 @@ static __global__ void rope_vision(
 template 
 static void rope_norm_cuda(const T *            x,
                            D *                  dst,
-                           const int            ne0,
-                           const int            ne1,
+                           const int            ne00,
+                           const int            ne01,
+                           const int            ne02,
+                           const int            s01,
+                           const int            s02,
+                           const int            s03,
                            const int            s1,
                            const int            s2,
+                           const int            s3,
                            const int            n_dims,
                            const int            nr,
                            const int32_t *      pos,
@@ -304,31 +351,36 @@ static void rope_norm_cuda(const T *            x,
                            const int64_t *      row_indices,
                            const int            set_rows_stride,
                            cudaStream_t         stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_norm<<>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     } else {
         rope_norm<<>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     }
 }
 
 template 
 static void rope_neox_cuda(const T *            x,
                            D *                  dst,
-                           const int            ne0,
-                           const int            ne1,
+                           const int            ne00,
+                           const int            ne01,
+                           const int            ne02,
+                           const int            s01,
+                           const int            s02,
+                           const int            s03,
                            const int            s1,
                            const int            s2,
+                           const int            s3,
                            const int            n_dims,
                            const int            nr,
                            const int32_t *      pos,
@@ -341,55 +393,92 @@ static void rope_neox_cuda(const T *            x,
                            const int64_t *      row_indices,
                            const int            set_rows_stride,
                            cudaStream_t         stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_neox<<>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     } else {
         rope_neox<<>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
-            freq_factors, row_indices, set_rows_stride);
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
+            attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
     }
 }
 
-template
-static void rope_multi_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, const bool is_imrope, cudaStream_t stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+template 
+static void rope_multi_cuda(const T *            x,
+                            T *                  dst,
+                            const int            ne00,
+                            const int            ne01,
+                            const int            ne02,
+                            const int            s01,
+                            const int            s02,
+                            const int            s03,
+                            const int            s1,
+                            const int            s2,
+                            const int            s3,
+                            const int            n_dims,
+                            const int            nr,
+                            const int32_t *      pos,
+                            const float          freq_scale,
+                            const float          freq_base,
+                            const float          ext_factor,
+                            const float          attn_factor,
+                            const rope_corr_dims corr_dims,
+                            const float *        freq_factors,
+                            const mrope_sections sections,
+                            const bool           is_imrope,
+                            cudaStream_t         stream) {
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     if (freq_factors == nullptr) {
         rope_multi<<>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     } else {
         rope_multi<<>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
     }
 }
 
-template
-static void rope_vision_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, cudaStream_t stream) {
-    GGML_ASSERT(ne0 % 2 == 0);
+template 
+static void rope_vision_cuda(const T *            x,
+                             T *                  dst,
+                             const int            ne00,
+                             const int            ne01,
+                             const int            ne02,
+                             const int            s01,
+                             const int            s02,
+                             const int            s03,
+                             const int            s1,
+                             const int            s2,
+                             const int            s3,
+                             const int            n_dims,
+                             const int            nr,
+                             const int32_t *      pos,
+                             const float          freq_scale,
+                             const float          freq_base,
+                             const float          ext_factor,
+                             const float          attn_factor,
+                             const rope_corr_dims corr_dims,
+                             const float *        freq_factors,
+                             const mrope_sections sections,
+                             cudaStream_t         stream) {
+    GGML_ASSERT(ne00 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const int  n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nr, n_blocks_x, 1);
     // break down (head_dim, heads, seq) into (CUDA_ROPE_BLOCK_SIZE, x, heads * seq)
     // where x ~= ceil(head_dim / CUDA_ROPE_BLOCK_SIZE);
@@ -398,11 +487,11 @@ static void rope_vision_cuda(
 
     if (freq_factors == nullptr) {
         rope_vision<<>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections);
     } else {
         rope_vision<<>>(
-            x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
+            x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
             attn_factor, corr_dims, theta_scale, freq_factors, sections);
     }
 }
@@ -445,6 +534,11 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
 
     const size_t s01 = src0->nb[1] / ggml_type_size(src0->type);
     const size_t s02 = src0->nb[2] / ggml_type_size(src0->type);
+    const size_t s03 = src0->nb[3] / ggml_type_size(src0->type);
+
+    const size_t s1 = dst->nb[1] / ggml_type_size(dst->type);
+    const size_t s2 = dst->nb[2] / ggml_type_size(dst->type);
+    const size_t s3 = dst->nb[3] / ggml_type_size(dst->type);
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
@@ -495,57 +589,63 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
     // compute
     if (is_neox) {
         if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
-            rope_neox_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                  freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                  s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                  ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                  set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
-            rope_neox_cuda((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                 freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                 s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                 ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                 set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
-            rope_neox_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
-                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                freq_factors, row_indices, set_rows_stride, stream);
+            rope_neox_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_mrope && !is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_multi_cuda(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
+            rope_multi_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                     s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                     corr_dims, freq_factors, sections, is_imrope, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_multi_cuda(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
+            rope_multi_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                     s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                     corr_dims, freq_factors, sections, is_imrope, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else if (is_vision) {
         if (src0->type == GGML_TYPE_F32) {
-            rope_vision_cuda(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+            rope_vision_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                      s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                      corr_dims, freq_factors, sections, stream);
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_vision_cuda(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
+            rope_vision_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
+                                      s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
+                                      corr_dims, freq_factors, sections, stream);
         } else {
             GGML_ABORT("fatal error");
         }
     } else {
         if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
-            rope_norm_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                  freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                  s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                  ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                  set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
-            rope_norm_cuda((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
-                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                 freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                 s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                 ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                 set_rows_stride, stream);
         } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
-            rope_norm_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
-                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
-                                                freq_factors, row_indices, set_rows_stride, stream);
+            rope_norm_cuda((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
+                                                s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
+                                                ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
+                                                set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }

From 39bf692af1cba2a1072e4a42425611bf1ec2807d Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" 
Date: Mon, 9 Feb 2026 00:24:08 +0100
Subject: [PATCH 157/206] [Model] Qwen3.5 dense and MoE support (no vision)
 (#19435)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Unified delta net handling

* Remove old methods.

* Refactor and optimize

* Adapt autoregressive version from @ymcki

* Change to decay mask approach

* Fix bad permute

* Qwen 3.5 support

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret 

* Further fixes

* Use inheritance, remove unneeded conts

* Not like this!

* Remove ggml.h explicit import

* Remove transformers, fix the views

* ACTUALLY fix views, make super calls explicit in conversion.

* Fix conversion again

* Remove extra ggml.h imports

---------

Co-authored-by: Sigbjørn Skjæret 
---
 convert_hf_to_gguf.py          |  78 +++--
 gguf-py/gguf/constants.py      |  59 ++++
 gguf-py/gguf/tensor_mapping.py |   6 +-
 src/CMakeLists.txt             |   3 +
 src/llama-arch.cpp             |  61 ++++
 src/llama-arch.h               |   2 +
 src/llama-context.cpp          |   2 +-
 src/llama-model.cpp            | 154 ++++++++
 src/models/delta.cpp           | 618 +++++++++++++++++++++++++++++++++
 src/models/kimi-linear.cpp     |   1 -
 src/models/models.h            | 102 +++++-
 src/models/qwen3-5.cpp         | 421 ++++++++++++++++++++++
 src/models/qwen3-5moe.cpp      |  52 +++
 src/models/qwen3next.cpp       | 372 +-------------------
 14 files changed, 1532 insertions(+), 399 deletions(-)
 create mode 100644 src/models/delta.cpp
 create mode 100644 src/models/qwen3-5.cpp
 create mode 100644 src/models/qwen3-5moe.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 843c00a896..e64756a74a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4102,39 +4102,27 @@ class Qwen2MoeModel(TextModel):
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # handle aggregated expert tensors
-        # GGUF stores dimensions reversed from PyTorch, so:
-        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
-        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
-        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
+        # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
+        # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
+        # This matches the individual expert stacking path below (which stacks
+        # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
-            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
-            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(permuted, mapped, bid)
+            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
+            yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
-                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
-            yield from super().modify_tensors(perm_up, mapped_up, bid)
+            # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
+            n_ff = data_torch.shape[1] // 2
+            gate = data_torch[:, :n_ff, :].contiguous()
+            up = data_torch[:, n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
@@ -4344,6 +4332,40 @@ class Qwen3NextModel(Qwen2MoeModel):
             yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
+class Qwen3_5Model(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5
+
+    # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
+    _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
+        # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
+        if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
+            which = "a" if "in_proj_a" in name else "b"
+            if bid not in self._pending_ba:
+                self._pending_ba[bid] = (which, data_torch)
+                return
+            prev_which, prev_tensor = self._pending_ba.pop(bid)
+            assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
+            b_tensor = prev_tensor if prev_which == "b" else data_torch
+            a_tensor = prev_tensor if prev_which == "a" else data_torch
+            ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
+            return
+        else:
+            # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
+            # (norm correction, A_log to A etc.) for free
+            # Qwen2Moe already does the gate_up conversion properly, just use that
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
+class Qwen3_5MoeModel(Qwen3_5Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
+
+
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 3af4fffe95..8a3fab1e1c 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -382,6 +382,8 @@ class MODEL_ARCH(IntEnum):
     QWEN3            = auto()
     QWEN3MOE         = auto()
     QWEN3NEXT        = auto()
+    QWEN3_5          = auto()
+    QWEN3_5_MOE      = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
     PHI2             = auto()
@@ -812,6 +814,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
+    MODEL_ARCH.QWEN3_5:          "qwen3_5",
+    MODEL_ARCH.QWEN3_5_MOE:      "qwen3_5moe",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
     MODEL_ARCH.PHI2:             "phi2",
@@ -1784,6 +1788,61 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_BETA_ALPHA,
         MODEL_TENSOR.SSM_OUT
     ],
+    MODEL_ARCH.QWEN3_5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
+    MODEL_ARCH.QWEN3_5_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT,
+    ],
     MODEL_ARCH.QWEN3VL: [
         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 167ade7803..43f32c7b52 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -228,6 +228,7 @@ class TensorNameMap:
             "transformer_encoder.{bid}.qkv",                                       # neobert
             "layers.{bid}.attn.Wqkv",                                              # modern-bert
             "model.layers.{bid}.self_attn.language_expert_query_key_value",        # cogvlm
+            "model.layers.{bid}.linear_attn.in_proj_qkv",                          # qwen3.5
         ),
 
         # Attention query
@@ -358,8 +359,9 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj", # afmoe
-            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
+            "model.layers.{bid}.self_attn.gate_proj",   # afmoe
+            "model.layers.{bid}.self_attn.g_proj",      # step3.5 head-wise attention gate
+            "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
         ),
 
         # Feed-forward norm
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 2115fc4255..0c164617a1 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -57,6 +57,7 @@ add_library(llama
             models/deci.cpp
             models/deepseek.cpp
             models/deepseek2.cpp
+            models/delta.cpp
             models/dots1.cpp
             models/dream.cpp
             models/ernie4-5-moe.cpp
@@ -122,6 +123,8 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
+            models/qwen3-5.cpp
+            models/qwen3-5moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index bd78f1e556..fce46772d7 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -35,6 +35,8 @@ static const std::map LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
+    { LLM_ARCH_QWEN3_5,          "qwen3_5"          },
+    { LLM_ARCH_QWEN3_5_MOE,      "qwen3_5moe"       },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
     { LLM_ARCH_PHI2,             "phi2"             },
@@ -985,6 +987,63 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
+        case LLM_ARCH_QWEN3_5:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_QWEN3_5_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_HUNYUAN_DENSE:
@@ -2674,6 +2733,8 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_KIMI_LINEAR:
             return true;
         default:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index e8263369b8..a392ecce2b 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -39,6 +39,8 @@ enum llm_arch {
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
     LLM_ARCH_QWEN3NEXT,
+    LLM_ARCH_QWEN3_5,
+    LLM_ARCH_QWEN3_5_MOE,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
     LLM_ARCH_PHI2,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index a6df893a31..80b9a7d46a 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
         return std::max(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max(1024u, 8u*model.n_tensors());
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 674d06c891..8fc61aee37 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2412,6 +2412,25 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                // Load linear attention (gated delta net) parameters
+                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);
+
+                // Mark recurrent layers (linear attention layers)
+                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
+                }
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7094,6 +7113,129 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
 
+                        // Shared experts
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN3_5:
+                {
+                    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 == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    const int64_t ba_dim = n_v_heads * 2;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        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);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Full attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        // Dense FFN for all layers
+                        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_QWEN3_5_MOE:
+                {
+                    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 == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    const int64_t ba_dim = n_v_heads * 2;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        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);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Full attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        // MoE FFN
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+
                         // Shared experts
                         layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
                         layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
@@ -7545,6 +7687,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
+        arch == LLM_ARCH_QWEN3_5 ||
+        arch == LLM_ARCH_QWEN3_5_MOE ||
         arch == LLM_ARCH_NEMOTRON_H ||
         arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8343,6 +8487,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique(*this, params);
             } break;
+        case LLM_ARCH_QWEN3_5:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
+        case LLM_ARCH_QWEN3_5_MOE:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 llm = std::make_unique(*this, params);
@@ -8603,6 +8755,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN3_5:
+        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
diff --git a/src/models/delta.cpp b/src/models/delta.cpp
new file mode 100644
index 0000000000..d1d9837d09
--- /dev/null
+++ b/src/models/delta.cpp
@@ -0,0 +1,618 @@
+#include "models.h"
+#include "ggml.h"
+#include 
+#include 
+#include 
+
+llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
+
+/**
+ * Unified Delta Net implementation supporting both GDA and KDA modes.
+ *
+ * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
+ *   - Per-head gating, broadcasts over K dimension
+ *
+ * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
+ *   - Per-key gating
+ *
+ * The mode is auto-detected based on g's dimensionality.
+ *
+ * Tensor dimension convention:
+ *   GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
+ *   PyTorch: dim 0 is outermost, dim -1 is innermost
+ *   So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
+ */
+
+// Helper to get a slice along dimension 2 (n_chunks dimension)
+static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
+    return ggml_view_4d(ctx, t,
+        t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3],
+        chunk * t->nb[2]);
+}
+
+/**
+ * Unified chunked Delta Net implementation.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
+ * @param g         Gate tensor:
+ *                    GDA: [H_v, n_tokens, n_seqs]
+ *                    KDA: [S_k, H_v, n_tokens, n_seqs]
+ * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ * @param causal_mask   Lower triangular mask [chunk_size, chunk_size]
+ * @param identity      Identity matrix [chunk_size, chunk_size]
+ * @param diag_mask     Diagonal mask [chunk_size, chunk_size]
+ * @param il            Layer index (for debugging callbacks)
+ * @param chunk_size    Chunk size for chunked processing
+ * @param eps_norm      Epsilon for L2 normalization
+ *
+ * @return Pair of (output_tokens, new_state)
+ */
+std::pair llm_graph_context_delta::build_delta_net_unified_chunking(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state_reshaped,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    // Detect KDA vs GDA based on g's shape
+    // GDA: g has shape [H_v, n_tokens, n_seqs]
+    // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
+    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
+
+    // Validate tensor shapes
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(H_k == H_v);
+
+    if (is_kda) {
+        // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
+        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
+    } else {
+        // GDA: g shape [H_v, n_tokens, n_seqs]
+        GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    }
+
+    // L2 normalize q and k
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf((float)S_v);
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    // Permute tensors to working format [S, n_tokens, H, n_seqs]
+    // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    if (is_kda) {
+        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
+    } else {
+        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+    }
+    beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state_reshaped, "state_in", il);
+
+    // Padding for chunk processing
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    // Reshape to chunks
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+    beta   = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    // Reshape g for chunks
+    ggml_tensor * g_cumsum;
+    ggml_tensor * g_cumsum_t;
+    if (is_kda) {
+        // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
+        g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
+        // Cumsum along chunk_size dimension (ne[1])
+        // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
+        g = ggml_cont(ctx0, ggml_transpose(ctx0, g));  // [chunk_size, S_k, n_chunks, H_k * n_seqs]
+        g_cumsum_t = ggml_cumsum(ctx0, g);
+        g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t));  // [S_k, chunk_size, n_chunks, H_k * n_seqs]
+    } else {
+        // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
+        g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+        g_cumsum = ggml_cumsum(ctx0, g);
+        g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
+    }
+
+    cb(g_cumsum, "g_cumsum", il);
+
+    // Build attention matrix A for the WY representation solve
+    // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
+    // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+    // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
+
+    ggml_tensor * k_decay;
+    ggml_tensor * decay_mask = nullptr;
+    ggml_tensor * g_exp_pos = nullptr;
+
+    if (is_kda) {
+        // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
+        // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+        // By putting S_k in dim 0, mul_mat implicitly sums over it
+
+        const int64_t CHB = n_chunks * H_k * n_seqs;
+
+        // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
+        // Reshape to [chunk_size, S_k, CHB] then build decay mask
+        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
+        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
+
+        // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
+        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
+        decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
+
+        cb(decay_mask, "decay_mask_kda", il);
+
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+        decay_mask = ggml_exp(ctx0, decay_mask);
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
+        decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
+
+        // Reshape k and k_beta for broadcasting with decay_mask
+        // k_i: indexed at position i (dim 2 of decay_mask)
+        // k_beta_j: indexed at position j (dim 1 of decay_mask)
+        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
+        ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
+
+        // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
+        ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
+
+        // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
+        k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
+        k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
+
+        // g_exp_pos is still needed for later (kbeta_gexp, etc.)
+        g_exp_pos = ggml_exp(ctx0, g_cumsum);
+    } else {
+        // GDA: Use decay mask approach (g broadcasts over K dimension)
+        // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
+        ggml_tensor * gcs_i = g_cumsum;
+        ggml_tensor * gcs_j = g_cumsum_t;
+        g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
+        ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+        decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+
+        cb(decay_mask, "decay_mask", il);
+
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+        decay_mask = ggml_exp(ctx0, decay_mask);
+        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+        ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+        k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    }
+
+    ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+
+    cb(attn, "attn_pre_solve", il);
+
+    // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+    ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn = ggml_add(ctx0, attn, identity);
+
+    cb(attn, "attn_solved", il);
+
+    // Compute u = A @ v and w = A @ (g.exp() * k)
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
+    cb(kbeta_gexp, "kbeta_gexp", il);
+
+    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
+        ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il);
+
+    // Attention scores q @ k^T with decay
+    // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
+    // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
+    ggml_tensor * attn_kq;
+    if (is_kda) {
+        // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
+        const int64_t CHB = n_chunks * H_k * n_seqs;
+
+        // Rebuild decay mask (same structure as k_decay)
+        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
+        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
+        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
+        ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
+
+        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
+        decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
+        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
+
+        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
+        decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
+
+        // q_j: indexed at position j, k_i: indexed at position i
+        ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
+        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
+
+        // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
+        ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
+
+        // mul_mat sums over S_k
+        attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
+        attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
+    } else {
+        // GDA: Use decay mask
+        attn_kq = ggml_mul_mat(ctx0, k, q);
+        attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+        attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    }
+    cb(attn_kq, "attn_kq", il);
+
+    // Compute g_last and g_diff for state updates
+    ggml_tensor * g_last;
+    ggml_tensor * g_diff_exp;
+    ggml_tensor * g_last_exp;
+
+    if (is_kda) {
+        // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
+        // Get last element along chunk_size dimension (ne[1])
+        g_last = ggml_view_4d(ctx0, g_cumsum,
+            g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
+            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+            (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
+        g_last = ggml_cont(ctx0, g_last);
+        g_last_exp = ggml_exp(ctx0, g_last);
+
+        // g_diff = g_last - g_cumsum
+        ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
+            g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
+        ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
+        g_diff_exp = ggml_exp(ctx0, g_diff);
+    } else {
+        // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
+        g_last = ggml_view_4d(ctx0, g_cumsum,
+            1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+        g_last = ggml_cont(ctx0, g_last);
+        g_last_exp = ggml_exp(ctx0, g_last);
+
+        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+        g_diff_exp = ggml_exp(ctx0, g_diff);
+    }
+
+    cb(g_last, "g_last", il);
+    cb(g_last_exp, "g_last_exp", il);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
+    cb(key_gdiff, "key_gdiff", il);
+
+    // Process chunks
+    ggml_tensor * new_state = state_reshaped;
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
+
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
+            S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = k_cumdecay @ state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il);
+
+        // v_new = v - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q * g.exp()) @ state
+        ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // output = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // State update: state = state * g_last_exp + key_gdiff^T @ v_new
+        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
+
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+
+        if (is_kda) {
+            // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
+            // State: [S_v, S_v, H_v, n_seqs]
+            // Need to reshape g_last_exp to broadcast correctly over V dimension only
+            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
+                1, gexp_last_chunk->ne[0], H_v, n_seqs);  // [1, S_k, H_v, n_seqs]
+            // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
+            gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
+        } else {
+            // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
+            // Broadcasts over both K and V dimensions
+            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
+                gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
+        }
+
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, gexp_last_chunk),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // Truncate padding and permute back
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+        S_v, n_tokens, H_v, n_seqs,
+        ggml_row_size(core_attn_out->type, S_v),
+        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    cb(output_tokens, "output_tokens", il);
+
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+
+/**
+ * Unified autoregressive Delta Net implementation (single token processing).
+ *
+ * This implementation uses matrix multiplication instead of elementwise operations + summation,
+ * which is more efficient and mathematically equivalent. See inline comments for equivalences.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, 1, n_seqs]
+ * @param k         Key tensor [S_k, H_k, 1, n_seqs]
+ * @param v         Value tensor [S_v, H_v, 1, n_seqs]
+ * @param g         Gate tensor:
+ *                    GDA: [H_v, 1, n_seqs]
+ *                    KDA: [S_k, H_v, 1, n_seqs]
+ * @param beta      Beta tensor [H_v, 1, 1, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ * @param il        Layer index (for debugging callbacks)
+ * @param eps_norm  Epsilon for L2 normalization
+ *
+ * @return Pair of (output_tokens, new_state)
+ */
+std::pair llm_graph_context_delta::build_delta_net_unified_autoregressive(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // Autoregressive mode is for single token
+
+    // Detect KDA vs GDA based on g's shape
+    // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
+    // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
+    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
+
+    // Validate shapes
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(H_k == H_v);
+
+    if (is_kda) {
+        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
+    } else {
+        GGML_ASSERT(g->ne[0] == H_v);
+    }
+
+    // L2 normalize q and k
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf((float)S_v);
+    q = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    // Reshape g and beta for broadcasting
+    ggml_tensor * g_t;
+    ggml_tensor * beta_t;
+
+    if (is_kda) {
+        // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
+        // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
+        g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
+    } else {
+        // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
+        // For state multiplication, broadcasts over both K and V
+        g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    }
+
+    beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // State decay: state = state * exp(g)
+    if (is_kda) {
+        // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
+        // Need to broadcast g_t over V dimension (ne[0] of state)
+        // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
+        ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
+        state = ggml_mul(ctx0, state, g_broadcast);
+    } else {
+        // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
+        state = ggml_mul(ctx0, state, g_t);
+    }
+
+    // Equivalence to previous version:
+    // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
+    // Current:  k_state = state_t @ k_t using matrix multiplication
+    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
+    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+    ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
+    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
+
+    // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
+    ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
+
+    // Equivalence to previous version:
+    // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
+    // Current:  state += v_diff^T @ k_beta^T using matrix multiplication
+    // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
+    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
+
+    // Equivalence to previous version:
+    // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
+    // Current:  core_attn_out = state_t @ q using matrix multiplication
+    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
+    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
+    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
+    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+
+/**
+ * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
+ *
+ * Input tensor format matches qwen3next conventions:
+ * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
+ * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
+ * @param g         Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
+ * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
+ * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
+ */
+std::pair llm_graph_context_delta::build_delta_net_unified(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm) {
+
+    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
+    const int64_t n_tokens = q->ne[2];
+
+    if (n_tokens == 1) {
+        return build_delta_net_unified_autoregressive(
+            ctx0, q, k, v, g, beta, state, il, eps_norm);
+    }
+    return build_delta_net_unified_chunking(
+        ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
+        il, chunk_size, eps_norm);
+}
diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp
index 0f037d1a39..d9ee698075 100644
--- a/src/models/kimi-linear.cpp
+++ b/src/models/kimi-linear.cpp
@@ -1,5 +1,4 @@
 #include "models.h"
-#include "ggml.h"
 
 #define CHUNK_SIZE 64
 
diff --git a/src/models/models.h b/src/models/models.h
index cfcbb9aaa5..2a750c168e 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -17,6 +17,53 @@ struct llm_graph_context_mamba : public llm_graph_context {
 
 };
 
+struct llm_graph_context_delta : public llm_graph_context_mamba {
+    llm_graph_context_delta(const llm_graph_params & params);
+
+    virtual ~llm_graph_context_delta() = default;
+
+    std::pair build_delta_net_unified_chunking(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm);
+
+    std::pair build_delta_net_unified_autoregressive(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il,
+        float         eps_norm);
+
+    std::pair build_delta_net_unified(
+        ggml_context * ctx0,
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il,
+        int64_t       chunk_size,
+        float         eps_norm);
+};
+
 // Base class for RWKV-related models
 struct llm_build_rwkv6_base : public llm_graph_context {
     const llama_model & model;
@@ -476,7 +523,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
-struct llm_build_qwen3next : public llm_graph_context_mamba {
+struct llm_build_qwen3next : public llm_graph_context_delta {
     llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
     ggml_tensor * build_layer_attn(
@@ -534,6 +581,59 @@ private:
     const llama_model & model;
 };
 
+struct llm_build_qwen3_5 : public llm_graph_context_delta {
+    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
+
+protected:
+    // Tag type for subclass constructors that need to call build_graph() themselves
+    // (to ensure virtual dispatch works correctly)
+    struct defer_graph_build_t {};
+
+    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
+
+    void build_graph();
+
+    virtual ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    const llama_model & model;
+
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int   il);
+
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    std::pair build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+};
+
+struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
+    llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
+
+protected:
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il) override;
+};
+
 struct llm_build_qwen : public llm_graph_context {
     llm_build_qwen(const llama_model & model, const llm_graph_params & params);
 };
diff --git a/src/models/qwen3-5.cpp b/src/models/qwen3-5.cpp
new file mode 100644
index 0000000000..0947299d73
--- /dev/null
+++ b/src/models/qwen3-5.cpp
@@ -0,0 +1,421 @@
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_delta(params), model(model) {
+    build_graph();
+}
+
+// virtual call in constructor fix
+llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
+    llm_graph_context_delta(params), model(model) {
+}
+
+void llm_build_qwen3_5::build_graph() {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "model.embed_tokens", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        if (hparams.is_recurrent(il)) {
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, 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);
+        }
+
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        ggml_tensor * ffn_residual = cur;
+
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_ffn", il);
+
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_qwen3_5::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", 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);
+
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", 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,
+            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);
+
+    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,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+std::pair llm_build_qwen3_5::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    if (model.layers[il].wqkv) {
+        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+        cb(z, "z", il);
+
+        return { qkv_mixed, z };
+
+    }
+    // legacy path for combined in_proj_qkvz
+    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
+    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
+
+    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
+    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+
+    int64_t split_sizes_qkvz[4] = {
+        head_k_dim,
+        head_k_dim,
+        head_v_dim * num_v_heads / num_k_heads,
+        head_v_dim * num_v_heads / num_k_heads
+    };
+
+    ggml_tensor * query =
+        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
+                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
+    cb(query, "q", il);
+
+    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
+                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                    split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
+    cb(key, "k", il);
+
+    ggml_tensor * value =
+        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
+                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                    (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
+    cb(value, "v", il);
+
+    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
+                                mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
+                                (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
+    z = ggml_cont(ctx0, z);
+    cb(z, "z", il);
+
+    ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+    cb(query_flat, "query_flat", il);
+
+    ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
+    cb(key_flat, "key_flat", il);
+
+    ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(value_flat, "value_flat", il);
+
+    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
+    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
+    cb(qkv_mixed, "qkv_mixed", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
+    cb(mixed_ba, "linear_attn_mixed_ba", il);
+
+    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
+    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+
+    int64_t split_sizes_ba[2] = {
+        num_v_heads / num_k_heads,
+        num_v_heads / num_k_heads
+    };
+
+    ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
+                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
+    cb(b, "b", il);
+
+    ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
+                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
+                                   split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
+    cb(a, "a", il);
+
+    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
+
+    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
+    cb(gate, "gate", il);
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
+    cb(state, "state_predelta", il);
+
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        int64_t repeat_factor = num_v_heads / num_k_heads;
+
+        ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
+        ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
+
+        ggml_tensor * q_repeated =
+            ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
+        ggml_tensor * k_repeated =
+            ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
+
+        q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
+        k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    std::pair attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
+            gate, beta, state, causal_mask, identity, diag_mask,
+            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
+
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Qwen3.5 Dense always uses dense FFN
+    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);
+    return cur;
+}
diff --git a/src/models/qwen3-5moe.cpp b/src/models/qwen3-5moe.cpp
new file mode 100644
index 0000000000..a488443218
--- /dev/null
+++ b/src/models/qwen3-5moe.cpp
@@ -0,0 +1,52 @@
+#include "models.h"
+
+llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
+    llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
+    build_graph();
+}
+
+ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Check if this is an MoE layer
+    if (model.layers[il].ffn_gate_inp != nullptr) {
+        // MoE branch
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used, LLM_FFN_SILU,
+                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // Add shared experts if present
+        if (model.layers[il].ffn_up_shexp != nullptr) {
+            ggml_tensor * ffn_shexp =
+                build_ffn(cur,
+                    model.layers[il].ffn_up_shexp, NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            // Apply shared expert gating (sigmoid)
+            ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+            cb(shared_gate, "shared_expert_gate", il);
+
+            shared_gate = ggml_sigmoid(ctx0, shared_gate);
+            cb(shared_gate, "shared_expert_gate_sigmoid", il);
+
+            ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+            cb(ffn_shexp, "ffn_shexp_gated", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = moe_out;
+        }
+    } else {
+        // Dense FFN branch (fallback)
+        cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
+    }
+    return cur;
+}
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 99b1a76a48..0335f5ab76 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -1,10 +1,9 @@
-#include "ggml.h"
 #include "models.h"
 
 #define CHUNK_SIZE 64
 
 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_mamba(params), model(model) {
+    llm_graph_context_delta(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -86,362 +85,6 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
-// utility to get one slice from the third dimension
-// input dim:  [x, y, c, b]
-// output dim: [x, y, 1, b]
-static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
-    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
-        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
-}
-
-std::pair llm_build_qwen3next::build_delta_net_chunking(
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il) {
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
-
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-
-    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
-
-    const float eps_norm = hparams.f_norm_rms_eps;
-
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf(S_v);
-
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-
-    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
-
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state, "state_in", il);
-
-    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
-    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
-
-    // Do padding
-    const int64_t chunk_size = CHUNK_SIZE;
-
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-
-    cb(q, "q_pad", il);
-    cb(k, "k_pad", il);
-    cb(v, "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g, "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-
-    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
-    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
-    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
-    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * gcs_j_broadcast =
-        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-
-    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-    decay_mask = ggml_exp(ctx0, decay_mask);
-    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-
-    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-
-    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn                     = ggml_add(ctx0, attn, identity);
-    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
-    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * k_cumdecay =
-        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
-    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
-    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
-    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
-
-
-    // vectorized calculation of key_gdiff
-    // improved from the chunked version:
-    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
-    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
-    //   key_gdiff = key * g_diff.unsqueeze(-1)
-    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-
-    // get last element in g_cumsum along chunk_size dimension (ne0)
-    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
-    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-    g_last = ggml_cont(ctx0, g_last);
-    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
-    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
-    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
-                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
-    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
-
-    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
-    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
-
-
-    // state to be updated per chunk
-    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
-    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
-
-    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
-        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
-
-        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
-        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
-
-        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
-        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
-
-        // shape: (chunk_size, 1, H_v * n_seqs)
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
-
-        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-        // replaced by precomputed attn_kq
-        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
-
-        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
-
-        // v_new = v_i - v_prime
-        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
-        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
-
-        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-        new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
-            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    // truncate padded tokens
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-            S_v, n_tokens, H_v, n_seqs,
-            ggml_row_size(core_attn_out->type, S_v),
-            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
-            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-    cb(output_tokens, "output_tokens", il);
-
-    // permute back to (S_v, H_v, n_tokens, n_seqs)
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    return {output_tokens, new_state};
-}
-
-std::pair llm_build_qwen3next::build_delta_net_autoregressive(
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il) {
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
-
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-
-    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
-
-    const float eps_norm = hparams.f_norm_rms_eps;
-
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf(S_v);
-
-    q    = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
-
-    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    // Apply exponential to g_t
-    g_t = ggml_exp(ctx0, g_t);
-
-    // Apply the gated delta rule for the single timestep
-    // last_recurrent_state = last_recurrent_state * g_t
-    state = ggml_mul(ctx0, state, g_t);
-
-    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
-    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
-    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
-    // we need to sum over dim=-2, so we transpose, sum, then transpose again
-    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
-
-    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
-    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    // delta = (v_t - kv_mem) * beta_t
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
-    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
-
-    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
-    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
-    state                   = ggml_add(ctx0, state, k_t_delta);
-
-    // Compute the attention output
-    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
-    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
-    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
-    // again, since it's over dim = -2, transpose, sum, transpose back
-    ggml_tensor * core_attn_out =
-        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
-
-    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
-    cb(core_attn_out, "output_tokens", il);
-    cb(state, "new_state", il);
-
-    return {core_attn_out, state};
-}
-
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
@@ -752,7 +395,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
     cb(state, "state_predelta", il);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
@@ -781,13 +424,10 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
-    std::pair attn_out; // pair of (output, new_state)
-    if (n_seq_tokens == 1) {
-        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
-    } else {
-        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
-    }
+    std::pair attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
+            gate, beta, state, causal_mask, identity, diag_mask,
+            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
+
     ggml_tensor * output    = attn_out.first;
     ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);

From 1e8924fd65ad349d1d838412a2172292618f3bbf Mon Sep 17 00:00:00 2001
From: Hugo 
Date: Mon, 9 Feb 2026 06:12:02 +0000
Subject: [PATCH 158/206] cmake : add variable to skip installing tests
 (#19370)

When packaging downstream, there's usually little point in installing
test. The default behaviour remains the same.
---
 CMakeLists.txt       | 1 +
 tests/CMakeLists.txt | 8 ++++++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6d4ed67020..55f3d594db 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -109,6 +109,7 @@ option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)
 
 # 3rd party libs
 option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index c9436c5995..350bffc315 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -11,7 +11,9 @@ function(llama_build source)
 
     add_executable(${TEST_TARGET} ${TEST_SOURCES})
     target_link_libraries(${TEST_TARGET} PRIVATE common)
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
 endfunction()
 
 function(llama_test target)
@@ -100,7 +102,9 @@ function(llama_build_and_test source)
     endif()
 
     add_executable(${TEST_TARGET} ${TEST_SOURCES})
-    install(TARGETS ${TEST_TARGET} RUNTIME)
+    if (LLAMA_TESTS_INSTALL)
+        install(TARGETS ${TEST_TARGET} RUNTIME)
+    endif()
     target_link_libraries(${TEST_TARGET} PRIVATE common)
 
     add_test(

From f5e7734ff2e1d2e22015f4a9da9a52c70240a064 Mon Sep 17 00:00:00 2001
From: Kevin Pouget 
Date: Mon, 9 Feb 2026 13:15:42 +0100
Subject: [PATCH 159/206] ggml-virtgpu: add backend documentation (#19354)

* ggml-virtgpu: add backend documentation

Assisted-by-AI: Claude Code

* CODEOWNERS: add /docs/backend/GGML-VirtGPU/ -> kpouget

* README: add the link to docs/backend/GGML-VirtGPU/ggml-virt.md

* docs/ggml-virt: add link to testing + configuration

* Revert "CODEOWNERS: add /docs/backend/GGML-VirtGPU/ -> kpouget"

This reverts commit 8ece8e72e24d305f308505c08ebb75804546374e.

* drop the ggml- prefix

* s/ggerganov/ggml-org

* Relocate VirtGPU.md

* reorganize the text

* turn turn the ascii diagram into a mermaid

* README.md: update the link to the main doc
---
 README.md                             |   1 +
 docs/backend/VirtGPU.md               | 180 +++++++++++++++++++++
 docs/backend/VirtGPU/configuration.md | 174 ++++++++++++++++++++
 docs/backend/VirtGPU/development.md   | 220 ++++++++++++++++++++++++++
 4 files changed, 575 insertions(+)
 create mode 100644 docs/backend/VirtGPU.md
 create mode 100644 docs/backend/VirtGPU/configuration.md
 create mode 100644 docs/backend/VirtGPU/development.md

diff --git a/README.md b/README.md
index dac020ad37..5c11f38048 100644
--- a/README.md
+++ b/README.md
@@ -288,6 +288,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
+| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
 
 ## Obtaining and quantizing models
 
diff --git a/docs/backend/VirtGPU.md b/docs/backend/VirtGPU.md
new file mode 100644
index 0000000000..c81468da13
--- /dev/null
+++ b/docs/backend/VirtGPU.md
@@ -0,0 +1,180 @@
+# GGML-VirtGPU Backend
+
+The GGML-VirtGPU backend enables GGML applications to run machine
+learning computations on host hardware while the application itself
+runs inside a virtual machine.  It uses host-guest shared memory to
+efficiently share data buffers between the two sides.
+
+This backend relies on the virtio-gpu, and VirglRenderer API Remoting
+(APIR) component. The backend is split into two libraries:
+- a GGML implementation (the "remoting frontend"), running in the
+  guest and interacting with the virtgpu device
+- a VirglRenderer APIR compatible library (the "remoting backend"),
+  running in the host and interacting with Virglrenderer and an actual
+  GGML device backend.
+
+## OS support
+
+| OS       | Status            | Backend     | CI testing  | Notes
+| -------- | ----------------- | ----------- | ----------- | -----
+| MacOS 14 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14
+| MacOS 15 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14 or MacOS 15
+| MacOS 26 | Not tested        |             |             |
+| Linux    | Under development | ggml-vulkan | not working | Working locally, CI running into deadlocks
+
+
+## Architecture Overview
+
+The GGML-VirtGPU backend consists of three main components:
+
+```mermaid
+graph TD
+    %% Nodes
+
+ subgraph GuestVM ["Guest VM - Frontend"]
+        App([GGML Application
llama.cpp, etc.])
+
+        direction TB
+        Interface[GGML Backend Interface]
+        Comm["GGML-VirtGPU
(hypercalls + shared mem)"]
+
+        App --> Interface
+        Interface --> Comm
+    end
+
+    API[virtio-gpu / virglrenderer API]
+
+    subgraph HostSystem [Host System - Backend]
+        direction TB
+        Dispatcher[GGML-VirtGPU-Backend]
+        BackendLib[GGML Backend library
Metal / Vulkan / CPU / ...]
+
+        Dispatcher --> BackendLib
+    end
+
+    %% Connections
+    Comm --> API
+    API --> HostSystem
+```
+
+### Key Components
+
+1. **Guest-side Frontend** (`ggml-virtgpu/`): Implements the GGML backend interface and forwards operations to the host
+2. **Host-side Backend** (`ggml-virtgpu/backend/`): Receives forwarded operations and executes them on actual hardware backends
+3. **Communication Layer**: Uses virtio-gpu hypercalls and shared memory for efficient data transfer
+
+## Features
+
+- **Dynamic backend loading** on the host side (CPU, CUDA, Metal, etc.)
+- **Zero-copy data transfer** via host-guest shared memory pages
+
+## Communication Protocol
+
+### Hypercalls and Shared Memory
+
+The backend uses two primary communication mechanisms:
+
+1. **Hypercalls (`DRM_IOCTL_VIRTGPU_EXECBUFFER`)**: Trigger remote execution from guest to host
+2. **Shared Memory Pages**: Zero-copy data transfer for tensors and parameters
+
+#### Shared Memory Layout
+
+Each connection uses two shared memory buffers:
+
+- **Data Buffer** (24 MiB): For command/response data and tensor transfers
+- **Reply Buffer** (16 KiB): For command replies and status information
+- **Data Buffers**: Dynamically allocated host-guest shared buffers
+  served as GGML buffers.
+
+### APIR Protocol
+
+The Virglrender API Remoting protocol defines three command types:
+
+- `HANDSHAKE`: Protocol version negotiation and capability discovery
+- `LOADLIBRARY`: Dynamic loading of backend libraries on the host
+- `FORWARD`: API function call forwarding
+
+### Binary Serialization
+
+Commands and data are serialized using a custom binary protocol with:
+
+- Fixed-size encoding for basic types
+- Variable-length arrays with size prefixes
+- Buffer bounds checking
+- Error recovery mechanisms
+
+## Supported Operations
+
+### Device Operations
+- Device enumeration and capability queries
+- Memory information (total/free)
+- Backend type detection
+
+### Buffer Operations
+- Buffer allocation and deallocation
+- Tensor data transfer (host ↔ guest)
+- Memory copying and clearing
+
+### Computation Operations
+- Graph execution forwarding
+
+## Build Requirements
+
+### Guest-side Dependencies
+- `libdrm` for DRM/virtio-gpu communication
+- C++20 compatible compiler
+- CMake 3.14+
+
+### Host-side Dependencies
+- virglrenderer with APIR support (pending upstream review)
+- Target backend libraries (libggml-metal, libggml-vulkan, etc.)
+
+## Configuration
+
+### Environment Variables
+
+- `GGML_VIRTGPU_BACKEND_LIBRARY`: Path to the host-side backend library
+- `GGML_VIRTGPU_DEBUG`: Enable debug logging
+
+### Build Options
+
+- `GGML_VIRTGPU`: Enable the VirtGPU backend (`ON` or `OFF`, default: `OFF`)
+- `GGML_VIRTGPU_BACKEND`: Build the host-side backend component (`ON`, `OFF` or `ONLY`, default: `OFF`)
+
+### System Requirements
+
+- VM with virtio-gpu support
+- VirglRenderer with APIR patches
+- Compatible backend libraries on host
+
+## Limitations
+
+- **VM-specific**: Only works in virtual machines with virtio-gpu support
+- **Host dependency**: Requires properly configured host-side backend
+- **Latency**: Small overhead from VM escaping for each operation
+
+
+* This work is pending upstream changes in the VirglRenderer
+  project.
+  * The backend can be tested with Virglrenderer compiled from source
+  using this PR:
+  https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590
+* This work is pending changes in the VMM/hypervisor running the
+  virtual machine, which need to know how to route the newly
+  introduced APIR capset.
+  * The environment variable `VIRGL_ROUTE_VENUS_TO_APIR=1` allows
+    using the Venus capset, until the relevant hypervisors have been
+    patched. However, setting this flag breaks the Vulkan/Venus normal
+    behavior.
+  * The environment variable `GGML_REMOTING_USE_APIR_CAPSET` tells the
+    `ggml-virtgpu` backend to use the APIR capset. This will become
+    the default when the relevant hypervisors have been patched.
+
+* This work focused on improving the performance of llama.cpp running
+  on MacOS containers, and is mainly tested on this platform. The
+  linux support (via `krun`) is in progress.
+
+## See Also
+
+- [Development and Testing](VirtGPU/development.md)
+- [Backend configuration](VirtGPU/configuration.md)
diff --git a/docs/backend/VirtGPU/configuration.md b/docs/backend/VirtGPU/configuration.md
new file mode 100644
index 0000000000..597862d5c8
--- /dev/null
+++ b/docs/backend/VirtGPU/configuration.md
@@ -0,0 +1,174 @@
+# GGML-VirtGPU Backend Configuration
+
+This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
+
+## Environment Variables Overview
+
+The ggml-virtgpu backend uses environment variables for configuration across three main components:
+- **Frontend (Guest)**: GGML applications running in VMs
+- **Hypervisor**: Virglrenderer/APIR system
+- **Backend (Host)**: Host-side GGML backend integration
+
+## Frontend (Guest-side) Configuration
+
+### GGML_REMOTING_USE_APIR_CAPSET
+- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Controls which virtio-gpu capability set to use for communication
+- **Values**:
+  - Set (any value): Use the APIR capset (long-term setup)
+  - Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
+- **Default**: Unset (Venus capset)
+- **Usage**:
+  ```bash
+  export GGML_REMOTING_USE_APIR_CAPSET=1  # Use APIR capset
+  # or leave unset for Venus capset
+  ```
+
+## Hypervisor (Virglrenderer/APIR) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### VIRGL_APIR_BACKEND_LIBRARY
+- **Location**: `virglrenderer/src/apir/apir-context.c`
+- **Configuration Key**: `apir.load_library.path`
+- **Type**: File path string
+- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
+- **Required**: Yes
+- **Example**:
+  ```bash
+  export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
+  ```
+
+### VIRGL_ROUTE_VENUS_TO_APIR
+- **Location**: `virglrenderer/src/apir/apir-renderer.h`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
+- **Status**: will be removed once hypervisors support APIR natively
+- **Warning**: Breaks normal Vulkan/Venus functionality
+- **Usage**:
+  ```bash
+  export VIRGL_ROUTE_VENUS_TO_APIR=1  # For testing with an unmodified hypervisor
+  ```
+
+### VIRGL_APIR_LOG_TO_FILE
+- **Location**: `virglrenderer/src/apir/apir-renderer.c`
+- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
+- **Required**: No (optional debugging)
+- **Default**: Logging to `stderr`
+- **Usage**:
+  ```bash
+  export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
+  ```
+
+## Backend (Host-side) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+- **Configuration Key**: `ggml.library.path`
+- **Type**: File path string
+- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
+- **Required**: **Yes** - backend initialization fails without this
+- **Examples**:
+  ```bash
+  # macOS with Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+
+  # Linux with CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
+
+  # macOS or Linux with Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
+  ```
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_REG
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+- **Configuration Key**: `ggml.library.reg`
+- **Type**: Function symbol name string
+- **Purpose**: Name of the backend registration function to call after loading the library
+- **Required**: No (defaults to `ggml_backend_init`)
+- **Default**: `ggml_backend_init`
+- **Examples**:
+  ```bash
+  # Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+  # CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
+
+  # Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
+
+  # Generic fallback (default)
+  # export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
+  ```
+
+### APIR_LLAMA_CPP_LOG_TO_FILE
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
+- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the GGML backend to specified file
+- **Required**: No (optional debugging)
+- **Usage**:
+  ```bash
+  export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
+  ```
+
+## Configuration Flow
+
+The configuration system works as follows:
+
+1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
+
+2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
+   - `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
+   - `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+   - `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+   - this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
+
+3. **Backend Initialization**: The backend queries the configuration via callbacks:
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
+
+4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
+
+## Error Messages
+
+Common error scenarios and their messages:
+
+- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
+- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
+
+## Example Complete Configuration
+
+Here's an example configuration for a macOS host with Metal backend:
+
+```bash
+# Hypervisor environment
+export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
+
+# Backend configuration
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+# Optional logging
+export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
+export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
+
+# Guest configuration
+export GGML_REMOTING_USE_APIR_CAPSET=1
+```
diff --git a/docs/backend/VirtGPU/development.md b/docs/backend/VirtGPU/development.md
new file mode 100644
index 0000000000..ca2e47772a
--- /dev/null
+++ b/docs/backend/VirtGPU/development.md
@@ -0,0 +1,220 @@
+# Development and Testing
+
+## Development
+
+### Code Generation
+
+The backend uses code generation from YAML configuration:
+
+```bash
+# Regenerate protocol code
+cd ggml-virtgpu/
+python regenerate_remoting.py
+```
+
+### Adding New Operations
+
+1. Add function definition to `ggmlremoting_functions.yaml`
+2. Regenerate code with `regenerate_remoting.py`
+3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
+4. Implement host-side handling in `backend-dispatched-*.cpp`
+
+## Testing
+
+This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
+
+### Prerequisites
+
+The testing setup requires:
+
+- macOS host system
+- Container runtime with `libkrun` provider (podman machine)
+- Access to development patchset for VirglRenderer
+
+### Required Patchsets
+
+The backend requires patches that are currently under review:
+
+- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
+- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
+- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
+
+### Build Instructions
+
+#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
+
+```bash
+# Build the backend that runs natively on macOS
+mkdir llama.cpp
+cd llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
+
+cmake -S . -B $LLAMA_MAC_BUILD \
+      -DGGML_NATIVE=OFF \
+      -DLLAMA_CURL=ON \
+      -DGGML_REMOTINGBACKEND=ONLY \
+      -DGGML_METAL=ON
+
+TARGETS="ggml-metal"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
+
+# Build additional tools for native benchmarking
+EXTRA_TARGETS="llama-run llama-bench"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
+```
+
+#### 2. Build virglrenderer (Host-side, macOS)
+
+```bash
+# Build virglrenderer with APIR support
+mkdir virglrenderer
+git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
+cd src
+
+VIRGL_BUILD_DIR=$PWD/build
+
+# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
+
+meson setup $VIRGL_BUILD_DIR \
+      -Dvenus=true \
+      -Dapir=true
+
+ninja -C $VIRGL_BUILD_DIR
+```
+
+#### 3. Build ggml-virtgpu (Guest-side, Linux)
+
+Option A: Build from a script:
+
+```bash
+# Inside a Linux container
+mkdir llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_LINUX_BUILD=$PWD//build-virtgpu
+
+cmake -S . -B $LLAMA_LINUX_BUILD \
+      -DGGML_VIRTGPU=ON
+
+ninja -C $LLAMA_LINUX_BUILD
+```
+
+Option B: Build container image with frontend:
+
+```bash
+cat << EOF > remoting.containerfile
+FROM quay.io/fedora/fedora:43
+USER 0
+
+WORKDIR /app/remoting
+
+ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
+ARG LLAMA_CPP_VERSION="master"
+ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
+ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
+
+RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
+
+RUN git clone "\${LLAMA_CPP_REPO}" src \\
+ && git -C src fetch origin \${LLAMA_CPP_VERSION} \\
+ && git -C src reset --hard FETCH_HEAD
+
+RUN mkdir -p build \\
+ && cd src \\
+ && set -o pipefail \\
+ && cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
+ && cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
+
+ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
+EOF
+
+mkdir -p empty_dir
+podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
+```
+
+### Environment Setup
+
+#### Set krunkit Environment Variables
+
+```bash
+# Define the base directories (adapt these paths to your system)
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
+LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
+
+# For krunkit to load the custom virglrenderer library
+export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
+
+# For Virglrenderer to load the ggml-remotingbackend library
+export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
+
+# For llama.cpp remotingbackend to load the ggml-metal backend
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
+```
+
+#### Launch Container Environment
+
+```bash
+# Set container provider to libkrun
+export CONTAINERS_MACHINE_PROVIDER=libkrun
+podman machine start
+```
+
+#### Verify Environment
+
+Confirm that krunkit is using the correct virglrenderer library:
+
+```bash
+lsof -c krunkit | grep virglrenderer
+# Expected output:
+# krunkit 50574 user  txt  REG  1,14  2273912  10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
+```
+
+### Running Tests
+
+#### Launch Test Container
+
+```bash
+# Optional model caching
+mkdir -p models
+PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
+
+podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
+```
+
+#### Test llama.cpp in Container
+
+```bash
+
+# Run performance benchmark
+/app/remoting/build/bin/llama-bench -m ./llama3.2
+```
+
+Expected output (performance may vary):
+```
+| model                          |       size |     params | backend    | ngl |          test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         pp512 |        991.30 ± 0.66 |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         tg128 |         85.71 ± 0.11 |
+```
+
+### Troubleshooting
+
+#### SSH Environment Variable Issues
+
+⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
+
+**Workaround 1: Replace system library**
+```bash
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build  # ⚠️ adapt to your system
+BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
+VIRGL_LIB=libvirglrenderer.1.dylib
+
+cd $BREW_VIRGL_DIR
+mv $VIRGL_LIB ${VIRGL_LIB}.orig
+ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
+```

From 972f323e73bf0b28358ccaa3b9aa02779421f260 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Mon, 9 Feb 2026 14:57:51 +0200
Subject: [PATCH 160/206] revert : "[Model] Qwen3.5 dense and MoE support (no
 vision) (#19435)" (#19453)

This reverts commit 39bf692af1cba2a1072e4a42425611bf1ec2807d.
---
 convert_hf_to_gguf.py          |  78 ++---
 gguf-py/gguf/constants.py      |  59 ----
 gguf-py/gguf/tensor_mapping.py |   6 +-
 src/CMakeLists.txt             |   3 -
 src/llama-arch.cpp             |  61 ----
 src/llama-arch.h               |   2 -
 src/llama-context.cpp          |   2 +-
 src/llama-model.cpp            | 154 --------
 src/models/delta.cpp           | 618 ---------------------------------
 src/models/kimi-linear.cpp     |   1 +
 src/models/models.h            | 102 +-----
 src/models/qwen3-5.cpp         | 421 ----------------------
 src/models/qwen3-5moe.cpp      |  52 ---
 src/models/qwen3next.cpp       | 372 +++++++++++++++++++-
 14 files changed, 399 insertions(+), 1532 deletions(-)
 delete mode 100644 src/models/delta.cpp
 delete mode 100644 src/models/qwen3-5.cpp
 delete mode 100644 src/models/qwen3-5moe.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index e64756a74a..843c00a896 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4102,27 +4102,39 @@ class Qwen2MoeModel(TextModel):
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # handle pre-packed expert tensors (e.g. Qwen3.5 MoE, Qwen3Next)
-        # HF stores these using nn.Linear convention: [n_expert, out_features, in_features]
-        # This matches the individual expert stacking path below (which stacks
-        # per-expert [out, in] weights into [n_expert, out, in]), so no permute is needed.
+        # handle aggregated expert tensors
+        # GGUF stores dimensions reversed from PyTorch, so:
+        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
+        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
+        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # HF: [n_expert, n_embd, n_ff] → GGML: {n_ff, n_embd, n_expert} ✓
-            yield from super().modify_tensors(data_torch, mapped, bid)
+            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
+            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
+            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
+            permuted = data_torch.permute(0, 2, 1).contiguous()
+            yield from super().modify_tensors(permuted, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            # HF: [n_expert, 2*n_ff, n_embd] → split on dim=1
-            n_ff = data_torch.shape[1] // 2
-            gate = data_torch[:, :n_ff, :].contiguous()
-            up = data_torch[:, n_ff:, :].contiguous()
-            # gate/up: [n_expert, n_ff, n_embd] → GGML: {n_embd, n_ff, n_expert} ✓
-            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
-            mapped_gate = f"{base_name}.gate_proj.weight"
-            mapped_up = f"{base_name}.up_proj.weight"
-            yield from super().modify_tensors(gate, mapped_gate, bid)
-            yield from super().modify_tensors(up, mapped_up, bid)
+            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            split_dim = data_torch.shape[-1] // 2
+            gate = data_torch[..., :split_dim].contiguous()
+            up = data_torch[..., split_dim:].contiguous()
+            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+            base_name = name.removesuffix(".weight")
+            base = base_name.rsplit('.', 1)[0]
+            mapped_gate = f"{base}.gate_proj.weight"
+            mapped_up = f"{base}.up_proj.weight"
+            perm_gate = gate.permute(0, 2, 1).contiguous()
+            perm_up = up.permute(0, 2, 1).contiguous()
+            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
+            yield from super().modify_tensors(perm_up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
@@ -4332,40 +4344,6 @@ class Qwen3NextModel(Qwen2MoeModel):
             yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Qwen3_5ForCausalLM", "Qwen3_5TextForCausalLM")
-class Qwen3_5Model(Qwen3NextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3_5
-
-    # Stores whichever of in_proj_a/in_proj_b is seen first, keyed by layer
-    _pending_ba: dict[int | None, tuple[str, Tensor]] = {}
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Handle split in_proj_b + in_proj_a → concatenated SSM_BETA_ALPHA
-        # safetensors sorts alphabetically so in_proj_a arrives before in_proj_b
-        if "in_proj_a.weight" in name or "in_proj_b.weight" in name:
-            which = "a" if "in_proj_a" in name else "b"
-            if bid not in self._pending_ba:
-                self._pending_ba[bid] = (which, data_torch)
-                return
-            prev_which, prev_tensor = self._pending_ba.pop(bid)
-            assert prev_which != which, f"duplicate in_proj_{which} for layer {bid}"
-            b_tensor = prev_tensor if prev_which == "b" else data_torch
-            a_tensor = prev_tensor if prev_which == "a" else data_torch
-            ba_combined = torch.cat([b_tensor, a_tensor], dim=0)
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.SSM_BETA_ALPHA, bid, ".weight"), ba_combined)
-            return
-        else:
-            # Qwen3Next uses .qkvz tensor, so we use the super to get the other functionalities
-            # (norm correction, A_log to A etc.) for free
-            # Qwen2Moe already does the gate_up conversion properly, just use that
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3_5MoeForCausalLM", "Qwen3_5MoeTextForCausalLM")
-class Qwen3_5MoeModel(Qwen3_5Model):
-    model_arch = gguf.MODEL_ARCH.QWEN3_5_MOE
-
-
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 8a3fab1e1c..3af4fffe95 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -382,8 +382,6 @@ class MODEL_ARCH(IntEnum):
     QWEN3            = auto()
     QWEN3MOE         = auto()
     QWEN3NEXT        = auto()
-    QWEN3_5          = auto()
-    QWEN3_5_MOE      = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
     PHI2             = auto()
@@ -814,8 +812,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
-    MODEL_ARCH.QWEN3_5:          "qwen3_5",
-    MODEL_ARCH.QWEN3_5_MOE:      "qwen3_5moe",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
     MODEL_ARCH.PHI2:             "phi2",
@@ -1788,61 +1784,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.SSM_BETA_ALPHA,
         MODEL_TENSOR.SSM_OUT
     ],
-    MODEL_ARCH.QWEN3_5: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
-        MODEL_TENSOR.ATTN_GATE,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-        MODEL_TENSOR.SSM_A,
-        MODEL_TENSOR.SSM_CONV1D,
-        MODEL_TENSOR.SSM_DT,
-        MODEL_TENSOR.SSM_NORM,
-        MODEL_TENSOR.SSM_IN,
-        MODEL_TENSOR.SSM_BETA_ALPHA,
-        MODEL_TENSOR.SSM_OUT,
-    ],
-    MODEL_ARCH.QWEN3_5_MOE: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
-        MODEL_TENSOR.ATTN_GATE,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.FFN_GATE_INP,
-        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
-        MODEL_TENSOR.FFN_UP_SHEXP,
-        MODEL_TENSOR.FFN_DOWN_SHEXP,
-        MODEL_TENSOR.FFN_GATE_SHEXP,
-        MODEL_TENSOR.FFN_DOWN_EXP,
-        MODEL_TENSOR.FFN_UP_EXP,
-        MODEL_TENSOR.FFN_GATE_EXP,
-        MODEL_TENSOR.SSM_A,
-        MODEL_TENSOR.SSM_CONV1D,
-        MODEL_TENSOR.SSM_DT,
-        MODEL_TENSOR.SSM_NORM,
-        MODEL_TENSOR.SSM_IN,
-        MODEL_TENSOR.SSM_BETA_ALPHA,
-        MODEL_TENSOR.SSM_OUT,
-    ],
     MODEL_ARCH.QWEN3VL: [
         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 43f32c7b52..167ade7803 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -228,7 +228,6 @@ class TensorNameMap:
             "transformer_encoder.{bid}.qkv",                                       # neobert
             "layers.{bid}.attn.Wqkv",                                              # modern-bert
             "model.layers.{bid}.self_attn.language_expert_query_key_value",        # cogvlm
-            "model.layers.{bid}.linear_attn.in_proj_qkv",                          # qwen3.5
         ),
 
         # Attention query
@@ -359,9 +358,8 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj",   # afmoe
-            "model.layers.{bid}.self_attn.g_proj",      # step3.5 head-wise attention gate
-            "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5
+            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
 
         # Feed-forward norm
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 0c164617a1..2115fc4255 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -57,7 +57,6 @@ add_library(llama
             models/deci.cpp
             models/deepseek.cpp
             models/deepseek2.cpp
-            models/delta.cpp
             models/dots1.cpp
             models/dream.cpp
             models/ernie4-5-moe.cpp
@@ -123,8 +122,6 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
-            models/qwen3-5.cpp
-            models/qwen3-5moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index fce46772d7..bd78f1e556 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -35,8 +35,6 @@ static const std::map LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
-    { LLM_ARCH_QWEN3_5,          "qwen3_5"          },
-    { LLM_ARCH_QWEN3_5_MOE,      "qwen3_5moe"       },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
     { LLM_ARCH_PHI2,             "phi2"             },
@@ -987,63 +985,6 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
-        case LLM_ARCH_QWEN3_5:
-            return {
-                LLM_TENSOR_TOKEN_EMBD,
-                LLM_TENSOR_OUTPUT_NORM,
-                LLM_TENSOR_OUTPUT,
-                LLM_TENSOR_ATTN_NORM,
-                LLM_TENSOR_ATTN_POST_NORM,
-                LLM_TENSOR_ATTN_Q,
-                LLM_TENSOR_ATTN_Q_NORM,
-                LLM_TENSOR_ATTN_K,
-                LLM_TENSOR_ATTN_K_NORM,
-                LLM_TENSOR_ATTN_V,
-                LLM_TENSOR_ATTN_OUT,
-                LLM_TENSOR_ATTN_QKV,
-                LLM_TENSOR_ATTN_GATE,
-                LLM_TENSOR_FFN_GATE,
-                LLM_TENSOR_FFN_DOWN,
-                LLM_TENSOR_FFN_UP,
-                LLM_TENSOR_SSM_A_NOSCAN,
-                LLM_TENSOR_SSM_CONV1D,
-                LLM_TENSOR_SSM_DT,
-                LLM_TENSOR_SSM_BETA_ALPHA,
-                LLM_TENSOR_SSM_IN,
-                LLM_TENSOR_SSM_NORM,
-                LLM_TENSOR_SSM_OUT,
-            };
-        case LLM_ARCH_QWEN3_5_MOE:
-            return {
-                LLM_TENSOR_TOKEN_EMBD,
-                LLM_TENSOR_OUTPUT_NORM,
-                LLM_TENSOR_OUTPUT,
-                LLM_TENSOR_ATTN_NORM,
-                LLM_TENSOR_ATTN_POST_NORM,
-                LLM_TENSOR_ATTN_Q,
-                LLM_TENSOR_ATTN_Q_NORM,
-                LLM_TENSOR_ATTN_K,
-                LLM_TENSOR_ATTN_K_NORM,
-                LLM_TENSOR_ATTN_V,
-                LLM_TENSOR_ATTN_OUT,
-                LLM_TENSOR_ATTN_QKV,
-                LLM_TENSOR_ATTN_GATE,
-                LLM_TENSOR_FFN_GATE_INP,
-                LLM_TENSOR_FFN_GATE_EXPS,
-                LLM_TENSOR_FFN_DOWN_EXPS,
-                LLM_TENSOR_FFN_UP_EXPS,
-                LLM_TENSOR_FFN_GATE_INP_SHEXP,
-                LLM_TENSOR_FFN_GATE_SHEXP,
-                LLM_TENSOR_FFN_DOWN_SHEXP,
-                LLM_TENSOR_FFN_UP_SHEXP,
-                LLM_TENSOR_SSM_A_NOSCAN,
-                LLM_TENSOR_SSM_CONV1D,
-                LLM_TENSOR_SSM_DT,
-                LLM_TENSOR_SSM_BETA_ALPHA,
-                LLM_TENSOR_SSM_IN,
-                LLM_TENSOR_SSM_NORM,
-                LLM_TENSOR_SSM_OUT,
-            };
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_HUNYUAN_DENSE:
@@ -2733,8 +2674,6 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_KIMI_LINEAR:
             return true;
         default:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index a392ecce2b..e8263369b8 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -39,8 +39,6 @@ enum llm_arch {
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
     LLM_ARCH_QWEN3NEXT,
-    LLM_ARCH_QWEN3_5,
-    LLM_ARCH_QWEN3_5_MOE,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
     LLM_ARCH_PHI2,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 80b9a7d46a..a6df893a31 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_QWEN3_5 || model.arch == LLM_ARCH_QWEN3_5_MOE || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
         return std::max(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max(1024u, 8u*model.n_tensors());
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 8fc61aee37..674d06c891 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2412,25 +2412,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-
-                // Load linear attention (gated delta net) parameters
-                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);
-
-                // Mark recurrent layers (linear attention layers)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
-                }
-            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7113,129 +7094,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
 
-                        // Shared experts
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
-                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
-                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3_5:
-                {
-                    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 == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    const int64_t ba_dim = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        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);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        // Dense FFN for all layers
-                        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_QWEN3_5_MOE:
-                {
-                    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 == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    const int64_t ba_dim = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        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);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Full attention layers
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
-                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, key_dim * 2 + value_dim * 2 }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        // MoE FFN
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-
                         // Shared experts
                         layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
                         layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
@@ -7687,8 +7545,6 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
-        arch == LLM_ARCH_QWEN3_5 ||
-        arch == LLM_ARCH_QWEN3_5_MOE ||
         arch == LLM_ARCH_NEMOTRON_H ||
         arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8487,14 +8343,6 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique(*this, params);
             } break;
-        case LLM_ARCH_QWEN3_5:
-            {
-                llm = std::make_unique(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3_5_MOE:
-            {
-                llm = std::make_unique(*this, params);
-            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 llm = std::make_unique(*this, params);
@@ -8755,8 +8603,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
-        case LLM_ARCH_QWEN3_5:
-        case LLM_ARCH_QWEN3_5_MOE:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
diff --git a/src/models/delta.cpp b/src/models/delta.cpp
deleted file mode 100644
index d1d9837d09..0000000000
--- a/src/models/delta.cpp
+++ /dev/null
@@ -1,618 +0,0 @@
-#include "models.h"
-#include "ggml.h"
-#include 
-#include 
-#include 
-
-llm_graph_context_delta::llm_graph_context_delta(const llm_graph_params & params) : llm_graph_context_mamba(params) {}
-
-/**
- * Unified Delta Net implementation supporting both GDA and KDA modes.
- *
- * GDA (Gated Delta Attention): g has shape [H, T, B] in GGML (PyTorch: [B, T, H])
- *   - Per-head gating, broadcasts over K dimension
- *
- * KDA (Key-wise Delta Attention): g has shape [K, H, T, B] in GGML (PyTorch: [B, T, H, K])
- *   - Per-key gating
- *
- * The mode is auto-detected based on g's dimensionality.
- *
- * Tensor dimension convention:
- *   GGML: ne[0] is innermost (fastest varying), ne[3] is outermost
- *   PyTorch: dim 0 is outermost, dim -1 is innermost
- *   So GGML [A, B, C, D] corresponds to PyTorch [D, C, B, A]
- */
-
-// Helper to get a slice along dimension 2 (n_chunks dimension)
-static ggml_tensor * get_slice_2d(ggml_context * ctx, ggml_tensor * t, int64_t chunk) {
-    return ggml_view_4d(ctx, t,
-        t->ne[0], t->ne[1], 1, t->ne[3],
-        t->nb[1], t->nb[2], t->nb[3],
-        chunk * t->nb[2]);
-}
-
-/**
- * Unified chunked Delta Net implementation.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, n_tokens, n_seqs]
- *                    KDA: [S_k, H_v, n_tokens, n_seqs]
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param causal_mask   Lower triangular mask [chunk_size, chunk_size]
- * @param identity      Identity matrix [chunk_size, chunk_size]
- * @param diag_mask     Diagonal mask [chunk_size, chunk_size]
- * @param il            Layer index (for debugging callbacks)
- * @param chunk_size    Chunk size for chunked processing
- * @param eps_norm      Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair llm_graph_context_delta::build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state_reshaped,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, n_tokens, n_seqs] (4D with ne[0]=S_k)
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate tensor shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state_reshaped->ne[0] == S_v && state_reshaped->ne[1] == S_v && state_reshaped->ne[2] == H_v && state_reshaped->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        // KDA: g shape [S_k, H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs);
-    } else {
-        // GDA: g shape [H_v, n_tokens, n_seqs]
-        GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Permute tensors to working format [S, n_tokens, H, n_seqs]
-    // Input: [S, H, n_tokens, n_seqs] -> permute(0, 2, 1, 3) -> [S, n_tokens, H, n_seqs]
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    if (is_kda) {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
-    } else {
-        g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-    }
-    beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
-
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state_reshaped, "state_in", il);
-
-    // Padding for chunk processing
-    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
-    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
-
-    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
-    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
-    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
-    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
-    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
-
-
-    cb(q, "q_pad", il);
-    cb(k, "k_pad", il);
-    cb(v, "v_pad", il);
-    cb(beta, "beta_pad", il);
-    cb(g, "g_pad", il);
-
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
-
-    cb(v_beta, "v_beta", il);
-    cb(k_beta, "k_beta", il);
-
-    // Reshape to chunks
-    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-    beta   = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
-
-    // Reshape g for chunks
-    ggml_tensor * g_cumsum;
-    ggml_tensor * g_cumsum_t;
-    if (is_kda) {
-        // KDA: g [S_k, n_tokens+pad, H_k, n_seqs] -> [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, S_k, chunk_size, n_chunks, H_k * n_seqs);
-        // Cumsum along chunk_size dimension (ne[1])
-        // GGML cumsum operates on ne[0], so we need to transpose, cumsum, transpose back
-        g = ggml_cont(ctx0, ggml_transpose(ctx0, g));  // [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        g_cumsum_t = ggml_cumsum(ctx0, g);
-        g_cumsum = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum_t));  // [S_k, chunk_size, n_chunks, H_k * n_seqs]
-    } else {
-        // GDA: g [n_tokens+pad, 1, H_k, n_seqs] -> [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-        g_cumsum = ggml_cumsum(ctx0, g);
-        g_cumsum_t = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_k * n_seqs);
-    }
-
-    cb(g_cumsum, "g_cumsum", il);
-
-    // Build attention matrix A for the WY representation solve
-    // For GDA: A[j,i] = sum_k(k[j,k] * exp(g[j] - g[i]) * k[i,k]) = (k @ k^T) * exp(g[j] - g[i])
-    // For KDA: A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    // KDA uses decay mask with S_k packed into batch to compute exp(g[j,k] - g[i,k]) per-key
-
-    ggml_tensor * k_decay;
-    ggml_tensor * decay_mask = nullptr;
-    ggml_tensor * g_exp_pos = nullptr;
-
-    if (is_kda) {
-        // KDA: Use decay mask with S_k in leading dimension for efficient mul_mat reduction
-        // A[j,i] = sum_k(k_beta[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-        // By putting S_k in dim 0, mul_mat implicitly sums over it
-
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // g_cumsum_t is [chunk_size, S_k, n_chunks, H_k * n_seqs]
-        // Reshape to [chunk_size, S_k, CHB] then build decay mask
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-
-        // Build decay mask: [chunk_size, chunk_size, S_k, CHB]
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        decay_mask = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        cb(decay_mask, "decay_mask_kda", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB] for mul_mat reduction over S_k
-        decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // Reshape k and k_beta for broadcasting with decay_mask
-        // k_i: indexed at position i (dim 2 of decay_mask)
-        // k_beta_j: indexed at position j (dim 1 of decay_mask)
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-        ggml_tensor * k_beta_j = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, 1, CHB);
-
-        // decay_k_beta_j[s,j,i,b] = decay[s,j,i,b] * k_beta[s,j,b]
-        ggml_tensor * decay_k_beta_j = ggml_mul(ctx0, decay_mask, k_beta_j);
-
-        // mul_mat sums over S_k: result[j,1,i,CHB] = sum_s decay_k_beta_j[s,j,i,b] * k_i[s,1,i,b]
-        k_decay = ggml_mul_mat(ctx0, decay_k_beta_j, k_i);
-        k_decay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, k_decay, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-
-        // g_exp_pos is still needed for later (kbeta_gexp, etc.)
-        g_exp_pos = ggml_exp(ctx0, g_cumsum);
-    } else {
-        // GDA: Use decay mask approach (g broadcasts over K dimension)
-        // g_cumsum [chunk_size, 1, n_chunks, H_v * n_seqs]
-        ggml_tensor * gcs_i = g_cumsum;
-        ggml_tensor * gcs_j = g_cumsum_t;
-        g_exp_pos = ggml_exp(ctx0, g_cumsum_t);
-        ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
-        decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-        cb(decay_mask, "decay_mask", il);
-
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-        decay_mask = ggml_exp(ctx0, decay_mask);
-        decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
-
-        ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-        k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    }
-
-    ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_solve", il);
-
-    // Solve triangular system: (I + L) @ X = I, where L is strictly lower triangular
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-    ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn = ggml_add(ctx0, attn, identity);
-
-    cb(attn, "attn_solved", il);
-
-    // Compute u = A @ v and w = A @ (g.exp() * k)
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, g_exp_pos);
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0,
-        ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    // Attention scores q @ k^T with decay
-    // For GDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j] - g[i]) * k[i,k])
-    // For KDA: attn_kq[j,i] = sum_k(q[j,k] * exp(g[j,k] - g[i,k]) * k[i,k])
-    ggml_tensor * attn_kq;
-    if (is_kda) {
-        // KDA: Same approach as k_decay - use decay_mask with S_k in leading dim
-        const int64_t CHB = n_chunks * H_k * n_seqs;
-
-        // Rebuild decay mask (same structure as k_decay)
-        ggml_tensor * gcs = ggml_reshape_3d(ctx0, g_cumsum_t, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, gcs, chunk_size, 1, S_k, CHB);
-        ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, gcs, 1, chunk_size, S_k, CHB);
-        ggml_tensor * gcs_j_bc = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, S_k, CHB);
-        ggml_tensor * decay_mask_kq = ggml_sub(ctx0, gcs_j_bc, gcs_i);
-
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-        decay_mask_kq = ggml_exp(ctx0, decay_mask_kq);
-        decay_mask_kq = ggml_mul(ctx0, decay_mask_kq, diag_mask);
-
-        // Permute to [S_k, chunk_size_j, chunk_size_i, CHB]
-        decay_mask_kq = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask_kq, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
-
-        // q_j: indexed at position j, k_i: indexed at position i
-        ggml_tensor * q_j = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
-        ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
-
-        // decay_q_j[s,j,i,b] = decay[s,j,i,b] * q[s,j,b]
-        ggml_tensor * decay_q_j = ggml_mul(ctx0, decay_mask_kq, q_j);
-
-        // mul_mat sums over S_k
-        attn_kq = ggml_mul_mat(ctx0, decay_q_j, k_i);
-        attn_kq = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, attn_kq, chunk_size, chunk_size, n_chunks, H_k * n_seqs)));
-    } else {
-        // GDA: Use decay mask
-        attn_kq = ggml_mul_mat(ctx0, k, q);
-        attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
-        attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
-    }
-    cb(attn_kq, "attn_kq", il);
-
-    // Compute g_last and g_diff for state updates
-    ggml_tensor * g_last;
-    ggml_tensor * g_diff_exp;
-    ggml_tensor * g_last_exp;
-
-    if (is_kda) {
-        // KDA: g_cumsum [S_k, chunk_size, n_chunks, H_k * n_seqs]
-        // Get last element along chunk_size dimension (ne[1])
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            g_cumsum->ne[0], 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[1] - 1) * g_cumsum->nb[1]);
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        // g_diff = g_last - g_cumsum
-        ggml_tensor * g_last_broadcast = ggml_repeat_4d(ctx0, g_last,
-            g_cumsum->ne[0], g_cumsum->ne[1], g_cumsum->ne[2], g_cumsum->ne[3]);
-        ggml_tensor * g_diff = ggml_sub(ctx0, g_last_broadcast, g_cumsum);
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    } else {
-        // GDA: g_cumsum [chunk_size, 1, n_chunks, H_k * n_seqs]
-        g_last = ggml_view_4d(ctx0, g_cumsum,
-            1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
-            g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
-            (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
-        g_last = ggml_cont(ctx0, g_last);
-        g_last_exp = ggml_exp(ctx0, g_last);
-
-        ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
-        g_diff_exp = ggml_exp(ctx0, g_diff);
-    }
-
-    cb(g_last, "g_last", il);
-    cb(g_last_exp, "g_last_exp", il);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
-    cb(key_gdiff, "key_gdiff", il);
-
-    // Process chunks
-    ggml_tensor * new_state = state_reshaped;
-    ggml_tensor * core_attn_out = nullptr;
-
-    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
-        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk);
-        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk);
-        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk);
-        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
-        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, g_exp_pos, chunk);
-
-        cb(attn_chunk, "attn_chunk", il);
-
-        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3),
-            S_v, S_v, 1, H_v * n_seqs);
-
-        // v_prime = k_cumdecay @ state
-        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
-        cb(v_prime, "v_prime_chunk", il);
-
-        // v_new = v - v_prime
-        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
-        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-        cb(v_new, "v_new_chunk", il);
-
-        // attn_inter = (q * g.exp()) @ state
-        ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
-        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-        cb(attn_inter, "attn_inter_chunk", il);
-
-        // output = attn_inter + attn @ v_new
-        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
-        cb(v_attn, "v_attn_chunk", il);
-
-        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
-        cb(core_attn_out_chunk, "core_attn_out_chunk", il);
-
-        core_attn_out = core_attn_out == nullptr
-            ? core_attn_out_chunk
-            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
-
-        // State update: state = state * g_last_exp + key_gdiff^T @ v_new
-        ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
-        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
-
-        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
-
-        if (is_kda) {
-            // KDA: g_last_exp [S_k, 1, n_chunks, H_k * n_seqs]
-            // State: [S_v, S_v, H_v, n_seqs]
-            // Need to reshape g_last_exp to broadcast correctly over V dimension only
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                1, gexp_last_chunk->ne[0], H_v, n_seqs);  // [1, S_k, H_v, n_seqs]
-            // Transpose to [S_k, 1, H_v, n_seqs] then broadcast
-            gexp_last_chunk = ggml_cont(ctx0, ggml_permute(ctx0, gexp_last_chunk, 1, 0, 2, 3));
-        } else {
-            // GDA: g_last_exp [1, 1, n_chunks, H_k * n_seqs]
-            // Broadcasts over both K and V dimensions
-            gexp_last_chunk = ggml_reshape_4d(ctx0, gexp_last_chunk,
-                gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs);
-        }
-
-        new_state = ggml_add(ctx0,
-            ggml_mul(ctx0, new_state, gexp_last_chunk),
-            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
-    }
-
-    // Truncate padding and permute back
-    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
-        S_v, n_tokens, H_v, n_seqs,
-        ggml_row_size(core_attn_out->type, S_v),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
-        ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    cb(output_tokens, "output_tokens", il);
-
-    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
-    output_tokens = ggml_cont(ctx0, output_tokens);
-
-    return {output_tokens, new_state};
-}
-
-
-/**
- * Unified autoregressive Delta Net implementation (single token processing).
- *
- * This implementation uses matrix multiplication instead of elementwise operations + summation,
- * which is more efficient and mathematically equivalent. See inline comments for equivalences.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, 1, n_seqs]
- * @param k         Key tensor [S_k, H_k, 1, n_seqs]
- * @param v         Value tensor [S_v, H_v, 1, n_seqs]
- * @param g         Gate tensor:
- *                    GDA: [H_v, 1, n_seqs]
- *                    KDA: [S_k, H_v, 1, n_seqs]
- * @param beta      Beta tensor [H_v, 1, 1, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- * @param il        Layer index (for debugging callbacks)
- * @param eps_norm  Epsilon for L2 normalization
- *
- * @return Pair of (output_tokens, new_state)
- */
-std::pair llm_graph_context_delta::build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t S_k      = q->ne[0];
-    const int64_t H_k      = q->ne[1];
-    const int64_t n_tokens = q->ne[2];
-    const int64_t n_seqs   = q->ne[3];
-
-    const int64_t S_v = v->ne[0];
-    const int64_t H_v = v->ne[1];
-
-    GGML_ASSERT(n_tokens == 1);  // Autoregressive mode is for single token
-
-    // Detect KDA vs GDA based on g's shape
-    // GDA: g has shape [H_v, 1, n_seqs] or [H_v, n_tokens, n_seqs]
-    // KDA: g has shape [S_k, H_v, 1, n_seqs] or [S_k, H_v, n_tokens, n_seqs]
-    const bool is_kda = (g->ne[0] == S_k && g->ne[1] == H_v);
-
-    // Validate shapes
-    GGML_ASSERT(v->ne[2] == n_tokens);
-    GGML_ASSERT(k->ne[2] == n_tokens);
-    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
-    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
-    GGML_ASSERT(H_k == H_v);
-
-    if (is_kda) {
-        GGML_ASSERT(g->ne[0] == S_k && g->ne[1] == H_v);
-    } else {
-        GGML_ASSERT(g->ne[0] == H_v);
-    }
-
-    // L2 normalize q and k
-    q = ggml_l2_norm(ctx0, q, eps_norm);
-    k = ggml_l2_norm(ctx0, k, eps_norm);
-
-    const float scale = 1.0f / sqrtf((float)S_v);
-    q = ggml_scale(ctx0, q, scale);
-    beta = ggml_sigmoid(ctx0, beta);
-
-    cb(q, "q_in", il);
-    cb(k, "k_in", il);
-    cb(v, "v_in", il);
-    cb(beta, "beta_in", il);
-    cb(g, "g_in", il);
-
-    // Reshape g and beta for broadcasting
-    ggml_tensor * g_t;
-    ggml_tensor * beta_t;
-
-    if (is_kda) {
-        // KDA: g [S_k, H_v, 1, n_seqs] -> [S_k, 1, H_k, n_seqs]
-        // For state multiplication, need [1, S_k, H_v, n_seqs] to broadcast over V only
-        g_t = ggml_reshape_4d(ctx0, g, S_k, 1, H_k, n_seqs);
-    } else {
-        // GDA: g [H_v, 1, n_seqs] -> [1, 1, H_k, n_seqs]
-        // For state multiplication, broadcasts over both K and V
-        g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
-    }
-
-    beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
-
-    // Apply exponential to g_t
-    g_t = ggml_exp(ctx0, g_t);
-
-    // State decay: state = state * exp(g)
-    if (is_kda) {
-        // KDA: g_t [S_k, 1, H_k, n_seqs], state [S_v, S_v, H_v, n_seqs]
-        // Need to broadcast g_t over V dimension (ne[0] of state)
-        // Permute g_t to [1, S_k, H_k, n_seqs] for correct broadcasting
-        ggml_tensor * g_broadcast = ggml_cont(ctx0, ggml_permute(ctx0, g_t, 1, 0, 2, 3));
-        state = ggml_mul(ctx0, state, g_broadcast);
-    } else {
-        // GDA: g_t [1, 1, H_k, n_seqs] broadcasts over both dimensions
-        state = ggml_mul(ctx0, state, g_t);
-    }
-
-    // Equivalence to previous version:
-    // Previous: kv_mem = sum_k(state * k) using elementwise mult + sum_rows
-    // Current:  k_state = state_t @ k_t using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * k_t = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
-    ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k_t);
-
-    // v_diff = v - k_state (equivalent to v - kv_mem in previous version)
-    ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
-    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, k_state);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k_t, beta_t);
-
-    // Equivalence to previous version:
-    // Previous: state += k.unsqueeze(-1) * delta where delta = (v - kv_mem) * beta
-    // Current:  state += v_diff^T @ k_beta^T using matrix multiplication
-    // These are equivalent because: outer_product(k, v_diff * beta) = v_diff^T @ k^T
-    state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
-
-    // Equivalence to previous version:
-    // Previous: core_attn_out = sum_k(state * q) using elementwise mult + sum_rows
-    // Current:  core_attn_out = state_t @ q using matrix multiplication
-    // These are equivalent because: sum_k(A * B) = A @ B when dimensions align
-    q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
-    state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-    ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
-    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
-    cb(core_attn_out, "output_tokens", il);
-    cb(state, "new_state", il);
-
-    return {core_attn_out, state};
-}
-
-
-/**
- * Main entry point that dispatches to chunked or autoregressive based on n_tokens.
- *
- * Input tensor format matches qwen3next conventions:
- * @param q         Query tensor [S_k, H_k, n_tokens, n_seqs]
- * @param k         Key tensor [S_k, H_k, n_tokens, n_seqs]
- * @param v         Value tensor [S_v, H_v, n_tokens, n_seqs]
- * @param g         Gate tensor (GDA: [H_v, n_tokens, n_seqs], KDA: [S_k, H_v, n_tokens, n_seqs])
- * @param beta      Beta tensor [H_v, 1, n_tokens, n_seqs]
- * @param state     State tensor [S_v, S_v * H_v, 1, n_seqs]
- */
-std::pair llm_graph_context_delta::build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm) {
-
-    // Input format: [S, H, n_tokens, n_seqs] (matching qwen3next convention)
-    const int64_t n_tokens = q->ne[2];
-
-    if (n_tokens == 1) {
-        return build_delta_net_unified_autoregressive(
-            ctx0, q, k, v, g, beta, state, il, eps_norm);
-    }
-    return build_delta_net_unified_chunking(
-        ctx0, q, k, v, g, beta, state, causal_mask, identity, diag_mask,
-        il, chunk_size, eps_norm);
-}
diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp
index d9ee698075..0f037d1a39 100644
--- a/src/models/kimi-linear.cpp
+++ b/src/models/kimi-linear.cpp
@@ -1,4 +1,5 @@
 #include "models.h"
+#include "ggml.h"
 
 #define CHUNK_SIZE 64
 
diff --git a/src/models/models.h b/src/models/models.h
index 2a750c168e..cfcbb9aaa5 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -17,53 +17,6 @@ struct llm_graph_context_mamba : public llm_graph_context {
 
 };
 
-struct llm_graph_context_delta : public llm_graph_context_mamba {
-    llm_graph_context_delta(const llm_graph_params & params);
-
-    virtual ~llm_graph_context_delta() = default;
-
-    std::pair build_delta_net_unified_chunking(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-
-    std::pair build_delta_net_unified_autoregressive(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        int           il,
-        float         eps_norm);
-
-    std::pair build_delta_net_unified(
-        ggml_context * ctx0,
-        ggml_tensor * q,
-        ggml_tensor * k,
-        ggml_tensor * v,
-        ggml_tensor * g,
-        ggml_tensor * beta,
-        ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
-        ggml_tensor * diag_mask,
-        int           il,
-        int64_t       chunk_size,
-        float         eps_norm);
-};
-
 // Base class for RWKV-related models
 struct llm_build_rwkv6_base : public llm_graph_context {
     const llama_model & model;
@@ -523,7 +476,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
-struct llm_build_qwen3next : public llm_graph_context_delta {
+struct llm_build_qwen3next : public llm_graph_context_mamba {
     llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
     ggml_tensor * build_layer_attn(
@@ -581,59 +534,6 @@ private:
     const llama_model & model;
 };
 
-struct llm_build_qwen3_5 : public llm_graph_context_delta {
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params);
-
-protected:
-    // Tag type for subclass constructors that need to call build_graph() themselves
-    // (to ensure virtual dispatch works correctly)
-    struct defer_graph_build_t {};
-
-    llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t);
-
-    void build_graph();
-
-    virtual ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
-
-    const llama_model & model;
-
-private:
-    ggml_tensor * build_layer_attn(
-    llm_graph_input_attn_kv * inp_attn,
-                ggml_tensor * cur,
-                ggml_tensor * inp_pos,
-                        int   il);
-
-    ggml_tensor * build_layer_attn_linear(
-         llm_graph_input_rs * inp,
-                ggml_tensor * cur,
-                ggml_tensor * causal_mask,
-                ggml_tensor * identity,
-                ggml_tensor * diag_mask,
-                        int   il);
-
-    ggml_tensor * build_norm_gated(
-                ggml_tensor * input,
-                ggml_tensor * weights,
-                ggml_tensor * gate,
-                        int   layer);
-
-    std::pair build_qkvz(
-                ggml_tensor * input,
-                        int   il);
-};
-
-struct llm_build_qwen3_5_moe : public llm_build_qwen3_5 {
-    llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params);
-
-protected:
-    ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il) override;
-};
-
 struct llm_build_qwen : public llm_graph_context {
     llm_build_qwen(const llama_model & model, const llm_graph_params & params);
 };
diff --git a/src/models/qwen3-5.cpp b/src/models/qwen3-5.cpp
deleted file mode 100644
index 0947299d73..0000000000
--- a/src/models/qwen3-5.cpp
+++ /dev/null
@@ -1,421 +0,0 @@
-#include "models.h"
-
-#define CHUNK_SIZE 64
-
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
-    build_graph();
-}
-
-// virtual call in constructor fix
-llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
-    llm_graph_context_delta(params), model(model) {
-}
-
-void llm_build_qwen3_5::build_graph() {
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-    cb(inpL, "model.embed_tokens", -1);
-
-    auto * inp = build_inp_mem_hybrid();
-
-    ggml_tensor * inp_pos     = build_inp_pos();
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    ggml_tensor * causal_mask =
-        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
-                    GGML_TRI_TYPE_LOWER);
-
-    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
-    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
-
-    ggml_build_forward_expand(gf, causal_mask);
-    ggml_build_forward_expand(gf, identity);
-    ggml_build_forward_expand(gf, diag_mask);
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
-        cb(cur, "attn_norm", il);
-
-        if (hparams.is_recurrent(il)) {
-            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
-        } else {
-            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, 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);
-        }
-
-        cur = ggml_add(ctx0, cur, inpSA);
-        cb(cur, "attn_residual", il);
-
-        ggml_tensor * ffn_residual = cur;
-
-        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
-        cb(attn_post_norm, "attn_post_norm", il);
-
-        cur = build_layer_ffn(attn_post_norm, il);
-        cb(cur, "ffn_out", il);
-
-        cur = ggml_add(ctx0, cur, ffn_residual);
-        cb(cur, "post_ffn", il);
-
-        inpL = cur;
-    }
-    cur = inpL;
-
-    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
-
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-
-    cur = build_lora_mm(model.output, cur);
-
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_norm_gated(
-        ggml_tensor * input,
-        ggml_tensor * weights,
-        ggml_tensor * gate,
-        int           layer) {
-    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
-    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
-
-    return ggml_mul(ctx0, normalized, gated_silu);
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn(
-        llm_graph_input_attn_kv * inp,
-        ggml_tensor *             cur,
-        ggml_tensor *             inp_pos,
-        int                       il) {
-    const int64_t n_embd_head = hparams.n_embd_head_v;
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
-    cb(Qcur_full, "Qcur_full", il);
-
-    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
-    cb(Qcur, "Qcur_reshaped", il);
-
-    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Qcur, "Qcur_normed", 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);
-
-    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
-    cb(Kcur, "Kcur_normed", il);
-
-    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
-        ggml_element_size(Qcur_full) * n_embd_head * 2,
-        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
-        ggml_element_size(Qcur_full) * n_embd_head);
-    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
-    cb(gate, "gate_reshaped", 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,
-            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);
-
-    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,
-                nullptr, nullptr,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-    cb(cur, "attn_pregate", il);
-
-    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
-    cb(gate_sigmoid, "gate_sigmoid", il);
-
-    cur = ggml_mul(ctx0, cur, gate_sigmoid);
-    cb(cur, "attn_gated", il);
-
-    cur = build_lora_mm(model.layers[il].wo, cur);
-    cb(cur, "attn_output", il);
-
-    return cur;
-}
-
-std::pair llm_build_qwen3_5::build_qkvz(
-                ggml_tensor * input,
-                        int   il) {
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    if (model.layers[il].wqkv) {
-        ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
-        qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
-        cb(qkv_mixed, "linear_attn_qkv_mixed", il);
-
-        ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
-        cb(z, "z", il);
-
-        return { qkv_mixed, z };
-
-    }
-    // legacy path for combined in_proj_qkvz
-    ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
-    cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
-
-    int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_qkvz[4] = {
-        head_k_dim,
-        head_k_dim,
-        head_v_dim * num_v_heads / num_k_heads,
-        head_v_dim * num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * query =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
-    cb(query, "q", il);
-
-    ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
-                                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                    split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
-    cb(key, "k", il);
-
-    ggml_tensor * value =
-        ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
-                    mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                    (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
-    cb(value, "v", il);
-
-    ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
-                                mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
-                                (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
-    z = ggml_cont(ctx0, z);
-    cb(z, "z", il);
-
-    ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(query_flat, "query_flat", il);
-
-    ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
-    cb(key_flat, "key_flat", il);
-
-    ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(value_flat, "value_flat", il);
-
-    ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
-    qkv_mixed               = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
-    cb(qkv_mixed, "qkv_mixed", il);
-
-    return { qkv_mixed, z };
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
-        llm_graph_input_rs * inp,
-        ggml_tensor *        cur,
-        ggml_tensor *        causal_mask,
-        ggml_tensor *        identity,
-        ggml_tensor *        diag_mask,
-        int                  il) {
-    const auto * mctx_cur = inp->mctx;
-
-    const int64_t d_inner      = hparams.ssm_d_inner;
-    const int64_t n_seqs       = ubatch.n_seqs;
-    const int64_t head_k_dim   = hparams.ssm_d_state;
-    const int64_t num_k_heads  = hparams.ssm_n_group;
-    const int64_t num_v_heads  = hparams.ssm_dt_rank;
-    const int64_t head_v_dim   = d_inner / num_v_heads;
-    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-    const auto kv_head = mctx_cur->get_head();
-
-    GGML_ASSERT(n_seqs != 0);
-    GGML_ASSERT(ubatch.equal_seqs());
-    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-    auto qkvz = build_qkvz(cur, il);
-    ggml_tensor * qkv_mixed = qkvz.first;
-    ggml_tensor * z         = qkvz.second;
-
-    ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
-    cb(mixed_ba, "linear_attn_mixed_ba", il);
-
-    int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
-    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
-
-    int64_t split_sizes_ba[2] = {
-        num_v_heads / num_k_heads,
-        num_v_heads / num_k_heads
-    };
-
-    ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
-    cb(b, "b", il);
-
-    ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
-                                   mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
-                                   split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
-    cb(a, "a", il);
-
-    ggml_tensor * beta  = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
-    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
-    cb(alpha_softplus, "a_softplus", il);
-    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);
-    cb(gate, "gate", il);
-
-    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
-    const int64_t conv_kernel_size = conv_kernel->ne[0];
-    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
-    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
-    cb(qkv_mixed, "qkv_mixed_permuted", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
-    cb(conv_states_all, "conv_states_updated", il);
-
-    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
-    cb(conv_output_proper, "conv_output_raw", il);
-
-    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
-    cb(conv_output_silu, "conv_output_silu", il);
-
-    ggml_tensor * conv_qkv_mix = conv_output_silu;
-
-    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
-    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
-
-    ggml_tensor * q_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
-    cb(q_conv, "q_conv", il);
-    ggml_tensor * k_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(k_conv, "k_conv", il);
-    ggml_tensor * v_conv =
-        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
-                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
-    cb(v_conv, "v_conv", il);
-
-    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
-    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
-
-    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
-    cb(state, "state_predelta", il);
-
-    if (num_k_heads != num_v_heads) {
-        GGML_ASSERT(num_v_heads % num_k_heads == 0);
-        int64_t repeat_factor = num_v_heads / num_k_heads;
-
-        ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-        ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
-
-        ggml_tensor * q_repeated =
-            ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-        ggml_tensor * k_repeated =
-            ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
-
-        q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-        k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
-    }
-
-    cb(q_conv, "q_conv_predelta", il);
-    cb(k_conv, "k_conv_predelta", il);
-    cb(v_conv, "v_conv_predelta", il);
-
-    std::pair attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    ggml_build_forward_expand(gf,
-                              ggml_cpy(ctx0, new_state,
-                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
-                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
-    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
-
-    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
-
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(final_output, "final_output", il);
-
-    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
-    cb(cur, "linear_attn_out", il);
-
-    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-    return cur;
-}
-
-ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Qwen3.5 Dense always uses dense FFN
-    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);
-    return cur;
-}
diff --git a/src/models/qwen3-5moe.cpp b/src/models/qwen3-5moe.cpp
deleted file mode 100644
index a488443218..0000000000
--- a/src/models/qwen3-5moe.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-#include "models.h"
-
-llm_build_qwen3_5_moe::llm_build_qwen3_5_moe(const llama_model & model, const llm_graph_params & params) :
-    llm_build_qwen3_5(model, params, defer_graph_build_t{}) {
-    build_graph();
-}
-
-ggml_tensor * llm_build_qwen3_5_moe::build_layer_ffn(ggml_tensor * cur, const int il) {
-    // Check if this is an MoE layer
-    if (model.layers[il].ffn_gate_inp != nullptr) {
-        // MoE branch
-        ggml_tensor * moe_out =
-            build_moe_ffn(cur,
-                model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
-                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
-                nullptr,
-                n_expert, n_expert_used, LLM_FFN_SILU,
-                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
-        cb(moe_out, "ffn_moe_out", il);
-
-        // Add shared experts if present
-        if (model.layers[il].ffn_up_shexp != nullptr) {
-            ggml_tensor * ffn_shexp =
-                build_ffn(cur,
-                    model.layers[il].ffn_up_shexp, NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(ffn_shexp, "ffn_shexp", il);
-
-            // Apply shared expert gating (sigmoid)
-            ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
-            cb(shared_gate, "shared_expert_gate", il);
-
-            shared_gate = ggml_sigmoid(ctx0, shared_gate);
-            cb(shared_gate, "shared_expert_gate_sigmoid", il);
-
-            ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
-            cb(ffn_shexp, "ffn_shexp_gated", il);
-
-            cur = ggml_add(ctx0, moe_out, ffn_shexp);
-            cb(cur, "ffn_out", il);
-        } else {
-            cur = moe_out;
-        }
-    } else {
-        // Dense FFN branch (fallback)
-        cur = llm_build_qwen3_5::build_layer_ffn(cur, il);
-    }
-    return cur;
-}
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 0335f5ab76..99b1a76a48 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -1,9 +1,10 @@
+#include "ggml.h"
 #include "models.h"
 
 #define CHUNK_SIZE 64
 
 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
-    llm_graph_context_delta(params), model(model) {
+    llm_graph_context_mamba(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -85,6 +86,362 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair llm_build_qwen3next::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair llm_build_qwen3next::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
 ggml_tensor * llm_build_qwen3next::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
@@ -395,7 +752,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
-    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim,  num_v_heads, n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
     cb(state, "state_predelta", il);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
@@ -424,10 +781,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    std::pair attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
-            gate, beta, state, causal_mask, identity, diag_mask,
-            il, CHUNK_SIZE, hparams.f_norm_rms_eps);
-
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
     ggml_tensor * output    = attn_out.first;
     ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);

From 81ddc60cb3b980a4503a9a0177b079dfa562c60e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Mon, 9 Feb 2026 15:09:30 +0200
Subject: [PATCH 161/206] ci : add metal server workflows (#19293)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ci : add metal server workflows

* cont : try fix python init

* cont : move to a separate workflow that runs only on master

* cont : fix num jobs

Co-authored-by: Sigbjørn Skjæret 

---------

Co-authored-by: Sigbjørn Skjæret 
---
 .github/workflows/server-metal.yml | 73 ++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 .github/workflows/server-metal.yml

diff --git a/.github/workflows/server-metal.yml b/.github/workflows/server-metal.yml
new file mode 100644
index 0000000000..1d707bef44
--- /dev/null
+++ b/.github/workflows/server-metal.yml
@@ -0,0 +1,73 @@
+name: Server-Metal
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
+
+env:
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+  LLAMA_LOG_VERBOSITY: 10
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  server-metal:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    name: server-metal (${{ matrix.wf_name }})
+    strategy:
+      matrix:
+        build_type: [Release]
+        wf_name: ["GPUx1"]
+        include:
+          - build_type: Release
+            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx1, backend-sampling"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2"
+            wf_name:    "GPUx2"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx2, backend-sampling"
+      fail-fast: false
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
+          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
+        run: |
+          cd tools/server/tests
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+          export ${{ matrix.extra_args }}
+          pytest -v -x -m "not slow"

From 292f6908cdc6abb5c38581e34fa141973e5aba82 Mon Sep 17 00:00:00 2001
From: Sascha Rogmann <59577610+srogmann@users.noreply.github.com>
Date: Mon, 9 Feb 2026 14:30:50 +0100
Subject: [PATCH 162/206] spec : remove check rate (#19377)

* spec: remove parameter spec-ngram-check-rate

* spec : renamed statistics vars

* spec : add n_call_begin, n_call_accept

* spec : don't enable key-map-stats
---
 common/arg.cpp               | 10 -------
 common/common.h              |  1 -
 common/ngram-map.cpp         |  7 ++---
 common/ngram-map.h           |  8 ++----
 common/speculative.cpp       | 55 ++++++++++++++++--------------------
 docs/speculative.md          | 13 ++++-----
 tools/server/server-task.cpp |  4 ---
 7 files changed, 36 insertions(+), 62 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 5fbc9022c0..9c85696ebd 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -3437,16 +3437,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.ngram_size_m = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(common_arg(
-        {"--spec-ngram-check-rate"}, "N",
-        string_format("ngram check rate for ngram-simple/ngram-map speculative decoding (default: %d)", params.speculative.ngram_check_rate),
-        [](common_params & params, int value) {
-            if (value < 1) {
-                throw std::invalid_argument("ngram check rate must be at least 1");
-            }
-            params.speculative.ngram_check_rate = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--spec-ngram-min-hits"}, "N",
         string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits),
diff --git a/common/common.h b/common/common.h
index 398ebb0960..b284244530 100644
--- a/common/common.h
+++ b/common/common.h
@@ -269,7 +269,6 @@ struct common_params_speculative {
 
     uint16_t ngram_size_n     = 12; // ngram size for lookup
     uint16_t ngram_size_m     = 48; // mgram size for speculative tokens
-    uint16_t ngram_check_rate =  1; // check rate for ngram lookup
     uint16_t ngram_min_hits   =  1; // minimum hits at ngram/mgram lookup for mgram to be proposed
 
     std::shared_ptr ngram_mod;
diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp
index c5b8fc75ed..2b876a6e99 100644
--- a/common/ngram-map.cpp
+++ b/common/ngram-map.cpp
@@ -231,10 +231,9 @@ void common_ngram_map_draft(common_ngram_map & map,
         GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
     }
 
-    // Only check every check_rate tokens to save compute
-    // i.e., perform check if (cur_len - idx_last_check) >= check_rate
-    if (map.idx_last_check + map.check_rate > cur_len) {
-        return;
+    if (map.idx_last_check  > cur_len) {
+        // Should not happen because of common_ngram_map_begin().
+        GGML_ABORT("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
     }
     map.idx_last_check = cur_len;
 
diff --git a/common/ngram-map.h b/common/ngram-map.h
index 9668bd5a7c..41b9530449 100644
--- a/common/ngram-map.h
+++ b/common/ngram-map.h
@@ -24,7 +24,6 @@
 struct common_ngram_simple_config {
     uint16_t   size_ngram;      // size of n-grams to lookup in self-mode
     uint16_t   size_mgram;      // size of m-grams to draft in self-mode
-    uint16_t   check_rate;      // check for speculative decoding without draft model for each check_rate token
 };
 
 // Searches for a n-gram in the history and checks whether a draft sequence should be generated.
@@ -66,15 +65,14 @@ struct common_ngram_map {
     bool key_only;       // true if only key n-grams are used, no values.
 
     std::vector keys; // key n-grams which occur several times in token-history
-    uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token
     uint16_t min_hits;   // minimum number of key hits to consider a draft
 
-    bool     show_key_map_stats = false; // true, if statitics of the key_map should be printed.
+    bool     show_key_map_stats = false; // true, if statistics of the key_map should be printed.
 
     common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys,
-                     uint16_t check_rate, uint16_t min_hits)
+                     uint16_t min_hits)
         : size_key(sz_key), size_value(sz_value), key_only(only_keys),
-          check_rate(check_rate), min_hits(min_hits) {
+          min_hits(min_hits) {
         key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used
     }
 
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 84d2556ceb..3e68c38e49 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -113,13 +113,14 @@ static bool common_speculative_are_compatible(
 struct common_speculative_state {
     const enum common_speculative_type type;
 
-    // TODO: rename to n_call_draft, n_gen_drafts, n_acc_drafts, n_gen_tokens, n_acc_tokens
-    // TODO: add n_call_begin, n_call_accept
-    size_t drafts_call_count       = 0; // number of times this implementation was called.
-    size_t drafts_generated_count  = 0; // number of times a draft or part was generated by this implementation.
-    size_t drafts_accepted_count   = 0; // number of times a draft or part was accepted by the target model.
-    size_t drafts_generated_tokens = 0; // number of tokens generated by this implementation.
-    size_t drafts_accepted_tokens  = 0; // number of tokens accepted by the target model.
+    size_t n_call_begin  = 0; // number of times this implementation was called for refresh.
+    size_t n_call_draft  = 0; // number of times this implementation was called for generation.
+    size_t n_call_accept = 0; // number of times this implementation was called for accumulation.
+
+    size_t n_gen_drafts = 0; // number of times a draft or part was generated by this implementation.
+    size_t n_acc_drafts = 0; // number of times a draft or part was accepted by the target model.
+    size_t n_gen_tokens = 0; // number of tokens generated by this implementation.
+    size_t n_acc_tokens = 0; // number of tokens accepted by the target model.
 
     // TODO: track performance of most recent calls
     const bool gen_perf = true; // whether to generate performance stats.
@@ -465,8 +466,6 @@ struct common_speculative_state_eagle3 : public common_speculative_state {
 struct common_speculative_state_ngram_simple : public common_speculative_state {
     common_ngram_simple_config config;
 
-    uint16_t check_id = 0; // used to control the frequency of generating drafts
-
     common_speculative_state_ngram_simple(
             enum common_speculative_type type,
             common_ngram_simple_config config)
@@ -481,11 +480,6 @@ struct common_speculative_state_ngram_simple : public common_speculative_state {
             const llama_tokens & prompt_tgt,
             llama_token id_last,
             llama_tokens & result) override {
-        ++check_id;
-        if (check_id < config.check_rate) {
-            return;
-        }
-        check_id = 0;
 
         result = common_ngram_simple_draft(config, prompt_tgt, id_last);
         GGML_UNUSED(params);
@@ -752,10 +746,9 @@ static common_ngram_map get_common_ngram_map(const common_speculative_config & c
     uint16_t size_key   = config.params.ngram_size_n;
     uint16_t size_value = config.params.ngram_size_m;
     bool     key_only   = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
-    uint16_t check_rate = config.params.ngram_check_rate;
     uint16_t min_hits   = config.params.ngram_min_hits;
 
-    return common_ngram_map(size_key, size_value, key_only, check_rate, min_hits);
+    return common_ngram_map(size_key, size_value, key_only, min_hits);
 }
 
 static common_speculative_state_ngram_cache create_state_ngram_cache(
@@ -931,12 +924,10 @@ common_speculative * common_speculative_init(
 
                 uint16_t ngram_size_key   = ngram_map.size_key;
                 uint16_t mgram_size_value = ngram_map.size_value;
-                uint16_t check_rate       = ngram_map.check_rate;
 
                 auto config_simple = common_ngram_simple_config {
                     /* .size_ngram      = */ ngram_size_key,
-                    /* .size_mgram      = */ mgram_size_value,
-                    /* .check_rate      = */ check_rate
+                    /* .size_mgram      = */ mgram_size_value
                 };
                 auto state = std::make_unique(
                     /* .type            = */ config.type,
@@ -997,6 +988,7 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr
     for (auto & impl : spec->impls) {
         common_time_meas tm(impl->t_begin_us, !impl->gen_perf);
         impl->begin(prompt);
+        impl->n_call_begin++;
     }
 }
 
@@ -1013,17 +1005,17 @@ llama_tokens common_speculative_draft(
         {
             common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
             impl->draft(params, prompt_tgt, id_last, result);
-            impl->drafts_call_count++;
+            impl->n_call_draft++;
         }
 
         if (!result.empty()) {
             LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
                     common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(),
-                    impl.get()->drafts_call_count, result.size());
+                    impl.get()->n_call_draft, result.size());
 
             spec->curr_impl = impl.get(); // set current implementation for stats
-            impl->drafts_generated_count++;
-            impl->drafts_generated_tokens += result.size();
+            impl->n_gen_drafts++;
+            impl->n_gen_tokens += result.size();
 
             break; // We have a draft, so break out of the loop and return it.
         }
@@ -1044,11 +1036,12 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
     {
         common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
         if (n_accepted > 0) {
-            impl->drafts_accepted_count++;
-            impl->drafts_accepted_tokens += n_accepted;
+            impl->n_acc_drafts++;
+            impl->n_acc_tokens += n_accepted;
         }
 
         impl->accept(n_accepted);
+        impl->n_call_accept++;
     }
 }
 
@@ -1069,13 +1062,13 @@ void common_speculative_print_stats(const common_speculative * spec) {
             str_perf = "";
         }
 
-        LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
+        LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
                 common_speculative_type_to_str(impl->type).c_str(),
-                impl->drafts_call_count,
-                impl->drafts_generated_count,
-                impl->drafts_accepted_count,
-                impl->drafts_generated_tokens,
-                impl->drafts_accepted_tokens,
+                impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
+                impl->n_gen_drafts,
+                impl->n_acc_drafts,
+                impl->n_gen_tokens,
+                impl->n_acc_tokens,
                 str_perf.c_str());
     }
 }
diff --git a/docs/speculative.md b/docs/speculative.md
index 03afab5b41..29da332875 100644
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -119,8 +119,6 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
                                         of lookup n-gram (default: 12)
 --spec-ngram-size-m N                   ngram size M for ngram-simple/ngram-map speculative decoding, length
                                         of draft m-gram (default: 48)
---spec-ngram-check-rate N               ngram check rate for ngram-simple/ngram-map speculative decoding
-                                        (default: 1)
 --spec-ngram-min-hits N                 minimum hits for ngram-map speculative decoding (default: 1)
 ```
 
@@ -153,10 +151,6 @@ Sets the size M of the draft m-gram for n-gram map based speculative decoding.
 The m-gram size determines how many tokens to draft when a match is found.
 Larger values can provide more speedup but may reduce acceptance rate.
 
-### `--spec-ngram-check-rate R`
-
-This option aims at performance if the n-gram lookup in history is to costly. A lookup will be executed at every R tokens (default is 1, every token).
-
 ### `--spec-ngram-min-hits H`
 
 This option defines how often a key has to appear in the token history to be used as a draft (default is 1).
@@ -175,7 +169,12 @@ draft acceptance rate = 0.70312 (   90 accepted /   128 generated)
 statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms
 ```
 
-- `#calls`: number of calls of this implementations
+```
+statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts = 26, #gen tokens = 1248, #acc tokens = 968, dur(b,g,a) = 2.234, 1.427, 0.016 ms
+```
+
+
+- `#calls(b,g,a)`: number of calls of begin (new prompt), generation and accumulation of this implementations
 - `#gen drafts`: number of drafts generated by this implementation
 - `#acc drafts`: number of drafts accepted (partially) by the main model
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 2d25db63b7..a137427c69 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -80,7 +80,6 @@ json task_params::to_json(bool only_metrics) const {
             {"speculative.type",          common_speculative_type_to_str(speculative.type)},
             {"speculative.ngram_size_n",  speculative.ngram_size_n},
             {"speculative.ngram_size_m",  speculative.ngram_size_m},
-            {"speculative.ngram_c_rate",  speculative.ngram_check_rate},
             {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
             {"timings_per_token",         timings_per_token},
             {"post_sampling_probs",       post_sampling_probs},
@@ -144,7 +143,6 @@ json task_params::to_json(bool only_metrics) const {
         {"speculative.type",          common_speculative_type_to_str(speculative.type)},
         {"speculative.ngram_size_n",  speculative.ngram_size_n},
         {"speculative.ngram_size_m",  speculative.ngram_size_m},
-        {"speculative.ngram_c_rate",  speculative.ngram_check_rate},
         {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
         {"timings_per_token",         timings_per_token},
         {"post_sampling_probs",       post_sampling_probs},
@@ -257,12 +255,10 @@ task_params server_task::params_from_json_cmpl(
 
     params.speculative.ngram_size_n     = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
     params.speculative.ngram_size_m     = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
-    params.speculative.ngram_check_rate = json_value(data, "speculative.ngram_c_rate", defaults.speculative.ngram_check_rate);
     params.speculative.ngram_min_hits   = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);
 
     params.speculative.ngram_size_n     = std::max(std::min(1, (int) params.speculative.ngram_size_n),     1024);
     params.speculative.ngram_size_m     = std::max(std::min(1, (int) params.speculative.ngram_size_m),     1024);
-    params.speculative.ngram_check_rate = std::max(std::min(1, (int) params.speculative.ngram_check_rate), 1024);
     params.speculative.ngram_min_hits   = std::max(std::min(1, (int) params.speculative.ngram_min_hits),   1024);
 
     // Use OpenAI API logprobs only if n_probs wasn't provided

From 820ebfa6f45347c77e9ddfcc670e44109d6df43f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=86=90=ED=9D=AC=EC=A4=80?= 
Date: Tue, 10 Feb 2026 00:22:57 +0900
Subject: [PATCH 163/206] Server: log when converting requests to chat
 completions format (#19457)

* Log converting requests

* Print as debug instead of info [no ci]

---------

Co-authored-by: openingnow <>
---
 tools/server/server-context.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 8ec8451339..ceafcac179 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -3584,6 +3584,8 @@ void server_routes::init_routes() {
         auto res = create_response();
         std::vector files;
         json body = convert_responses_to_chatcmpl(json::parse(req.body));
+        SRV_DBG("%s\n", "Request converted: OpenAI Responses -> OpenAI Chat Completions");
+        SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
             body,
             meta->chat_params,
@@ -3600,6 +3602,8 @@ void server_routes::init_routes() {
         auto res = create_response();
         std::vector files;
         json body = convert_anthropic_to_oai(json::parse(req.body));
+        SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
+        SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
             body,
             meta->chat_params,
@@ -3616,6 +3620,8 @@ void server_routes::init_routes() {
         auto res = create_response();
         std::vector files;
         json body = convert_anthropic_to_oai(json::parse(req.body));
+        SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
+        SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
             body,
             meta->chat_params,

From 262364e31d1da43596fe84244fba44e94a0de64e Mon Sep 17 00:00:00 2001
From: Tarek Dakhran 
Date: Mon, 9 Feb 2026 17:30:32 +0100
Subject: [PATCH 164/206] mtmd: Implement tiling for LFM2-VL (#19454)

---
 tools/mtmd/clip.cpp | 136 ++++++++++++++++++++++++++++++++++++++++++--
 tools/mtmd/mtmd.cpp |  19 ++++++-
 2 files changed, 147 insertions(+), 8 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 9fa5afc390..614fe66fde 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -10,6 +10,7 @@
 #include "ggml-backend.h"
 #include "gguf.h"
 
+#include 
 #include 
 #include 
 #include 
@@ -1116,9 +1117,8 @@ struct clip_model_loader {
                 case PROJECTOR_TYPE_LFM2:
                     {
                         get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
-                        // ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
-                        // config above specifies number of tokens after downsampling, while here it is before, relax lowerbound to 64
-                        hparams.set_limit_image_tokens(64, 1024);
+                        // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json
+                        hparams.set_limit_image_tokens(64, 256);
                     } break;
                 case PROJECTOR_TYPE_PIXTRAL:
                 case PROJECTOR_TYPE_LIGHTONOCR:
@@ -2807,6 +2807,119 @@ private:
     }
 };
 
+// ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py
+// some of the logic is similar to llava_uhd, but with different hyperparameters and some logic is unique (e.g. grid layout)
+struct lfm2_vl_image_processor {
+    // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json
+    static constexpr int   min_tiles            = 2;
+    static constexpr int   max_tiles            = 10;
+    static constexpr float max_pixels_tolerance = 2.0f;
+    static constexpr int   tile_size            = 512;
+
+    static llava_uhd::slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
+        llava_uhd::slice_instructions inst;
+        const auto & params  = ctx->model.hparams;
+        const int align_size = params.patch_size * params.n_merge;
+
+        inst.interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR;
+        inst.interpolation_refined  = img_tool::RESIZE_ALGO_BILINEAR;
+        inst.overview_size          = img_tool::calc_size_preserved_ratio(original_size, align_size, params.image_min_pixels, params.image_max_pixels);
+
+        // tile if either dimension exceeds tile_size with tolerance
+        const bool needs_tiling = original_size.width > tile_size * max_pixels_tolerance || original_size.height > tile_size * max_pixels_tolerance;
+
+        if (!needs_tiling) {
+            inst.refined_size = clip_image_size{0, 0};
+            inst.grid_size    = clip_image_size{0, 0};
+            return inst;
+        }
+
+        const clip_image_size grid = get_grid_layout(original_size.height, original_size.width);
+
+        inst.grid_size    = grid;
+        inst.refined_size = clip_image_size{tile_size * grid.width, tile_size * grid.height};
+
+        LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d, grid size: %d x %d\n",
+                __func__,
+                original_size.width, original_size.height,
+                inst.overview_size.width, inst.overview_size.height,
+                inst.refined_size.width, inst.refined_size.height,
+                grid.width, grid.height);
+
+        for (int row = 0; row < grid.height; row++) {
+            for (int col = 0; col < grid.width; col++) {
+                llava_uhd::slice_coordinates slice;
+                slice.x    = col * tile_size;
+                slice.y    = row * tile_size;
+                slice.size = clip_image_size{tile_size, tile_size};
+                inst.slices.push_back(slice);
+                LOG_DBG("%s: slice %d: x=%d, y=%d, size=%d x %d\n",
+                        __func__, (int)inst.slices.size() - 1,
+                        slice.x, slice.y, slice.size.width, slice.size.height);
+            }
+        }
+
+        return inst;
+    }
+
+private:
+    static clip_image_size find_closest_aspect_ratio(
+            float aspect_ratio,
+            const std::vector & target_ratios,
+            int width, int height) {
+        float best_ratio_diff = std::numeric_limits::max();
+        clip_image_size best_ratio = {1, 1};
+        const float area = static_cast(width * height);
+
+        for (const auto & ratio : target_ratios) {
+            const float target_aspect_ratio = static_cast(ratio.width) / ratio.height;
+            const float ratio_diff = std::abs(aspect_ratio - target_aspect_ratio);
+            if (ratio_diff < best_ratio_diff) {
+                best_ratio_diff = ratio_diff;
+                best_ratio = ratio;
+            } else if (ratio_diff == best_ratio_diff) {
+                const float target_area = static_cast(tile_size * tile_size * ratio.width * ratio.height);
+                if (area > 0.5f * target_area) {
+                    best_ratio = ratio;
+                }
+            }
+        }
+        return best_ratio;
+    }
+
+    static std::vector get_target_ratios() {
+        std::vector ratios;
+        for (int n = min_tiles; n <= max_tiles; n++) {
+            for (int w = 1; w <= n; w++) {
+                for (int h = 1; h <= n; h++) {
+                    if (w * h >= min_tiles && w * h <= max_tiles) {
+                        bool found = false;
+                        for (const auto & r : ratios) {
+                            if (r.width == w && r.height == h) {
+                                found = true;
+                                break;
+                            }
+                        }
+                        if (!found) {
+                            ratios.push_back({w, h});
+                        }
+                    }
+                }
+            }
+        }
+        std::sort(ratios.begin(), ratios.end(), [](const clip_image_size & a, const clip_image_size & b) {
+            return a.width * a.height < b.width * b.height;
+        });
+        return ratios;
+    }
+
+    static clip_image_size get_grid_layout(int height, int width) {
+        const float aspect_ratio = static_cast(width) / height;
+        const auto ratios = get_target_ratios();
+        return find_closest_aspect_ratio(aspect_ratio, ratios, width, height);
+    }
+};
+
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, struct clip_image_f32_batch * res_imgs) {
@@ -3021,6 +3134,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
             } break;
 
         case PROJECTOR_TYPE_LFM2:
+            {
+                auto const inst = lfm2_vl_image_processor::get_slice_instructions(ctx, original_size);
+                std::vector imgs = llava_uhd::slice_image(img, inst);
+
+                for (size_t i = 0; i < imgs.size(); ++i) {
+                    clip_image_f32_ptr res(clip_image_f32_init());
+                    normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
+                    res_imgs->entries.push_back(std::move(res));
+                }
+
+                res_imgs->grid_x = inst.grid_size.width;
+                res_imgs->grid_y = inst.grid_size.height;
+            } break;
+
         case PROJECTOR_TYPE_KIMIVL:
             {
                 GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
@@ -3032,8 +3159,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
                 const std::array pad_color = {122, 116, 104};
 
                 clip_image_u8 resized_img;
-                const bool pad = (ctx->proj_type() != PROJECTOR_TYPE_LFM2);
-                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, pad, pad_color);
+                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, true, 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));
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index d037e834f3..b7636279cb 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -85,6 +85,7 @@ enum mtmd_slice_tmpl {
     MTMD_SLICE_TMPL_MINICPMV_2_6,
     MTMD_SLICE_TMPL_LLAMA4,
     MTMD_SLICE_TMPL_IDEFICS3,
+    MTMD_SLICE_TMPL_LFM2,
 };
 
 const char * mtmd_default_marker() {
@@ -307,9 +308,19 @@ struct mtmd_context {
             img_end = "<|im_end|>";
 
         } else if (proj == PROJECTOR_TYPE_LFM2) {
-            img_beg = "<|image_start|>";
-            img_end = "<|image_end|>";
-
+            // multi-tile:
+            //   <|image_start|>
+            //     <|img_row_1_col_1|> (tile) <|img_row_1_col_2|> (tile) ...
+            //     <|img_thumbnail|> (thumbnail)
+            //   <|image_end|>
+            // single-tile:
+            //   <|image_start|> (image) <|image_end|>
+            img_beg            = "<|image_start|>";
+            img_end            = "<|image_end|>";
+            slice_tmpl         = MTMD_SLICE_TMPL_LFM2;
+            sli_img_start_tmpl = "<|img_row_%d_col_%d|>";
+            tok_ov_img_start   = {lookup_token("<|img_thumbnail|>")};
+            ov_img_first       = false;
         } else if (proj == PROJECTOR_TYPE_GLM4V) {
             img_beg = "<|begin_of_image|>";
             img_end = "<|end_of_image|>";
@@ -562,11 +573,13 @@ struct mtmd_tokenizer {
             }
 
             // handle llava-uhd style preprocessing
+            const bool has_tiling_grid = batch_f32.grid_x > 0 && batch_f32.grid_y > 0;
             if (
                 ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
                 || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
                 || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
                 || ctx->slice_tmpl == MTMD_SLICE_TMPL_IDEFICS3
+                || (ctx->slice_tmpl == MTMD_SLICE_TMPL_LFM2 && has_tiling_grid)
             ) {
                 const int n_col = batch_f32.grid_x;
                 const int n_row = batch_f32.grid_y;

From 98e57ca422c5adb33663a6406c2f2d5b7d255da7 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen 
Date: Mon, 9 Feb 2026 22:14:12 +0100
Subject: [PATCH 165/206] chat: fix case where template accepts type content
 only (#19419)

* chat: fix case where template accepts type content only

* rm stray log

* reuse render_message_to_json
---
 common/chat.cpp          | 39 +++++++++++++++++++++++++++++++++++----
 common/chat.h            |  2 ++
 common/jinja/caps.cpp    | 13 +++++++++----
 common/jinja/caps.h      |  4 +++-
 common/jinja/runtime.cpp |  6 ++++++
 5 files changed, 55 insertions(+), 9 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index 2bf4632669..47a34d5822 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -380,15 +380,46 @@ std::vector common_chat_msgs_parse_oaicompat(const json & messa
     return msgs;
 }
 
-json common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text) {
+static json render_message_to_json(const std::vector & msgs, const jinja::caps & c) {
+    if (!c.supports_string_content && !c.supports_typed_content) {
+        LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
+    }
+
+    bool only_string_accepted =  c.supports_string_content && !c.supports_typed_content;
+    bool only_typed_accepted  = !c.supports_string_content &&  c.supports_typed_content;
+
     json messages = json::array();
     for (const auto & msg : msgs) {
-        json jmsg = msg.to_json_oaicompat(concat_typed_text);
-        messages.push_back(jmsg);
+        if (only_string_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
+            messages.push_back(jmsg);
+        } else if (only_typed_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            if (jmsg.at("content").is_string()) {
+                jmsg["content"] = json::array({
+                    json{
+                        {"type", "text"},
+                        {"text", jmsg.at("content").get()},
+                    }
+                });
+            }
+            messages.push_back(jmsg);
+        } else {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            messages.push_back(jmsg);
+        }
     }
     return messages;
 }
 
+// DEPRECATED: only used in tests
+json common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text) {
+    jinja::caps c;
+    c.supports_string_content = true;
+    c.supports_typed_content = !concat_typed_text;
+    return render_message_to_json(msgs, c);
+}
+
 std::vector common_chat_tools_parse_oaicompat(const json & tools) {
     std::vector result;
 
@@ -3020,7 +3051,7 @@ static common_chat_params common_chat_templates_apply_jinja(
         : *tmpls->template_default;
     const auto & src = tmpl.source();
     const auto & caps = tmpl.original_caps();
-    params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
+    params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
     params.add_generation_prompt = inputs.add_generation_prompt;
     params.tool_choice = inputs.tool_choice;
     params.reasoning_format = inputs.reasoning_format;
diff --git a/common/chat.h b/common/chat.h
index 24aa4aab5c..1bf43f7261 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -240,6 +240,8 @@ bool common_chat_templates_support_enable_thinking(const common_chat_templates *
 
 // Parses a JSON array of messages in OpenAI's chat completion API format.
 std::vector common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
+
+// DEPRECATED: only used in tests
 nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text = false);
 
 std::vector common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp
index f27490f1fb..dbaaed500a 100644
--- a/common/jinja/caps.cpp
+++ b/common/jinja/caps.cpp
@@ -63,7 +63,8 @@ static void caps_print_stats(value & v, const std::string & path) {
 
 std::map caps::to_map() const {
     return {
-        {"requires_typed_content", requires_typed_content},
+        {"supports_string_content", supports_string_content},
+        {"supports_typed_content", supports_typed_content},
         {"supports_tools", supports_tools},
         {"supports_tool_calls", supports_tool_calls},
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
@@ -89,7 +90,7 @@ caps caps_get(jinja::program & prog) {
         return v->stats.ops.find(op_name) != v->stats.ops.end();
     };
 
-    // case: typed content requirement
+    // case: typed content support
     caps_try_execute(
         prog,
         [&]() {
@@ -105,12 +106,16 @@ caps caps_get(jinja::program & prog) {
             // tools
             return json{nullptr};
         },
-        [&](bool, value & messages, value &) {
+        [&](bool success, value & messages, value &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
                 // accessed as an array
-                result.requires_typed_content = true;
+                result.supports_typed_content = true;
+            }
+            if (!success) {
+                // failed to execute with content as string
+                result.supports_string_content = false;
             }
         }
     );
diff --git a/common/jinja/caps.h b/common/jinja/caps.h
index 77df117baa..e694e7bfaa 100644
--- a/common/jinja/caps.h
+++ b/common/jinja/caps.h
@@ -14,7 +14,9 @@ struct caps {
     bool supports_parallel_tool_calls = true;
     bool supports_preserve_reasoning = false; // support assistant message with reasoning_content
 
-    bool requires_typed_content = false; // default: use string content
+    // one of the 2 content capabilities must be true
+    bool supports_string_content = true;
+    bool supports_typed_content = false;
 
     // for reporting on server
     std::map to_map() const;
diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp
index 4453d86e6d..cc012c892f 100644
--- a/common/jinja/runtime.cpp
+++ b/common/jinja/runtime.cpp
@@ -446,6 +446,12 @@ value for_statement::execute_impl(context & ctx) {
 
     value iterable_val = iter_expr->execute(scope);
 
+    // mark the variable being iterated as used for stats
+    if (ctx.is_get_stats) {
+        iterable_val->stats.used = true;
+        iterable_val->stats.ops.insert("array_access");
+    }
+
     if (iterable_val->is_undefined()) {
         JJ_DEBUG("%s", "For loop iterable is undefined, skipping loop");
         iterable_val = mk_val();

From a0d585537cb7f0352c4859acbad64d5084dbe964 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Tue, 10 Feb 2026 08:07:16 +0200
Subject: [PATCH 166/206] cuda : extend GGML_OP_PAD to work with non-cont src0
 (#19429)

* cuda : extend GGML_OP_PAD to work with non-cont src0

* tests : add permuted pad
---
 ggml/src/ggml-cpu/ops.cpp       |  3 +--
 ggml/src/ggml-cuda/ggml-cuda.cu |  3 ++-
 ggml/src/ggml-cuda/pad.cu       | 23 +++++++++++++----------
 tests/test-backend-ops.cpp      | 21 ++++++++++++---------
 4 files changed, 28 insertions(+), 22 deletions(-)

diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index ce15b18ce0..ed45350207 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -7629,8 +7629,7 @@ static void ggml_compute_forward_pad_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-    GGML_ASSERT( dst->nb[0] == sizeof(float));
+    assert(dst->nb[0] == sizeof(float));
 
     const int ith = params->ith;
     const int nth = params->nth;
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 9e77c231c8..b163468789 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -4834,8 +4834,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUM_ROWS:
         case GGML_OP_MEAN:
         case GGML_OP_GROUP_NORM:
-        case GGML_OP_PAD:
             return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_PAD:
+            return true;
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_ARANGE:
diff --git a/ggml/src/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu
index 660c192e48..31cd00f778 100644
--- a/ggml/src/ggml-cuda/pad.cu
+++ b/ggml/src/ggml-cuda/pad.cu
@@ -7,7 +7,7 @@ __device__ __forceinline__ int64_t wrap_around(int64_t coord, int64_t size) {
     return (coord + size) % size;
 }
 
-static __global__ void pad_f32(const float * src, float * dst,
+static __global__ void pad_f32(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, 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,
@@ -34,11 +34,8 @@ static __global__ void pad_f32(const float * src, float * dst,
             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;
+            const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
 
             dst[dst_idx] = src[src_idx];
         } else {
@@ -57,21 +54,21 @@ static __global__ void pad_f32(const float * src, float * dst,
         const int64_t i02 = wrap_around(i2 - lp2, ne02);
         const int64_t i03 = wrap_around(i3 - lp3, ne03);
 
-        const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
+        const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
 
         dst[dst_idx] = src[src_idx];
     }
 }
 
 
-static void pad_f32_cuda(const float * src, float * dst,
+static void pad_f32_cuda(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, 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,
     const bool circular, 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<<>>(src, dst,
+    pad_f32<<>>(src, s00, s01, s02, s03, dst,
                                                          lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
                                                          ne0, ne1, ne2, ne3, circular);
 }
@@ -82,9 +79,10 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float *             dst_d  = (float *) dst->data;
     cudaStream_t        stream = ctx.stream();
 
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    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];
@@ -96,7 +94,12 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int32_t rp3      = ((const int32_t *) (dst->op_params))[7];
     const int32_t circular = ((const int32_t *) (dst->op_params))[8];
 
-    pad_f32_cuda(src0_d, dst_d,
+    const size_t s00 = nb00 / ggml_type_size(src0->type);
+    const size_t s01 = nb01 / ggml_type_size(src0->type);
+    const size_t s02 = nb02 / ggml_type_size(src0->type);
+    const size_t s03 = nb03 / ggml_type_size(src0->type);
+
+    pad_f32_cuda(src0_d, s00, s01, s02, s03, dst_d,
                  lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
                  dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
                  (bool) circular, stream);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 6fe1780f3b..56dadb9b36 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5894,33 +5894,36 @@ struct test_pad_ext : public test_case {
     const int rp2;
     const int lp3;
     const int rp3;
-    const bool v;
+    const int tfrm; // 0 - none, 1 - non-cont, 2 - perm
     const bool circular;
 
     std::string vars() override {
-        return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v, circular);
+        return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, tfrm, circular);
     }
 
     test_pad_ext(ggml_type type = GGML_TYPE_F32,
             std::array 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,
-            bool v = false, bool circular = false)
+            int tfrm = 0, bool circular = false)
         : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3),
-          v(v), circular(circular) {}
+          tfrm(tfrm), circular(circular) {}
 
     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");
 
-        if (v) {
+        if (tfrm == 1) {
             a = ggml_view_4d(ctx, a, (a->ne[0] + 1) / 2, (a->ne[1] + 1) / 2, (a->ne[2] + 1) / 2, (a->ne[3] + 1) / 2, a->nb[1], a->nb[2], a->nb[3], 0);
             ggml_set_name(a, "view of a");
+        } else if (tfrm == 2) {
+            a = ggml_permute(ctx, a, 2, 1, 0, 3);
+            ggml_set_name(a, "permuted a");
         }
 
         ggml_tensor * out = circular
             ? ggml_pad_ext_circular(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
-            : ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+            : ggml_pad_ext         (ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
         ggml_set_name(out, "out");
 
         return out;
@@ -8198,10 +8201,10 @@ static std::vector> make_test_cases_eval() {
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 }));
 
-    for (bool v : {false, true}) {
+    for (int tfrm : {0, 1, 2}) {
         for (bool circular : {false, true}) {
-            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v, circular));
-            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v, circular));
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, tfrm, circular));
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, tfrm, circular));
         }
     }
 

From 52e38faf8c1c8e0d32fca7a43d5efd3a2f5efdc8 Mon Sep 17 00:00:00 2001
From: hipudding 
Date: Tue, 10 Feb 2026 14:18:59 +0800
Subject: [PATCH 167/206] CANN: implement quantized MUL_MAT_ID for MoE models
 (#19228)

Implement ggml_cann_mul_mat_id_quant function to support quantized matrix
multiplication for Mixture of Experts (MoE) architectures on CANN backend.

Key features:
- Support Q4_0 and Q8_0 quantized weight formats
- Use IndexSelect to dynamically route expert-specific weights based on indices
- Leverage WeightQuantBatchMatmulV2 for efficient quantized computation
- Handle automatic F16 type conversion for hardware compatibility
- Support both per-expert and broadcast input modes

Implementation details:
- Extract expert weights and scales using CANN IndexSelect operation
- Process each batch and expert combination independently
- Create proper tensor views with correct stride for matmul operations
- Automatic input/output type casting to/from F16 as needed

Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 291 ++++++++++++++++++++-----------
 1 file changed, 192 insertions(+), 99 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 87ac05748e..fc7c3e3b72 100644
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -3286,130 +3286,223 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor
 }
 
 /**
- * @brief Performs expert-specific matrix multiplication (MoE) with
- * quantized precision using the CANN backend.
+ * @brief Performs quantized matrix multiplication for Mixture of Experts (MoE)
+ * models using the CANN backend.
  *
- * This function executes a matrix multiplication operation tailored for
- * Mixture of Experts (MoE) models, where the input tensor is multiplied
- * with expert-specific quantized weight matrices. It leverages the CANN
- * backend to perform efficient low-precision computations and stores the
- * quantized result in the destination tensor `dst`.
+ * This function implements MUL_MAT_ID operation for quantized weight matrices
+ * (Q4_0 and Q8_0 formats). It selects expert-specific weight matrices based on
+ * the provided expert indices, and computes matrix multiplication using CANN's
+ * WeightQuantBatchMatmulV2 operator.
  *
- * Quantization techniques reduce memory footprint and improve performance
- * by using lower-bit representations (e.g., int8) instead of floating-point.
- * This function is designed to work with such formats and may incorporate
- * optimizations like identity-based fast paths or routing masks for sparse
- * expert selection.
+ * The function performs the following steps:
+ * 1. Converts input/output tensors to F16 format if necessary
+ * 2. Uses IndexSelect to extract expert-specific weights and scales based on indices
+ * 3. Performs quantized matrix multiplication for each expert using WeightQuantBatchMatmulV2
+ * 4. Converts output back to the target type if needed
  *
- * @param ctx The context for executing CANN backend operations.
- * @param dst The destination tensor where the quantized MoE multiplication result
- * will be stored.
+ * Tensor shapes:
+ * - dst:  [M, K, N, 1] - output tensor
+ * - src0: [D, M, A, 1] - quantized weight matrices (Q4_0 or Q8_0)
+ * - src1: [D, B, N, 1] - input activations (B = K for per-expert input, or B = 1 for broadcast)
+ * - ids:  [K, N] - expert indices for routing
  *
- * @note This function assumes quantized data types and is designed for
- * MoE architectures with potential sparse expert routing.
+ * @param ctx The CANN backend context for operation execution.
+ * @param dst The destination tensor where the multiplication result will be stored.
+ *
+ * @note Only Q4_0 and Q8_0 quantization formats are supported.
+ * @note The function handles automatic type conversion to/from F16 as needed by the hardware.
  */
 static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    // TODO: Use aclnnGroupedMatMul
-    //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 * ids  = dst->src[2];  //ids	[K, N]
+    // dst:  [M, K, N, 1]
+    // src0: [D, M, A, 1] - quantized weights
+    // src1: [D, B, N, 1] - input activations, B = K or B = 1
+    // ids:  [K, N] - expert indices
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * ids  = dst->src[2];
 
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_ASSERT(src1->ne[2] == ids->ne[1]);
 
-    // copy index from npu to cpu
-    int64_t n_as  = ne02;        // A
-    int64_t n_ids = ids->ne[0];  // K
+    const int64_t        n_batches        = ids->ne[1];
+    const int64_t        n_select_experts = ids->ne[0];
+    const enum ggml_type type             = src0->type;
 
-    std::vector ids_host(ggml_nbytes(ids));
-    ACL_CHECK(aclrtMemcpyAsync(ids_host.data(), ggml_nbytes(ids), ids->data, ggml_nbytes(ids),
-                               ACL_MEMCPY_DEVICE_TO_HOST, ctx.stream()));
-    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+    const int32_t group_size = QK8_0;  // Both Q4_0 and Q8_0 use group size of 32
+    GGML_ASSERT(group_size == QK4_0);
 
-    char * src0_original = (char *) src0->data;
-    char * src1_original = (char *) src1->data;
-    char * dst_original  = (char *) dst->data;
+    // Calculate element size for quantized weights
+    const float weight_elem_size =
+        (type == GGML_TYPE_Q4_0) ? 0.5f :
+        (type == GGML_TYPE_Q8_0) ? 1.0f :
+                                   (GGML_ABORT("MUL_MAT_ID only supports Q4_0 and Q8_0"), 0.0f);
 
-    ggml_tensor src0_row = *src0;
-    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row  = *dst;
+    // Calculate scale offset in memory
+    const size_t weight_size     = src0->ne[0] * src0->ne[1] * src0->ne[2] * weight_elem_size;
+    const size_t scale_elem_size = sizeof(uint16_t);
+    char *       scale_data      = (char *) src0->data + weight_size;
 
-    const enum ggml_type type = dst->src[0]->type;
-    float                weight_elem_size;
-    if (type == GGML_TYPE_Q4_0) {
-        weight_elem_size = float(sizeof(uint8_t)) / 2;
-    } else if (type == GGML_TYPE_Q8_0) {
-        weight_elem_size = float(sizeof(uint8_t));
-    } else {
-        GGML_ABORT("MUL_MAT_ID only support quant type Q4_0 and Q8_0 ");
-    }
+    // Allocate buffers for selected expert weights and scales
+    const size_t         selected_weight_size = src0->ne[0] * src0->ne[1] * n_select_experts * weight_elem_size;
+    ggml_cann_pool_alloc selected_weight_alloc(ctx.pool(), selected_weight_size);
+    void *               selected_weight_buffer = selected_weight_alloc.get();
 
-    // src0_row [D, M, 1, 1] weight without permute
-    src0_row.ne[2]       = 1;
-    src0_row.ne[3]       = 1;
-    src0_row.nb[0]       = weight_elem_size;
-    src0_row.nb[1]       = weight_elem_size * ne00;
-    src0_row.nb[2]       = weight_elem_size * ne00;
-    src0_row.nb[3]       = weight_elem_size * ne00;
-    size_t weight_stride = ne00 * ne01 * weight_elem_size;
-    size_t weight_size   = weight_stride * ne02 * ne03;
+    const size_t selected_scale_size = (src0->ne[0] / group_size) * src0->ne[1] * n_select_experts * scale_elem_size;
+    ggml_cann_pool_alloc selected_scale_alloc(ctx.pool(), selected_scale_size);
+    void *               selected_scale_buffer = selected_scale_alloc.get();
 
-    // scale [D, M, 1, 1] -> scale && permute
-    size_t scale_elem_size = sizeof(uint16_t);
-    size_t scale_stride    = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
+    // Helper lambda to allocate and cast tensor to F16 if needed
+    constexpr size_t f16_elem_size      = sizeof(uint16_t);
+    auto             prepare_f16_buffer = [&](ggml_tensor * tensor, ggml_cann_pool_alloc & allocator,
+                                  bool need_cast = false) -> void * {
+        if (tensor->type == GGML_TYPE_F16) {
+            return tensor->data;
+        }
 
-    // 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;
+        size_t total_size = f16_elem_size;
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            total_size *= tensor->ne[i];
+        }
+        void * buffer = allocator.alloc(total_size);
 
-    // 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;
+        if (need_cast == false) {
+            return buffer;
+        }
 
-    //create weight for one row
-    ggml_cann_pool_alloc weight_allocator(ctx.pool());
-    void *               weight_buffer = weight_allocator.alloc(nb02);
-    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);
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = tensor->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
 
-            // If B = 1 (broadcast), always use 0; otherwise, use id.
-            int64_t i11 = (ne11 == 1 ? 0 : id);
-            int64_t i12 = iid1;
+        acl_tensor_ptr src_tensor = ggml_cann_create_tensor(tensor);
+        acl_tensor_ptr f16_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, src_tensor.get(), f16_tensor.get(), ACL_FLOAT16);
 
-            int64_t i1 = id;
-            int64_t i2 = i12;
+        return buffer;
+    };
 
-            void * src0_tmp_ptr  = src0_original + i02 * weight_stride;
-            void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride;
-            void * src1_tmp_ptr  = src1_original + i11 * nb11 + i12 * nb12;
-            void * dst_tmp_ptr   = dst_original + i1 * nb1 + i2 * nb2;
+    // Prepare input and output buffers
+    ggml_cann_pool_alloc input_alloc(ctx.pool());
+    void *               input_buffer = prepare_f16_buffer(src1, input_alloc, true);
 
-            // mem cpy
-            ACL_CHECK(aclrtMemcpyAsync(weight_buffer, weight_stride, src0_tmp_ptr, weight_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
-            void * scale_buffer = (char *) weight_buffer + weight_stride;
-            ACL_CHECK(aclrtMemcpyAsync(scale_buffer, scale_stride, scale_tmp_ptr, scale_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    ggml_cann_pool_alloc output_alloc(ctx.pool());
+    void *               output_buffer = prepare_f16_buffer(dst, output_alloc, false);
 
-            src0_row.data  = weight_buffer;
-            src1_row.data  = src1_tmp_ptr;
-            dst_row.data   = dst_tmp_ptr;
-            dst_row.src[0] = &src0_row;
-            dst_row.src[1] = &src1_row;
+    // Process each batch
+    for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        // Create index tensor for current batch
+        const size_t   index_offset  = batch_idx * ids->nb[1];
+        acl_tensor_ptr batch_indices = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, index_offset);
 
-            ggml_cann_mul_mat(ctx, &dst_row);
+        // Select quantized weights using expert indices
+        // Q4_0 stores 2 values per byte, Q8_0 stores 1 value per byte
+        const int64_t weight_d         = (type == GGML_TYPE_Q4_0) ? src0->ne[0] / 2 : src0->ne[0];
+        const int64_t weight_m         = src0->ne[1];
+        const int64_t weight_n_experts = src0->ne[2];
+
+        int64_t weight_ne[3] = { weight_d, weight_m, weight_n_experts };
+        size_t  weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t), weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr all_weights =
+            ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, 3);
+
+        int64_t selected_weight_ne[3] = { weight_d, weight_m, n_select_experts };
+        size_t  selected_weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t),
+                                          weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr selected_weights = ggml_cann_create_tensor(selected_weight_buffer, ACL_INT8, sizeof(int8_t),
+                                                                  selected_weight_ne, selected_weight_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_weights.get(), 0, batch_indices.get(), selected_weights.get());
+
+        // Select scales using the same expert indices
+        const int64_t scale_d     = src0->ne[0] / group_size;
+        int64_t       scale_ne[3] = { scale_d, weight_m, weight_n_experts };
+        size_t scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size, scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr all_scales =
+            ggml_cann_create_tensor(scale_data, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 3);
+
+        int64_t selected_scale_ne[3] = { scale_d, weight_m, n_select_experts };
+        size_t  selected_scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size,
+                                         scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr selected_scales = ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size,
+                                                                 selected_scale_ne, selected_scale_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_scales.get(), 0, batch_indices.get(), selected_scales.get());
+
+        // Process each expert for current batch
+        // IndexSelect output layout: [D, M, K] in contiguous format
+        // WeightQuantBatchMatmulV2 expects: [M, D] with row-major stride
+        for (int64_t expert_idx = 0; expert_idx < n_select_experts; expert_idx++) {
+            // Determine input offset: broadcast if src1->ne[1]==1, otherwise use per-expert input
+            const size_t input_offset =
+                (batch_idx * src1->ne[1] + (src1->ne[1] == 1 ? 0 : expert_idx)) * src1->ne[0] * f16_elem_size;
+            const size_t output_offset = (batch_idx * dst->ne[1] + expert_idx) * dst->ne[0] * f16_elem_size;
+
+            // Create weight view for current expert: [D, M, K] -> [M, D]
+            int64_t      weight_view_ne[2]  = { weight_m, src0->ne[0] };
+            float        weight_view_nb[2]  = { src0->ne[0] * weight_elem_size, weight_elem_size };
+            const size_t weight_view_offset = expert_idx * selected_weight_nb[2];
+
+            acl_tensor_ptr weight_view =
+                ggml_cann_create_tensor(selected_weight_buffer, ggml_cann_type_mapping(type), weight_elem_size,
+                                        weight_view_ne, weight_view_nb, 2, ACL_FORMAT_ND, weight_view_offset);
+
+            // Create scale view for current expert: [D, M, K] -> [M, D]
+            int64_t      scale_view_ne[2]  = { weight_m, scale_d };
+            size_t       scale_view_nb[2]  = { selected_scale_nb[1], selected_scale_nb[0] };
+            const size_t scale_view_offset = expert_idx * selected_scale_nb[2];
+
+            acl_tensor_ptr scale_view =
+                ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size, scale_view_ne,
+                                        scale_view_nb, 2, ACL_FORMAT_ND, scale_view_offset);
+
+            // Create input activation tensor [D, 1]
+            int64_t input_ne[2] = { src1->ne[0], 1 };
+            size_t  input_nb[2] = { f16_elem_size, src1->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, f16_elem_size, input_ne,
+                                                                  input_nb, 2, ACL_FORMAT_ND, input_offset);
+
+            // Create output tensor [M, 1]
+            int64_t output_ne[2] = { dst->ne[0], 1 };
+            size_t  output_nb[2] = { f16_elem_size, dst->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, output_ne,
+                                                                   output_nb, 2, ACL_FORMAT_ND, output_offset);
+
+            // Perform quantized matrix multiplication
+            GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, input_tensor.get(), weight_view.get(),
+                                    scale_view.get(), nullptr, nullptr, nullptr, nullptr, group_size,
+                                    output_tensor.get());
         }
     }
-    return;
+
+    // Cast output back to original type if we used a temporary F16 buffer
+    if (dst->type != GGML_TYPE_F16) {
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = dst->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
+
+        acl_tensor_ptr f16_output =
+            ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        acl_tensor_ptr dst_tensor = ggml_cann_create_tensor(dst);
+
+        aclnn_cast(ctx, f16_output.get(), dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
 }
 
 void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) {

From f0bfe54f552f4783588f333b90d73920a57c5096 Mon Sep 17 00:00:00 2001
From: Raul Torres <138264735+rauletorresc@users.noreply.github.com>
Date: Tue, 10 Feb 2026 06:19:30 +0000
Subject: [PATCH 168/206] CANN: Remove unnecessary wrapper for
 `gml_backend_buft_is_cann` (#18968)

---
 ggml/src/ggml-cann/ggml-cann.cpp | 89 +++++++++++++-------------------
 1 file changed, 37 insertions(+), 52 deletions(-)

diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 6b2dbdd359..3f3de9f0bc 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -794,19 +794,44 @@ struct ggml_backend_cann_buffer_context {
     ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
 };
 
+// cann buffer type
 /**
- * @brief Check if a buffer is a CANN buffer.
- *
- * This function checks if a given buffer is a CANN buffer by comparing its
- * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
- *
- * @param buffer The buffer to check.
- * @return true if the buffer is a CANN buffer, false otherwise.
+ * @brief Structure representing context information for a specific backend
+ * buffer type.
  */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft);
+struct ggml_backend_cann_buffer_type_context {
+    int32_t     device; /**< Device identifier associated with the buffer context. */
+    std::string name;   /**< Name associated with the buffer context. */
+};
 
-static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_is_cann(buffer->buft);
+/**
+ * @brief Retrieves the name associated with a CANN buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN buffer type context.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
+
+    return buft_ctx->name.c_str();
+}
+
+/**
+ * @brief Checks if the backend buffer type is associated with the CANN backend.
+ *
+ * This function checks whether the provided backend buffer type is associated
+ * with the CANN backend based on the comparison of its name retrieval function
+ * pointer.
+ *
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the buffer type is associated with the CANN
+ * backend, otherwise false.
+ */
+static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
 }
 
 /**
@@ -1271,7 +1296,7 @@ static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer,
 static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
                                                 const ggml_tensor *   src,
                                                 ggml_tensor *         dst) {
-    if (ggml_backend_buffer_is_cann(src->buffer)) {
+    if (ggml_backend_buft_is_cann(src->buffer->buft)) {
         ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context;
         ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context;
 
@@ -1335,31 +1360,6 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .reset           = */ NULL,
 };
 
-// cann buffer type
-/**
- * @brief Structure representing context information for a specific backend
- * buffer type.
- */
-struct ggml_backend_cann_buffer_type_context {
-    int32_t     device; /**< Device identifier associated with the buffer context. */
-    std::string name;   /**< Name associated with the buffer context. */
-};
-
-/**
- * @brief Retrieves the name associated with a CANN buffer type.
- *
- * This function returns the descriptive name associated with the specified
- * CANN buffer type context.
- *
- * @param buft Pointer to the buffer type context.
- * @return Const pointer to the C-style string containing the name.
- */
-static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
-
-    return buft_ctx->name.c_str();
-}
-
 /**
  * @brief Allocates a new CANN buffer of the specified type and size.
  *
@@ -1997,7 +1997,7 @@ static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t      backend_src,
 
     GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src));
 
-    if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) {
+    if (!ggml_backend_buft_is_cann(src->buffer->buft) || !ggml_backend_buft_is_cann(dst->buffer->buft)) {
         return false;
     }
 
@@ -2523,21 +2523,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
     GGML_UNUSED(dev);
 }
 
-/**
- * @brief Checks if the backend buffer type is associated with the CANN backend.
- *
- * This function checks whether the provided backend buffer type is associated
- * with the CANN backend based on the comparison of its name retrieval function
- * pointer.
- *
- * @param buft Pointer to the backend buffer type to check.
- * @return bool Returns true if the buffer type is associated with the CANN
- * backend, otherwise false.
- */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
-}
-
 /**
  * @brief Records an event on the CANN backend stream.
  *

From 66d403c48098a999406c4850b9d7e6256b73d6da Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Tue, 10 Feb 2026 07:30:41 +0100
Subject: [PATCH 169/206] tts : fix typos in README.md [no ci] (#19463)

---
 tools/tts/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/tts/README.md b/tools/tts/README.md
index 48302c070b..4749bb9f5a 100644
--- a/tools/tts/README.md
+++ b/tools/tts/README.md
@@ -34,7 +34,7 @@ $ build/bin/llama-quantize models/outetts-0.2-0.5B-f16.gguf \
 ```
 The quantized model will be `models/outetts-0.2-0.5B-q8_0.gguf`.
 
-Next we do something simlar for the audio decoder. First download or checkout
+Next we do something similar for the audio decoder. First download or checkout
 the model for the voice decoder:
 ```console
 $ pushd models
@@ -42,7 +42,7 @@ $ git clone --branch main --single-branch --depth 1 https://huggingface.co/novat
 $ cd WavTokenizer-large-speech-75token && git lfs install && git lfs pull
 $ popd
 ```
-This model file is PyTorch checkpoint (.ckpt) and we first need to convert it to
+This model file is a PyTorch checkpoint (.ckpt) and we first need to convert it to
 huggingface format:
 ```console
 (venv) python tools/tts/convert_pt_to_hf.py \

From 854b09f0d7825dd9b8ca542a7f63c2374d66121a Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" 
Date: Tue, 10 Feb 2026 09:01:37 +0100
Subject: [PATCH 170/206] convert : move experts permutation from Qwen2MoeModel
 to Qwen3VLMoeTextModel (#19445)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add special case for Qwen3VLMoe

* Fix down path, remove arrows and checkmarks

* ws

* Moved to Qwen3VL

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret 

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret 

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret 

---------

Co-authored-by: Sigbjørn Skjæret 
---
 convert_hf_to_gguf.py | 65 ++++++++++++++++++++++++++++---------------
 1 file changed, 43 insertions(+), 22 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 843c00a896..0951469149 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4109,37 +4109,29 @@ class Qwen2MoeModel(TextModel):
         # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
-            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
-            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(permuted, mapped, bid)
+            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
+            yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
                 raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
-            yield from super().modify_tensors(perm_up, mapped_up, bid)
+            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
+            n_ff = data_torch.shape[-2] // 2
+            gate = data_torch[..., :n_ff, :].contiguous()
+            up = data_torch[..., n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
             # skip visual tensors
             return
+
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -4535,6 +4527,35 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         if name.startswith("model.visual."):
             return
 
+        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
+        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
+            name = name.replace("language_model.", "")
+            mapped = f"{name}.weight" if not name.endswith(".weight") else name
+            permuted = data_torch.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
+            return
+
+        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
+            name = name.replace("language_model.", "")
+            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            split_dim = data_torch.shape[-1] // 2
+            gate = data_torch[..., :split_dim].contiguous()
+            up = data_torch[..., split_dim:].contiguous()
+            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+            base_name = name.removesuffix(".weight")
+            base = base_name.rsplit('.', 1)[0]
+            mapped_gate = f"{base}.gate_proj.weight"
+            mapped_up = f"{base}.up_proj.weight"
+            perm_gate = gate.permute(0, 2, 1).contiguous()
+            perm_up = up.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
+            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
+            return
+
         yield from super().modify_tensors(data_torch, name, bid)
 
 

From 6948adc90d77949e7802616d4c030396cf03b9c7 Mon Sep 17 00:00:00 2001
From: k4ss4n <128936199+k4ss4n@users.noreply.github.com>
Date: Tue, 10 Feb 2026 10:57:48 +0100
Subject: [PATCH 171/206] ggml : use noexcept overload for is_regular_file in
 backend registration (#19452)

using noexcept std::filesystem::directory_entry::is_regular_file
overload prevents abnormal termination upon throwing an error
(as caused by symlinks to non-existent folders on linux)

Resolves: #18560
---
 ggml/src/ggml-backend-reg.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 8a693f84af..311fa5fe36 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -471,9 +471,10 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
 
     int best_score = 0;
     fs::path best_path;
+    std::error_code ec;
 
     for (const auto & search_path : search_paths) {
-        if (std::error_code ec; !fs::exists(search_path, ec)) {
+        if (!fs::exists(search_path, ec)) {
             if (ec) {
                 GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
             } else {
@@ -483,7 +484,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         }
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
         for (const auto & entry : dir_it) {
-            if (entry.is_regular_file()) {
+            if (entry.is_regular_file(ec)) {
                 auto filename = entry.path().filename();
                 auto ext = entry.path().extension();
                 if (filename.native().find(file_prefix) == 0 && ext == file_extension) {

From c03a5a46f0b9d05dd6099d64ab6ed091feabdb97 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?=
 <1478977+Alcpz@users.noreply.github.com>
Date: Tue, 10 Feb 2026 10:47:45 +0000
Subject: [PATCH 172/206] ggml-cpu: arm64: q6_K repack gemm and gemv (and
 generic) implementations (dotprod) (#19360)

* First working version of GEMM and GEMV

* interleave loads and compute

* Clang-format

* Added missing fallback. Removed tested TODO.

* Swap M and N to be consistent with the repack template convention
---
 ggml/src/ggml-cpu/arch-fallback.h     |  16 +-
 ggml/src/ggml-cpu/arch/arm/repack.cpp | 400 +++++++++++++++++++++++-
 ggml/src/ggml-cpu/repack.cpp          | 425 ++++++++++++++------------
 ggml/src/ggml-cpu/repack.h            |   4 +
 4 files changed, 643 insertions(+), 202 deletions(-)

diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index 427c1146e4..c6eb75b230 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -43,6 +43,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -55,7 +56,8 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
-#    define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
+#define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_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
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -76,6 +78,7 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
@@ -84,6 +87,7 @@
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -107,6 +111,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -119,6 +124,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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
@@ -143,6 +149,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -155,6 +162,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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
@@ -186,6 +194,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -197,6 +206,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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
@@ -227,6 +237,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -239,6 +250,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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
@@ -271,6 +283,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_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
@@ -283,6 +296,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_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
diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp
index 99bb70274c..fd05c609f7 100644
--- a/ggml/src/ggml-cpu/arch/arm/repack.cpp
+++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp
@@ -1072,6 +1072,195 @@ void ggml_gemv_q5_K_8x8_q8_K(int                        n,
     ggml_gemv_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemv_q6_K_8x4_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) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 4;
+
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    col_groups = ncols_interleaved / 4;
+    const uint8x16_t m4b        = vdupq_n_u8(0x0f);
+    const uint8x16_t mask_lo    = vdupq_n_u8(0x03);
+    const uint8x16_t mask_hi    = vdupq_n_u8(0x30);
+
+    // 1x8 tile = 2 x 4
+    float32x4_t acc_f32[2];
+
+    const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy;
+
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+        for (int i = 0; i < col_groups; i++) {
+            acc_f32[i] = vdupq_n_f32(0);
+        }
+
+        for (int b = 0; b < nb; b++) {
+            float32x4_t q6_d_0     = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d));      // d0 d1 d2 d3
+            float32x4_t q6_d_1     = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4));  // d4 d5 d6 d7
+            float32x4_t q8_d       = vdupq_n_f32(q8_ptr[b].d);
+            float32x4_t sb_scale_0 = vmulq_f32(q6_d_0, q8_d);
+            float32x4_t sb_scale_1 = vmulq_f32(q6_d_1, q8_d);
+
+            int32x4_t acc[col_groups];
+            for (int i = 0; i < col_groups; i++) {
+                acc[i] = vdupq_n_s32(0);
+            }
+
+            // Load all 16 scales once and widen to int16 (Q6_K has 16 scales per block)
+            // Reused for bias and dequantization later
+            int16_t q6_scales[16 * 8];
+            for (int i = 0; i < 16; i++) {
+                int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
+                vst1q_s16(q6_scales + i * 8, scales);
+            }
+
+            // Compute bias per column using q8 bsums and preloaded scales to skip the -32 shift
+            int32x4_t bias_lo = vdupq_n_s32(0);
+            int32x4_t bias_hi = vdupq_n_s32(0);
+
+            // Load bsums in chunks of 4 to process with vectorized operations
+            for (int i = 0; i < 16; i += 4) {
+                int16x4_t bsums_vec   = vld1_s16(q8_ptr[b].bsums + i);
+                int16x4_t scales_lo_0 = vld1_s16(q6_scales + (i + 0) * 8);
+                int16x4_t scales_hi_0 = vld1_s16(q6_scales + (i + 0) * 8 + 4);
+                int16x4_t scales_lo_1 = vld1_s16(q6_scales + (i + 1) * 8);
+                int16x4_t scales_hi_1 = vld1_s16(q6_scales + (i + 1) * 8 + 4);
+                int16x4_t scales_lo_2 = vld1_s16(q6_scales + (i + 2) * 8);
+                int16x4_t scales_hi_2 = vld1_s16(q6_scales + (i + 2) * 8 + 4);
+                int16x4_t scales_lo_3 = vld1_s16(q6_scales + (i + 3) * 8);
+                int16x4_t scales_hi_3 = vld1_s16(q6_scales + (i + 3) * 8 + 4);
+
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_0, bsums_vec, 0);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_0, bsums_vec, 0);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_1, bsums_vec, 1);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_1, bsums_vec, 1);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_2, bsums_vec, 2);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_2, bsums_vec, 2);
+                bias_lo = vmlal_lane_s16(bias_lo, scales_lo_3, bsums_vec, 3);
+                bias_hi = vmlal_lane_s16(bias_hi, scales_hi_3, bsums_vec, 3);
+            }
+            bias_lo = vshlq_n_s32(bias_lo, 5);
+            bias_hi = vshlq_n_s32(bias_hi, 5);
+
+            // Process two 128-value halves per superblock
+            for (int half = 0; half < 2; half++) {
+                const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
+                const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
+
+                // A subblock (sb) is a set of weights that share the scale
+                // Since q6_K scales are per 16 elements
+                // num sbs -> 256 elements / (16 elements/scale * 2 elements/byte * 2 halves)
+                for (int sb = 0; sb < QK_K / 64; sb++) {
+                    const int8_t * q8_base_l = q8_ptr[b].qs + half * 128 + sb * 16;
+                    const int8_t * q8_base_h = q8_base_l + 64;
+
+                    // Load and duplicate q8 values (each register covers four interleaved columns of q6)
+                    int8x16_t q8_l[4];
+                    int8x16_t q8_h[4];
+                    for (int i = 0; i < 4; i++) {
+                        q8_l[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_l + i * 4));
+                        q8_h[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_h + i * 4));
+                    }
+
+                    const int ql_off_base = sb * QK_K / 2;
+                    const int qh_off_base = ql_off_base & 255;  // wraps after 256 bytes
+
+                    // Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
+                    uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
+                    uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
+                    uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
+                    uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
+
+                    // Adjust qh for subblocks 2 and 3 (shift right by 2)
+                    if (sb > 1) {
+                        q6_qh_0.val[0] = vshrq_n_u8(q6_qh_0.val[0], 2);
+                        q6_qh_0.val[1] = vshrq_n_u8(q6_qh_0.val[1], 2);
+                        q6_qh_0.val[2] = vshrq_n_u8(q6_qh_0.val[2], 2);
+                        q6_qh_0.val[3] = vshrq_n_u8(q6_qh_0.val[3], 2);
+                        q6_qh_1.val[0] = vshrq_n_u8(q6_qh_1.val[0], 2);
+                        q6_qh_1.val[1] = vshrq_n_u8(q6_qh_1.val[1], 2);
+                        q6_qh_1.val[2] = vshrq_n_u8(q6_qh_1.val[2], 2);
+                        q6_qh_1.val[3] = vshrq_n_u8(q6_qh_1.val[3], 2);
+                    }
+
+                    const uint8x16_t q6_ql[8] = { q6_ql_0.val[0], q6_ql_0.val[1], q6_ql_0.val[2], q6_ql_0.val[3],
+                                                  q6_ql_1.val[0], q6_ql_1.val[1], q6_ql_1.val[2], q6_ql_1.val[3] };
+                    const uint8x16_t q6_qh[8] = { q6_qh_0.val[0], q6_qh_0.val[1], q6_qh_0.val[2], q6_qh_0.val[3],
+                                                  q6_qh_1.val[0], q6_qh_1.val[1], q6_qh_1.val[2], q6_qh_1.val[3] };
+
+                    // Process column groups (0-3, 4-7)
+                    for (int g = 0; g < col_groups; g++) {
+                        int32x4_t sb_acc_l = vdupq_n_s32(0);
+                        int32x4_t sb_acc_h = vdupq_n_s32(0);
+
+                        for (int chunk = 0; chunk < 4; chunk++) {
+                            const int idx = chunk * 2 + g;
+
+                            const uint8x16_t q6_qs_l = q6_ql[idx];
+                            const uint8x16_t q6_qs_h = q6_qh[idx];
+
+                            // Extract high 2 bits for upper nibble reconstruction
+                            const uint8x16_t q6_qs_hh = vandq_u8(q6_qs_h, mask_hi);
+
+                            // q6 = (low4 | high2<<4), without -32 bias (handled via bsums)
+                            const int8x16_t q6_l =
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_qs_l, m4b), vandq_u8(q6_qs_h, mask_lo), 4));
+                            const int8x16_t q6_h = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_l, 4), q6_qs_hh));
+
+                            sb_acc_l = vdotq_s32(sb_acc_l, q6_l, q8_l[chunk]);
+                            sb_acc_h = vdotq_s32(sb_acc_h, q6_h, q8_h[chunk]);
+                        }
+
+                        const int scale_idx_l = half * 8 + sb;
+                        const int scale_idx_h = half * 8 + sb + 4;
+
+                        const int32x4_t scale_vec_l = vmovl_s16(vld1_s16(q6_scales + scale_idx_l * 8 + g * 4));
+                        const int32x4_t scale_vec_h = vmovl_s16(vld1_s16(q6_scales + scale_idx_h * 8 + g * 4));
+
+                        acc[g] = vmlaq_s32(acc[g], sb_acc_l, scale_vec_l);
+                        acc[g] = vmlaq_s32(acc[g], sb_acc_h, scale_vec_h);
+                    }
+                }
+            }  // for half
+
+            // Bias correction
+            acc[0] = vsubq_s32(acc[0], bias_lo);
+            acc[1] = vsubq_s32(acc[1], bias_hi);
+
+            // Apply superblock scale (no mins for q6_K)
+            // acc[g] has [c0, c1, c2, c3]
+            float32x4_t w_0123 = vmulq_f32(vcvtq_f32_s32(acc[0]), sb_scale_0);
+            float32x4_t w_4567 = vmulq_f32(vcvtq_f32_s32(acc[1]), sb_scale_1);
+
+            acc_f32[0] = vaddq_f32(acc_f32[0], w_0123);
+            acc_f32[1] = vaddq_f32(acc_f32[1], w_4567);
+        }  // for b
+
+        int base = x * ncols_interleaved;
+        vst1q_f32(s + base, acc_f32[0]);
+        vst1q_f32(s + base + 4, acc_f32[1]);
+    }  // for x
+    return;
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemv_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q6_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,
@@ -1177,15 +1366,14 @@ void ggml_gemv_q6_K_8x8_q8_K(int                        n,
                         q8_h[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base_h + i * 8));
                     }
 
-                    // TODO: Test other qh repack patterns to reduce loads
                     const int ql_off_base = sb * QK_K / 2;
                     const int qh_off_base = ql_off_base & 255;  // wraps after 256 bytes
 
                     // Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
-                    ggml_uint8x16x4_t q6_ql_0 = ggml_vld1q_u8_x4(ql_base + ql_off_base);
-                    ggml_uint8x16x4_t q6_ql_1 = ggml_vld1q_u8_x4(ql_base + ql_off_base + 64);
-                    ggml_uint8x16x4_t q6_qh_0 = ggml_vld1q_u8_x4(qh_base + qh_off_base);
-                    ggml_uint8x16x4_t q6_qh_1 = ggml_vld1q_u8_x4(qh_base + qh_off_base + 64);
+                    uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
+                    uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
+                    uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
+                    uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
 
                     // Adjust qh for subblocks 2 and 3 (shift right by 2)
                     if (sb > 1) {
@@ -3474,6 +3662,208 @@ void ggml_gemm_q5_K_8x8_q8_K(int                        n,
     ggml_gemm_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemm_q6_K_8x4_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) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 4;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    q8_k_blocklen = 4;
+    constexpr int    col_groups    = ncols_interleaved / 4;
+    constexpr int    acc_size      = q8_k_blocklen * col_groups;  // 4 rows, 2 column groups
+    const uint8x16_t m4b           = vdupq_n_u8(0x0f);
+    const uint8x16_t mask_lo       = vdupq_n_u8(0x03);
+    const uint8x16_t mask_hi       = vdupq_n_u8(0x30);
+    const int8x16_t  m32s          = vdupq_n_s8(32);
+
+    float32x4_t acc_f32[acc_size];
+
+    for (int y = 0; y < nr / q8_k_blocklen; y++) {
+        const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+            for (int i = 0; i < acc_size; i++) {
+                acc_f32[i] = vdupq_n_f32(0);
+            }
+
+            for (int b = 0; b < nb; b++) {
+                float32x4_t q6_d_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d));
+                float32x4_t q6_d_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4));
+                float32x4_t q8_d_0123 = vld1q_f32(q8_ptr[b].d);
+
+                float32x4_t sbd_scale_0123[q8_k_blocklen];
+                float32x4_t sbd_scale_4567[q8_k_blocklen];
+
+                sbd_scale_0123[0] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 0);
+                sbd_scale_4567[0] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 0);
+                sbd_scale_0123[1] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 1);
+                sbd_scale_4567[1] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 1);
+                sbd_scale_0123[2] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 2);
+                sbd_scale_4567[2] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 2);
+                sbd_scale_0123[3] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 3);
+                sbd_scale_4567[3] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 3);
+
+                int32x4_t acc_s32[acc_size];
+                for (int i = 0; i < acc_size; i++) {
+                    acc_s32[i] = vdupq_n_s32(0);
+                }
+
+                int16_t q6_scales[8 * 16];
+                for (int i = 0; i < 16; i++) {
+                    int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
+                    vst1q_s16(q6_scales + i * 8, scales);
+                }
+
+                for (int half = 0; half < 2; half++) {
+                    const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
+                    const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
+
+                    for (int sb = 0; sb < QK_K / 64; sb++) {
+                        int32x4_t acc_lo[acc_size];
+                        int32x4_t acc_hi[acc_size];
+                        for (int i = 0; i < acc_size; i++) {
+                            acc_lo[i] = vdupq_n_s32(0);
+                            acc_hi[i] = vdupq_n_s32(0);
+                        }
+
+                        const int8_t * q8_base_l = q8_ptr[b].qs + half * 512 + sb * 64;
+                        const int8_t * q8_base_h = q8_ptr[b].qs + half * 512 + 256 + sb * 64;
+
+                        // 4 rows * 16 elements per scale
+                        // 4 reads of 16 bytes each
+                        constexpr int reads_per_sb = 4;
+                        int8x16_t     q8_l[reads_per_sb];
+                        int8x16_t     q8_h[reads_per_sb];
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            q8_l[k] = vld1q_s8(q8_base_l + 16 * k);
+                            q8_h[k] = vld1q_s8(q8_base_h + 16 * k);
+                        }
+
+                        const int ql_off_base = sb * QK_K / 2;
+                        const int qh_off_base = ql_off_base & 255;
+
+                        uint8x16_t q6_ql_0123[reads_per_sb];
+                        uint8x16_t q6_ql_4567[reads_per_sb];
+                        uint8x16_t q6_qh_0123[reads_per_sb];
+                        uint8x16_t q6_qh_4567[reads_per_sb];
+
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            q6_ql_0123[k] = vld1q_u8(ql_base + ql_off_base + k * 32);
+                            q6_ql_4567[k] = vld1q_u8(ql_base + ql_off_base + k * 32 + 16);
+                            q6_qh_0123[k] = vld1q_u8(qh_base + qh_off_base + k * 32);
+                            q6_qh_4567[k] = vld1q_u8(qh_base + qh_off_base + k * 32 + 16);
+                        }
+
+                        if (sb > 1) {
+                            for (int k = 0; k < reads_per_sb; k++) {
+                                q6_qh_0123[k] = vshrq_n_u8(q6_qh_0123[k], 2);
+                                q6_qh_4567[k] = vshrq_n_u8(q6_qh_4567[k], 2);
+                            }
+                        }
+
+                        for (int k = 0; k < reads_per_sb; k++) {
+                            // q = (ql | qh) - 32
+                            const uint8x16_t hbit_lo_0123 = vandq_u8(q6_qh_0123[k], mask_lo);
+                            const uint8x16_t hbit_hi_0123 = vandq_u8(q6_qh_0123[k], mask_hi);
+                            const uint8x16_t hbit_lo_4567 = vandq_u8(q6_qh_4567[k], mask_lo);
+                            const uint8x16_t hbit_hi_4567 = vandq_u8(q6_qh_4567[k], mask_hi);
+
+                            const int8x16_t q6_0123_lo = vsubq_s8(
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_0123[k], m4b), hbit_lo_0123, 4)), m32s);
+                            const int8x16_t q6_0123_hi = vsubq_s8(
+                                vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_0123[k], 4), hbit_hi_0123)), m32s);
+
+                            acc_lo[0] = vdotq_laneq_s32(acc_lo[0], q6_0123_lo, q8_l[k], 0);  //  0..3  r0 c0123
+                            acc_lo[1] = vdotq_laneq_s32(acc_lo[1], q6_0123_lo, q8_l[k], 1);  //  0..3  r1 c0123
+                            acc_lo[2] = vdotq_laneq_s32(acc_lo[2], q6_0123_lo, q8_l[k], 2);  //  0..3  r2 c0123
+                            acc_lo[3] = vdotq_laneq_s32(acc_lo[3], q6_0123_lo, q8_l[k], 3);  //  0..3  r3 c0123
+
+                            acc_hi[0] = vdotq_laneq_s32(acc_hi[0], q6_0123_hi, q8_h[k], 0);  // 64..67 r0 c0123
+                            acc_hi[1] = vdotq_laneq_s32(acc_hi[1], q6_0123_hi, q8_h[k], 1);  // 64..67 r1 c0123
+                            acc_hi[2] = vdotq_laneq_s32(acc_hi[2], q6_0123_hi, q8_h[k], 2);  // 64..67 r2 c0123
+                            acc_hi[3] = vdotq_laneq_s32(acc_hi[3], q6_0123_hi, q8_h[k], 3);  // 64..67 r3 c0123
+
+                            const int8x16_t q6_4567_lo = vsubq_s8(
+                                vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_4567[k], m4b), hbit_lo_4567, 4)), m32s);
+                            const int8x16_t q6_4567_hi = vsubq_s8(
+                                vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_4567[k], 4), hbit_hi_4567)), m32s);
+
+                            acc_lo[4] = vdotq_laneq_s32(acc_lo[4], q6_4567_lo, q8_l[k], 0);  //  0..3  r0 c4567
+                            acc_lo[5] = vdotq_laneq_s32(acc_lo[5], q6_4567_lo, q8_l[k], 1);  //  0..3  r1 c4567
+                            acc_lo[6] = vdotq_laneq_s32(acc_lo[6], q6_4567_lo, q8_l[k], 2);  //  0..3  r2 c4567
+                            acc_lo[7] = vdotq_laneq_s32(acc_lo[7], q6_4567_lo, q8_l[k], 3);  //  0..3  r3 c4567
+
+                            acc_hi[4] = vdotq_laneq_s32(acc_hi[4], q6_4567_hi, q8_h[k], 0);  // 64..67 r0 c4567
+                            acc_hi[5] = vdotq_laneq_s32(acc_hi[5], q6_4567_hi, q8_h[k], 1);  // 64..67 r1 c4567
+                            acc_hi[6] = vdotq_laneq_s32(acc_hi[6], q6_4567_hi, q8_h[k], 2);  // 64..67 r2 c4567
+                            acc_hi[7] = vdotq_laneq_s32(acc_hi[7], q6_4567_hi, q8_h[k], 3);  // 64..67 r3 c4567
+                        }
+
+                        // Scale and bias
+                        const int scale_idx_l = half * 8 + sb;
+                        const int scale_idx_h = half * 8 + sb + 4;
+
+                        for (int g = 0; g < col_groups; g++) {
+                            const int16x4_t scales_l16  = vld1_s16(q6_scales + scale_idx_l * 8 + g * 4);
+                            const int16x4_t scales_h16  = vld1_s16(q6_scales + scale_idx_h * 8 + g * 4);
+                            const int32x4_t scale_vec_l = vmovl_s16(scales_l16);
+                            const int32x4_t scale_vec_h = vmovl_s16(scales_h16);
+                            const int       acc_offset  = g * q8_k_blocklen;
+
+                            for (int row = 0; row < q8_k_blocklen; row++) {
+                                const int idx = row * 2 + g;
+                                acc_s32[idx]  = vmlaq_s32(acc_s32[idx], acc_lo[acc_offset + row], scale_vec_l);
+                                acc_s32[idx]  = vmlaq_s32(acc_s32[idx], acc_hi[acc_offset + row], scale_vec_h);
+                            }
+                        }
+                    }
+                }
+
+                // Finally we apply the superblock scales
+                for (int row = 0; row < q8_k_blocklen; row++) {
+                    const int       idx0     = 2 * row;
+                    const int       idx1     = 2 * row + 1;
+                    const int32x4_t acc_0123 = acc_s32[idx0];
+                    const int32x4_t acc_4567 = acc_s32[idx1];
+
+                    acc_f32[idx0] = vmlaq_f32(acc_f32[idx0], vcvtq_f32_s32(acc_0123), sbd_scale_0123[row]);
+                    acc_f32[idx1] = vmlaq_f32(acc_f32[idx1], vcvtq_f32_s32(acc_4567), sbd_scale_4567[row]);
+                }
+            }  // for b
+
+            for (int i = 0; i < q8_k_blocklen; i++) {
+                int row = y * q8_k_blocklen + i;
+                for (int j = 0; j < 2; j++) {
+                    int col    = x * ncols_interleaved + j * 4;
+                    int offset = row * bs + col;
+                    vst1q_f32(s + offset, acc_f32[2 * i + j]);
+                }
+            }
+        }  // for x
+    }  // for y
+    return;
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemm_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q6_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,
diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp
index 24e8ab4618..4cb7cdeb07 100644
--- a/ggml/src/ggml-cpu/repack.cpp
+++ b/ggml/src/ggml-cpu/repack.cpp
@@ -256,6 +256,200 @@ template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTR
     ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row);
 }
 
+template 
+static void ggml_gemv_q6_K_NxM_q8_K_generic_impl(int                        n,
+                                                 float * GGML_RESTRICT      s,
+                                                 size_t                     bs,
+                                                 const void * GGML_RESTRICT vx,
+                                                 const void * GGML_RESTRICT vy,
+                                                 int                        nr,
+                                                 int                        nc) {
+    constexpr int blocklen          = M;
+    constexpr int ncols_interleaved = N;
+    const int     qk                = QK_K;
+    const int     nb                = n / qk;
+    const int     blocks_per_half   = 64 / blocklen;
+
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[8];
+
+    const block_q8_K * a_ptr = (const block_q8_K *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) {
+            sumf[j] = 0.0f;
+        }
+
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
+                const int base_h = base_l + 64;
+
+                const int scale_idx_l = base_l / 16;
+                const int scale_idx_h = base_h / 16;
+
+                const int qh_shift_l = ((base_l % 128) / 32) * 2;
+                const int qh_shift_h = ((base_h % 128) / 32) * 2;
+
+                const int qh_half_l = (base_l / 128) * 32;
+                const int qh_half_h = (base_h / 128) * 32;
+
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
+                    const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
+
+                    int sumi_l = 0;
+                    int sumi_h = 0;
+
+                    for (int i = 0; i < blocklen; i++) {
+                        const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
+                        const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
+                        const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
+
+                        const int qh_idx_l    = qh_half_l + ((base_l + i) % 32);
+                        const int qh_chunk_l  = qh_idx_l / blocklen;
+                        const int qh_pos_l    = qh_idx_l % blocklen;
+                        const int qh_offset_l = qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
+                        const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
+
+                        const int qh_idx_h    = qh_half_h + ((base_h + i) % 32);
+                        const int qh_chunk_h  = qh_idx_h / blocklen;
+                        const int qh_pos_h    = qh_idx_h % blocklen;
+                        const int qh_offset_h = qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
+                        const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
+
+                        const int q_l = ((hi_2_l << 4) | l_4) - 32;
+                        const int q_h = ((hi_2_h << 4) | hi_4) - 32;
+
+                        const int8_t a_l = a_ptr[l].qs[base_l + i];
+                        const int8_t a_h = a_ptr[l].qs[base_h + i];
+
+                        sumi_l += q_l * a_l;
+                        sumi_h += q_h * a_h;
+                    }
+
+                    sumf[j] +=
+                        (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
+                }
+            }
+        }
+
+        for (int j = 0; j < ncols_interleaved; j++) {
+            s[x * ncols_interleaved + j] = sumf[j];
+        }
+    }
+}
+
+template 
+static void ggml_gemm_q6_K_NxM_q8_K_generic_impl(int                        n,
+                                                 float * GGML_RESTRICT      s,
+                                                 size_t                     bs,
+                                                 const void * GGML_RESTRICT vx,
+                                                 const void * GGML_RESTRICT vy,
+                                                 int                        nr,
+                                                 int                        nc) {
+    constexpr int blocklen          = M;
+    constexpr int ncols_interleaved = N;
+    const int     qk                = QK_K;
+    const int     nb                = n / qk;
+    const int     blocks_per_half   = 64 / blocklen;
+    const int     q8_half_stride    = 512;
+    const int     q8_low_high_step  = 256;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+
+    float sumf[4][8];
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
+
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumf[m][j] = 0.0f;
+                }
+            }
+
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
+                    const int base_h = base_l + 64;
+
+                    const int scale_idx_l = base_l / 16;
+                    const int scale_idx_h = base_h / 16;
+
+                    const int qh_shift_l = ((base_l % 128) / 32) * 2;
+                    const int qh_shift_h = ((base_h % 128) / 32) * 2;
+
+                    const int qh_half_l = (base_l / 128) * 32;
+                    const int qh_half_h = (base_h / 128) * 32;
+
+                    const int q8_base = (k / blocks_per_half) * q8_half_stride + (k % blocks_per_half) * (blocklen * 4);
+
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
+                            const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
+
+                            int sumi_l = 0;
+                            int sumi_h = 0;
+
+                            for (int i = 0; i < blocklen; i++) {
+                                const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
+                                const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
+                                const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
+
+                                const int qh_idx_l   = qh_half_l + ((base_l + i) % 32);
+                                const int qh_chunk_l = qh_idx_l / blocklen;
+                                const int qh_pos_l   = qh_idx_l % blocklen;
+                                const int qh_offset_l =
+                                    qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
+                                const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
+
+                                const int qh_idx_h   = qh_half_h + ((base_h + i) % 32);
+                                const int qh_chunk_h = qh_idx_h / blocklen;
+                                const int qh_pos_h   = qh_idx_h % blocklen;
+                                const int qh_offset_h =
+                                    qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
+                                const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
+
+                                const int q_l = ((hi_2_l << 4) | l_4) - 32;
+                                const int q_h = ((hi_2_h << 4) | hi_4) - 32;
+
+                                const int8_t q8_l = a_ptr[l].qs[q8_base + m * blocklen + i];
+                                const int8_t q8_h = a_ptr[l].qs[q8_base + m * blocklen + i + q8_low_high_step];
+
+                                sumi_l += q_l * q8_l;
+                                sumi_h += q_h * q8_h;
+                            }
+
+                            sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
+                                          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" {
 
 void ggml_gemv_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) {
@@ -704,94 +898,12 @@ void ggml_gemv_q5_K_8x8_q8_K_generic(int                        n,
 }
 
 
+void ggml_gemv_q6_K_8x4_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) {
+    ggml_gemv_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q6_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) {
-    constexpr int qk = QK_K;
-    const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
-
-    assert(n % qk == 0);
-    assert(nc % ncols_interleaved == 0);
-
-    UNUSED(bs);
-    UNUSED(nr);
-
-    float sumf[8];
-
-    const block_q8_K * a_ptr = (const block_q8_K *) vy;
-    for (int x = 0; x < nc / ncols_interleaved; x++) {
-        const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
-
-        for (int j = 0; j < ncols_interleaved; j++) {
-            sumf[j] = 0.0f;
-        }
-
-        for (int l = 0; l < nb; l++) {
-
-
-            for (int k = 0; k < 16; k++) {
-                // k = 0.. 7 weights 0-63 low, 64-127 high
-                // k = 8..15 weights 128-191 low, 192-255 high
-                const int base_l = (k / 8) * 128 + (k % 8) * 8;
-                const int base_h = base_l + 64;
-
-                const int scale_idx_l = base_l / 16;
-                const int scale_idx_h = base_h / 16;
-
-                // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
-                const int qh_shift_l = ((base_l % 128) / 32) * 2;
-                const int qh_shift_h = ((base_h % 128) / 32) * 2;
-
-                // qh_half: offset to the correct 32-byte half (0 or 32)
-                const int qh_half_l = (base_l / 128) * 32;
-                const int qh_half_h = (base_h / 128) * 32;
-
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    // Interleaved scales
-                    const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
-                    const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
-
-                    int sumi_l = 0;
-                    int sumi_h = 0;
-
-                    for (int i = 0; i < blocklen; i++) {
-                        const int ql_pos = k * 64 + j * 8 + i;
-                        const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
-                        const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
-
-                        // qh indexing with 8-byte interleaving (like q5_K)
-                        const int qh_byte_l   = qh_half_l + ((base_l + i) % 32);
-                        const int qh_chunk_l  = qh_byte_l / 8;
-                        const int qh_pos_l    = qh_byte_l % 8;
-                        const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
-                        const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
-
-                        const int qh_byte_h   = qh_half_h + ((base_h + i) % 32);
-                        const int qh_chunk_h  = qh_byte_h / 8;
-                        const int qh_pos_h    = qh_byte_h % 8;
-                        const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
-                        const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
-
-                        const int q_l = ((hi_2_l << 4) | l_4) - 32;
-                        const int q_h = ((hi_2_h << 4) | hi_4) - 32;
-
-                        const int8_t a_l = a_ptr[l].qs[base_l + i];
-                        const int8_t a_h = a_ptr[l].qs[base_h + i];
-
-                        sumi_l += q_l * a_l;
-                        sumi_h += q_h * a_h;
-                    }
-
-                    sumf[j] +=
-                        (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
-                }
-            }
-        }
-
-        for (int j = 0; j < ncols_interleaved; j++) {
-            s[x * ncols_interleaved + j] = sumf[j];
-        }
-    }
+    ggml_gemv_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, 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) {
@@ -1485,109 +1597,12 @@ void ggml_gemm_q5_K_8x8_q8_K_generic(int                        n,
     }
 }
 
-void ggml_gemm_q6_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) {
-    const int qk                = QK_K;
-    const int nb                = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen          = 8;
+void ggml_gemm_q6_K_8x4_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) {
+    ggml_gemm_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
+}
 
-    assert(n % qk == 0);
-    assert(nr % 4 == 0);
-    assert(nc % ncols_interleaved == 0);
-
-    UNUSED(bs);
-
-    float sumf[4][8];
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
-
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumf[m][j] = 0.0f;
-                }
-            }
-
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < 16; k++) {
-                    // k = 0.. 7 weights 0-63 low, 64-127 high
-                    // k = 8..15 weights 128-191 low, 192-255 high
-                    const int base_l = (k / 8) * 128 + (k % 8) * 8;
-                    const int base_h = base_l + 64;
-
-                    const int scale_idx_l = base_l / 16;
-                    const int scale_idx_h = base_h / 16;
-
-                    // Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
-                    const int qh_shift_l = ((base_l % 128) / 32) * 2;
-                    const int qh_shift_h = ((base_h % 128) / 32) * 2;
-
-                    // qh_half: offset to the correct 32-byte half (0 or 32)
-                    const int qh_half_l = (base_l / 128) * 32;
-                    const int qh_half_h = (base_h / 128) * 32;
-
-                    // Activation base indices for q8_Kx4 interleaved format
-                    // Layout: 128-value halves (k/8), then 8-value sub-blocks (k%8) with stride 32
-                    const int q8_base = (k / 8) * 512 + (k % 8) * 32;
-
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            // Interleaved scales
-                            const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
-                            const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
-
-                            int sumi_l = 0;
-                            int sumi_h = 0;
-
-                            for (int i = 0; i < blocklen; i++) {
-                                const int ql_pos = k * 64 + j * 8 + i;
-                                const int l_4    = b_ptr[l].ql[ql_pos] & 0xF;
-                                const int hi_4   = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
-
-                                const int qh_idx_l    = qh_half_l + ((base_l + i) % 32);
-                                const int qh_chunk_l  = qh_idx_l / 8;
-                                const int qh_pos_l    = qh_idx_l % 8;
-                                const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
-                                const int hi_2_l      = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
-
-                                const int qh_idx_h    = qh_half_h + ((base_h + i) % 32);
-                                const int qh_chunk_h  = qh_idx_h / 8;
-                                const int qh_pos_h    = qh_idx_h % 8;
-                                const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
-                                const int hi_2_h      = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
-
-                                const int q_l = ((hi_2_l << 4) | l_4) - 32;
-                                const int q_h = ((hi_2_h << 4) | hi_4) - 32;
-
-                                const int8_t q8_l = a_ptr[l].qs[q8_base + m * 8 + i];
-                                const int8_t q8_h = a_ptr[l].qs[q8_base + m * 8 + i + 256];
-
-                                sumi_l += q_l * q8_l;
-                                sumi_h += q_h * q8_h;
-                            }
-
-                            sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
-                                          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];
-                }
-            }
-        }
-    }
+void ggml_gemm_q6_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) {
+   ggml_gemm_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, 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) {
@@ -2097,18 +2112,18 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
     }
 
     const int end_ls = QK_K * 4 / blck_size_interleave;
-    // Interleave Q6_K quants by taking 8 bytes at a time
+    // Interleave Q6_K quants by taking blck_size_interleave bytes at a time
     for (int i = 0; i < end_ls; ++i) {
         int src_id     = i % n_blocks;
         int src_offset = (i / n_blocks) * blck_size_interleave;
         int dst_offset = i * blck_size_interleave;
 
         uint64_t elem_ls;
-        memcpy(&elem_ls, &in[src_id].ql[src_offset], sizeof(uint64_t));
-        memcpy(&out.ql[dst_offset], &elem_ls, sizeof(uint64_t));
+        memcpy(&elem_ls, &in[src_id].ql[src_offset], blck_size_interleave);
+        memcpy(&out.ql[dst_offset], &elem_ls, blck_size_interleave);
     }
 
-    // Interleave high bits using same 8-byte pattern as low bits
+    // Interleave high bits using same chunk size as low bits
     const int end_hs = end_ls / 2;
     for (int i = 0; i < end_hs; ++i) {
         int src_id     = i % n_blocks;
@@ -2116,8 +2131,8 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
         int dst_offset = i * blck_size_interleave;
 
         uint64_t elem_hs;
-        memcpy(&elem_hs, &in[src_id].qh[src_offset], sizeof(uint64_t));
-        memcpy(&out.qh[dst_offset], &elem_hs, sizeof(uint64_t));
+        memcpy(&elem_hs, &in[src_id].qh[src_offset], blck_size_interleave);
+        memcpy(&out.qh[dst_offset], &elem_hs, blck_size_interleave);
     }
 
     // The below logic is designed so as to unpack and rearrange scales in Q6_K
@@ -2262,7 +2277,7 @@ static int repack_q5_K_to_q5_K_8_bl(struct ggml_tensor *       t,
 
 static int repack_q6_K_to_q6_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q6_K);
-    GGML_ASSERT(interleave_block == 8);
+    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
     constexpr int nrows_interleaved = 8;
 
     block_q6_Kx8 * dst = (block_q6_Kx8 *)t->data;
@@ -2511,6 +2526,10 @@ template <> int repack(struct ggml_tensor * t, const void * da
     return repack_q5_K_to_q5_K_8_bl(t, 8, data, data_size);
 }
 
+template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q6_K_to_q6_K_8_bl(t, 4, data, data_size);
+}
+
 template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) {
     return repack_q6_K_to_q6_K_8_bl(t, 8, data, data_size);
 }
@@ -2575,6 +2594,10 @@ template <> void gemv(int n, float * s, size_t
     ggml_gemv_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
@@ -2634,6 +2657,10 @@ template <> void gemm(int n, float * s, size_t
     ggml_gemm_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
@@ -3043,6 +3070,7 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits q5_K_8x8_q8_K;
 
     // instance for Q6_K
+    static const ggml::cpu::repack::tensor_traits q6_K_8x4_q8_K;
     static const ggml::cpu::repack::tensor_traits q6_K_8x8_q8_K;
 
     // instance for Q2
@@ -3107,6 +3135,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                 return &q6_K_8x8_q8_K;
             }
         }
+        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &q6_K_8x4_q8_K;
+            }
+        }
     } else if (cur->type == GGML_TYPE_IQ4_NL) {
         if (ggml_cpu_has_avx2()) {
             if (cur->ne[1] % 8 == 0) {
diff --git a/ggml/src/ggml-cpu/repack.h b/ggml/src/ggml-cpu/repack.h
index 855320eeeb..39b6b48238 100644
--- a/ggml/src/ggml-cpu/repack.h
+++ b/ggml/src/ggml-cpu/repack.h
@@ -112,6 +112,7 @@ void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q4_K_8x4_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_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_q5_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_q6_K_8x4_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_q6_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);
@@ -122,6 +123,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemm_q4_K_8x4_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_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_q5_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_q6_K_8x4_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_q6_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);
@@ -142,6 +144,7 @@ void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q4_K_8x4_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_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_q5_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_q6_K_8x4_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_q6_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);
@@ -152,6 +155,7 @@ void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemm_q4_K_8x4_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_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_q5_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_q6_K_8x4_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_q6_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);

From 9a9635272942541a2722135a3d9fffd5d3af0d9f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen 
Date: Tue, 10 Feb 2026 14:37:50 +0100
Subject: [PATCH 173/206] test: fix IMROPE perf test case (#19465)

---
 tests/test-backend-ops.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 56dadb9b36..3bdb3d7ba9 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8523,7 +8523,7 @@ static std::vector> make_test_cases_perf() {
                     test_cases.emplace_back(new test_rope(type, { 80,  32, 512, 1},  20, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (stablelm)
                     test_cases.emplace_back(new test_rope(type, { 64,   8, 512, 1},  64, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (falcon 40B)
                     test_cases.emplace_back(new test_rope(type, {128,  12, 512, 1}, 128, GGML_ROPE_TYPE_MROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl 2B)
-                    test_cases.emplace_back(new test_rope(type, {128,  12, 2, 1}, 128, GGML_ROPE_TYPE_IMROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,imrope (qwen3vl 2B)
+                    test_cases.emplace_back(new test_rope(type, {128,  12, 512, 1}, 128, GGML_ROPE_TYPE_IMROPE,  512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,imrope (qwen3vl 2B)
                     test_cases.emplace_back(new test_rope(type, { 80,  16, 2, 1},  80, GGML_ROPE_TYPE_VISION, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl ViT)
                 }
             }

From fc0fe4004985d6749a7a05e250d161f9dbe41d65 Mon Sep 17 00:00:00 2001
From: JJJYmmm <92386084+JJJYmmm@users.noreply.github.com>
Date: Wed, 11 Feb 2026 00:00:26 +0800
Subject: [PATCH 174/206] models : support qwen3.5 series (#19468)

* support qwen3.5 series

* remove deepstack for now, and some code clean

* code clean

* add FULL_ATTENTION_INTERVAL metadata

* code clean

* reorder v heads for linear attention to avoid expensive interleaved repeat
---
 convert_hf_to_gguf.py          |  97 ++++-
 convert_hf_to_gguf_update.py   |   1 +
 gguf-py/gguf/constants.py      |  66 ++-
 gguf-py/gguf/gguf_writer.py    |   3 +
 gguf-py/gguf/tensor_mapping.py |   9 +-
 src/CMakeLists.txt             |   2 +
 src/llama-arch.cpp             |  65 ++-
 src/llama-arch.h               |   6 +-
 src/llama-context.cpp          |   2 +-
 src/llama-model.cpp            | 202 ++++++++-
 src/llama-model.h              |   4 +
 src/llama-vocab.cpp            |  11 +
 src/llama-vocab.h              |   1 +
 src/models/models.h            | 119 +++++
 src/models/qwen35.cpp          | 740 +++++++++++++++++++++++++++++++
 src/models/qwen35moe.cpp       | 774 +++++++++++++++++++++++++++++++++
 tools/mtmd/models/qwen3vl.cpp  |   4 +-
 17 files changed, 2096 insertions(+), 10 deletions(-)
 create mode 100644 src/models/qwen35.cpp
 create mode 100644 src/models/qwen35moe.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 0951469149..61eb9839e0 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1261,6 +1261,9 @@ class TextModel(ModelBase):
         if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
             # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
             res = "exaone-moe"
+        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
+            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
+            res = "qwen35"
 
         if res is None:
             logger.warning("\n")
@@ -4287,6 +4290,7 @@ class Qwen3NextModel(Qwen2MoeModel):
         self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
         self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
         self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
@@ -4351,7 +4355,7 @@ class RND1Model(Qwen2MoeModel):
             self.gguf_writer.add_mask_token_id(mask_token_id)
 
 
-@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration")
+@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
 class Qwen3VLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -4397,6 +4401,10 @@ class Qwen3VLVisionModel(MmprojModel):
         if name.startswith("model.language_model.") or name.startswith("lm_head."):
             return
 
+        # Skip MTP tensors
+        if name.startswith("mtp."):
+            return
+
         if name.startswith("model.visual."):
             name = name.replace("model.visual.", "visual.", 1)
 
@@ -4559,6 +4567,93 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+class _LinearAttentionVReorderBase(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
+    """reorders V heads from grouped to tiled order for ggml broadcast
+
+    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+
+    Linear attention may has num_k_heads < num_v_heads. The HF weights store
+    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
+    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
+    We reorder V heads to tiled order so ggml_repeat can replace the expensive
+    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
+    """
+
+    @staticmethod
+    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
+        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
+        shape = list(tensor.shape)
+        if dim < 0:
+            dim += len(shape)
+        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
+        tensor = tensor.reshape(*new_shape)
+        perm = list(range(len(new_shape)))
+        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
+        return tensor.permute(*perm).contiguous().reshape(*shape)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
+        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
+
+        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_per_k = num_v_heads // num_k_heads
+
+            if ".in_proj_qkv." in name:
+                # QKV weight: reorder only the V rows
+                q_dim = head_k_dim * num_k_heads
+                k_dim = head_k_dim * num_k_heads
+                q = data_torch[:q_dim]
+                k = data_torch[q_dim:q_dim + k_dim]
+                v = data_torch[q_dim + k_dim:]
+                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([q, k, v], dim=0)
+
+            elif ".in_proj_z." in name:
+                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)
+
+            elif ".in_proj_b." in name or ".in_proj_a." in name:
+                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)
+
+            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
+                # A_log / dt_bias: 1D parameters with num_v_heads elements
+                if data_torch.ndim == 1:
+                    data_torch = self._reorder_v_heads(
+                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
+                    ).squeeze(-1)
+                else:
+                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)
+
+            elif ".conv1d" in name:
+                # Conv1d kernel: reorder only the V channel portion
+                data = data_torch.squeeze()
+                qk_channels = head_k_dim * num_k_heads * 2
+                qk_part = data[:qk_channels]
+                v_part = data[qk_channels:]
+                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([qk_part, v_part], dim=0)
+
+            elif ".out_proj." in name:
+                # Out projection weight: reorder columns (input dimension)
+                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5ForConditionalGeneration")
+class Qwen3_5TextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35
+
+
+@ModelBase.register("Qwen3_5MoeForConditionalGeneration")
+class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35MOE
+
+
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GPT2
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 2811f7f884..a683451508 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -148,6 +148,7 @@ models = [
     {"name": "youtu",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
     {"name": "solar-open",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
     {"name": "exaone-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
+    {"name": "qwen35",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", }
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 3af4fffe95..ec36b6292a 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -142,6 +142,7 @@ class Keys:
         EMBEDDING_SCALE                   = "{arch}.embedding_scale"
         TOKEN_SHIFT_COUNT                 = "{arch}.token_shift_count"
         INTERLEAVE_MOE_LAYER_STEP         = "{arch}.interleave_moe_layer_step"
+        FULL_ATTENTION_INTERVAL           = "{arch}.full_attention_interval"
         ACTIVATION_SPARSITY_SCALE         = "{arch}.activation_sparsity_scale"
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
@@ -384,6 +385,8 @@ class MODEL_ARCH(IntEnum):
     QWEN3NEXT        = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
+    QWEN35           = auto()
+    QWEN35MOE        = auto()
     PHI2             = auto()
     PHI3             = auto()
     PHIMOE           = auto()
@@ -557,13 +560,14 @@ class MODEL_TENSOR(IntEnum):
     SSM_D                = auto()
     SSM_NORM             = auto()
     SSM_OUT              = auto()
+    SSM_ALPHA            = auto() # qwen3.5
     SSM_BETA_ALPHA       = auto() # qwen3next
     SSM_CONV1D_Q         = auto() # Kimi Linear
     SSM_CONV1D_K         = auto() # Kimi Linear
     SSM_CONV1D_V         = auto() # Kimi Linear
     SSM_F_A              = auto() # Kimi Linear
     SSM_F_B              = auto() # Kimi Linear
-    SSM_BETA             = auto() # Kimi Linear
+    SSM_BETA             = auto() # Kimi Linear qwen3.5
     SSM_G_A              = auto() # Kimi Linear
     SSM_G_B              = auto() # Kimi Linear
     TIME_MIX_W0          = auto()
@@ -814,6 +818,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
+    MODEL_ARCH.QWEN35:           "qwen35",
+    MODEL_ARCH.QWEN35MOE:        "qwen35moe",
     MODEL_ARCH.PHI2:             "phi2",
     MODEL_ARCH.PHI3:             "phi3",
     MODEL_ARCH.PHIMOE:           "phimoe",
@@ -985,13 +991,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.SSM_D:                     "blk.{bid}.ssm_d",
     MODEL_TENSOR.SSM_NORM:                  "blk.{bid}.ssm_norm",
     MODEL_TENSOR.SSM_OUT:                   "blk.{bid}.ssm_out",
+    MODEL_TENSOR.SSM_ALPHA:                 "blk.{bid}.ssm_alpha",            # qwen3.5
     MODEL_TENSOR.SSM_BETA_ALPHA:            "blk.{bid}.ssm_ba",
     MODEL_TENSOR.SSM_CONV1D_Q:              "blk.{bid}.ssm_conv1d_q",         # Kimi Linear
     MODEL_TENSOR.SSM_CONV1D_K:              "blk.{bid}.ssm_conv1d_k",         # Kimi Linear
     MODEL_TENSOR.SSM_CONV1D_V:              "blk.{bid}.ssm_conv1d_v",         # Kimi Linear
     MODEL_TENSOR.SSM_F_A:                   "blk.{bid}.ssm_f_a",              # Kimi Linear
     MODEL_TENSOR.SSM_F_B:                   "blk.{bid}.ssm_f_b",              # Kimi Linear
-    MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear
+    MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear qwen3.5
     MODEL_TENSOR.SSM_G_A:                   "blk.{bid}.ssm_g_a",              # Kimi Linear
     MODEL_TENSOR.SSM_G_B:                   "blk.{bid}.ssm_g_b",              # Kimi Linear
     MODEL_TENSOR.TIME_MIX_W0:               "blk.{bid}.time_mix_w0",
@@ -1818,6 +1825,61 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.QWEN35: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_ALPHA,
+        MODEL_TENSOR.SSM_OUT
+    ],
+    MODEL_ARCH.QWEN35MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_ALPHA,
+        MODEL_TENSOR.SSM_OUT
+    ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 62172b24c3..a237537c8d 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -708,6 +708,9 @@ class GGUFWriter:
     def add_leading_dense_block_count(self, length: int) -> None:
         self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
 
+    def add_full_attention_interval(self, interval: int) -> None:
+        self.add_uint32(Keys.LLM.FULL_ATTENTION_INTERVAL.format(arch=self.arch), interval)
+
     def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
         if isinstance(length, int):
             self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 167ade7803..71dd7e887b 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -228,6 +228,7 @@ class TensorNameMap:
             "transformer_encoder.{bid}.qkv",                                       # neobert
             "layers.{bid}.attn.Wqkv",                                              # modern-bert
             "model.layers.{bid}.self_attn.language_expert_query_key_value",        # cogvlm
+            "model.layers.{bid}.linear_attn.in_proj_qkv",                          # qwen3.5
         ),
 
         # Attention query
@@ -359,6 +360,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.ATTN_GATE: (
             "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.linear_attn.in_proj_z",  # qwen3.5
             "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
 
@@ -823,6 +825,10 @@ class TensorNameMap:
             "model.layers.layers.{bid}.mixer.out_proj",  # plamo2
         ),
 
+        MODEL_TENSOR.SSM_ALPHA: (
+            "model.layers.{bid}.linear_attn.in_proj_a",  # qwen3.5
+        ),
+
         MODEL_TENSOR.SSM_BETA_ALPHA: (
             "model.layers.{bid}.linear_attn.in_proj_ba",  # qwen3next
         ),
@@ -844,7 +850,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.f_b_proj",
         ),
         MODEL_TENSOR.SSM_BETA: (
-            "model.layers.{bid}.self_attn.b_proj",
+            "model.layers.{bid}.linear_attn.in_proj_b",  # qwen3.5
+            "model.layers.{bid}.self_attn.b_proj",       # Kimi Linear
         ),
         MODEL_TENSOR.SSM_G_A: (
             "model.layers.{bid}.self_attn.g_a_proj",
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 2115fc4255..fdda05d3ea 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -122,6 +122,8 @@ add_library(llama
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
             models/qwen3next.cpp
+            models/qwen35.cpp
+            models/qwen35moe.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index bd78f1e556..a943d40dc4 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -37,6 +37,8 @@ static const std::map LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
+    { LLM_ARCH_QWEN35,           "qwen35"           },
+    { LLM_ARCH_QWEN35MOE,        "qwen35moe"        },
     { LLM_ARCH_PHI2,             "phi2"             },
     { LLM_ARCH_PHI3,             "phi3"             },
     { LLM_ARCH_PHIMOE,           "phimoe"           },
@@ -195,6 +197,7 @@ static const std::map LLM_KV_NAMES = {
     { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
     { LLM_KV_TOKEN_SHIFT_COUNT,                 "%s.token_shift_count"                 },
     { LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         "%s.interleave_moe_layer_step"         },
+    { LLM_KV_FULL_ATTENTION_INTERVAL,           "%s.full_attention_interval"           },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,                   "%s.attention.head_count"                   },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,                "%s.attention.head_count_kv"                },
@@ -366,6 +369,7 @@ static const std::map LLM_TENSOR_NAMES = {
     { LLM_TENSOR_SSM_CONV1D,                             "blk.%d.ssm_conv1d" },
     { LLM_TENSOR_SSM_DT,                                 "blk.%d.ssm_dt" },
     { LLM_TENSOR_SSM_BETA_ALPHA,                         "blk.%d.ssm_ba" },
+    { LLM_TENSOR_SSM_ALPHA,                              "blk.%d.ssm_alpha" },
     { LLM_TENSOR_SSM_IN,                                 "blk.%d.ssm_in" },
     { LLM_TENSOR_SSM_NORM,                               "blk.%d.ssm_norm" },
     { LLM_TENSOR_SSM_OUT,                                "blk.%d.ssm_out" },
@@ -968,7 +972,6 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_ATTN_OUT,
                 LLM_TENSOR_ATTN_QKV,
                 LLM_TENSOR_ATTN_GATE,
-                LLM_TENSOR_FFN_NORM,
                 LLM_TENSOR_FFN_GATE_INP,
                 LLM_TENSOR_FFN_GATE_EXPS,
                 LLM_TENSOR_FFN_DOWN_EXPS,
@@ -985,6 +988,63 @@ static std::set llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SSM_NORM,
                 LLM_TENSOR_SSM_OUT,
             };
+        case LLM_ARCH_QWEN35:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA,
+                LLM_TENSOR_SSM_ALPHA,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_QWEN35MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA,
+                LLM_TENSOR_SSM_ALPHA,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_HUNYUAN_DENSE:
@@ -2456,6 +2516,7 @@ static const std::map LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SSM_X,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_DT,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_OUT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_ALPHA,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_BETA_ALPHA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -2675,6 +2736,8 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
             return true;
         default:
             return false;
diff --git a/src/llama-arch.h b/src/llama-arch.h
index e8263369b8..4f7b51e70d 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -41,6 +41,8 @@ enum llm_arch {
     LLM_ARCH_QWEN3NEXT,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
+    LLM_ARCH_QWEN35,
+    LLM_ARCH_QWEN35MOE,
     LLM_ARCH_PHI2,
     LLM_ARCH_PHI3,
     LLM_ARCH_PHIMOE,
@@ -199,6 +201,7 @@ enum llm_kv {
     LLM_KV_EMBEDDING_SCALE,
     LLM_KV_TOKEN_SHIFT_COUNT,
     LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
+    LLM_KV_FULL_ATTENTION_INTERVAL,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -404,13 +407,14 @@ enum llm_tensor {
     LLM_TENSOR_SSM_NORM,
     LLM_TENSOR_SSM_OUT,
     LLM_TENSOR_SSM_BETA_ALPHA,      // qwen3next
+    LLM_TENSOR_SSM_ALPHA,           // qwen3.5
     // Kimi Linear KDA (using SSM_ prefix for consistency)
     LLM_TENSOR_SSM_CONV1D_Q,        // kimi: Q conv1d weight
     LLM_TENSOR_SSM_CONV1D_K,        // kimi: K conv1d weight
     LLM_TENSOR_SSM_CONV1D_V,        // kimi: V conv1d weight
     LLM_TENSOR_SSM_F_A,             // kimi: forget gate projection A
     LLM_TENSOR_SSM_F_B,             // kimi: forget gate projection B
-    LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient
+    LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient and qwen3.5
     LLM_TENSOR_SSM_G_A,             // kimi: output gate projection A
     LLM_TENSOR_SSM_G_B,             // kimi: output gate projection B
     LLM_TENSOR_TIME_MIX_W0,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index a6df893a31..1a35ce369c 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
         return std::max(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max(1024u, 8u*model.n_tensors());
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 674d06c891..7a06e96c87 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -125,6 +125,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
         case LLM_TYPE_31B_A3_5B:     return "31B.A3.5B";
+        case LLM_TYPE_35B_A3B:       return "35B.A3B";
         case LLM_TYPE_48B_A3B:       return "48B.A3B";
         case LLM_TYPE_80B_A3B:       return "80B.A3B";
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
@@ -2403,8 +2404,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
                 // Mark recurrent layers (linear attention layers)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0); // TODO: extract the magic 4 from "full_attention_interval"
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
                 }
 
                 switch (hparams.n_layer) {
@@ -2412,6 +2417,62 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN35:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
+
+                // Load linear attention (gated delta net) parameters
+                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);
+
+                // Mark recurrent layers (linear attention layers)
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
+                }
+
+                switch (hparams.n_layer) {
+                    case 24: type = LLM_TYPE_2B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_QWEN35MOE:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
+
+                // Load linear attention (gated delta net) parameters
+                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);
+
+                // Mark recurrent layers (linear attention layers)
+                {
+                    uint32_t full_attn_interval = 4;
+                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+                    }
+                }
+
+                switch (hparams.n_layer) {
+                    case 28: type = LLM_TYPE_35B_A3B; break;
+                    case 48: type = LLM_TYPE_80B_A3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7101,6 +7162,131 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
                     }
                 } break;
+            case LLM_ARCH_QWEN35MOE:
+                {
+                    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);
+                    }
+
+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        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);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            // Q/K normalization for attention layers
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            // Create tensors with calculated dimensions
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+
+                        // Shared experts
+                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, n_ff_shexp }, 0);
+                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, n_ff_shexp }, 0);
+                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { n_ff_shexp, n_embd }, 0);
+                    }
+                } break;
+            case LLM_ARCH_QWEN35:
+                {
+                    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);
+                    }
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        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);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            // Q/K normalization for attention layers
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            // Create tensors with calculated dimensions
+                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "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);
+                    }
+                } break;
             case LLM_ARCH_MIMO2:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -7545,6 +7731,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
+        arch == LLM_ARCH_QWEN35 ||
+        arch == LLM_ARCH_QWEN35MOE ||
         arch == LLM_ARCH_NEMOTRON_H ||
         arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
@@ -8343,6 +8531,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique(*this, params);
             } break;
+        case LLM_ARCH_QWEN35:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
+        case LLM_ARCH_QWEN35MOE:
+            {
+                llm = std::make_unique(*this, params);
+            } break;
         case LLM_ARCH_MISTRAL3:
             {
                 llm = std::make_unique(*this, params);
@@ -8611,6 +8807,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
             return LLAMA_ROPE_TYPE_MROPE;
         case LLM_ARCH_QWEN3VL:
         case LLM_ARCH_QWEN3VLMOE:
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
             return LLAMA_ROPE_TYPE_IMROPE;
 
         case LLM_ARCH_GLM4:
diff --git a/src/llama-model.h b/src/llama-model.h
index 7b580043b3..adc8ff6479 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -118,6 +118,7 @@ enum llm_type {
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
     LLM_TYPE_31B_A3_5B,
+    LLM_TYPE_35B_A3B, // Qwen3.5
     LLM_TYPE_48B_A3B, // Kimi Linear
     LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
@@ -322,6 +323,9 @@ struct llama_layer {
     // qwen3next
     struct ggml_tensor * ssm_beta_alpha = nullptr;
 
+    // qwen3.5
+    struct ggml_tensor * ssm_alpha = nullptr;
+
     // rwkv
     struct ggml_tensor * time_mix_w1         = nullptr;
     struct ggml_tensor * time_mix_w2         = nullptr;
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 6d6bdfa090..62e137fb84 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -368,6 +368,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_QWEN35:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_PORO:
             case LLAMA_VOCAB_PRE_TYPE_BLOOM:
             case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
@@ -1926,6 +1933,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "kormo") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                 clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "qwen35") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN35;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "stablelm2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 28c3a82b91..718238fb86 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -54,6 +54,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN      = 43,
     LLAMA_VOCAB_PRE_TYPE_YOUTU           = 44,
     LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE      = 45,
+    LLAMA_VOCAB_PRE_TYPE_QWEN35          = 46,
 };
 
 struct LLM_KV;
diff --git a/src/models/models.h b/src/models/models.h
index cfcbb9aaa5..3c66d32531 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -476,6 +476,7 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
+
 struct llm_build_qwen3next : public llm_graph_context_mamba {
     llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
 private:
@@ -534,6 +535,124 @@ private:
     const llama_model & model;
 };
 
+struct llm_build_qwen35 : public llm_graph_context_mamba {
+    llm_build_qwen35(const llama_model & model, const llm_graph_params & params);
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int * sections,
+                        int   il);
+
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair build_delta_net_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    // returns pair of qkv, z
+    std::pair build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+
+    const llama_model & model;
+};
+
+struct llm_build_qwen35moe : public llm_graph_context_mamba {
+    llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params);
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int * sections,
+                        int   il);
+
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair build_delta_net_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                ggml_tensor * diag_mask,
+                        int   il);
+
+    // returns pair of output and new state
+    std::pair build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    // returns pair of qkv, z
+    std::pair build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+
+    const llama_model & model;
+};
+
 struct llm_build_qwen : public llm_graph_context {
     llm_build_qwen(const llama_model & model, const llm_graph_params & params);
 };
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
new file mode 100644
index 0000000000..592c170457
--- /dev/null
+++ b/src/models/qwen35.cpp
@@ -0,0 +1,740 @@
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params), model(model) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    cb(inpL, "model.input_embed", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Determine layer type and build appropriate attention mechanism
+        if (hparams.is_recurrent(il)) {
+            // Linear attention layer (gated delta net)
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            // Full attention layer
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, 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);
+        }
+
+        // Residual connection
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        // Save the tensor before post-attention norm for residual connection
+        ggml_tensor * ffn_residual = cur;
+
+        // Post-attention norm
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        // Dense FFN layer - without residual connection
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_ffn", il);
+
+        // Input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // Final norm
+    cur = build_norm(cur, model.output_norm, nullptr, 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);
+}
+
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair llm_build_qwen35::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair llm_build_qwen35::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+std::pair llm_build_qwen35::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+    qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+    cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+    ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+    cb(z, "z", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int *                     sections,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
+
+    // Qwen3Next uses a single Q projection that outputs query + gate
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    // Apply Q normalization
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", 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);
+
+    // Apply K normalization
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", il);
+
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+    // Apply MRoPE
+    Qcur = ggml_rope_multi(
+            ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    Kcur = ggml_rope_multi(
+            ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, 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);
+
+    // Attention computation
+    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,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    // Input projections
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+    beta  = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+    cb(beta, "beta", il);
+    ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+    alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+    cb(alpha, "alpha", il);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus
+    cb(gate, "gate", il);
+
+    // Get convolution states from cache
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+    // Build the convolution states tensor
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    // Calculate convolution kernel size
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    // Update convolution state cache
+    // Extract the last (conv_kernel_size - 1) states from conv_input
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    // Apply SSM convolution
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    // Calculate the total conv dimension
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    // Extract the convolved Q, K, V from conv_output
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    // Unsqueeze them
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    cb(state, "state_predelta", il);
+
+    // if head keys and value keys are different, repeat Q/K to match V's head count
+    // V heads are in tiled order (from conversion), so simple tiled repeat works
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+        k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    // Update the recurrent states
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    // Reshape both attn_out_final and z to 2D tensors for normalization
+    // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // Apply gated normalization: self.norm(core_attn_out, z)
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    // Output projection
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    // Reshape back to original dimensions
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Qwen3.5 does not use MoE FFN
+    GGML_ASSERT(model.layers[il].ffn_gate_inp == nullptr);
+
+    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);
+
+    return cur;
+}
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
new file mode 100644
index 0000000000..0db8f825c6
--- /dev/null
+++ b/src/models/qwen35moe.cpp
@@ -0,0 +1,774 @@
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params), model(model) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    cb(inpL, "model.input_embed", -1);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * causal_mask =
+        ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+                    GGML_TRI_TYPE_LOWER);
+
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+    ggml_build_forward_expand(gf, causal_mask);
+    ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Determine layer type and build appropriate attention mechanism
+        if (hparams.is_recurrent(il)) {
+            // Linear attention layer (gated delta net)
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+        } else {
+            // Full attention layer
+            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, 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);
+        }
+
+        // Residual connection
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "attn_residual", il);
+
+        // Save the tensor before post-attention norm for residual connection
+        ggml_tensor * ffn_residual = cur;
+
+        // Post-attention norm
+        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+        cb(attn_post_norm, "attn_post_norm", il);
+
+        // MOE FFN layer
+        cur = build_layer_ffn(attn_post_norm, il);
+        cb(cur, "ffn_out", il);
+
+        // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+        cur = ggml_add(ctx0, cur, ffn_residual);
+        cb(cur, "post_moe", il);
+
+        // Input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // Final norm
+    cur = build_norm(cur, model.output_norm, nullptr, 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);
+}
+
+// utility to get one slice from the third dimension
+// input dim:  [x, y, c, b]
+// output dim: [x, y, 1, b]
+static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
+    return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
+        t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
+}
+
+std::pair llm_build_qwen35moe::build_delta_net_chunking(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        ggml_tensor * causal_mask,
+        ggml_tensor * identity,
+        ggml_tensor * diag_mask,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q = ggml_scale(ctx0, q, scale);
+
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    cb(q, "q_perm", il);
+    cb(k, "k_perm", il);
+    cb(v, "v_perm", il);
+    cb(beta, "beta_perm", il);
+    cb(g, "g_perm", il);
+    cb(state, "state_in", il);
+
+    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+    // Do padding
+    const int64_t chunk_size = CHUNK_SIZE;
+
+    const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+    const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+    q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+    k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+    v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+    g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+    beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+    cb(q, "q_pad", il);
+    cb(k, "k_pad", il);
+    cb(v, "v_pad", il);
+    cb(beta, "beta_pad", il);
+    cb(g, "g_pad", il);
+
+    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+    cb(v_beta, "v_beta", il);
+    cb(k_beta, "k_beta", il);
+
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * gcs_j_broadcast =
+        ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+    cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+    decay_mask = ggml_exp(ctx0, decay_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+    cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
+    cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
+
+    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+    cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * k_cumdecay =
+        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+    cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+    attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+    attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+    cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+    // vectorized calculation of key_gdiff
+    // improved from the chunked version:
+    //   g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+    //   g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+    //   key_gdiff = key * g_diff.unsqueeze(-1)
+    //   kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+    //   last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+    // get last element in g_cumsum along chunk_size dimension (ne0)
+    // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+    ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+                                        g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+                                        (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+    g_last = ggml_cont(ctx0, g_last);
+    cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+    cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+    cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+    ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+                                                 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+    ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+    cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+    ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+    cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+    // state to be updated per chunk
+    ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+    cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+    // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+    ggml_tensor * core_attn_out = nullptr;
+
+    for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+        // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+        ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+        // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+        ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+        // shape: (chunk_size, 1, H_v * n_seqs)
+        ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+        // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        // replaced by precomputed attn_kq
+        ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+        cb(attn_chunk, "attn_chunk", il);
+
+        ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+        // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+        cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+        // v_new = v_i - v_prime
+        ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+        ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+        cb(v_new, "v_new_chunk", il);
+
+        // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        ggml_tensor * q_g_exp    = ggml_mul(ctx0, q_chunk, gexp_chunk);
+        ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+        cb(attn_inter, "attn_inter_chunk", il);
+
+        // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+        cb(v_attn, "v_attn_chunk", il);
+
+        ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+        cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+        core_attn_out = core_attn_out == nullptr
+            ? core_attn_out_chunk
+            : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+        // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+        ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+        //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+        ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+        // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+        ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+        new_state = ggml_add(ctx0,
+            ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+            ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+    }
+
+    // truncate padded tokens
+    ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+            S_v, n_tokens, H_v, n_seqs,
+            ggml_row_size(core_attn_out->type, S_v),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+            ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+    cb(output_tokens, "output_tokens", il);
+
+    // permute back to (S_v, H_v, n_tokens, n_seqs)
+    output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+    output_tokens = ggml_cont(ctx0, output_tokens);
+
+    return {output_tokens, new_state};
+}
+
+std::pair llm_build_qwen35moe::build_delta_net_autoregressive(
+        ggml_tensor * q,
+        ggml_tensor * k,
+        ggml_tensor * v,
+        ggml_tensor * g,
+        ggml_tensor * beta,
+        ggml_tensor * state,
+        int           il) {
+    const int64_t S_k      = q->ne[0];
+    const int64_t H_k      = q->ne[1];
+    const int64_t n_tokens = q->ne[2];
+    const int64_t n_seqs   = q->ne[3];
+
+    const int64_t S_v = v->ne[0];
+    const int64_t H_v = v->ne[1];
+
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
+    GGML_ASSERT(v->ne[2] == n_tokens);
+    GGML_ASSERT(k->ne[2] == n_tokens);
+    GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+    GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+    GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+    GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+    GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+    GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
+
+    const float eps_norm = hparams.f_norm_rms_eps;
+
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
+
+    const float scale = 1.0f / sqrtf(S_v);
+
+    q    = ggml_scale(ctx0, q, scale);
+    beta = ggml_sigmoid(ctx0, beta);
+
+    cb(q, "q_in", il);
+    cb(k, "k_in", il);
+    cb(v, "v_in", il);
+    cb(beta, "beta_in", il);
+    cb(g, "g_in", il);
+
+    state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
+
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
+
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
+
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
+
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
+    cb(state, "new_state", il);
+
+    return {core_attn_out, state};
+}
+
+std::pair llm_build_qwen35moe::build_qkvz(
+                ggml_tensor * input,
+                        int   il) {
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+    qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+    cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+    ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+    cb(z, "z", il);
+
+    return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35moe::build_norm_gated(
+        ggml_tensor * input,
+        ggml_tensor * weights,
+        ggml_tensor * gate,
+        int           layer) {
+    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+    return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
+        llm_graph_input_attn_kv * inp,
+        ggml_tensor *             cur,
+        ggml_tensor *             inp_pos,
+        int *                     sections,
+        int                       il) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
+
+    // Qwen3Next uses a single Q projection that outputs query + gate
+    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
+    cb(Qcur_full, "Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
+    cb(Qcur, "Qcur_reshaped", il);
+
+    // Apply Q normalization
+    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "Qcur_normed", 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);
+
+    // Apply K normalization
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "Kcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
+        ggml_element_size(Qcur_full) * n_embd_head * 2,
+        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+        ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "gate_reshaped", il);
+
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+    // Apply IMRoPE
+    Qcur = ggml_rope_multi(
+            ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow
+            );
+
+    Kcur = ggml_rope_multi(
+            ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, 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);
+
+    // Attention computation
+    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,
+                nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_pregate", il);
+
+    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+    cb(gate_sigmoid, "gate_sigmoid", il);
+
+    cur = ggml_mul(ctx0, cur, gate_sigmoid);
+    cb(cur, "attn_gated", il);
+
+    cur = build_lora_mm(model.layers[il].wo, cur);
+    cb(cur, "attn_output", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        cur,
+        ggml_tensor *        causal_mask,
+        ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
+        int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const int64_t d_inner      = hparams.ssm_d_inner;
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t head_k_dim   = hparams.ssm_d_state;
+    const int64_t num_k_heads  = hparams.ssm_n_group;
+    const int64_t num_v_heads  = hparams.ssm_dt_rank;
+    const int64_t head_v_dim   = d_inner / num_v_heads;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    // Input projections
+    auto qkvz = build_qkvz(cur, il);
+    ggml_tensor * qkv_mixed = qkvz.first;
+    ggml_tensor * z         = qkvz.second;
+
+    ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+    beta  = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+    cb(beta, "beta", il);
+    ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+    alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+    cb(alpha, "alpha", il);
+
+    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+    cb(alpha_softplus, "a_softplus", il);
+    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus
+    cb(gate, "gate", il);
+
+    // Get convolution states from cache
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+    // Build the convolution states tensor
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    // Calculate convolution kernel size
+    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
+    const int64_t conv_kernel_size = conv_kernel->ne[0];
+    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+    conv_states                    = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+    cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    // Update convolution state cache
+    // Extract the last (conv_kernel_size - 1) states from conv_input
+    ggml_tensor * last_conv_states =
+        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+    cb(last_conv_states, "last_conv_states", il);
+
+    ggml_tensor * state_update_target =
+        ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+    cb(state_update_target, "state_update_target", il);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    cb(conv_states_all, "conv_states_updated", il);
+
+    // Apply SSM convolution
+    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+    cb(conv_output_proper, "conv_output_raw", il);
+
+    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+    cb(conv_output_silu, "conv_output_silu", il);
+
+    ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+    // Calculate the total conv dimension
+    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+    // Extract the convolved Q, K, V from conv_output
+    ggml_tensor * q_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+    cb(q_conv, "q_conv", il);
+    ggml_tensor * k_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(k_conv, "k_conv", il);
+    ggml_tensor * v_conv =
+        ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+                     2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+    cb(v_conv, "v_conv", il);
+
+    // Unsqueeze them
+    q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+    v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state               = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+    cb(state, "state_predelta", il);
+
+    // if head keys and value keys are different, repeat Q/K to match V's head count
+    // V heads are in tiled order (from conversion), so simple tiled repeat works
+    if (num_k_heads != num_v_heads) {
+        GGML_ASSERT(num_v_heads % num_k_heads == 0);
+        q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+        k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+    }
+
+    cb(q_conv, "q_conv_predelta", il);
+    cb(k_conv, "k_conv_predelta", il);
+    cb(v_conv, "v_conv_predelta", il);
+
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    std::pair attn_out; // pair of (output, new_state)
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
+    ggml_tensor * output    = attn_out.first;
+    ggml_tensor * new_state = attn_out.second;
+    cb(output, "attn_output", il);
+    cb(new_state, "new_state", il);
+
+    // Update the recurrent states
+    ggml_build_forward_expand(gf,
+                              ggml_cpy(ctx0, new_state,
+                                       ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                                                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    // Reshape both attn_out_final and z to 2D tensors for normalization
+    // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+    ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+    // Apply gated normalization: self.norm(core_attn_out, z)
+    ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    cb(final_output, "final_output", il);
+
+    // Output projection
+    cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+    cb(cur, "linear_attn_out", il);
+
+    // Reshape back to original dimensions
+    cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+    return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int il) {
+    // Check if this is an MoE layer
+    GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
+
+    ggml_tensor * moe_out =
+        build_moe_ffn(cur,
+            model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+            model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+            nullptr,
+            n_expert, n_expert_used, LLM_FFN_SILU,
+            true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+    cb(moe_out, "ffn_moe_out", il);
+
+    // Add shared experts if present - following Qwen3Next reference implementation
+    if (model.layers[il].ffn_up_shexp != nullptr) {
+        ggml_tensor * ffn_shexp =
+            build_ffn(cur,
+                model.layers[il].ffn_up_shexp, NULL, NULL,
+                model.layers[il].ffn_gate_shexp, NULL, NULL,
+                model.layers[il].ffn_down_shexp, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "ffn_shexp", il);
+
+        // Apply shared expert gating as in the reference implementation
+        // The shared expert has its own gate that is sigmoided
+        // Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token)
+        ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+        cb(shared_gate, "shared_expert_gate", il);
+
+        // Apply sigmoid to the gate
+        shared_gate = ggml_sigmoid(ctx0, shared_gate);
+        cb(shared_gate, "shared_expert_gate_sigmoid", il);
+
+
+        // Apply the gate to the shared expert output
+        ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+        cb(ffn_shexp, "ffn_shexp_gated", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "ffn_out", il);
+    } else {
+        cur = moe_out;
+    }
+
+    return cur;
+}
diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp
index 35a42cb84d..5ecb10fe43 100644
--- a/tools/mtmd/models/qwen3vl.cpp
+++ b/tools/mtmd/models/qwen3vl.cpp
@@ -182,7 +182,9 @@ ggml_cgraph * clip_graph_qwen3vl::build() {
         model.mm_1_w, model.mm_1_b,
         ffn_op_type::FFN_GELU, -1);
 
-    embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension
+    if (deepstack_features) {
+        embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0);
+    } // concat along the feature dimension
 
     // build the graph
     ggml_build_forward_expand(gf, embeddings);

From 57487a64c88c152ac72f3aea09bd1cc491b2f61e Mon Sep 17 00:00:00 2001
From: Nikhil Jain 
Date: Tue, 10 Feb 2026 08:04:00 -0800
Subject: [PATCH 175/206] [WebGPU] Plug memory leaks and free resources on
 shutdown (#19315)

* Fix memory leaks in shader lib, backend, backend_context, buffer_context, and webgpu_buf_pool

* Free pools

* Cleanup

* More cleanup

* Run clang-format

* Fix arg-parser and tokenizer test errors that free an unallocated buffer

* Fix device lost callback to not print on device teardown

* Fix include and run clang-format

* remove unused unused

* Update binary ops

---------

Co-authored-by: Reese Levine 
---
 .../ggml-webgpu/ggml-webgpu-shader-lib.hpp    | 81 ++++++++---------
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          | 89 +++++++++++--------
 2 files changed, 94 insertions(+), 76 deletions(-)

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index 6997f6bdd3..63f797f142 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -4,6 +4,7 @@
 #include "ggml.h"
 #include "pre_wgsl.hpp"
 
+#include 
 #include 
 #include 
 
@@ -18,9 +19,9 @@
 #define GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE 512u
 
 struct ggml_webgpu_processed_shader {
-    std::string wgsl;
-    std::string variant;
-    void *      decisions;
+    std::string           wgsl;
+    std::string           variant;
+    std::shared_ptr decisions;
 };
 
 // Same hash combine function as in boost
@@ -192,13 +193,13 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_flash_attn_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                         = preprocessor.preprocess(shader_src, defines);
-    result.variant                                      = variant;
-    ggml_webgpu_flash_attn_shader_decisions * decisions = new ggml_webgpu_flash_attn_shader_decisions();
-    decisions->q_tile                                   = q_tile;
-    decisions->kv_tile                                  = kv_tile;
-    decisions->wg_size                                  = wg_size;
-    result.decisions                                    = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->q_tile  = q_tile;
+    decisions->kv_tile = kv_tile;
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -270,11 +271,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_pad_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -305,11 +306,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_shader(
     }
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_argsort_shader_decisions * decisions = new ggml_webgpu_argsort_shader_decisions();
-    decisions->wg_size                               = wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -324,11 +325,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_argsort_merge_shader(
     uint32_t wg_size = std::min(GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE, context.max_wg_size);
     defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_argsort_shader_decisions * decisions = new ggml_webgpu_argsort_shader_decisions();
-    decisions->wg_size                               = wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -391,11 +392,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_set_rows_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -457,11 +458,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 
@@ -527,11 +528,11 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
 
     defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
     ggml_webgpu_processed_shader result;
-    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
-    result.variant                                   = variant;
-    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
-    decisions->wg_size                               = context.max_wg_size;
-    result.decisions                                 = decisions;
+    result.wgsl        = preprocessor.preprocess(shader_src, defines);
+    result.variant     = variant;
+    auto decisions     = std::make_shared();
+    decisions->wg_size = context.max_wg_size;
+    result.decisions   = decisions;
     return result;
 }
 #endif  // GGML_WEBGPU_SHADER_LIB_HPP
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index f7ceca1121..32e120266a 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -186,11 +186,17 @@ struct webgpu_buf_pool {
     void cleanup() {
         std::lock_guard lock(mutex);
         for (auto & bufs : free) {
-            bufs.host_buf.Destroy();
-            bufs.dev_buf.Destroy();
+            if (bufs.host_buf) {
+                bufs.host_buf.Destroy();
+            }
+            if (bufs.dev_buf) {
+                bufs.dev_buf.Destroy();
+            }
         }
         free.clear();
     }
+
+    ~webgpu_buf_pool() { this->cleanup(); }
 };
 
 #ifdef GGML_WEBGPU_GPU_PROFILE
@@ -252,13 +258,15 @@ struct webgpu_gpu_profile_buf_pool {
         }
         free.clear();
     }
+
+    ~webgpu_gpu_profile_buf_pool() { this->cleanup(); }
 };
 #endif
 
 struct webgpu_pipeline {
     wgpu::ComputePipeline pipeline;
     std::string           name;
-    void *                context = nullptr;
+    std::shared_ptr context = nullptr;
 };
 
 struct webgpu_command {
@@ -319,6 +327,23 @@ struct webgpu_global_context_struct {
     wgpu::Buffer debug_host_buf;
     wgpu::Buffer debug_dev_buf;
 #endif
+
+    ~webgpu_global_context_struct() {
+        if (this->get_tensor_staging_buf) {
+            this->get_tensor_staging_buf.Destroy();
+            this->get_tensor_staging_buf = nullptr;
+        }
+#ifdef GGML_WEBGPU_DEBUG
+        if (this->debug_host_buf) {
+            this->debug_host_buf.Destroy();
+            this->debug_host_buf = nullptr;
+        }
+        if (this->debug_dev_buf) {
+            this->debug_dev_buf.Destroy();
+            this->debug_dev_buf = nullptr;
+        }
+#endif
+    }
 };
 
 typedef std::shared_ptr webgpu_global_context;
@@ -744,7 +769,6 @@ static const char * ggml_backend_webgpu_name(ggml_backend_t backend) {
     return ctx->name.c_str();
 }
 
-// TODO: implement proper cleanup
 static void ggml_backend_webgpu_free(ggml_backend_t backend) {
     ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context;
     WEBGPU_LOG_DEBUG("ggml_backend_webgpu_free(" << ctx->name << ")");
@@ -788,9 +812,8 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) {
     std::cout << "ggml_webgpu: gpu/cpu ratio: " << (total_cpu > 0.0 ? total_gpu / total_cpu : 0.0) << "\n";
 #endif
 
-#if !defined(GGML_WEBGPU_CPU_PROFILE) && !defined(GGML_WEBGPU_GPU_PROFILE)
-    GGML_UNUSED(ctx);
-#endif
+    delete ctx;
+    delete backend;
 }
 
 static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
@@ -896,8 +919,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g
         ctx->pad_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast(pipeline.context);
+    auto * decisions = static_cast(pipeline.context.get());
 
     const uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -941,7 +963,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g
          .size    = ggml_webgpu_tensor_binding_size(ctx, dst) }
     };
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -975,8 +997,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx,
         ctx->set_rows_pipelines.emplace(key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast(pipeline.context);
+    auto * decisions = static_cast(pipeline.context.get());
 
     std::optional error_bufs = std::nullopt;
     if (key.i64_idx) {
@@ -1028,7 +1049,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx,
     } else {
         threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3];
     }
-    uint32_t wg_x = CEIL_DIV(threads, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(threads, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1,
                                      error_bufs);
 }
@@ -1297,10 +1318,9 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx,
         ctx->flash_attn_pipelines.emplace(key, pipeline);
     }
 
-    ggml_webgpu_flash_attn_shader_decisions decisions =
-        *static_cast(pipeline.context);
+    auto * decisions = static_cast(pipeline.context.get());
 
-    uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions.q_tile);
+    uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions->q_tile);
     uint32_t wg_x        = wg_per_head * Q->ne[2] * Q->ne[3];  // wg per head * number of heads * number of batches
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
@@ -1331,8 +1351,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
         ctx->unary_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast(pipeline.context);
+    auto * decisions = static_cast(pipeline.context.get());
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1392,7 +1411,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -1425,8 +1444,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
         ctx->binary_pipelines.emplace(pipeline_key, pipeline);
     }
 
-    ggml_webgpu_generic_shader_decisions decisions =
-        *static_cast(pipeline.context);
+    auto * decisions = static_cast(pipeline.context.get());
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1471,7 +1489,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
+    uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -1821,8 +1839,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr
         argsort_pipeline.context = processed.decisions;
         ctx->argsort_pipelines.emplace(order, argsort_pipeline);
     }
-    ggml_webgpu_argsort_shader_decisions argsort_decisions =
-        *static_cast(argsort_pipeline.context);
+    auto * argsort_decisions = static_cast(argsort_pipeline.context.get());
 
     webgpu_pipeline argsort_merge_pipeline;
     it = ctx->argsort_merge_pipelines.find(order);
@@ -1839,13 +1856,13 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr
 
     const uint32_t src_ne0 = (uint32_t) src->ne[0];
     const uint32_t nrows   = (uint32_t) ggml_nrows(src);
-    const uint32_t npr     = CEIL_DIV(src_ne0, argsort_decisions.wg_size);
+    const uint32_t npr     = CEIL_DIV(src_ne0, argsort_decisions->wg_size);
     const uint32_t block_size =
-        is_top_k ? std::min(argsort_decisions.wg_size, (uint32_t) dst->ne[0]) : argsort_decisions.wg_size;
+        is_top_k ? std::min(argsort_decisions->wg_size, (uint32_t) dst->ne[0]) : argsort_decisions->wg_size;
     uint32_t out_ne0 = src_ne0;
     if (is_top_k) {
         if (npr > 1) {
-            const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions.wg_size;
+            const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions->wg_size;
             out_ne0                  = (npr - 1) * block_size + std::min(last_tile, block_size);
         } else {
             out_ne0 = block_size;
@@ -2198,7 +2215,10 @@ static ggml_backend_i ggml_backend_webgpu_i = {
 
 static void ggml_backend_webgpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_backend_webgpu_buffer_context * ctx = static_cast(buffer->context);
-    ctx->buffer.Destroy();
+    if (ctx != nullptr && ctx->buffer != nullptr) {
+        ctx->buffer.Destroy();
+        delete ctx;
+    }
 }
 
 // Returns the "fake" base pointer.
@@ -2926,12 +2946,12 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
     dev_desc.SetDeviceLostCallback(
         wgpu::CallbackMode::AllowSpontaneous,
         [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
+            if (reason == wgpu::DeviceLostReason::Destroyed) {
+                return;
+            }
             GGML_UNUSED(device);
-            GGML_UNUSED(reason);
-            GGML_UNUSED(message);
-            //TODO: uncomment once proper free logic is in place
-            //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason),
-            //std::string(message).c_str());
+            GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason),
+                           std::string(message).c_str());
         });
     dev_desc.SetUncapturedErrorCallback(
         [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) {
@@ -3365,10 +3385,7 @@ static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) {
     return ctx->device_count;
 }
 
-// TODO: Does this need to be thread safe? Is it only called once?
-// TODO: move most logic to device_init function so backend can be freed/initialized properly
 // Only one device is supported for now
-
 static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) {
     GGML_ASSERT(index == 0);
     WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()");

From 612db61886e0fd523f89a0f93be0ef19d450b7d7 Mon Sep 17 00:00:00 2001
From: Oliver Simons 
Date: Tue, 10 Feb 2026 22:31:19 +0100
Subject: [PATCH 176/206] CUDA : Update CCCL-tag for 3.2 to final release from
 RC (#19486)

CCCL 3.2 has been released since it was added to llama.cpp as part of
the backend-sampling PR, and it makes sense to update from RC to final
released version.

https://github.com/NVIDIA/cccl/releases/tag/v3.2.0
---
 ggml/src/ggml-cuda/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt
index d313c1ac9a..262f88204e 100644
--- a/ggml/src/ggml-cuda/CMakeLists.txt
+++ b/ggml/src/ggml-cuda/CMakeLists.txt
@@ -64,7 +64,7 @@ if (CUDAToolkit_FOUND)
         FetchContent_Declare(
             CCCL
             GIT_REPOSITORY https://github.com/nvidia/cccl.git
-            GIT_TAG        v3.2.0-rc2
+            GIT_TAG        v3.2.0
             GIT_SHALLOW    TRUE
         )
 

From 2cce9fddb7064da34bf69a8539cebd8032713011 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 11 Feb 2026 05:38:13 +0100
Subject: [PATCH 177/206] llama : refactor sampling_info to use buffer_view
 template (#19368)

* llama : refactor sampling_info to use buffer_view template

This commit updates the sampling_info struct in llama-context to use a
buffer_view template for the logits, probs, sampled tokens, and
candidates buffers.

The motivation for this is to simplify the code, improve type safety
and readability.
---
 src/llama-context.cpp | 189 ++++++++++++++++++++----------------------
 src/llama-context.h   |  23 ++---
 src/llama-impl.h      |  10 +++
 3 files changed, 107 insertions(+), 115 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 1a35ce369c..6b43ca1926 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -677,7 +677,7 @@ enum llama_pooling_type llama_context::pooling_type() const {
 float * llama_context::get_logits() {
     output_reorder();
 
-    return logits;
+    return logits.data;
 }
 
 int64_t llama_context::output_resolve_row(int32_t i) const {
@@ -715,7 +715,7 @@ float * llama_context::get_logits_ith(int32_t i) {
     output_reorder();
 
     try {
-        if (logits == nullptr) {
+        if (logits.data == nullptr) {
             throw std::runtime_error("no logits");
         }
 
@@ -739,7 +739,7 @@ float * llama_context::get_logits_ith(int32_t i) {
             throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
         }
 
-        return logits + j*model.vocab.n_tokens();
+        return logits.data + j*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -753,11 +753,11 @@ float * llama_context::get_logits_ith(int32_t i) {
 float * llama_context::get_embeddings() {
     output_reorder();
 
-    return embd;
+    return embd.data;
 }
 
 llama_token * llama_context::get_sampled_tokens()  const{
-    return sampling.sampled;
+    return sampling.sampled.data;
 }
 
 float * llama_context::get_embeddings_ith(int32_t i) {
@@ -766,7 +766,7 @@ float * llama_context::get_embeddings_ith(int32_t i) {
     output_reorder();
 
     try {
-        if (embd == nullptr) {
+        if (embd.data == nullptr) {
             throw std::runtime_error("no embeddings");
         }
 
@@ -791,7 +791,7 @@ float * llama_context::get_embeddings_ith(int32_t i) {
         }
 
         const uint32_t n_embd_out = model.hparams.n_embd_out();
-        return embd + j*n_embd_out;
+        return embd.data + j*n_embd_out;
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
@@ -814,14 +814,14 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
 llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.sampled == nullptr) {
+    if (!sampling.sampled.has_data()) {
         return LLAMA_TOKEN_NULL;
     }
 
     try {
         const int64_t row = output_resolve_row(idx);
-        GGML_ASSERT(row < (int64_t) sampling.sampled_size);
-        return sampling.sampled[row];
+        GGML_ASSERT(row < (int64_t) sampling.sampled.size);
+        return sampling.sampled.data[row];
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled token id %d, reason: %s\n", __func__, idx, err.what());
         return LLAMA_TOKEN_NULL;
@@ -831,7 +831,7 @@ llama_token llama_context::get_sampled_token_ith(int32_t idx) {
 float * llama_context::get_sampled_probs_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.probs == nullptr) {
+    if (!sampling.probs.has_data()) {
         return nullptr;
     }
 
@@ -840,7 +840,7 @@ float * llama_context::get_sampled_probs_ith(int32_t idx) {
         if ((size_t) row >= sampling.probs_count.size() || sampling.probs_count[row] == 0) {
             return nullptr;
         }
-        return sampling.probs + row*model.vocab.n_tokens();
+        return sampling.probs.data + row*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled probs id %d, reason: %s\n", __func__, idx, err.what());
         return nullptr;
@@ -850,7 +850,7 @@ float * llama_context::get_sampled_probs_ith(int32_t idx) {
 float * llama_context::get_sampled_logits_ith(int32_t idx) {
     output_reorder();
 
-    if (sampling.logits == nullptr) {
+    if (!sampling.logits.has_data()) {
         return nullptr;
     }
 
@@ -859,7 +859,7 @@ float * llama_context::get_sampled_logits_ith(int32_t idx) {
         if ((size_t) row >= sampling.logits_count.size() || sampling.logits_count[row] == 0) {
             return nullptr;
         }
-        return sampling.logits + row*model.vocab.n_tokens();
+        return sampling.logits.data + row*model.vocab.n_tokens();
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid backend sampled logits id %d, reason: %s\n", __func__, idx, err.what());
         return nullptr;
@@ -871,10 +871,10 @@ const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
 
     try {
         const int64_t row = output_resolve_row(idx);
-        if (sampling.candidates != nullptr &&
+        if (sampling.candidates.has_data() &&
             (size_t) row < sampling.candidates_count.size() &&
             sampling.candidates_count[row] > 0) {
-            return sampling.candidates + row*model.vocab.n_tokens();
+            return sampling.candidates.data + row*model.vocab.n_tokens();
         }
     } catch (const std::exception & err) {
         // fallback to full vocab list
@@ -886,7 +886,7 @@ const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
 size_t llama_context::get_sampled_candidates_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.candidates == nullptr) {
+    if (!sampling.candidates.has_data()) {
         return 0;
     }
 
@@ -905,7 +905,7 @@ size_t llama_context::get_sampled_candidates_count(int32_t idx) {
 size_t llama_context::get_sampled_logits_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.logits == nullptr) {
+    if (!sampling.logits.has_data()) {
         return model.vocab.n_tokens();
     }
 
@@ -924,7 +924,7 @@ size_t llama_context::get_sampled_logits_count(int32_t idx) {
 size_t llama_context::get_sampled_probs_count(int32_t idx) {
     output_reorder();
 
-    if (sampling.probs == nullptr) {
+    if (!sampling.probs.has_data()) {
         return 0;
     }
 
@@ -1254,16 +1254,16 @@ int llama_context::encode(const llama_batch & batch_inp) {
     auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
 
     // extract logits
-    if (logits && t_logits) {
+    if (logits.data && t_logits) {
         ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
         GGML_ASSERT(backend_res != nullptr);
-        GGML_ASSERT(logits != nullptr);
+        GGML_ASSERT(logits.data != nullptr);
 
-        ggml_backend_tensor_get_async(backend_res, t_logits, logits, 0, n_tokens*n_vocab*sizeof(float));
+        ggml_backend_tensor_get_async(backend_res, t_logits, logits.data, 0, n_tokens*n_vocab*sizeof(float));
     }
 
     // extract embeddings
-    if (embd && t_embd) {
+    if (embd.data && t_embd) {
         ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
         GGML_ASSERT(backend_embd != nullptr);
 
@@ -1271,11 +1271,11 @@ int llama_context::encode(const llama_batch & batch_inp) {
             case LLAMA_POOLING_TYPE_NONE:
                 {
                     // extract token embeddings
-                    GGML_ASSERT(embd != nullptr);
+                    GGML_ASSERT(embd.data != nullptr);
                     const uint32_t n_embd_out = hparams.n_embd_out();
 
-                    GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd_size);
-                    ggml_backend_tensor_get_async(backend_embd, t_embd, embd, 0, n_tokens*n_embd_out*sizeof(float));
+                    GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd.size);
+                    ggml_backend_tensor_get_async(backend_embd, t_embd, embd.data, 0, n_tokens*n_embd_out*sizeof(float));
                 } break;
             case LLAMA_POOLING_TYPE_MEAN:
             case LLAMA_POOLING_TYPE_CLS:
@@ -1323,7 +1323,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
         cross.n_embd = t_embd->ne[0];
         cross.n_enc  = t_embd->ne[1];
         cross.v_embd.resize(cross.n_embd*cross.n_enc);
-        memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
+        memcpy(cross.v_embd.data(), embd.data, ggml_nbytes(t_embd));
 
         const auto & batch = balloc->get_batch();
 
@@ -1363,11 +1363,10 @@ static std::map build_seq_to_output_row(const llama_ubat
 
 static void copy_tensor_async_ints(
     const std::map & tensor_map,
-    llama_token * sampled,
-    size_t sampled_size,
+    const buffer_view & sampled,
     const std::map & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (sampled == nullptr) {
+    if (!sampled.has_data()) {
         return;
     }
 
@@ -1378,23 +1377,23 @@ static void copy_tensor_async_ints(
         }
 
         const uint32_t row = it->second;
-        GGML_ASSERT(row < sampled_size);
+        GGML_ASSERT(row < sampled.size);
 
         GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        ggml_backend_tensor_get_async(backend, tensor, sampled + row, 0, sizeof(sampled[row]));
+        ggml_backend_tensor_get_async(backend, tensor, sampled.data + row, 0, sizeof(sampled.data[row]));
     }
 }
 
 static void copy_tensor_async_floats(
     const std::map & tensor_map,
-    float * dst,
+    const buffer_view & dst,
     size_t stride,
     std::vector & counts,
     const std::map & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr) {
+    if (!dst.has_data()) {
         return;
     }
 
@@ -1410,7 +1409,7 @@ static void copy_tensor_async_floats(
         GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        float * row_ptr = dst + (size_t) row * stride;
+        float * row_ptr = dst.data + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
 
         // Update the actual number of logits/probabilities that were written for this row.
@@ -1420,12 +1419,12 @@ static void copy_tensor_async_floats(
 
 static void copy_tensor_async_candidates(
     const std::map & tensor_map,
-    llama_token * dst,
+    const buffer_view & dst,
     size_t stride,
     std::vector & counts,
     const std::map & seq_to_row,
     ggml_backend_sched_t sched) {
-    if (dst == nullptr) {
+    if (!dst.has_data()) {
         return;
     }
 
@@ -1441,7 +1440,7 @@ static void copy_tensor_async_candidates(
         GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        llama_token * row_ptr = dst + (size_t) row * stride;
+        llama_token * row_ptr = dst.data + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
 
         // Update the actual number of candidates that were written.
@@ -1671,22 +1670,22 @@ int llama_context::decode(const llama_batch & batch_inp) {
         }
 
         // extract logits
-        if (logits && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
+        if (logits.data && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
             ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
             GGML_ASSERT(backend_res != nullptr);
-            GGML_ASSERT(logits != nullptr);
+            GGML_ASSERT(logits.data != nullptr);
 
-            float * logits_out = logits + n_outputs_prev*n_vocab;
+            float * logits_out = logits.data + n_outputs_prev*n_vocab;
 
             if (n_outputs) {
                 GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+                GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits.size);
                 ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
             }
         }
 
         // extract embeddings
-        if (embd && t_embd && n_outputs > 0) {
+        if (embd.data && t_embd && n_outputs > 0) {
             ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
             GGML_ASSERT(backend_embd != nullptr);
 
@@ -1694,13 +1693,13 @@ int llama_context::decode(const llama_batch & batch_inp) {
                 case LLAMA_POOLING_TYPE_NONE:
                     {
                         // extract token embeddings
-                        GGML_ASSERT(embd != nullptr);
+                        GGML_ASSERT(embd.data != nullptr);
                         const uint32_t n_embd_out = hparams.n_embd_out();
-                        float * embd_out = embd + n_outputs_prev*n_embd_out;
+                        float * embd_out = embd.data + n_outputs_prev*n_embd_out;
 
                         if (n_outputs) {
                             GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
-                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd_size);
+                            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd.size);
                             ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd_out*sizeof(float));
                         }
                     } break;
@@ -1747,7 +1746,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
             const auto stride = n_vocab;
 
             // async copy the sampling data from the backend to the host
-            copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
+            copy_tensor_async_ints(res->t_sampled, sampling.sampled, seq_to_output_row, sched.get());
 
             copy_tensor_async_floats    (res->t_sampled_logits, sampling.logits,     stride, sampling.logits_count,     seq_to_output_row, sched.get());
             copy_tensor_async_floats    (res->t_sampled_probs,  sampling.probs,      stride, sampling.probs_count,      seq_to_output_row, sched.get());
@@ -1841,19 +1840,14 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
 
-    logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    embd_size   = has_embd ? n_embd_out*n_outputs_max : 0;
+    logits.size = has_logits ? n_vocab*n_outputs_max : 0;
+    embd.size   = has_embd ? n_embd_out*n_outputs_max : 0;
 
     // Allocate backend sampling output buffers if there are backend samplers configured.
     const bool has_sampling = !sampling.samplers.empty();
     if (has_sampling) {
-        sampling.logits_size     = n_vocab*n_outputs_max;
-        sampling.probs_size      = n_vocab*n_outputs_max;
-        sampling.sampled_size    =         n_outputs_max;
-        sampling.candidates_size = n_vocab*n_outputs_max;
-
-        backend_float_count = sampling.logits_size  + sampling.probs_size;
-        backend_token_count = sampling.sampled_size + sampling.candidates_size;
+        backend_float_count = 2 * n_vocab * n_outputs_max;      // logits + probs
+        backend_token_count = (1 + n_vocab) * n_outputs_max;    // sampled + candidates
     }
 
     if (output_ids.empty()) {
@@ -1863,7 +1857,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
     const size_t new_size  =
-        (logits_size + embd_size + backend_float_count) * sizeof(float) +
+        (logits.size + embd.size + backend_float_count) * sizeof(float) +
         (                          backend_token_count) * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
@@ -1878,8 +1872,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
             // TODO: not needed?
             buf_output = nullptr;
-            logits = nullptr;
-            embd = nullptr;
+            logits.data = nullptr;
+            embd.data = nullptr;
         }
 
         auto * buft = ggml_backend_cpu_buffer_type();
@@ -1898,35 +1892,32 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
 
-    logits = nullptr;
-    embd   = nullptr;
-
     size_t offset = 0;
     uint8_t * base = (uint8_t *) output_base;
 
-    logits = has_logits ? output_base : nullptr;
-    offset += logits_size * sizeof(float);
+    logits = has_logits ? buffer_view{output_base, logits.size} : buffer_view{nullptr, 0};
+    offset += logits.size * sizeof(float);
 
-    embd = has_embd ? (float *) (base + offset) : nullptr;
-    offset += embd_size * sizeof(float);
+    embd = has_embd ? buffer_view{(float *) (base + offset), embd.size} : buffer_view{nullptr, 0};
+    offset += embd.size * sizeof(float);
 
-    sampling.logits     = nullptr;
-    sampling.probs      = nullptr;
-    sampling.sampled    = nullptr;
-    sampling.candidates = nullptr;
+    sampling.logits     = {nullptr, 0};
+    sampling.probs      = {nullptr, 0};
+    sampling.sampled    = {nullptr, 0};
+    sampling.candidates = {nullptr, 0};
 
     if (has_sampling) {
-        sampling.logits = (float *) (base + offset);
-        offset += sampling.logits_size * sizeof(float);
+        sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.logits.size * sizeof(float);
 
-        sampling.probs = (float *) (base + offset);
-        offset += sampling.probs_size * sizeof(float);
+        sampling.probs = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.probs.size * sizeof(float);
 
-        sampling.sampled = (llama_token *) (base + offset);
-        offset += sampling.sampled_size * sizeof(llama_token);
+        sampling.sampled = {(llama_token *) (base + offset), (size_t)n_outputs_max};
+        offset += sampling.sampled.size * sizeof(llama_token);
 
-        sampling.candidates = (llama_token *) (base + offset);
-        offset += sampling.candidates_size * sizeof(llama_token);
+        sampling.candidates = {(llama_token *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+        offset += sampling.candidates.size * sizeof(llama_token);
 
         // The count vectors keep track of the actual number of logits/probs/candidates
         // copied from the backend for each output row.
@@ -1939,7 +1930,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         std::fill(sampling.probs_count.begin(),      sampling.probs_count.end(),      0);
         std::fill(sampling.candidates_count.begin(), sampling.candidates_count.end(), 0);
 
-        std::fill_n(sampling.sampled, sampling.sampled_size, LLAMA_TOKEN_NULL);
+        std::fill_n(sampling.sampled.data, sampling.sampled.size, LLAMA_TOKEN_NULL);
     }
 
     // set all ids as invalid (negative)
@@ -1958,38 +1949,38 @@ void llama_context::output_reorder() {
         const uint64_t i0 = output_swaps[s].i0;
         const uint64_t i1 = output_swaps[s].i1;
 
-        if (logits_size > 0) {
+        if (logits.size > 0) {
             for (uint64_t k = 0; k < n_vocab; k++) {
-                std::swap(logits[i0*n_vocab + k], logits[i1*n_vocab + k]);
+                std::swap(logits.data[i0*n_vocab + k], logits.data[i1*n_vocab + k]);
             }
         }
 
-        if (embd_size > 0) {
+        if (embd.size > 0) {
             for (uint64_t k = 0; k < n_embd; k++) {
-                std::swap(embd[i0*n_embd + k], embd[i1*n_embd + k]);
+                std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]);
             }
         }
 
-        if (sampling.logits && sampling.logits_size > 0) {
+        if (sampling.logits.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.logits[i0*n_vocab + k], sampling.logits[i1*n_vocab + k]);
+                std::swap(sampling.logits.data[i0*n_vocab + k], sampling.logits.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.probs && sampling.probs_size > 0) {
+        if (sampling.probs.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.probs[i0*n_vocab + k], sampling.probs[i1*n_vocab + k]);
+                std::swap(sampling.probs.data[i0*n_vocab + k], sampling.probs.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.candidates && sampling.candidates_size > 0) {
+        if (sampling.candidates.has_data()) {
             for (uint64_t k = 0; k < n_vocab; ++k) {
-                std::swap(sampling.candidates[i0*n_vocab + k], sampling.candidates[i1*n_vocab + k]);
+                std::swap(sampling.candidates.data[i0*n_vocab + k], sampling.candidates.data[i1*n_vocab + k]);
             }
         }
 
-        if (sampling.sampled && sampling.sampled_size > 0) {
-            std::swap(sampling.sampled[i0], sampling.sampled[i1]);
+        if (sampling.sampled.has_data()) {
+            std::swap(sampling.sampled.data[i0], sampling.sampled.data[i1]);
         }
 
         if (!sampling.logits_count.empty()) {
@@ -2533,12 +2524,12 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     {
         LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
 
-        const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.vocab.n_tokens());
+        const uint64_t logits_size = std::min((uint64_t) this->logits.size, (uint64_t) n_outputs * model.vocab.n_tokens());
 
         io.write(&logits_size, sizeof(logits_size));
 
         if (logits_size) {
-            io.write(logits, logits_size * sizeof(float));
+            io.write(logits.data, logits_size * sizeof(float));
         }
     }
 
@@ -2546,12 +2537,12 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
     {
         LLAMA_LOG_DEBUG("%s: - writing embeddings\n", __func__);
 
-        const uint64_t embd_size = std::min((uint64_t) this->embd_size, (uint64_t) n_outputs * model.hparams.n_embd);
+        const uint64_t embd_size = std::min((uint64_t) this->embd.size, (uint64_t) n_outputs * model.hparams.n_embd);
 
         io.write(&embd_size, sizeof(embd_size));
 
         if (embd_size) {
-            io.write(embd, embd_size * sizeof(float));
+            io.write(embd.data, embd_size * sizeof(float));
         }
     }
 
@@ -2619,12 +2610,12 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
         uint64_t logits_size;
         io.read_to(&logits_size, sizeof(logits_size));
 
-        if (this->logits_size < logits_size) {
+        if (this->logits.size < logits_size) {
             throw std::runtime_error("logits buffer too small");
         }
 
         if (logits_size) {
-            io.read_to(this->logits, logits_size * sizeof(float));
+            io.read_to(this->logits.data, logits_size * sizeof(float));
         }
     }
 
@@ -2635,12 +2626,12 @@ size_t llama_context::state_read_data(llama_io_read_i & io) {
         uint64_t embd_size;
         io.read_to(&embd_size, sizeof(embd_size));
 
-        if (this->embd_size < embd_size) {
+        if (this->embd.size < embd_size) {
             throw std::runtime_error("embeddings buffer too small");
         }
 
         if (embd_size) {
-            io.read_to(this->embd, embd_size * sizeof(float));
+            io.read_to(this->embd.data, embd_size * sizeof(float));
         }
     }
 
diff --git a/src/llama-context.h b/src/llama-context.h
index 8e71cdd1dc..d995117574 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -4,6 +4,7 @@
 #include "llama-cparams.h"
 #include "llama-graph.h"
 #include "llama-adapter.h"
+#include "llama-impl.h"
 
 #include "ggml-cpp.h"
 #include "ggml-opt.h"
@@ -269,29 +270,19 @@ private:
     std::unique_ptr memory;
 
     // decode output (2-dimensional array: [n_outputs][n_vocab])
-    size_t  logits_size = 0; // capacity (of floats) for logits
-    float * logits      = nullptr;
+    struct buffer_view  logits = {nullptr, 0};
 
     // embeddings output (2-dimensional array: [n_outputs][n_embd])
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
-    size_t  embd_size = 0; // capacity (of floats) for embeddings
-    float * embd      = nullptr;
+    struct buffer_view  embd = {nullptr, 0};
 
-    // TODO: simplify
     struct sampling_info {
         std::map samplers;
 
-        float       * logits      = nullptr;
-        size_t        logits_size = 0;
-
-        llama_token * sampled      = nullptr;
-        size_t        sampled_size = 0;
-
-        float       * probs        = nullptr;
-        size_t        probs_size   = 0;
-
-        llama_token * candidates   = nullptr;
-        size_t        candidates_size = 0;
+        struct buffer_view       logits     = {nullptr, 0};
+        struct buffer_view sampled    = {nullptr, 0};
+        struct buffer_view       probs      = {nullptr, 0};
+        struct buffer_view candidates = {nullptr, 0};
 
         std::vector logits_count;
         std::vector probs_count;
diff --git a/src/llama-impl.h b/src/llama-impl.h
index c3391e79f5..dfd9fee9f4 100644
--- a/src/llama-impl.h
+++ b/src/llama-impl.h
@@ -49,6 +49,16 @@ struct time_meas {
     int64_t & t_acc;
 };
 
+template 
+struct buffer_view {
+    T * data;
+    size_t size = 0;
+
+    bool has_data() const {
+        return data && size > 0;
+    }
+};
+
 void replace_all(std::string & s, const std::string & search, const std::string & replace);
 
 // TODO: rename to llama_format ?

From ceaa89b7867021c84f8806a6d8d2613213c90737 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 11 Feb 2026 07:51:12 +0200
Subject: [PATCH 178/206] metal : consolidate unary ops (#19490)

---
 ggml/src/ggml-metal/ggml-metal-device.cpp |  82 +--
 ggml/src/ggml-metal/ggml-metal-device.m   |  21 +-
 ggml/src/ggml-metal/ggml-metal-impl.h     |  75 ++-
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 237 +++------
 ggml/src/ggml-metal/ggml-metal-ops.h      |   4 -
 ggml/src/ggml-metal/ggml-metal.metal      | 614 +++++++---------------
 tests/test-backend-ops.cpp                |  15 +-
 7 files changed, 377 insertions(+), 671 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 4c4c3ce36c..949e344cc8 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -212,61 +212,69 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat(ggml_meta
 }
 
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal_library_t lib, const ggml_tensor * op) {
-    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-
     char base[256];
     char name[256];
 
-    const int64_t n = ggml_nelements(op);
+    int op_num = -1;
 
-    const char * op_str = "undefined";
     switch (op->op) {
-        case GGML_OP_SCALE:      op_str = "scale";      break;
-        case GGML_OP_FILL:       op_str = "fill";       break;
-        case GGML_OP_CLAMP:      op_str = "clamp";      break;
-        case GGML_OP_SQR:        op_str = "sqr";        break;
-        case GGML_OP_SQRT:       op_str = "sqrt";       break;
-        case GGML_OP_SIN:        op_str = "sin";        break;
-        case GGML_OP_COS:        op_str = "cos";        break;
-        case GGML_OP_LOG:        op_str = "log";        break;
-        case GGML_OP_LEAKY_RELU: op_str = "leaky_relu"; break;
+        case GGML_OP_SCALE:      op_num = OP_UNARY_NUM_SCALE;      break;
+        case GGML_OP_FILL:       op_num = OP_UNARY_NUM_FILL;       break;
+        case GGML_OP_CLAMP:      op_num = OP_UNARY_NUM_CLAMP;      break;
+        case GGML_OP_SQR:        op_num = OP_UNARY_NUM_SQR;        break;
+        case GGML_OP_SQRT:       op_num = OP_UNARY_NUM_SQRT;       break;
+        case GGML_OP_SIN:        op_num = OP_UNARY_NUM_SIN;        break;
+        case GGML_OP_COS:        op_num = OP_UNARY_NUM_COS;        break;
+        case GGML_OP_LOG:        op_num = OP_UNARY_NUM_LOG;        break;
+        case GGML_OP_LEAKY_RELU: op_num = OP_UNARY_NUM_LEAKY_RELU; break;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
-                case GGML_UNARY_OP_TANH:        op_str = "tanh";        break;
-                case GGML_UNARY_OP_RELU:        op_str = "relu";        break;
-                case GGML_UNARY_OP_SIGMOID:     op_str = "sigmoid";     break;
-                case GGML_UNARY_OP_GELU:        op_str = "gelu";        break;
-                case GGML_UNARY_OP_GELU_ERF:    op_str = "gelu_erf";    break;
-                case GGML_UNARY_OP_GELU_QUICK:  op_str = "gelu_quick";  break;
-                case GGML_UNARY_OP_SILU:        op_str = "silu";        break;
-                case GGML_UNARY_OP_ELU:         op_str = "elu";         break;
-                case GGML_UNARY_OP_NEG:         op_str = "neg";         break;
-                case GGML_UNARY_OP_ABS:         op_str = "abs";         break;
-                case GGML_UNARY_OP_SGN:         op_str = "sgn";         break;
-                case GGML_UNARY_OP_STEP:        op_str = "step";        break;
-                case GGML_UNARY_OP_HARDSWISH:   op_str = "hardswish";   break;
-                case GGML_UNARY_OP_HARDSIGMOID: op_str = "hardsigmoid"; break;
-                case GGML_UNARY_OP_EXP:         op_str = "exp";         break;
-                case GGML_UNARY_OP_SOFTPLUS:    op_str = "softplus";    break;
-                case GGML_UNARY_OP_EXPM1:       op_str = "expm1";       break;
+                case GGML_UNARY_OP_TANH:        op_num = OP_UNARY_NUM_TANH;        break;
+                case GGML_UNARY_OP_RELU:        op_num = OP_UNARY_NUM_RELU;        break;
+                case GGML_UNARY_OP_SIGMOID:     op_num = OP_UNARY_NUM_SIGMOID;     break;
+                case GGML_UNARY_OP_GELU:        op_num = OP_UNARY_NUM_GELU;        break;
+                case GGML_UNARY_OP_GELU_ERF:    op_num = OP_UNARY_NUM_GELU_ERF;    break;
+                case GGML_UNARY_OP_GELU_QUICK:  op_num = OP_UNARY_NUM_GELU_QUICK;  break;
+                case GGML_UNARY_OP_SILU:        op_num = OP_UNARY_NUM_SILU;        break;
+                case GGML_UNARY_OP_ELU:         op_num = OP_UNARY_NUM_ELU;         break;
+                case GGML_UNARY_OP_NEG:         op_num = OP_UNARY_NUM_NEG;         break;
+                case GGML_UNARY_OP_ABS:         op_num = OP_UNARY_NUM_ABS;         break;
+                case GGML_UNARY_OP_SGN:         op_num = OP_UNARY_NUM_SGN;         break;
+                case GGML_UNARY_OP_STEP:        op_num = OP_UNARY_NUM_STEP;        break;
+                case GGML_UNARY_OP_HARDSWISH:   op_num = OP_UNARY_NUM_HARDSWISH;   break;
+                case GGML_UNARY_OP_HARDSIGMOID: op_num = OP_UNARY_NUM_HARDSIGMOID; break;
+                case GGML_UNARY_OP_EXP:         op_num = OP_UNARY_NUM_EXP;         break;
+                case GGML_UNARY_OP_SOFTPLUS:    op_num = OP_UNARY_NUM_SOFTPLUS;    break;
+                case GGML_UNARY_OP_EXPM1:       op_num = OP_UNARY_NUM_EXPM1;       break;
                 default: GGML_ABORT("fatal error");
             } break;
         default: GGML_ABORT("fatal error");
     };
 
-    const char * suffix = "";
-    if (n % 4 == 0) {
-        suffix = "_4";
-    }
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(base, 256, "kernel_%s_%s%s", op_str, ggml_type_name(op->src[0]->type), suffix);
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+    const bool is_cnt = ggml_is_contiguous(op->src[0]) && ggml_nelements(op) < 32768;
+
+    snprintf(base, 256, "kernel_unary_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d_cnt=%d", base, op_num, is_cnt);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_UNARY + 0);
+        ggml_metal_cv_set_bool (cv, is_cnt, FC_UNARY + 1);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
+    res.c4  = is_c4;
+    res.cnt = is_cnt;
+
     return res;
 }
 
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 891d70c85a..50a2a3e7f7 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1011,6 +1011,15 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
     }
 
     switch (op->op) {
+        case GGML_OP_SCALE:
+        case GGML_OP_FILL:
+        case GGML_OP_CLAMP:
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+        case GGML_OP_SIN:
+        case GGML_OP_COS:
+        case GGML_OP_LOG:
+            return ggml_is_contiguous_rows(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_TANH:
@@ -1030,7 +1039,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_EXPM1:
-                    return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                    return ggml_is_contiguous_rows(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
                 default:
                     return false;
             }
@@ -1061,8 +1070,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ACC:
         case GGML_OP_REPEAT:
-        case GGML_OP_SCALE:
-        case GGML_OP_FILL:
         case GGML_OP_CONV_TRANSPOSE_1D:
             return true;
         case GGML_OP_CONV_TRANSPOSE_2D:
@@ -1070,14 +1077,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32) &&
                 op->src[1]->type == GGML_TYPE_F32 &&
                 op->type == GGML_TYPE_F32;
-        case GGML_OP_CLAMP:
-            return op->src[0]->type == GGML_TYPE_F32;
-        case GGML_OP_SQR:
-        case GGML_OP_SQRT:
-        case GGML_OP_SIN:
-        case GGML_OP_COS:
-        case GGML_OP_LOG:
-            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SUM:
             return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
         case GGML_OP_TRI:
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 77bb403c15..44141f8e3d 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -80,7 +80,8 @@
 #define FC_SSM_CONV                    900
 #define FC_SOLVE_TRI                   1000
 #define FC_COUNT_EQUAL                 1100
-#define FC_BIN                         1200
+#define FC_UNARY                       1200
+#define FC_BIN                         1300
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
@@ -89,6 +90,35 @@
 #define OP_FLASH_ATTN_EXT_VEC_NQPSG 1
 #define OP_FLASH_ATTN_EXT_VEC_NCPSG 32
 
+#define OP_UNARY_NUM_SCALE      10
+#define OP_UNARY_NUM_FILL       11
+#define OP_UNARY_NUM_CLAMP      12
+#define OP_UNARY_NUM_SQR        13
+#define OP_UNARY_NUM_SQRT       14
+#define OP_UNARY_NUM_SIN        15
+#define OP_UNARY_NUM_COS        16
+#define OP_UNARY_NUM_LOG        17
+#define OP_UNARY_NUM_LEAKY_RELU 18
+
+#define OP_UNARY_NUM_TANH        100
+#define OP_UNARY_NUM_RELU        101
+#define OP_UNARY_NUM_SIGMOID     102
+#define OP_UNARY_NUM_GELU        103
+#define OP_UNARY_NUM_GELU_ERF    104
+#define OP_UNARY_NUM_GELU_QUICK  105
+#define OP_UNARY_NUM_SILU        106
+#define OP_UNARY_NUM_ELU         107
+#define OP_UNARY_NUM_NEG         108
+#define OP_UNARY_NUM_ABS         109
+#define OP_UNARY_NUM_SGN         110
+#define OP_UNARY_NUM_STEP        111
+#define OP_UNARY_NUM_HARDSWISH   112
+#define OP_UNARY_NUM_HARDSIGMOID 113
+#define OP_UNARY_NUM_EXP         114
+#define OP_UNARY_NUM_SOFTPLUS    115
+#define OP_UNARY_NUM_EXPM1       116
+
+
 // kernel argument structs
 //
 // - element counters (e.g. ne00) typically use int32_t to reduce register usage
@@ -124,6 +154,31 @@ typedef struct {
     int32_t  dim;
 } ggml_metal_kargs_concat;
 
+typedef struct {
+    int32_t  ne00;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
+    float    slope;
+    float    scale;
+    float    bias;
+    float    val;
+    float    min;
+    float    max;
+} ggml_metal_kargs_unary;
+
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;
@@ -181,20 +236,6 @@ typedef struct {
     uint64_t nb3;
 } ggml_metal_kargs_repeat;
 
-typedef struct {
-    float scale;
-    float bias;
-} ggml_metal_kargs_scale;
-
-typedef struct {
-    float val;
-} ggml_metal_kargs_fill;
-
-typedef struct {
-    float min;
-    float max;
-} ggml_metal_kargs_clamp;
-
 typedef struct {
     int64_t  nk0;
     int64_t  ne00;
@@ -881,10 +922,6 @@ typedef struct {
     int      max_period;
 } ggml_metal_kargs_timestep_embedding;
 
-typedef struct {
-    float    slope;
-} ggml_metal_kargs_leaky_relu;
-
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index dbf25433c2..b159a8e7fd 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -287,17 +287,9 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
                 n_fuse = ggml_metal_op_acc(ctx, idx);
             } break;
         case GGML_OP_SCALE:
-            {
-                n_fuse = ggml_metal_op_scale(ctx, idx);
-            } break;
         case GGML_OP_FILL:
-            {
-                n_fuse = ggml_metal_op_fill(ctx, idx);
-            } break;
         case GGML_OP_CLAMP:
-            {
-                n_fuse = ggml_metal_op_clamp(ctx, idx);
-            } break;
+        case GGML_OP_LEAKY_RELU:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
         case GGML_OP_SIN:
@@ -426,10 +418,6 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_top_k(ctx, idx);
             } break;
-        case GGML_OP_LEAKY_RELU:
-            {
-                n_fuse = ggml_metal_op_leaky_relu(ctx, idx);
-            } break;
         case GGML_OP_TRI:
             {
                 n_fuse = ggml_metal_op_tri(ctx, idx);
@@ -722,119 +710,6 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
-int ggml_metal_op_scale(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float scale;
-    float bias;
-    memcpy(&scale, ((const int32_t *) op->op_params) + 0, sizeof(float));
-    memcpy(&bias,  ((const int32_t *) op->op_params) + 1, sizeof(float));
-
-    ggml_metal_kargs_scale args = {
-        /*.scale =*/ scale,
-        /*.bias  =*/ bias,
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
-int ggml_metal_op_fill(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    const float val = ggml_get_op_params_f32(op, 0);
-
-    ggml_metal_kargs_fill args = {
-        /*.val =*/ val
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
-int ggml_metal_op_clamp(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float min;
-    float max;
-    memcpy(&min, ((const int32_t *) op->op_params) + 0, sizeof(float));
-    memcpy(&max, ((const int32_t *) op->op_params) + 1, sizeof(float));
-
-    ggml_metal_kargs_clamp args = {
-        /*.min =*/ min,
-        /*.max =*/ max,
-    };
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
 int ggml_metal_op_unary(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
@@ -846,19 +721,79 @@ int ggml_metal_op_unary(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
-    int64_t n = ggml_nelements(op);
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
 
-    if (n % 4 == 0) {
-        n /= 4;
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
+    ggml_metal_kargs_unary args = {
+        /*.ne00  =*/ ne00,
+        /*.ne01  =*/ ne01,
+        /*.ne02  =*/ ne02,
+        /*.ne03  =*/ ne03,
+        /*.nb00  =*/ nb00,
+        /*.nb01  =*/ nb01,
+        /*.nb02  =*/ nb02,
+        /*.nb03  =*/ nb03,
+        /*.ne0   =*/ ne0,
+        /*.ne1   =*/ ne1,
+        /*.ne2   =*/ ne2,
+        /*.ne3   =*/ ne3,
+        /*.nb0   =*/ nb0,
+        /*.nb1   =*/ nb1,
+        /*.nb2   =*/ nb2,
+        /*.nb3   =*/ nb3,
+        /*.slope =*/ 0.0,
+        /*.scale =*/ 0.0,
+        /*.bias  =*/ 0.0,
+        /*.val   =*/ 0.0,
+        /*.min   =*/ 0.0,
+        /*.max   =*/ 0.0,
+    };
+
+    if (op->op == GGML_OP_LEAKY_RELU) {
+        args.slope = ggml_get_op_params_f32(op, 0);
+    }
+
+    if (op->op == GGML_OP_SCALE) {
+        args.scale = ggml_get_op_params_f32(op, 0);
+        args.bias  = ggml_get_op_params_f32(op, 1);
+    }
+
+    if (op->op == GGML_OP_FILL) {
+        args.val = ggml_get_op_params_f32(op, 0);
+    }
+
+    if (op->op == GGML_OP_CLAMP) {
+        args.min = ggml_get_op_params_f32(op, 0);
+        args.max = ggml_get_op_params_f32(op, 1);
     }
 
     auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
 
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         1);
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
+
+    if (pipeline.cnt) {
+        const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op);
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
+    } else {
+        const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
+
+        const int nth = MIN(args.ne00, nth_max);
+
+        const int nk0 = (args.ne00 + nth - 1)/nth;
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, nk0*ne01, ne02, ne03, nth, 1, 1);
+    }
 
     return 1;
 }
@@ -4084,42 +4019,6 @@ int ggml_metal_op_top_k(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
-int ggml_metal_op_leaky_relu(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    float slope;
-    memcpy(&slope, op->op_params, sizeof(float));
-
-    ggml_metal_kargs_leaky_relu args = {
-        /*.slope =*/ slope
-    };
-
-    auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
-
-    int64_t n = ggml_nelements(op);
-
-    if (n % 4 == 0) {
-        n /= 4;
-    }
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
-
-    return 1;
-}
-
 int ggml_metal_op_tri(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h
index 3c64e4f600..29456d70d5 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.h
+++ b/ggml/src/ggml-metal/ggml-metal-ops.h
@@ -46,9 +46,6 @@ size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op);
 int ggml_metal_op_concat            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_repeat            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_acc               (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_scale             (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_fill              (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_clamp             (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_unary             (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_glu               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_sum               (ggml_metal_op_t ctx, int idx);
@@ -86,7 +83,6 @@ int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_argmax            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_argsort           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_top_k             (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_leaky_relu        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_tri               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_opt_step_adamw    (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_opt_step_sgd      (ggml_metal_op_t ctx, int idx);
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 35cc3bbdfd..7d841341a1 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -895,6 +895,192 @@ enum ggml_sort_order {
     GGML_SORT_ORDER_DESC,
 };
 
+constant float GELU_COEF_A     = 0.044715f;
+constant float GELU_QUICK_COEF = -1.702f;
+constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+constant float SQRT_2_INV      = 0.70710678118654752440084436210484f;
+
+// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
+// ref: https://www.johndcook.com/blog/python_erf/
+constant float p_erf  = 0.3275911f;
+constant float a1_erf = 0.254829592f;
+constant float a2_erf = -0.284496736f;
+constant float a3_erf = 1.421413741f;
+constant float a4_erf = -1.453152027f;
+constant float a5_erf = 1.061405429f;
+
+template
+T erf_approx(T x) {
+    T sign_x = sign(x);
+    x = fabs(x);
+    T t = 1.0f / (1.0f + p_erf * x);
+    T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
+    return sign_x * y;
+}
+
+constant short FC_unary_op [[function_constant(FC_UNARY + 0)]];
+constant bool  FC_unary_cnt[[function_constant(FC_UNARY + 1)]];
+
+template 
+kernel void kernel_unary_impl(
+        constant ggml_metal_kargs_unary & args,
+        device const char * src0,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+#define FC_OP  FC_unary_op
+#define FC_CNT FC_unary_cnt
+
+    device const T0 * src0_ptr;
+    device       T  * dst_ptr;
+
+    int i0;
+
+    if (FC_CNT) {
+        i0 = tgpig.x;
+
+        src0_ptr = (device const T0 *) (src0);
+        dst_ptr  = (device       T  *) (dst);
+    } else {
+        const int i03 = tgpig.z;
+        const int i02 = tgpig.y;
+        const int k0  = tgpig.x/args.ne01;
+        const int i01 = tgpig.x - k0*args.ne01;
+
+        i0 = k0*ntg.x + tpitg.x;
+
+        src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
+        dst_ptr  = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1 );
+    }
+
+    {
+        //threadgroup_barrier(mem_flags::mem_none);
+
+        if (!FC_CNT) {
+            if (i0 >= args.ne0) {
+                return;
+            }
+        }
+
+        device const T0 & x = src0_ptr[i0];
+
+        if (FC_OP == OP_UNARY_NUM_SCALE) {
+            dst_ptr[i0] = args.scale * x + args.bias;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_FILL) {
+            dst_ptr[i0] = args.val;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_CLAMP) {
+            dst_ptr[i0] = clamp(x, args.min, args.max);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SQR) {
+            dst_ptr[i0] = x * x;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SQRT) {
+            dst_ptr[i0] = sqrt(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SIN) {
+            dst_ptr[i0] = sin(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_COS) {
+            dst_ptr[i0] = cos(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_LOG) {
+            dst_ptr[i0] = log(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_LEAKY_RELU) {
+            dst_ptr[i0] = T(x > 0.0f)*x + T(x <= 0.0f)*(x * args.slope);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_TANH) {
+            dst_ptr[i0] = precise::tanh(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_RELU) {
+            dst_ptr[i0] = fmax(0.0f, x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SIGMOID) {
+            dst_ptr[i0] = 1.0f / (1.0f + exp(-x));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU) {
+            dst_ptr[i0] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU_ERF) {
+            dst_ptr[i0] = 0.5f*x*(1.0f + erf_approx(SQRT_2_INV*x));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_GELU_QUICK) {
+            dst_ptr[i0] = x * (1.0f/(1.0f + exp(GELU_QUICK_COEF*x)));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SILU) {
+            dst_ptr[i0] = x / (1.0f + exp(-x));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_ELU) {
+            dst_ptr[i0] = T(x > 0.0f)*x + T(x <= 0.0f)*(exp(x) - 1.0f);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_NEG) {
+            dst_ptr[i0] = -x;
+        }
+
+        if (FC_OP == OP_UNARY_NUM_ABS) {
+            dst_ptr[i0] = fabs(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SGN) {
+            dst_ptr[i0] = T(x > 0.0f) - T(x < 0.0f);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_STEP) {
+            dst_ptr[i0] = T(x > 0.0f);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_HARDSWISH) {
+            dst_ptr[i0] = x * fmax(0.0f, fmin(1.0f, x/6.0f + 0.5f));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_HARDSIGMOID) {
+            dst_ptr[i0] = fmax(0.0f, fmin(1.0f, x/6.0f + 0.5f));
+        }
+
+        if (FC_OP == OP_UNARY_NUM_EXP) {
+            dst_ptr[i0] = exp(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_SOFTPLUS) {
+            dst_ptr[i0] = select(log(1.0f + exp(x)), x, x > 20.0f);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_EXPM1) {
+            // TODO: precise implementation
+            dst_ptr[i0] = exp(x) - 1.0f;
+        }
+    }
+
+#undef FC_OP
+#undef FC_CNT
+}
+
+typedef decltype(kernel_unary_impl) kernel_unary_t;
+
+template [[host_name("kernel_unary_f32_f32")]]   kernel kernel_unary_t kernel_unary_impl;
+template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl;
+
+
 // OP: 0 - add, 1 - sub, 2 - mul, 3 - div
 constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
 constant short FC_bin_f  [[function_constant(FC_BIN + 1)]];
@@ -1114,414 +1300,6 @@ template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat
 template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat;
 template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat;
 
-kernel void kernel_scale_f32(
-        constant ggml_metal_kargs_scale & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * args.scale + args.bias;
-}
-
-kernel void kernel_scale_f32_4(
-        constant ggml_metal_kargs_scale & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * args.scale + args.bias;
-}
-
-kernel void kernel_fill_f32(
-        constant ggml_metal_kargs_fill & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = args.val;
-}
-
-kernel void kernel_fill_f32_4(
-        constant ggml_metal_kargs_fill & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = args.val;
-}
-
-kernel void kernel_clamp_f32(
-        constant ggml_metal_kargs_clamp & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = clamp(src0[tpig], args.min, args.max);
-}
-
-kernel void kernel_clamp_f32_4(
-        constant ggml_metal_kargs_clamp & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = clamp(src0[tpig], args.min, args.max);
-}
-
-kernel void kernel_relu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = max(0.0f, src0[tpig]);
-}
-
-kernel void kernel_relu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = max(0.0f, src0[tpig]);
-}
-
-kernel void kernel_sigmoid_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
-}
-
-kernel void kernel_sigmoid_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
-}
-
-kernel void kernel_tanh_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = precise::tanh(src0[tpig]);
-}
-
-kernel void kernel_tanh_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = precise::tanh(src0[tpig]);
-}
-
-constant float GELU_COEF_A     = 0.044715f;
-constant float GELU_QUICK_COEF = -1.702f;
-constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
-constant float SQRT_2_INV      = 0.70710678118654752440084436210484f;
-
-kernel void kernel_gelu_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
-}
-
-kernel void kernel_gelu_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    // BEWARE !!!
-    // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
-    // This was observed with Falcon 7B and 40B models
-    //
-    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
-}
-
-kernel void kernel_gelu_quick_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
-}
-
-kernel void kernel_gelu_quick_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
-}
-
-// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
-// ref: https://www.johndcook.com/blog/python_erf/
-constant float p_erf  = 0.3275911f;
-constant float a1_erf = 0.254829592f;
-constant float a2_erf = -0.284496736f;
-constant float a3_erf = 1.421413741f;
-constant float a4_erf = -1.453152027f;
-constant float a5_erf = 1.061405429f;
-
-template
-T erf_approx(T x) {
-    T sign_x = sign(x);
-    x = fabs(x);
-    T t = 1.0f / (1.0f + p_erf * x);
-    T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
-    return sign_x * y;
-}
-
-kernel void kernel_gelu_erf_f32(
-    device const float * src0,
-    device       float * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f+erf_approx(x*SQRT_2_INV));
-}
-
-kernel void kernel_gelu_erf_f32_4(
-    device const float4 * src0,
-    device       float4 * dst,
-    uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-
-    dst[tpig] = 0.5f*x*(1.0f+erf_approx(x*SQRT_2_INV));
-}
-
-kernel void kernel_silu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-    dst[tpig] = x / (1.0f + exp(-x));
-}
-
-kernel void kernel_silu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-    dst[tpig] = x / (1.0f + exp(-x));
-}
-
-kernel void kernel_elu_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = (x > 0.0f) ? x : (exp(x) - 1.0f);
-}
-
-kernel void kernel_elu_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig][0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
-    dst[tpig][1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
-    dst[tpig][2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
-    dst[tpig][3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
-}
-
-kernel void kernel_sqr_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * src0[tpig];
-}
-
-kernel void kernel_sqr_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * src0[tpig];
-}
-
-kernel void kernel_sqrt_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sqrt(src0[tpig]);
-}
-
-kernel void kernel_sqrt_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sqrt(src0[tpig]);
-}
-
-kernel void kernel_sin_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sin(src0[tpig]);
-}
-
-kernel void kernel_sin_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sin(src0[tpig]);
-}
-
-kernel void kernel_cos_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = cos(src0[tpig]);
-}
-
-kernel void kernel_cos_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = cos(src0[tpig]);
-}
-
-kernel void kernel_log_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = log(src0[tpig]);
-}
-
-kernel void kernel_log_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = log(src0[tpig]);
-}
-
-kernel void kernel_neg_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = -src0[tpig];
-}
-
-kernel void kernel_neg_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = -src0[tpig];
-}
-
-kernel void kernel_abs_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = fabs(src0[tpig]);
-}
-
-kernel void kernel_abs_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = fabs(src0[tpig]);
-}
-
-kernel void kernel_sgn_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sign(src0[tpig]);
-}
-
-kernel void kernel_sgn_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = sign(src0[tpig]);
-}
-
-kernel void kernel_step_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = step(0.0f, src0[tpig]);
-}
-
-kernel void kernel_step_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = step(0.0f, src0[tpig]);
-}
-
-kernel void kernel_hardswish_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = x * fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardswish_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = x * fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardsigmoid_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_hardsigmoid_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
-}
-
-kernel void kernel_exp_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]);
-}
-
-kernel void kernel_exp_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]);
-}
-
-kernel void kernel_softplus_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float & x = src0[tpig];
-    dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
-}
-
-kernel void kernel_softplus_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    device const float4 & x = src0[tpig];
-    dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
-}
-
-kernel void kernel_expm1_f32(
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]) - 1.0f;
-}
-
-kernel void kernel_expm1_f32_4(
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = exp(src0[tpig]) - 1.0f;
-}
-
 kernel void kernel_reglu_f32(
         constant ggml_metal_kargs_glu & args,
         device const char * src0,
@@ -5072,24 +4850,6 @@ kernel void kernel_argsort_merge_f32_i32(
 template [[host_name("kernel_argsort_merge_f32_i32_asc")]]  kernel argsort_merge_t kernel_argsort_merge_f32_i32;
 template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32;
 
-kernel void kernel_leaky_relu_f32(
-        constant     ggml_metal_kargs_leaky_relu & args,
-        device const float * src0,
-        device       float * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float x = src0[tpig];
-    dst[tpig] = x > 0.0f ? x : x * args.slope;
-}
-
-kernel void kernel_leaky_relu_f32_4(
-        constant     ggml_metal_kargs_leaky_relu & args,
-        device const float4 * src0,
-        device       float4 * dst,
-        uint tpig[[thread_position_in_grid]]) {
-    const float4 x = src0[tpig];
-    dst[tpig] = float4(x > 0.0f)*x + float4(x <= 0.0f)*(x * args.slope);
-}
-
 constant bool FC_flash_attn_ext_pad_has_mask [[function_constant(FC_FLASH_ATTN_EXT_PAD + 0)]];
 
 constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 25)]];
@@ -9939,7 +9699,7 @@ kernel void kernel_opt_step_sgd_f32(
 
 template
 kernel void kernel_memset(
-        constant ggml_metal_kargs_fill & args,
+        constant ggml_metal_kargs_memset & args,
         device T * dst,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = args.val;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 3bdb3d7ba9..1f40d2fc38 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7882,20 +7882,27 @@ static std::vector> make_test_cases_eval() {
         test_cases.emplace_back(new test_round     (type));
         test_cases.emplace_back(new test_trunc     (type));
         test_cases.emplace_back(new test_sqr       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sqr       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_sqrt      (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sqrt      (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_log       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_log       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_sin       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_sin       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_cos       (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_cos       (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_clamp     (type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_clamp     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_leaky_relu(type, {7, 1, 5, 3}));
+        test_cases.emplace_back(new test_leaky_relu(type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_floor     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_floor     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_floor     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_ceil      (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_ceil      (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_ceil      (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_round     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_round     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_round     (type, {1024, 1024, 1, 1}));
         test_cases.emplace_back(new test_trunc     (type, {7, 1, 5, 3}));
-        test_cases.emplace_back(new test_trunc     (type, { 1024, 1024, 1, 1 }));
+        test_cases.emplace_back(new test_trunc     (type, {1024, 1024, 1, 1}));
     }
 
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));

From 89181c0b6d6f57fdf82c33bf93ede10433088573 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 11 Feb 2026 07:52:00 +0200
Subject: [PATCH 179/206] ggml : extend bin bcast for permuted src1 (#19484)

* tests : extend bin bcast for permuted src1

* cont : extend bin support

* cont : s0 is always 1

* tests : simplify
---
 ggml/src/ggml-cpu/binary-ops.cpp |  8 ++---
 ggml/src/ggml-cuda/binbcast.cu   | 62 ++++++++++++++++----------------
 tests/test-backend-ops.cpp       | 51 +++++++++++++++-----------
 3 files changed, 65 insertions(+), 56 deletions(-)

diff --git a/ggml/src/ggml-cpu/binary-ops.cpp b/ggml/src/ggml-cpu/binary-ops.cpp
index 14f5b43ae0..75e3829001 100644
--- a/ggml/src/ggml-cpu/binary-ops.cpp
+++ b/ggml/src/ggml-cpu/binary-ops.cpp
@@ -59,11 +59,7 @@ static void apply_binary_op(const ggml_compute_params * params, ggml_tensor * ds
     GGML_ASSERT(nb00 == sizeof(src0_t));
 
     const auto [ir0, ir1] = get_thread_range(params, src0);
-    const bool is_src1_contiguous = (nb10 == sizeof(src1_t));
-
-    if (!is_src1_contiguous) { // broadcast not implemented yet for non-contiguous
-        GGML_ASSERT(ggml_are_same_shape(src0, src1));
-    }
+    const bool is_src1_contiguous_rows = ggml_is_contiguous_rows(src1);
 
 #ifdef GGML_USE_ACCELERATE
     vDSP_fn_t vDSP_op = nullptr;
@@ -94,7 +90,7 @@ static void apply_binary_op(const ggml_compute_params * params, ggml_tensor * ds
         const src0_t * src0_ptr = (const src0_t *) ((const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
         const src1_t * src1_ptr = (const src1_t *) ((const char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
 
-        if (is_src1_contiguous) {
+        if (is_src1_contiguous_rows) {
             // src1 is broadcastable across src0 and dst in i1, i2, i3
             const int64_t nr0 = ne00 / ne10;
 
diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index 0e6d777b1e..7339fe0c07 100644
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -39,13 +39,16 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
                                    const uint3            ne11,
                                    const uint3            ne12,
                                    const uint3            ne13,
-                                   /*int s0, */ const int s1,
+                                 /*const int              s0,*/
+                                   const int              s1,
                                    const int              s2,
                                    const int              s3,
-                                   /*int s00,*/ const int s01,
+                                   const int              s00,
+                                   const int              s01,
                                    const int              s02,
                                    const int              s03,
-                                   /*int s10,*/ const int s11,
+                                   const int              s10,
+                                   const int              s11,
                                    const int              s12,
                                    const int              s13,
                                    src1_ptrs... src1s) {
@@ -72,11 +75,11 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
         const uint32_t i10 = fastmodulo(i0, ne10);
 
-        float result = src0_row ? (float) src0_row[i0] : 0.0f;
+        float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
         if constexpr (sizeof...(src1_ptrs) > 0) {
-            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
         } else {
-            result = bin_op(result, (float)src1[i_src1 + i10]);
+            result = bin_op(result, (float)src1[i_src1 + i10*s10]);
         }
 
         dst_row[i0] = (dst_t) result;
@@ -101,13 +104,16 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
                                            const uint3            ne11,
                                            const uint3            ne12,
                                            const uint3            ne13,
-                                           /*int s0, */ const int s1,
+                                         /*const int              s0,*/
+                                           const int              s1,
                                            const int              s2,
                                            const int              s3,
-                                           /*int s00,*/ const int s01,
+                                           const int              s00,
+                                           const int              s01,
                                            const int              s02,
                                            const int              s03,
-                                           /*int s10,*/ const int s11,
+                                           const int              s10,
+                                           const int              s11,
                                            const int              s12,
                                            const int              s13,
                                            src1_ptrs... src1s) {
@@ -135,11 +141,11 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
 
     const int i10 = fastmodulo(i0, ne10);
 
-    float result = src0_row ? (float) src0_row[i0] : 0.0f;
+    float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
     if constexpr (sizeof...(src1_ptrs) > 0) {
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
     } else {
-        result = bin_op(result, (float)src1[i_src1 + i10]);
+        result = bin_op(result, (float)src1[i_src1 + i10*s10]);
     }
 
     dst_row[i0] = (dst_t) result;
@@ -179,7 +185,7 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         cnb[3] *= cne[3];
     };
 
-    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) {
         for (int i = 0; i < 4; i++) {
             if (nr[i] != 1) {
                 break;
@@ -221,7 +227,7 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         size_t nb12 = cnb1[2];
         size_t nb13 = cnb1[3];
 
-        size_t s0 = nb0 / sizeof(dst_t);
+      //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);
@@ -251,10 +257,6 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         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);
@@ -284,31 +286,31 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
                 k_bin_bcast_unravel<<>>(
                     src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv, ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11,
                     ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
+                  /*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
                     <<>>(src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv,
                                                            ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11, ne12, ne13,
-                                                           /* s0, */ s1, s2, s3,
-                                                           /* s00,*/ s01, s02, s03,
-                                                           /* s10,*/ s11, s12, s13);
+                                                         /*s0,*/ s1,  s2,  s3,
+                                                           s00, s01, s02, s03,
+                                                           s10, s11, s12, s13);
             }
         } else {
             const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
             if constexpr (sizeof...(I) > 0) {
                 k_bin_bcast<<>>(
                     src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
+                  /*s0,*/ s1, s2,  s3,
+                    s00 ,s01, s02, s03,
+                    s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             } else {
                 k_bin_bcast<<>>(
                     src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13);
+                  /*s0,*/ s1,  s2,  s3,
+                    s00, s01, s02, s03,
+                    s10, s11, s12, s13);
             }
         }
     }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 1f40d2fc38..5d5e44a0c7 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2964,11 +2964,12 @@ struct test_bin_bcast : public test_case {
     const std::array ne;
     const std::array nr;
     int nf; // number of fused ops, nf == 1 -> single op (no fusion)
+    bool perm1; // permute src1?
 
     bool run_whole_graph() override { return nf > 1; }
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, nr, nf);
+        return VARS_TO_STR5(type, ne, nr, nf, perm1);
     }
 
     size_t op_size(ggml_tensor * t) override {
@@ -2978,8 +2979,9 @@ struct test_bin_bcast : public test_case {
     test_bin_bcast(op_t op, ggml_type type = GGML_TYPE_F32,
             std::array ne = {10, 10, 1, 1},
             std::array nr = {1, 2, 1, 1},
-            int nf = 1)
-        : op(op), type(type), ne(ne), nr(nr), nf(nf) {}
+            int nf = 1,
+            bool perm1 = false)
+        : op(op), type(type), ne(ne), nr(nr), nf(nf), perm1(perm1) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         GGML_ASSERT(nf <= 16);
@@ -2989,12 +2991,19 @@ struct test_bin_bcast : public test_case {
 
         ggml_tensor * b[16];
         for (int i = 0; i < nf; ++i) {
-            b[i] = ggml_new_tensor(ctx, type, 4, ne.data());
+            if (perm1) {
+                const int p[4] = { 1, 2, 0, 3 }; // hardcoded for now
+
+                b[i] = ggml_new_tensor_4d(ctx, type, ne[p[0]], ne[p[1]], ne[p[2]], ne[p[3]]);
+                b[i] = ggml_permute(ctx, b[i], p[0], p[1], p[2], p[3]);
+            } else {
+                b[i] = ggml_new_tensor(ctx, type, 4, ne.data());
+            }
             ggml_set_name(b[i], (std::string("b") + std::to_string(i)).c_str());
         }
 
         // The backward pass supports broadcasting only for GGML_ADD:
-        const bool grad_supported = op == ggml_add && ggml_are_same_shape(a, b[0]) && nf == 1;
+        const bool grad_supported = op == ggml_add && ggml_are_same_shape(a, b[0]) && nf == 1 && !perm1;
         if (grad_supported) {
             ggml_set_param(a);
             ggml_set_param(b[0]);
@@ -7477,25 +7486,27 @@ static std::vector> make_test_cases_eval() {
         }
     }
 
-    auto add_test_bin_bcast = [&](ggml_type type, std::array ne, std::array nr) {
+    auto add_test_bin_bcast = [&](ggml_type type, std::array ne, std::array nr, bool perm1 = false) {
         for (auto op : {ggml_add, ggml_sub, ggml_mul, ggml_div}) {
-            test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
+            test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr, 1, perm1));
         }
     };
     for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
-        add_test_bin_bcast(type, {1, 1, 8, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {1, 1, 1, 1}, {32, 1, 1, 1});
-        add_test_bin_bcast(type, {1, 1, 320, 320}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 1, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 1}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 1, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 1, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 1});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 2, 2});
-        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 2, 2, 2});
+        for (bool perm1 : {false, true}) {
+            add_test_bin_bcast(type, {1,  1,   8,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {1,  1,   1,   1}, {32, 1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {1,  1, 320, 320}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   1,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   1}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {2,  1, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  2, 1, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 2, 1}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 1, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  1, 2, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {1,  2, 2, 2}, perm1);
+            add_test_bin_bcast(type, {10, 5,   4,   3}, {2,  2, 2, 2}, perm1);
+        }
 
         // test case for k_bin_bcast_unravel in CUDA backend
         add_test_bin_bcast(type, {1, 1, 65536, 1}, {256, 1, 1, 1});

From 6d957078270f58d4ea14e8c205f5ef4e49be33f3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 11 Feb 2026 07:52:20 +0200
Subject: [PATCH 180/206] model : fix wavtokenizer embedding notions (#19479)

---
 src/llama-hparams.h |  1 -
 src/llama-model.cpp | 11 ++++++-----
 tools/tts/tts.cpp   |  2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 6c695bdbf6..706eda8441 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -42,7 +42,6 @@ struct llama_hparams {
 
     uint32_t n_ctx_train; // context size the model was trained on
     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;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 7a06e96c87..5816e9a954 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -523,7 +523,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used,    false);
 
     if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
-        ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
+        ml.get_key(LLM_KV_FEATURES_LENGTH,  hparams.n_embd);
+        ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd_out_impl);
 
         ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
         ml.get_key(LLM_KV_POSNET_BLOCK_COUNT,      hparams.posnet.n_layer);
@@ -6046,9 +6047,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_WAVTOKENIZER_DEC:
                 {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);
 
-                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
+                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
                     conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
 
                     // posnet
@@ -6144,8 +6145,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                     }
 
-                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
-                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
+                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, hparams.n_embd_out()}, 0);
+                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {hparams.n_embd_out()}, 0);
                 } break;
             case LLM_ARCH_BAILINGMOE:
                 {
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index 8c39fce8ba..ac55a8b1ca 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -1036,7 +1036,7 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
 
 #if 1
     // spectral operations
-    const int n_embd = llama_model_n_embd(model_cts);
+    const int n_embd = llama_model_n_embd_out(model_cts);
     const float * embd = llama_get_embeddings(ctx_cts);
 
     auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads);

From 8ee538ce73de1afbf6924a38c2d016e496c26497 Mon Sep 17 00:00:00 2001
From: thecaptain789 <257642323+thecaptain789@users.noreply.github.com>
Date: Wed, 11 Feb 2026 06:05:31 +0000
Subject: [PATCH 181/206] llama : correct typos 'occured' and 'occurences'
 (#19414)

Co-authored-by: thecaptain789 
---
 common/ngram-map.cpp | 2 +-
 common/ngram-map.h   | 4 ++--
 include/llama.h      | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/common/ngram-map.cpp b/common/ngram-map.cpp
index 2b876a6e99..ebf771a24a 100644
--- a/common/ngram-map.cpp
+++ b/common/ngram-map.cpp
@@ -461,7 +461,7 @@ void common_ngram_map_draft(common_ngram_map & map,
             slot_max = v;
         }
     }
-    // What is sum of the other occurences?
+    // What is sum of the other occurrences?
     uint32_t sum_occur = 0;
     for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
         if (v == slot_max) {
diff --git a/common/ngram-map.h b/common/ngram-map.h
index 41b9530449..d84e719151 100644
--- a/common/ngram-map.h
+++ b/common/ngram-map.h
@@ -44,7 +44,7 @@ llama_tokens common_ngram_simple_draft(
 // statistics of a m-gram after a known n-gram
 struct common_ngram_map_value {
     size_t   value_idx =  0;  // index of value m-gram in token-history (0 if unused)
-    uint16_t value_num =  0;  // number of occurences of this value m-gram after the key n-gram (0 in an unused values-slot)
+    uint16_t value_num =  0;  // number of occurrences of this value m-gram after the key n-gram (0 in an unused values-slot)
     int16_t n_accepted = -1;  // number of accepted tokens at last draft (-1 if unused)
 };
 
@@ -53,7 +53,7 @@ struct common_ngram_map_key {
     size_t   key_idx;   // index of key n-gram in token-history
     size_t   stat_idx;  // index of last token of stastistics computation (key_num, values)
 
-    uint16_t key_num;   // number of occurences of this key n-gram in token-history
+    uint16_t key_num;   // number of occurrences of this key n-gram in token-history
     common_ngram_map_value values[COMMON_NGRAM_MAX_VALUES]; // some known values after the key
 };
 
diff --git a/include/llama.h b/include/llama.h
index bf4e28a8be..46c3672e98 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -482,7 +482,7 @@ extern "C" {
     enum llama_params_fit_status {
         LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
         LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
-        LLAMA_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occured, e.g. because no model could be found at the specified path
+        LLAMA_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occurred, e.g. because no model could be found at the specified path
     };
 
     // fits mparams and cparams to free device memory (assumes system memory is unlimited)

From 73cd5e1b971d6223c58d88907672d1a153bd18d9 Mon Sep 17 00:00:00 2001
From: Max Krasnyansky 
Date: Tue, 10 Feb 2026 23:21:12 -0800
Subject: [PATCH 182/206] hexagon: Add ARGSORT, DIV, SQR, SQRT, SUM_ROWS, GEGLU
 (#19406)

* hexagon: add ARGSORT op

Co-authored-by: Yarden Tal 

* hexagon: argsort reject tensors with huge rows for now

* Adding support for DIV,SQR,SQRT,SUM_ROWS ops in hexagon backend

* hexagon : Add GEGLU op

* hexagon: fix editor config check

* hexagon: rewrite and optimize binary ops ADD/SUB/MUL/DIV/ADD_ID to use DMA

---------

Co-authored-by: Yarden Tal 
Co-authored-by: Manohara Hosakoppa Krishnamurthy 
---
 ggml/src/ggml-hexagon/ggml-hexagon.cpp   | 113 ++-
 ggml/src/ggml-hexagon/htp/CMakeLists.txt |   3 +
 ggml/src/ggml-hexagon/htp/act-ops.c      | 152 +++-
 ggml/src/ggml-hexagon/htp/argsort-ops.c  | 281 +++++++
 ggml/src/ggml-hexagon/htp/binary-ops.c   | 966 +++++++++++++++++------
 ggml/src/ggml-hexagon/htp/htp-msg.h      |  64 +-
 ggml/src/ggml-hexagon/htp/htp-ops.h      |   2 +
 ggml/src/ggml-hexagon/htp/hvx-arith.h    | 245 +++---
 ggml/src/ggml-hexagon/htp/hvx-base.h     |   6 +
 ggml/src/ggml-hexagon/htp/hvx-copy.h     |   2 -
 ggml/src/ggml-hexagon/htp/hvx-div.h      | 116 +++
 ggml/src/ggml-hexagon/htp/hvx-sigmoid.h  |  27 +
 ggml/src/ggml-hexagon/htp/hvx-sqrt.h     |  68 +-
 ggml/src/ggml-hexagon/htp/hvx-utils.h    |   1 +
 ggml/src/ggml-hexagon/htp/main.c         | 107 +++
 ggml/src/ggml-hexagon/htp/sum-rows-ops.c | 115 +++
 ggml/src/ggml-hexagon/htp/unary-ops.c    |  64 ++
 17 files changed, 1925 insertions(+), 407 deletions(-)
 create mode 100644 ggml/src/ggml-hexagon/htp/argsort-ops.c
 create mode 100644 ggml/src/ggml-hexagon/htp/hvx-div.h
 create mode 100644 ggml/src/ggml-hexagon/htp/sum-rows-ops.c

diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 4f0a1620fb..54f9986498 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -1935,11 +1935,6 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se
         return false;
     }
 
-    // TODO: add support for non-contigiuos tensors
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
-        return false;
-    }
-
     return true;
 }
 
@@ -1991,6 +1986,25 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses
     return true;
 }
 
+static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0];
+    const struct ggml_tensor * dst  = op;
+
+    if (!hex_supported_src0_type(src0->type)) {
+        return false;
+    }
+    if (!hex_supported_dst_type(dst->type)) {
+        return false;
+    }
+
+    // TODO: add support for non-contigiuos tensors
+    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess,
                                                const struct ggml_tensor *          op) {
     const struct ggml_tensor * src0 = op->src[0];
@@ -2111,6 +2125,26 @@ static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session *
     return true;
 }
 
+static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0]; // values
+    const struct ggml_tensor * dst  = op;         // indices
+
+    if (src0->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (dst->type != GGML_TYPE_I32) {
+        return false;
+    }
+
+    if (src0->ne[0] > (16*1024)) {
+        // reject tensors with huge rows for now
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
     const int32_t * op_params = &op->op_params[0];
 
@@ -2278,6 +2312,9 @@ static inline size_t init_binary_req(htp_general_req * req, dspqueue_buffer * bu
         case GGML_OP_SUB:
             req->op = HTP_OP_SUB;
             break;
+        case GGML_OP_DIV:
+            req->op = HTP_OP_DIV;
+            break;
         default:
             GGML_ABORT("ggml-hex: binary : unsupported op: %d\n", t->op);
             break;
@@ -2316,6 +2353,17 @@ static inline size_t init_get_rows_req(htp_general_req * req, dspqueue_buffer *
     return n_bufs;
 }
 
+static inline size_t init_argsort_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
+    req->op = HTP_OP_ARGSORT;
+    memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
+
+    size_t n_bufs = 0;
+    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
+    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
+
+    return n_bufs;
+}
+
 template 
 static inline size_t init_binary_id_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     switch (t->op) {
@@ -2370,6 +2418,16 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
             supported = true;
             break;
 
+        case GGML_OP_SQR:
+            req->op   = HTP_OP_SQR;
+            supported = true;
+            break;
+
+        case GGML_OP_SQRT:
+            req->op   = HTP_OP_SQRT;
+            supported = true;
+            break;
+
         case GGML_OP_UNARY:
             if (ggml_get_unary_op(t) == GGML_UNARY_OP_SILU) {
                 req->op   = HTP_OP_UNARY_SILU;
@@ -2387,6 +2445,9 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
             } else if (ggml_get_glu_op(t) == GGML_GLU_OP_SWIGLU_OAI) {
                 req->op   = HTP_OP_GLU_SWIGLU_OAI;
                 supported = true;
+            } else if (ggml_get_glu_op(t) == GGML_GLU_OP_GEGLU) {
+                req->op   = HTP_OP_GLU_GEGLU;
+                supported = true;
             }
             break;
 
@@ -2411,6 +2472,17 @@ static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * buf
     return n_bufs;
 }
 
+static inline size_t init_sum_rows_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
+    memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
+    req->op = HTP_OP_SUM_ROWS;
+
+    size_t n_bufs = 0;
+    n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ);
+    n_bufs += htp_req_buff_init(&req->dst,  &bufs[n_bufs], t,         DSPQBUF_TYPE_DSP_WRITE_CPU_READ);
+
+    return n_bufs;
+}
+
 static inline size_t init_rope_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) {
     memcpy(&req->op_params, &t->op_params, sizeof(t->op_params));
     req->op = HTP_OP_ROPE;
@@ -2519,6 +2591,7 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
             case GGML_OP_MUL:
             case GGML_OP_ADD:
             case GGML_OP_SUB:
+            case GGML_OP_DIV:
                 ggml_hexagon_dispatch_op>(sess, node, flags);
                 break;
             case GGML_OP_ADD_ID:
@@ -2528,6 +2601,13 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
             case GGML_OP_SCALE:
                 ggml_hexagon_dispatch_op(sess, node, flags);
                 break;
+            case GGML_OP_SQR:
+            case GGML_OP_SQRT:
+                ggml_hexagon_dispatch_op(sess, node, flags);
+                break;
+            case GGML_OP_SUM_ROWS:
+                ggml_hexagon_dispatch_op(sess, node, flags);
+                break;
             case GGML_OP_UNARY:
                 if ((ggml_get_unary_op(node) == GGML_UNARY_OP_SILU) ||
                         (ggml_get_unary_op(node) == GGML_UNARY_OP_GELU)) {
@@ -2536,7 +2616,8 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 break;
             case GGML_OP_GLU:
                 if ((ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU) ||
-                        (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI)) {
+                        (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI) ||
+                        (ggml_get_glu_op(node) == GGML_GLU_OP_GEGLU)) {
                     ggml_hexagon_dispatch_op(sess, node, flags);
                 }
                 break;
@@ -2564,6 +2645,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 ggml_hexagon_dispatch_op(sess, node, flags);
                 break;
 
+            case GGML_OP_ARGSORT:
+                ggml_hexagon_dispatch_op(sess, node, flags);
+                break;
+
             default:
                 GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
         }
@@ -2916,6 +3001,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_MUL:
         case GGML_OP_ADD:
         case GGML_OP_SUB:
+        case GGML_OP_DIV:
             supp = ggml_hexagon_supported_binary(sess, op);
             break;
 
@@ -2928,6 +3014,15 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_unary(sess, op);
             break;
 
+        case GGML_OP_SQR:
+        case GGML_OP_SQRT:
+            supp = ggml_hexagon_supported_unary(sess, op);
+            break;
+
+        case GGML_OP_SUM_ROWS:
+            supp = ggml_hexagon_supported_sum_rows(sess, op);
+            break;
+
         case GGML_OP_SOFT_MAX:
             supp = ggml_hexagon_supported_softmax(sess, op);
             break;
@@ -2943,7 +3038,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_GLU:
             {
                 const auto glu_op = ggml_get_glu_op(op);
-                if ((glu_op == GGML_GLU_OP_SWIGLU) || (glu_op == GGML_GLU_OP_SWIGLU_OAI)) {
+                if ((glu_op == GGML_GLU_OP_SWIGLU) || (glu_op == GGML_GLU_OP_SWIGLU_OAI) || (glu_op == GGML_GLU_OP_GEGLU)) {
                     supp = ggml_hexagon_supported_activations(sess, op);
                 }
                 break;
@@ -2968,6 +3063,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_cpy(sess, op);
             break;
 
+        case GGML_OP_ARGSORT:
+            supp = ggml_hexagon_supported_argsort(sess, op);
+            break;
+
         default:
             break;
     }
diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
index e8ef203045..2c23b60da3 100644
--- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
@@ -6,6 +6,7 @@ include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
 include_directories(
     ${HEXAGON_SDK_ROOT}/incs
     ${HEXAGON_SDK_ROOT}/incs/stddef
+    ${CMAKE_CURRENT_SOURCE_DIR}/../../../include
     ${CMAKE_CURRENT_SOURCE_DIR}/../..
     ${CMAKE_CURRENT_SOURCE_DIR}/..
     ${CMAKE_CURRENT_SOURCE_DIR}
@@ -21,6 +22,7 @@ add_library(${HTP_LIB} SHARED
     matmul-ops.c
     binary-ops.c
     unary-ops.c
+    sum-rows-ops.c
     softmax-ops.c
     act-ops.c
     rope-ops.c
@@ -28,6 +30,7 @@ add_library(${HTP_LIB} SHARED
     set-rows-ops.c
     get-rows-ops.c
     cpy-ops.c
+    argsort-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE
diff --git a/ggml/src/ggml-hexagon/htp/act-ops.c b/ggml/src/ggml-hexagon/htp/act-ops.c
index c3daf5adb2..950d836ad3 100644
--- a/ggml/src/ggml-hexagon/htp/act-ops.c
+++ b/ggml/src/ggml-hexagon/htp/act-ops.c
@@ -410,7 +410,7 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0,
             // gelu = x * sigmoid(1.702 * x) // current implementation
             hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0);
             hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -516,7 +516,7 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
 
             // silu = x * sigmoid(x)
             hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0);
-            hvx_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
+            hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0);
         }
 
         dma_queue_push_vtcm_to_ddr(dma_queue,
@@ -541,6 +541,143 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0,
          ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
+static const float GELU_COEF_A     = 0.044715f;
+static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+
+static void glu_geglu_f32_per_thread(const struct htp_tensor * src0,
+                                       const struct htp_tensor * src1,
+                                       struct htp_tensor *       dst,
+                                       const int32_t *           op_params,
+                                       struct htp_spad *         src0_spad,
+                                       struct htp_spad *         src1_spad,
+                                       struct htp_spad *         dst_spad,
+                                       uint32_t                  nth,
+                                       uint32_t                  ith,
+                                       uint32_t                  src0_nrows_per_thread,
+                                       dma_queue *               dma_queue) {
+    htp_act_preamble3;
+
+    size_t src0_row_size = nb01;
+    size_t src1_row_size = nb11;
+    size_t dst_row_size  = nb1;
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
+
+    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+
+    // no work for this thread
+    if (src0_start_row >= src0_end_row) {
+        return;
+    }
+
+    const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
+    const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
+    uint8_t * restrict data_dst        = (uint8_t *) dst->data;
+
+    const bool src1_valid = src1->ne[0];
+    const int  nc         = (src1_valid) ? ne00 : ne00 / 2;
+    if (!src1_valid) {
+        const int32_t swapped = op_params[1];
+        data_src1             = data_src0;
+        src1_row_size         = src0_row_size;
+
+        const size_t nc_in_bytes = nc * SIZEOF_FP32;
+        data_src0 += swapped ? nc_in_bytes : 0;
+        data_src1 += swapped ? 0 : nc_in_bytes;
+    }
+
+    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
+    const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
+    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+
+    uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
+    uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
+    uint8_t * restrict dst_spad_data  = dst_spad->data + (ith * dst_spad->size_per_thread);
+
+    // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0
+    size_t src0_spad_half_size = src0_spad->size_per_thread / 2;
+    size_t src1_spad_half_size = src1_spad->size_per_thread / 2;
+    size_t dst_spad_half_size  = dst_spad->size_per_thread / 2;
+
+    const int BLOCK = src0_spad_half_size / src0_row_size_aligned;  // How many rows can we process in one block
+    if (BLOCK == 0) {
+        FARF(ERROR,
+             "geglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n",
+             src0_spad->size_per_thread, src0_row_size_aligned);
+        return;
+    }
+
+    // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379
+    for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
+        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+
+        // Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
+        dma_queue_push_vtcm_to_ddr(dma_queue,
+            dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
+            dst_row_size, dst_row_size_aligned, 0);
+
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+            dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)),
+            src0_row_size_aligned, src0_row_size, block_size);
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+            dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)),
+            src1_row_size_aligned, src1_row_size, block_size);
+    }
+
+    for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
+        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+
+        float * dst_spad  = (float *) dma_queue_pop(dma_queue).src;
+        float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
+        float * src1_spad = (float *) dma_queue_pop(dma_queue).dst;
+
+        for (uint32_t ib = 0; ib < block_size; ib++) {
+            const uint8_t * src0_spad_ptr = (const uint8_t *)(src0_spad + ib * (src0_row_size_aligned / sizeof(float)));
+            const uint8_t * src1_spad_ptr = (const uint8_t *)(src1_spad + ib * (src1_row_size_aligned / sizeof(float)));
+            uint8_t *       dst_spad_ptr  = (uint8_t *)(dst_spad + ib * (dst_row_size_aligned / sizeof(float)));
+
+            // geglu tanh implementation
+            // geglu(x, g) = gelu(x) * g
+            // gelu(x) = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)))
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, src0_spad_ptr, nc);                       // res = x*x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, GELU_COEF_A, nc);   // res = res * GELU_COEF_A
+            hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 1.0f, nc);          // res = res + 1.0f
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc);       // res = res * x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, SQRT_2_OVER_PI, nc); // res = result * SQRT_2_OVER_PI
+            hvx_tanh_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc);         // res = tanh(res)
+            hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, 1.0f, nc);           // res = res + 1.0f
+            hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc);       // res = res * x
+            hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 0.5f, nc);          // res = res + 0.5f
+            hvx_mul_f32_aaa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, src1_spad_ptr, nc);       // res = res * g
+        }
+
+        dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
+                                   dst_row_size_aligned, block_size);
+
+        // prefetch N+2 loop iteration if any
+        const uint32_t pref_block = (ir + BLOCK * 2);
+        if (pref_block < src0_end_row) {
+            const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
+            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)),
+                                       src0_row_size_aligned, src0_row_size, pref_block_size);
+            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)),
+                                       src1_row_size_aligned, src1_row_size, pref_block_size);
+        }
+    }
+
+    dma_queue_flush(dma_queue);
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "geglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth,
+         ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3,
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
 static void unary_silu_f32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = (struct htp_ops_context *) data;
     unary_silu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
@@ -559,6 +696,12 @@ static void glu_swiglu_oai_f32(unsigned int n, unsigned int i, void * data) {
                                    &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
 }
 
+static void glu_geglu_f32(unsigned int n, unsigned int i, void * data) {
+    struct htp_ops_context * octx = (struct htp_ops_context *) data;
+    glu_geglu_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
+                               &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
+}
+
 static int execute_op_activations_f32(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
@@ -593,6 +736,11 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
             act_op_func = unary_gelu_f32;
             op_type     = "gelu-f32";
             break;
+
+        case HTP_OP_GLU_GEGLU:
+            act_op_func = glu_geglu_f32;
+            op_type     = "geglu-f32";
+            break;
         default:
             FARF(ERROR, "Unsupported activations Op %u\n", octx->op);
             return HTP_STATUS_NO_SUPPORT;
diff --git a/ggml/src/ggml-hexagon/htp/argsort-ops.c b/ggml/src/ggml-hexagon/htp/argsort-ops.c
new file mode 100644
index 0000000000..a4cee980be
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/argsort-ops.c
@@ -0,0 +1,281 @@
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "ggml.h"
+
+#include "hvx-utils.h"
+#include "hex-dma.h"
+
+#include "htp-ctx.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+
+#ifndef MIN
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
+
+struct htp_argsort_context {
+    struct htp_ops_context * octx;
+    uint32_t                 nrows_per_thread;
+};
+
+static inline bool all_greater_f32(HVX_Vector x, HVX_Vector y)
+{
+    const HVX_Vector one  = Q6_V_vsplat_R(1);
+    const HVX_Vector zero = Q6_V_vzero();
+
+    HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(x, y);
+    HVX_Vector matches = Q6_V_vmux_QVV(pred, one, zero);
+    HVX_Vector sum = hvx_vec_reduce_sum_i32(matches);
+    return hvx_vec_get_i32(sum) == 32;
+}
+
+// Sorts values and mirrors swaps to indices.
+static void quicksort_values_indices_asc(float * values, int32_t * indices, int left, int right) {
+    if (left >= right) return;
+
+    int pivot_idx = (left + right) / 2;
+    float pivot = values[pivot_idx];
+    int i = left;
+    int j = right;
+
+    HVX_Vector pivot_vec = hvx_vec_splat_f32(pivot);
+    while (i <= j) {
+        // Vectorized scan for i
+        while (i <= j) {
+            // Check if we have at least one full vector
+            if (i + 32 <= j) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + i);
+                if (all_greater_f32(pivot_vec, vals_vec)) {
+                    // If all elements are < pivot, we can skip this whole block
+                    i += 32;
+                    continue;
+                }
+            }
+
+            // Scalar fallback / cleanup
+            if (values[i] < pivot) {
+                i++;
+            } else {
+                break;
+            }
+        }
+
+        // Vectorized scan for j
+        while (i <= j) {
+            if (j - 32 >= i) {
+                // Load 32 elements ending at j.
+                // Since we want `values[j] > pivot`, let's load from j-31 to j.
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + j - 31);
+                if (all_greater_f32(vals_vec, pivot_vec)) {
+                    j -= 32;
+                    continue;
+                }
+            }
+
+            if (values[j] > pivot) {
+                j--;
+            } else {
+                break;
+            }
+        }
+
+        if (i <= j) {
+            float tmp_val = values[i];
+            values[i] = values[j];
+            values[j] = tmp_val;
+
+            int32_t tmp_idx = indices[i];
+            indices[i] = indices[j];
+            indices[j] = tmp_idx;
+            i++;
+            j--;
+        }
+    }
+
+    if (left < j) quicksort_values_indices_asc(values, indices, left, j);
+    if (i < right) quicksort_values_indices_asc(values, indices, i, right);
+}
+
+static void quicksort_values_indices_desc(float * values, int32_t * indices, int left, int right) {
+    if (left >= right) return;
+
+    int pivot_idx = (left + right) / 2;
+    float pivot = values[pivot_idx];
+    int i = left;
+    int j = right;
+
+    HVX_Vector pivot_vec = hvx_vec_splat_f32(pivot);
+
+    while (i <= j) {
+        // Vectorized scan for i (values[i] > pivot)
+        while (i <= j) {
+            if (i + 32 <= j) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + i);
+                if (all_greater_f32(vals_vec, pivot_vec)) {
+                    i += 32;
+                    continue;
+                }
+            }
+
+            if (values[i] > pivot) {
+                i++;
+            } else {
+                break;
+            }
+        }
+
+        // Vectorized scan for j (values[j] < pivot)
+        while (i <= j) {
+            if (j - 32 >= i) {
+                HVX_Vector vals_vec = *(HVX_UVector *)(values + j - 31);
+                if (all_greater_f32(pivot_vec, vals_vec)) {
+                    j -= 32;
+                    continue;
+                }
+            }
+
+            if (values[j] < pivot) {
+                j--;
+            } else {
+                break;
+            }
+        }
+
+        if (i <= j) {
+            float tmp_val = values[i];
+            values[i] = values[j];
+            values[j] = tmp_val;
+
+            int32_t tmp_idx = indices[i];
+            indices[i] = indices[j];
+            indices[j] = tmp_idx;
+            i++;
+            j--;
+        }
+    }
+
+    if (left < j) quicksort_values_indices_desc(values, indices, left, j);
+    if (i < right) quicksort_values_indices_desc(values, indices, i, right);
+}
+
+static void htp_argsort_f32(unsigned int n, unsigned int i, void * data) {
+    struct htp_argsort_context * actx = (struct htp_argsort_context *)data;
+    struct htp_ops_context * octx = actx->octx;
+
+    // Unpack context
+    const struct htp_tensor * src0 = &octx->src0;
+    const struct htp_tensor * dst = &octx->dst;
+
+    // Scratchpad memory
+    uint8_t * spad = octx->src0_spad.data + octx->src0_spad.size_per_thread * i;
+
+    // Dimensions
+    uint32_t ne00 = src0->ne[0];
+    uint32_t ne01 = src0->ne[1];
+    uint32_t ne02 = src0->ne[2];
+    uint32_t ne03 = src0->ne[3];
+
+    uint32_t nb01 = src0->nb[1];
+    //uint32_t nb02 = src0->nb[2];
+    //uint32_t nb03 = src0->nb[3];
+
+    uint32_t nb1 = dst->nb[1];
+    //uint32_t nb2 = dst->nb[2];
+    //uint32_t nb3 = dst->nb[3];
+
+    // Sort order
+    enum ggml_sort_order order = (enum ggml_sort_order) octx->op_params[0];
+
+    // Rows to process
+    uint32_t total_rows = ne01 * ne02 * ne03;
+    uint32_t rows_per_thread = actx->nrows_per_thread;
+    uint32_t start_row = rows_per_thread * i;
+    uint32_t end_row = MIN(start_row + rows_per_thread, total_rows);
+
+    // Scratchpad layout:
+    // We need space for one row of float data (values) and one row of int32 indices.
+    // values: ne00 * sizeof(float)
+    // indices: ne00 * sizeof(int32_t)
+    // Padded to 128 bytes.
+
+    size_t values_size = hex_round_up(ne00 * sizeof(float), 128);
+    float * values_buf = (float *) spad;
+    int32_t * indices_buf = (int32_t *) (spad + values_size);
+
+    for (uint32_t r = start_row; r < end_row; r++) {
+        uint32_t src_offset = r * nb01;
+        uint32_t dst_offset = r * nb1;
+
+        uint8_t * src_ptr = (uint8_t *) src0->data + src_offset;
+        uint8_t * dst_ptr = (uint8_t *) dst->data  + dst_offset;
+
+        hex_l2fetch(src_ptr, ne00 * sizeof(float), ne00 * sizeof(float), 1);
+        hvx_copy_f32_au((uint8_t*)values_buf, src_ptr, ne00);
+
+        // Initialize indices
+        for (uint32_t j = 0; j < ne00; j++) {
+            indices_buf[j] = j;
+        }
+
+        // Sort values and mirror swaps to indices
+        if (order == GGML_SORT_ORDER_ASC) {
+            quicksort_values_indices_asc(values_buf, indices_buf, 0, ne00 - 1);
+        } else {
+            quicksort_values_indices_desc(values_buf, indices_buf, 0, ne00 - 1);
+        }
+
+        // Copy indices back to DDR
+        hvx_copy_f32_ua(dst_ptr, (const uint8_t *) indices_buf, ne00);
+    }
+}
+
+int op_argsort(struct htp_ops_context * octx) {
+    // Check supported types
+    if (octx->src0.type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // Allocate scratchpad
+    // We need 1 row of float + 1 row of int32 per thread.
+    uint32_t ne00 = octx->src0.ne[0];
+    size_t values_size  = hex_round_up(ne00 * sizeof(float), 128);
+    size_t indices_size = hex_round_up(ne00 * sizeof(int32_t), 128);
+    size_t spad_per_thread = values_size + indices_size;
+
+    // Make sure we round up to 256 for alignment requirements
+    spad_per_thread = hex_round_up(spad_per_thread, 256);
+
+    size_t total_spad_size = spad_per_thread * octx->n_threads;
+
+    if (octx->ctx->vtcm_size < total_spad_size) {
+        FARF(ERROR, "argsort: VTCM size too small. Needed %zu, have %zu", total_spad_size, octx->ctx->vtcm_size);
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    octx->src0_spad.data = octx->ctx->vtcm_base;
+    octx->src0_spad.size = total_spad_size;
+    octx->src0_spad.size_per_thread = spad_per_thread;
+
+    FARF(HIGH, "argsort: %ux%ux%ux%u -> %ux%ux%ux%u (0x%x, 0x%x)",
+         octx->src0.ne[0], octx->src0.ne[1], octx->src0.ne[2], octx->src0.ne[3],
+         octx->dst.ne[0], octx->dst.ne[1], octx->dst.ne[2], octx->dst.ne[3],
+         octx->src0.data, octx->dst.data);
+
+    uint32_t total_rows = octx->src0.ne[1] * octx->src0.ne[2] * octx->src0.ne[3];
+    uint32_t n_jobs = MIN(total_rows, octx->n_threads);
+
+    struct htp_argsort_context actx;
+    actx.octx = octx;
+    actx.nrows_per_thread = (total_rows + n_jobs - 1) / n_jobs;
+
+    // Run jobs
+    worker_pool_run_func(octx->ctx->worker_pool, htp_argsort_f32, &actx, n_jobs);
+
+    return HTP_STATUS_OK;
+}
diff --git a/ggml/src/ggml-hexagon/htp/binary-ops.c b/ggml/src/ggml-hexagon/htp/binary-ops.c
index de22afe460..00dbcf8798 100644
--- a/ggml/src/ggml-hexagon/htp/binary-ops.c
+++ b/ggml/src/ggml-hexagon/htp/binary-ops.c
@@ -17,15 +17,37 @@
 #include "htp-msg.h"
 #include "htp-ops.h"
 
-typedef void (*hvx_elemwise_f32_func)(uint8_t * data_dst, const uint8_t * src0, const uint8_t * src1, const uint32_t num_elems);
+#ifndef MIN
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
 
-static hvx_elemwise_f32_func func_table_HVX[]     = { hvx_mul_f32, hvx_add_f32, hvx_sub_f32 };
-static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_aa, hvx_add_f32_aa, hvx_sub_f32_aa };
+// Context for binary operations
+struct htp_binary_context {
+    struct htp_ops_context * octx;
+    struct fastdiv_values dim1_div;
+    struct fastdiv_values dim2_div;
+    struct fastdiv_values dim12_div;
+
+    struct fastdiv_values src1_dim1_div; // ne11
+    struct fastdiv_values src1_dim2_div; // ne12
+    struct fastdiv_values src1_dim3_div; // ne13
+
+    uint32_t nrows_per_thread;
+    bool split_at_ne01;
+    bool split_at_ne02;
+
+    // Precomputed values
+    uint32_t block_max;
+    size_t   src0_row_size_aligned;
+    size_t   src1_row_size_aligned;
+    size_t   dst_row_size_aligned;
+    uint32_t src1_fetch_rows; // 1 or block_max
+    uint32_t src1_dma_stride; // 0 or stride
+};
 
 #define htp_binary_preamble            \
     const struct htp_tensor * src0 = &octx->src0; \
     const struct htp_tensor * src1 = &octx->src1; \
-    const struct htp_tensor * src2 = &octx->src2; \
     struct htp_tensor *       dst  = &octx->dst;  \
                                        \
     const uint32_t ne00 = src0->ne[0]; \
@@ -38,266 +60,696 @@ static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_aa, hvx_add_f3
     const uint32_t ne12 = src1->ne[2]; \
     const uint32_t ne13 = src1->ne[3]; \
                                        \
-    const uint32_t ne0 = dst->ne[0];   \
-    const uint32_t ne1 = dst->ne[1];   \
-    const uint32_t ne2 = dst->ne[2];   \
-    const uint32_t ne3 = dst->ne[3];   \
-                                       \
-    const uint32_t nb00 = src0->nb[0]; \
     const uint32_t nb01 = src0->nb[1]; \
     const uint32_t nb02 = src0->nb[2]; \
     const uint32_t nb03 = src0->nb[3]; \
                                        \
-    const uint32_t nb10 = src1->nb[0]; \
     const uint32_t nb11 = src1->nb[1]; \
     const uint32_t nb12 = src1->nb[2]; \
     const uint32_t nb13 = src1->nb[3]; \
                                        \
-    const uint32_t nb0 = dst->nb[0];   \
     const uint32_t nb1 = dst->nb[1];   \
     const uint32_t nb2 = dst->nb[2];   \
-    const uint32_t nb3 = dst->nb[3];   \
-                                       \
-    const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread;
+    const uint32_t nb3 = dst->nb[3];
 
-static void binary_job_f32_per_thread(struct htp_ops_context * octx,
-                                      uint8_t *                spad_data,
-                                      uint32_t                 nth,
-                                      uint32_t                 ith,
-                                      enum htp_op              op) {
-    htp_binary_preamble;
+static inline uint32_t calc_block_size(struct htp_binary_context * bctx, uint32_t ir, uint32_t end_row,
+                                uint32_t ne01, uint32_t ne02) {
+    uint32_t i03, i02, i01, rem;
+    i03 = fastdiv(ir, &bctx->dim12_div);
+    rem = ir - i03 * (ne02 * ne01);
+    i02 = fastdiv(rem, &bctx->dim1_div);
+    i01 = rem - i02 * ne01;
 
-    const size_t src0_row_size = nb01;
-    const size_t src1_row_size = nb11;
-    const size_t dst_row_size  = nb1;
+    uint32_t rows_left = end_row - ir;
+    uint32_t block_limit = rows_left;
 
-    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
-    const uint32_t src1_nrows = ne11 * ne12 * ne13;  // src1 rows
-
-    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
-    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
-
-    // no work for this thread
-    if (src0_start_row >= src0_end_row) {
-        return;
+    if (bctx->split_at_ne01) {
+        block_limit = MIN(block_limit, ne01 - i01);
+    }
+    if (bctx->split_at_ne02) {
+         uint32_t rows_in_plane = (ne02 * ne01) - rem;
+         block_limit = MIN(block_limit, rows_in_plane);
     }
 
-    uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
-
-    int is_aligned = 1;
-    int opt_path   = 0;
-    if ((0 == hex_is_aligned((void *) src0->data, VLEN)) || (0 == hex_is_aligned((void *) src1->data, VLEN)) ||
-        (0 == hex_is_aligned((void *) dst->data, VLEN))) {
-        is_aligned = 0;
-    }
-    if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) {
-        opt_path = 1;
-    }
-
-    hvx_elemwise_f32_func func_HVX = (1 == opt_path) ? func_table_HVX_opt[op] : func_table_HVX[op];
-
-    uint8_t * restrict spad_data_th = spad_data + (ith * src0_row_size);
-
-    const uint8_t * restrict src0_ptr = (const uint8_t *) src0->data + (src0_start_row * src0_row_size);
-    uint8_t * restrict dst_ptr        = (uint8_t *) dst->data + (src0_start_row * dst_row_size);
-
-    const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
-
-    const uint32_t ne02_ne01 = ne02 * ne01;
-
-    for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
-        const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
-        const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
-        const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
-
-        const uint32_t i13 = fastmodulo(i03, ne13, &octx->src1_div3);
-        const uint32_t i12 = fastmodulo(i02, ne12, &octx->src1_div2);
-        const uint32_t i11 = fastmodulo(i01, ne11, &octx->src1_div1);
-
-        const uint8_t * restrict src1_ptr = data_src1 + i13 * nb13 + i12 * nb12 + i11 * src1_row_size;
-
-        if (ir + 1 < src0_end_row) {
-            hex_l2fetch(src0_ptr + ne00, src0_row_size, src0_row_size, 1);
-            if (src1_row_size == src0_row_size) {
-                hex_l2fetch(src1_ptr, src1_row_size, src1_row_size, 1);
-            }
-        }
-
-        const uint32_t nr0 = ne00 / ne10;
-        if (nr0 > 1) {
-            if ((1 == is_aligned) && (nr0 == ne00)) {
-                hvx_splat_f32_a(spad_data_th, *(float *) src1_ptr, nr0);
-            } else {
-                for (uint32_t r = 0; r < nr0; r++) {
-                    memcpy(spad_data_th + r * nb11, (const uint8_t *) src1_ptr, nb11);
-                }
-            }
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) spad_data_th, ne00);
-        } else {
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, ne00);
-        }
-
-        src0_ptr += src0_row_size;
-        dst_ptr += dst_row_size;
-    }
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "binary-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path,
-         ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3,
-         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    return MIN(bctx->block_max, block_limit);
 }
 
-static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
-                                             uint8_t *                spad_data,
-                                             uint32_t                 nth,
-                                             uint32_t                 ith,
-                                             hvx_elemwise_f32_func    func_HVX) {
+// Macro for scalar op switch
+#define COMPUTE_SCALAR_OP(DST, SRC, VAL, N) \
+    switch (octx->op) { \
+        case HTP_OP_ADD: hvx_add_scalar_f32_aa(DST, SRC, VAL, N); break; \
+        case HTP_OP_SUB: hvx_sub_scalar_f32_aa(DST, SRC, VAL, N); break; \
+        case HTP_OP_MUL: hvx_mul_scalar_f32_aa(DST, SRC, VAL, N); break; \
+        case HTP_OP_DIV: hvx_mul_scalar_f32_aa(DST, SRC, 1.0f / (VAL), N); break; \
+        default: break; \
+    }
+
+// Macro for vector op switch (All Aligned)
+#define COMPUTE_VECTOR_OP_AAA(DST, SRC0, SRC1, N) \
+    switch (octx->op) { \
+        case HTP_OP_ADD: hvx_add_f32_aaa(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_SUB: hvx_sub_f32_aaa(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_MUL: hvx_mul_f32_aaa(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_DIV: hvx_div_f32_aaa(DST, SRC0, SRC1, N); break; \
+        default: break; \
+    }
+
+// Macro for vector op switch (Dst Aligned, Src0 Aligned, Src1 Unaligned)
+#define COMPUTE_VECTOR_OP_AAU(DST, SRC0, SRC1, N) \
+    switch (octx->op) { \
+        case HTP_OP_ADD: hvx_add_f32_aau(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_SUB: hvx_sub_f32_aau(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_MUL: hvx_mul_f32_aau(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_DIV: hvx_div_f32_aau(DST, SRC0, SRC1, N); break; \
+        default: break; \
+    }
+
+// Macro for vector op switch (All Unaligned - generic loop used in element repeat)
+#define COMPUTE_VECTOR_OP_UUU(DST, SRC0, SRC1, N) \
+    switch (octx->op) { \
+        case HTP_OP_ADD: hvx_add_f32_uuu(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_SUB: hvx_sub_f32_uuu(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_MUL: hvx_mul_f32_uuu(DST, SRC0, SRC1, N); break; \
+        case HTP_OP_DIV: hvx_div_f32_uuu(DST, SRC0, SRC1, N); break; \
+        default: break; \
+    }
+
+// 1. Scalar src1 (ne10 == 1)
+static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const size_t src0_row_size = nb01;
-    const size_t src1_row_size = nb11;
-    const size_t dst_row_size  = nb1;
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
 
-    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
+    size_t src0_spad_half    = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half     = octx->dst_spad.size_per_thread  / 2;
 
-    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
-    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
 
-    // no work for this thread
-    if (src0_start_row >= src0_end_row) {
-        return;
+    // Preamble
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
     }
 
-    uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
+    // Main loop
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
 
-    const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
-    const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
-    uint8_t * restrict data_dst        = (uint8_t *) dst->data;
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
 
-    const uint32_t ne02_ne01  = ne02 * ne01;
-    for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
-        // src0 indices
-        const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
-        const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
-        const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
 
-        // src1 indices
-        const int i11 = *(int32_t *) ((char *) src2->data + i01 * src2->nb[0] + i02 * src2->nb[1]);
-        assert(i11 >= 0 && i11 < ne11);
+        // src1 indices (broadcast/repeat)
+        uint32_t i13 = fastmodulo(i03, ne13, &bctx->src1_dim3_div);
+        uint32_t i12 = fastmodulo(i02, ne12, &bctx->src1_dim2_div);
+        uint32_t i11 = fastmodulo(i01, ne11, &bctx->src1_dim1_div);
 
-        float * restrict dst_ptr        = (float *) (data_dst + i03 * nb3 + i02 * nb2 + i01 * nb1);
-        const float * restrict src0_ptr = (const float *) (data_src0 + i03 * nb03 + i02 * nb02 + i01 * nb01);
-        const float * restrict src1_ptr = (const float *) (data_src1 + 0 + 0 + i11 * nb11);
+        uint8_t * src1_ptr = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
+        uint32_t s1_stride = (ne11 == 1) ? 0 : nb11;
 
-        if (ir + 1 < src0_end_row) {
-            hex_l2fetch(src0_ptr + ne00, src0_row_size, src0_row_size, 1);
-            if (src1_row_size == src0_row_size) {
-                hex_l2fetch(src1_ptr + ne10, src1_row_size, src1_row_size, 1);
-            }
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_dst  = d_spad + r * bctx->dst_row_size_aligned;
+            float val = *(float *)src1_ptr;
+            src1_ptr += s1_stride;
+            COMPUTE_SCALAR_OP(r_dst, r_src0, val, ne00);
         }
 
-        const uint32_t nr0 = ne00 / ne10;
-        if (nr0 > 1) {
-            for (uint32_t r = 0; r < nr0; r++) {
-                memcpy(spad_data + r * nb10, (const uint8_t *) src1_ptr, nb10);
-            }
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) spad_data, ne00);
-        } else {
-            func_HVX((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, (const uint8_t *) src1_ptr, ne00);
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             ir_prefetch += next_block_size;
         }
+        ir += current_block_size;
     }
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "add-id-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", ith, nth,
-         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1],
-         src1->ne[2], src1->ne[3], src2->ne[0], src2->ne[1], src2->ne[2], src2->ne[3], dst->ne[0], dst->ne[1],
-         dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    dma_queue_flush(q);
 }
 
-static void binary_job_dispatcher_f32(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = (struct htp_ops_context *) data;
+// 2. Vector Same Shape (ne1x == ne0x) or Simple Broadcast
+static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
+    htp_binary_preamble;
 
-    switch (octx->op) {
-        case HTP_OP_MUL:
-        case HTP_OP_ADD:
-        case HTP_OP_SUB:
-            binary_job_f32_per_thread(octx, octx->src1_spad.data, n, i, octx->op);
-            break;
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
 
-        case HTP_OP_ADD_ID:
-            binary_add_id_job_f32_per_thread(octx, octx->src0_spad.data, n, i, hvx_add_f32);
-            break;
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * src1_spad_base = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
 
-        default:
-            FARF(ERROR, "Unknown Binary Op %u", octx->op);
-            break;
+    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
+    size_t src1_spad_half = octx->src1_spad.size_per_thread / 2;
+    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
+
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint32_t i13 = (ne13 == 1) ? 0 : i03;
+        uint32_t i12 = (ne12 == 1) ? 0 : i02;
+        uint32_t i11 = (ne11 == 1) ? 0 : i01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * src1_base = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * s1_spad = src1_spad_base + spad_idx * src1_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        dma_queue_push(q, dma_make_ptr(s1_spad, src1_base), bctx->src1_row_size_aligned, bctx->src1_dma_stride, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
     }
+
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
+        uint8_t * s1_spad = (uint8_t *) dma_queue_pop(q).dst;
+
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_src1 = s1_spad + r * bctx->src1_row_size_aligned;
+            uint8_t * r_dst  = d_spad  + r * bctx->dst_row_size_aligned;
+            COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, ne00);
+        }
+
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+
+             uint32_t p13 = (ne13 == 1) ? 0 : p03;
+             uint32_t p12 = (ne12 == 1) ? 0 : p02;
+             uint32_t p11 = (ne11 == 1) ? 0 : p01;
+
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+             uint8_t * s1_next = (uint8_t *)src1->data + p13 * nb13 + p12 * nb12 + p11 * nb11;
+
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             dma_queue_push(q, dma_make_ptr(s1_spad, s1_next), bctx->src1_row_size_aligned, bctx->src1_dma_stride, ne00 * sizeof(float), next_block_size);
+
+             ir_prefetch += next_block_size;
+        }
+        ir += current_block_size;
+    }
+    dma_queue_flush(q);
+}
+
+// 3. Row Broadcast (ne11 == 1, ne12 == 1, single row src1)
+static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
+    htp_binary_preamble;
+
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
+
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * src1_spad = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
+
+    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
+
+    void * s1_ptr = (void *) src1_spad;
+
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
+    }
+
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
+
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_src1 = (uint8_t *)s1_ptr; // Constant
+            uint8_t * r_dst  = d_spad + r * bctx->dst_row_size_aligned;
+            COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, ne00);
+        }
+
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             ir_prefetch += next_block_size;
+        }
+        ir += current_block_size;
+    }
+    dma_queue_flush(q);
+}
+
+// 4. Vector Complex (ne10 == ne00, complex broadcast)
+static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
+    htp_binary_preamble;
+
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
+
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
+    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
+
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
+    }
+
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
+
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint32_t r_i01 = i01 + r;
+            uint32_t i13 = fastmodulo(i03, ne13, &bctx->src1_dim3_div);
+            uint32_t i12 = fastmodulo(i02, ne12, &bctx->src1_dim2_div);
+            uint32_t i11 = fastmodulo(r_i01, ne11, &bctx->src1_dim1_div);
+
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_src1 = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
+            uint8_t * r_dst  = d_spad + r * bctx->dst_row_size_aligned;
+
+            // Read src1 from DDR (unaligned)
+            COMPUTE_VECTOR_OP_AAU(r_dst, r_src0, r_src1, ne00);
+        }
+
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             ir_prefetch += next_block_size;
+        }
+        ir += current_block_size;
+    }
+    dma_queue_flush(q);
+}
+
+// 5. Element Repeat (ne10 != ne00)
+static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
+    htp_binary_preamble;
+
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
+
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
+    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
+
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
+    }
+
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
+
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint32_t r_i01 = i01 + r;
+            uint32_t i13 = fastmodulo(i03, ne13, &bctx->src1_dim3_div);
+            uint32_t i12 = fastmodulo(i02, ne12, &bctx->src1_dim2_div);
+            uint32_t i11 = fastmodulo(r_i01, ne11, &bctx->src1_dim1_div);
+
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_src1_row = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
+            uint8_t * r_dst  = d_spad + r * bctx->dst_row_size_aligned;
+
+            // Repeat src1 row
+            for (uint32_t c = 0; c < ne00; c += ne10) {
+                uint32_t len = MIN(ne10, ne00 - c);
+                // Use UUU for speed and simplicity
+                COMPUTE_VECTOR_OP_UUU(r_dst + c * sizeof(float), r_src0 + c * sizeof(float), r_src1_row, len);
+            }
+        }
+
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             ir_prefetch += next_block_size;
+        }
+        ir += current_block_size;
+    }
+    dma_queue_flush(q);
+}
+
+// 6. ADD_ID (src1 gathered via src2 indices)
+static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_binary_context * bctx = (struct htp_binary_context *) data;
+    struct htp_ops_context * octx = bctx->octx;
+
+    const struct htp_tensor * src0 = &octx->src0;
+    const struct htp_tensor * src1 = &octx->src1;
+    const struct htp_tensor * src2 = &octx->src2;
+    struct htp_tensor *       dst  = &octx->dst;
+
+    const uint32_t ne00 = src0->ne[0];
+    const uint32_t ne01 = src0->ne[1];
+    const uint32_t ne02 = src0->ne[2];
+    const uint32_t ne03 = src0->ne[3];
+    const uint32_t ne11 = src1->ne[1]; // for bounds check
+
+    const uint32_t nb01 = src0->nb[1];
+    const uint32_t nb02 = src0->nb[2];
+    const uint32_t nb03 = src0->nb[3];
+    const uint32_t nb11 = src1->nb[1]; // src1 row stride
+    const uint32_t nb1 = dst->nb[1];
+    const uint32_t nb2 = dst->nb[2];
+    const uint32_t nb3 = dst->nb[3];
+
+    const uint32_t total_rows = ne01 * ne02 * ne03;
+    const uint32_t start_row = bctx->nrows_per_thread * ith;
+    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    if (start_row >= end_row) return;
+
+    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
+    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+
+    dma_queue * q = octx->ctx->dma[ith];
+    uint32_t ir_prefetch = start_row;
+    int spad_idx = 0;
+
+    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
+        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        rem = ir_prefetch - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
+        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
+
+        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
+        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;
+
+        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
+        ir_prefetch += current_block_size;
+        spad_idx ^= 1;
+    }
+
+    for (uint32_t ir = start_row; ir < end_row; ) {
+        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
+        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
+
+        uint32_t i03, i02, i01, rem;
+        i03 = fastdiv(ir, &bctx->dim12_div);
+        rem = ir - i03 * (ne02 * ne01);
+        i02 = fastdiv(rem, &bctx->dim1_div);
+        i01 = rem - i02 * ne01;
+
+        for (uint32_t r = 0; r < current_block_size; r++) {
+            uint32_t r_i01 = i01 + r; // linear within block since we split at ne01
+
+            const int32_t idx = *(int32_t *)((char *)src2->data + r_i01 * src2->nb[0] + i02 * src2->nb[1]);
+
+            uint8_t * r_src1 = (uint8_t *)src1->data + idx * nb11;
+            uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned;
+            uint8_t * r_dst  = d_spad + r * bctx->dst_row_size_aligned;
+
+            hvx_add_f32_aau(r_dst, r_src0, r_src1, ne00);
+        }
+
+        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size);
+
+        if (ir_prefetch < end_row) {
+             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
+             uint32_t p03, p02, p01, prem;
+             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             prem = ir_prefetch - p03 * (ne02 * ne01);
+             p02 = fastdiv(prem, &bctx->dim1_div);
+             p01 = prem - p02 * ne01;
+             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
+             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
+             ir_prefetch += next_block_size;
+        }
+        ir += current_block_size;
+    }
+    dma_queue_flush(q);
 }
 
 static int execute_op_binary_f32(struct htp_ops_context * octx) {
-    int err = HTP_STATUS_OK;
-
     const struct htp_tensor * src0 = &octx->src0;
     const struct htp_tensor * src1 = &octx->src1;
     struct htp_tensor *       dst  = &octx->dst;
 
-    worker_callback_t binary_op_func;
-    const char *      op_type = NULL;
-
-    switch (octx->op) {
-        case HTP_OP_MUL:
-            binary_op_func = binary_job_dispatcher_f32;
-            op_type        = "mul-f32";
-            break;
-
-        case HTP_OP_ADD:
-            binary_op_func = binary_job_dispatcher_f32;
-            op_type        = "add-f32";
-            break;
-
-        case HTP_OP_SUB:
-            binary_op_func = binary_job_dispatcher_f32;
-            op_type        = "sub-f32";
-            break;
-
-        case HTP_OP_ADD_ID:
-            binary_op_func = binary_job_dispatcher_f32;
-            op_type        = "add-id-f32";
-            break;
-
-        default:
-            FARF(ERROR, "Unsupported binary-Op %u\n", octx->op);
-            return HTP_STATUS_NO_SUPPORT;
-    }
-
-    const int      n_threads  = octx->n_threads;
+    const uint32_t n_threads  = octx->n_threads;
     const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
 
-    const size_t src0_row_size = src0->nb[1];
-    const size_t src1_row_size = src1->nb[1];
-    const size_t dst_row_size  = dst->nb[1];
+    // Use packed row sizes for VTCM allocation
+    const size_t src0_row_size = src0->ne[0] * sizeof(float);
+    const size_t src1_row_size = src1->ne[0] * sizeof(float);
+    const size_t dst_row_size  = dst->ne[0] * sizeof(float);
 
-    // VTCM scratchpads for all tensors
-    octx->dst_spad.size  = hex_round_up(dst_row_size, 128) * n_threads;
-    octx->src0_spad.size = hex_round_up(src0_row_size, 128) * n_threads;
-    octx->src1_spad.size = hex_round_up(src1_row_size, 128) * n_threads;
+    // Align to VLEN
+    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
+    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
 
-    size_t spad_size = octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size;
+    bool is_add_id = (octx->op == HTP_OP_ADD_ID);
+    bool is_scalar = !is_add_id && (src1->ne[0] == 1);
 
-    FARF(HIGH,
-         "%s: (%ux%ux%ux%u) * (%ux%ux%ux%u) -> (%ux%ux%ux%u) : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n",
-         op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2],
-         src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size,
-         octx->dst_spad.size);
+    // Determine which kernel we will use to alloc memory and dispatch
+    bool use_vector_same = !is_add_id && !is_scalar && src1->ne[0] == src0->ne[0] &&
+               (src1->ne[1] == src0->ne[1] || src1->ne[1] == 1) &&
+               (src1->ne[2] == src0->ne[2] || src1->ne[2] == 1) &&
+               (src1->ne[3] == src0->ne[3] || src1->ne[3] == 1);
 
-    // Make sure the reserved vtcm size is sufficient
-    if (octx->ctx->vtcm_size < spad_size) {
-        FARF(ERROR, "binary-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type,
-             octx->ctx->vtcm_size, spad_size);
+    bool is_row_bcast = use_vector_same && (src1->ne[1] == 1 && src1->ne[2] == 1 && src1->ne[3] == 1);
+    bool use_complex = !is_add_id && !is_scalar && !use_vector_same && (src1->ne[0] == src0->ne[0]);
+    bool use_repeat  = !is_add_id && !is_scalar && !use_vector_same && (src1->ne[0] != src0->ne[0]);
+
+    size_t spad_row_total;
+    if (is_scalar) {
+        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+    } else if (is_row_bcast) {
+        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+    } else if (use_vector_same) {
+        spad_row_total = 2 * (src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned);
+    } else if (is_add_id) {
+        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned); // src1 read directly
+    } else {
+        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+    }
+
+    size_t rows_per_buffer = octx->ctx->vtcm_size / (n_threads * spad_row_total);
+    // Adjust for static src1 in row_bcast case
+    if (is_row_bcast) {
+        size_t needed_static = src1_row_size_aligned;
+        if (octx->ctx->vtcm_size < needed_static) return HTP_STATUS_VTCM_TOO_SMALL;
+        size_t avail = octx->ctx->vtcm_size - needed_static;
+        rows_per_buffer = avail / (n_threads * spad_row_total);
+    }
+
+    if (rows_per_buffer < 1) {
+         FARF(ERROR, "binary-f32: VTCM too small\n");
+         return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    octx->src0_spad.size_per_thread = rows_per_buffer * 2 * src0_row_size_aligned;
+    octx->dst_spad.size_per_thread  = rows_per_buffer * 2 * dst_row_size_aligned;
+
+    if (is_scalar || use_complex || use_repeat || is_add_id) {
+        octx->src1_spad.size_per_thread = 0;
+    } else if (is_row_bcast) {
+        octx->src1_spad.size_per_thread = 0;
+    } else {
+        octx->src1_spad.size_per_thread = rows_per_buffer * 2 * src1_row_size_aligned;
+    }
+
+    octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread;
+    if (is_row_bcast) {
+        octx->src1_spad.size = src1_row_size_aligned;
+    } else {
+        octx->src1_spad.size = n_threads * octx->src1_spad.size_per_thread;
+    }
+    octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;
+
+    if (octx->ctx->vtcm_size < (octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size)) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
@@ -305,39 +757,71 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) {
     octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
     octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
 
-    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
-        uint32_t n_jobs = MIN(n_threads, src0_nrows);
-
-        octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
-
-        octx->src0_div21 = init_fastdiv_values(src0->ne[2] * src0->ne[1]);
-        octx->src0_div3  = init_fastdiv_values(src0->ne[3]);
-        octx->src0_div2  = init_fastdiv_values(src0->ne[2]);
-        octx->src0_div1  = init_fastdiv_values(src0->ne[1]);
-
-        octx->src1_div21 = init_fastdiv_values(src1->ne[2] * src1->ne[1]);
-        octx->src1_div3  = init_fastdiv_values(src1->ne[3]);
-        octx->src1_div2  = init_fastdiv_values(src1->ne[2]);
-        octx->src1_div1  = init_fastdiv_values(src1->ne[1]);
-
-        worker_pool_run_func(octx->ctx->worker_pool, binary_op_func, octx, n_jobs);
+    if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
+        return HTP_STATUS_OK;
     }
 
-    return err;
+    uint32_t n_jobs = MIN(n_threads, src0_nrows);
+
+    dma_queue * q = octx->ctx->dma[0];
+    if (is_row_bcast) {
+        dma_queue_push(q, dma_make_ptr(octx->src1_spad.data, (const void *) src1->data), src1_row_size_aligned, 0, src1->ne[0] * sizeof(float), 1);
+    }
+
+    struct htp_binary_context bctx;
+    bctx.octx = octx;
+    bctx.nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
+    bctx.block_max = rows_per_buffer;
+    bctx.src0_row_size_aligned = src0_row_size_aligned;
+    bctx.src1_row_size_aligned = src1_row_size_aligned;
+    bctx.dst_row_size_aligned  = dst_row_size_aligned;
+
+    bctx.dim1_div = init_fastdiv_values(src0->ne[1]);
+    bctx.dim2_div = init_fastdiv_values(src0->ne[2]);
+    bctx.dim12_div = init_fastdiv_values(src0->ne[1] * src0->ne[2]);
+
+    bctx.src1_dim1_div = init_fastdiv_values(src1->ne[1]);
+    bctx.src1_dim2_div = init_fastdiv_values(src1->ne[2]);
+    bctx.src1_dim3_div = init_fastdiv_values(src1->ne[3]);
+
+    bool src0_contig_dim1 = (src0->nb[2] == src0->ne[1] * src0->nb[1]);
+    bool dst_contig_dim1  = (dst->nb[2] == src0->ne[1] * dst->nb[1]);
+
+    bool src0_contig_dim2 = (src0->nb[3] == src0->ne[2] * src0->nb[2]);
+    bool dst_contig_dim2  = (dst->nb[3] == src0->ne[2] * dst->nb[2]);
+
+    bctx.split_at_ne01 = (src0->ne[2] > 1) &&
+                         ((src1->ne[1] > 1) || (src1->ne[2] > 1) || !src0_contig_dim1 || !dst_contig_dim1);
+
+    bctx.split_at_ne02 = (src0->ne[3] > 1) &&
+                         ((src1->ne[2] > 1) || (src1->ne[3] > 1) || !src0_contig_dim2 || !dst_contig_dim2);
+
+    // Precompute specific kernel parameters
+    if (use_vector_same) {
+        bctx.src1_dma_stride = (src1->ne[1] == 1) ? 0 : src1->nb[1];
+        bctx.src1_fetch_rows = (src1->ne[1] == 1) ? 1 : rows_per_buffer;
+    }
+
+    worker_callback_t worker_func;
+    if (is_add_id) worker_func = binary_job_add_id;
+    else if (is_scalar) worker_func = binary_job_scalar;
+    else if (is_row_bcast) worker_func = binary_job_vector_row_broadcast;
+    else if (use_vector_same) worker_func = binary_job_vector_same_shape;
+    else if (use_complex) worker_func = binary_job_vector_complex;
+    else worker_func = binary_job_element_repeat;
+
+    if (is_row_bcast) {
+        dma_queue_pop(q);
+    }
+
+    worker_pool_run_func(octx->ctx->worker_pool, worker_func, &bctx, n_jobs);
+
+    return HTP_STATUS_OK;
 }
 
 int op_binary(struct htp_ops_context * octx) {
-    int err = HTP_STATUS_OK;
-
-    switch (octx->src0.type) {
-        case HTP_TYPE_F32:
-            err = execute_op_binary_f32(octx);
-            break;
-
-        default:
-            err = HTP_STATUS_NO_SUPPORT;
-            break;
+    if (octx->src0.type == HTP_TYPE_F32) {
+        return execute_op_binary_f32(octx);
     }
-
-    return err;
+    return HTP_STATUS_NO_SUPPORT;
 }
diff --git a/ggml/src/ggml-hexagon/htp/htp-msg.h b/ggml/src/ggml-hexagon/htp/htp-msg.h
index f49e8ee447..25403bb112 100644
--- a/ggml/src/ggml-hexagon/htp/htp-msg.h
+++ b/ggml/src/ggml-hexagon/htp/htp-msg.h
@@ -42,32 +42,36 @@ enum htp_data_type {
     HTP_TYPE_COUNT
 };
 
-// These values are manually translated over to HTP
-// !!!! DO NOT ALTER THE ORDER OF THE FIRST FOUR ENUMS !!!!
+// Do not reorder first 4 (used as an index)
 enum htp_op {
-    HTP_OP_MUL            = 0,
-    HTP_OP_ADD            = 1,
-    HTP_OP_SUB            = 2,
-    HTP_OP_DIV            = 3,
-    HTP_OP_MUL_MAT        = 4,
-    HTP_OP_MUL_MAT_ID     = 5,
-    HTP_OP_RMS_NORM       = 6,
-    HTP_OP_UNARY_SILU     = 7,
-    HTP_OP_UNARY_GELU     = 8,
-    HTP_OP_GLU_SWIGLU     = 9,
-    HTP_OP_GLU_SWIGLU_OAI = 10,
-    HTP_OP_SOFTMAX        = 11,
-    HTP_OP_ADD_ID         = 12,
-    HTP_OP_ROPE           = 13,
-    HTP_OP_FLASH_ATTN_EXT = 14,
-    HTP_OP_SET_ROWS       = 15,
-    HTP_OP_SCALE          = 16,
-    HTP_OP_GET_ROWS       = 17,
-    HTP_OP_CPY            = 18,
+    HTP_OP_MUL = 0,
+    HTP_OP_ADD = 1,
+    HTP_OP_SUB = 2,
+    HTP_OP_DIV = 3,
+    HTP_OP_MUL_MAT,
+    HTP_OP_MUL_MAT_ID,
+    HTP_OP_RMS_NORM,
+    HTP_OP_UNARY_SILU,
+    HTP_OP_UNARY_GELU,
+    HTP_OP_GLU_SWIGLU,
+    HTP_OP_GLU_SWIGLU_OAI,
+    HTP_OP_GLU_GEGLU,
+    HTP_OP_SOFTMAX,
+    HTP_OP_ADD_ID,
+    HTP_OP_ROPE,
+    HTP_OP_FLASH_ATTN_EXT,
+    HTP_OP_SET_ROWS,
+    HTP_OP_GET_ROWS,
+    HTP_OP_SCALE,
+    HTP_OP_CPY,
+    HTP_OP_ARGSORT,
+    HTP_OP_SQR,
+    HTP_OP_SQRT,
+    HTP_OP_SUM_ROWS,
     INVALID
 };
 
-static inline size_t htp_type_block_size(uint32_t t) {
+static inline size_t htp_t_block_size(uint32_t t) {
     switch (t) {
         case HTP_TYPE_F32:
             return 1;
@@ -103,22 +107,6 @@ static inline size_t htp_type_nbytes(uint32_t t) {
     return 0;
 }
 
-static const char * htp_type_name(uint32_t t) {
-    switch (t) {
-        case HTP_TYPE_F32:
-            return "fp32";
-        case HTP_TYPE_F16:
-            return "fp16";
-        case HTP_TYPE_Q4_0:
-            return "q4_0";
-        case HTP_TYPE_Q8_0:
-            return "q8_0";
-        case HTP_TYPE_MXFP4:
-            return "mxfp4";
-    }
-    return 0;
-}
-
 // Internal types
 #define QK_Q4_0x4x2  256  // 4x Q4_0 blocks packed with next 4x Q4_0 blocks (size in bytes 128)
 #define QK_Q8_0x4x2  256  // 4x Q8_0 blocks concat with next 4x Q8_0 blocks
diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index 602a2775a4..c0d72587ce 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -90,6 +90,7 @@ int op_matmul(struct htp_ops_context * octx);
 int op_matmul_id(struct htp_ops_context * octx);
 int op_binary(struct htp_ops_context * octx);
 int op_unary(struct htp_ops_context * octx);
+int op_sum_rows(struct htp_ops_context * octx);
 int op_activations(struct htp_ops_context * octx);
 int op_softmax(struct htp_ops_context * octx);
 int op_add_id(struct htp_ops_context * octx);
@@ -98,5 +99,6 @@ int op_flash_attn_ext(struct htp_ops_context * octx);
 int op_set_rows(struct htp_ops_context * octx);
 int op_get_rows(struct htp_ops_context * octx);
 int op_cpy(struct htp_ops_context * octx);
+int op_argsort(struct htp_ops_context * octx);
 
 #endif /* HTP_OPS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-arith.h b/ggml/src/ggml-hexagon/htp/hvx-arith.h
index 3449739a4f..2577cdd041 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-arith.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-arith.h
@@ -46,127 +46,76 @@
 #define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
 #endif
 
-// ADD variants
+// Generic macro to define alignment permutations for an op
+#define DEFINE_HVX_BINARY_OP_VARIANTS(OP_NAME, OP_MACRO) \
+static inline void OP_NAME##_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src0 % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src0 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) dst % 128 == 0); \
+    hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \
+} \
+static inline void OP_NAME##_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src0 % 128 == 0); \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src0 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    assert((uintptr_t) src1 % 128 == 0); \
+    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \
+} \
+static inline void OP_NAME##_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \
+    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \
+} \
 
-static inline void hvx_add_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_ADD);
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_add_f32, HVX_OP_ADD)
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_sub_f32, HVX_OP_SUB)
+DEFINE_HVX_BINARY_OP_VARIANTS(hvx_mul_f32, HVX_OP_MUL)
+
+// Dispatcher logic
+#define HVX_BINARY_DISPATCHER(OP_NAME) \
+static inline void OP_NAME(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) { \
+    if (hex_is_aligned((void *) dst, 128)) { \
+        if (hex_is_aligned((void *) src0, 128)) { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aaa(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_aau(dst, src0, src1, num_elems); \
+        } else { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aua(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_auu(dst, src0, src1, num_elems); \
+        } \
+    } else { \
+        if (hex_is_aligned((void *) src0, 128)) { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uaa(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_uau(dst, src0, src1, num_elems); \
+        } else { \
+            if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uua(dst, src0, src1, num_elems); \
+            else                                    OP_NAME##_uuu(dst, src0, src1, num_elems); \
+        } \
+    } \
 }
 
-static inline void hvx_add_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-static inline void hvx_add_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_ADD);
-}
-
-// SUB variants
-
-static inline void hvx_sub_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-static inline void hvx_sub_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_SUB);
-}
-
-// MUL variants
-
-static inline void hvx_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_au(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) dst % 128 == 0);
-    assert((unsigned long) src0 % 128 == 0);
-    hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    assert((unsigned long) src0 % 128 == 0);
-    assert((unsigned long) src1 % 128 == 0);
-    hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-static inline void hvx_mul_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
-    hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MUL);
-}
-
-// Dispatchers
-
-static inline void hvx_add_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_add_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_add_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_add_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_add_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_sub_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_sub_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_sub_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_sub_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_sub_f32_uu(dst, src0, src1, num_elems);
-    }
-}
-
-static inline void hvx_mul_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
-    if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src0, 128)) {
-        if (hex_is_aligned((void *) src1, 128)) {
-            hvx_mul_f32_aa(dst, src0, src1, num_elems);
-        } else {
-            hvx_mul_f32_au(dst, src0, src1, num_elems);
-        }
-    } else if (hex_is_aligned((void *) src0, 128) && hex_is_aligned((void *) src1, 128)) {
-        hvx_mul_f32_ua(dst, src0, src1, num_elems);
-    } else {
-        hvx_mul_f32_uu(dst, src0, src1, num_elems);
-    }
-}
+HVX_BINARY_DISPATCHER(hvx_add_f32)
+HVX_BINARY_DISPATCHER(hvx_sub_f32)
+HVX_BINARY_DISPATCHER(hvx_mul_f32)
 
 // Mul-Mul Optimized
-
 static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint8_t * restrict src2, const uint32_t num_elems) {
     assert((unsigned long) dst % 128 == 0);
     assert((unsigned long) src0 % 128 == 0);
@@ -443,6 +392,68 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t *
     }
 }
 
+//
+// Square
+//
+
+#define hvx_sqr_loop_body(dst_type, src_type, vec_store)           \
+    do {                                                                   \
+        dst_type * restrict vdst  = (dst_type *) dst;                      \
+        src_type * restrict vsrc = (src_type *) src;                       \
+                                                                           \
+        const uint32_t elem_size = sizeof(float);                          \
+        const uint32_t epv  = 128 / elem_size;                             \
+        const uint32_t nvec = n / epv;                                     \
+        const uint32_t nloe = n % epv;                                     \
+                                                                           \
+        uint32_t i = 0;                                                    \
+                                                                           \
+        _Pragma("unroll(4)")                                               \
+        for (; i < nvec; i++) {                                            \
+            vdst[i] = HVX_OP_MUL(vsrc[i], vsrc[i]);                        \
+        }                                                                  \
+        if (nloe) {                                                        \
+            HVX_Vector v = HVX_OP_MUL(vsrc[i], vsrc[i]);                   \
+            vec_store((void *) &vdst[i], nloe * elem_size, v);             \
+        }                                                                  \
+    } while(0)
+
+static inline void hvx_sqr_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqr_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqr_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqr_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqr_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    hvx_sqr_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqr_f32(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t num_elems) {
+    if (hex_is_aligned((void *) dst, 128)) {
+        if (hex_is_aligned((void *) src, 128)) {
+            hvx_sqr_f32_aa(dst, src, num_elems);
+        } else {
+            hvx_sqr_f32_au(dst, src, num_elems);
+        }
+    } else {
+        if (hex_is_aligned((void *) src, 128)) {
+            hvx_sqr_f32_ua(dst, src, num_elems);
+        } else {
+            hvx_sqr_f32_uu(dst, src, num_elems);
+        }
+    }
+}
+
 #undef HVX_OP_ADD
 #undef HVX_OP_SUB
 #undef HVX_OP_MUL
@@ -453,5 +464,7 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t *
 #undef hvx_scalar_loop_body
 #undef HVX_OP_MIN_SCALAR
 #undef HVX_OP_CLAMP_SCALAR
+#undef DEFINE_HVX_BINARY_OP_VARIANTS
+#undef HVX_BINARY_DISPATCHER
 
 #endif // HVX_ARITH_H
diff --git a/ggml/src/ggml-hexagon/htp/hvx-base.h b/ggml/src/ggml-hexagon/htp/hvx-base.h
index ffa6e18e64..12a1b7f128 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-base.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-base.h
@@ -66,6 +66,12 @@ static inline float hvx_vec_get_f32(HVX_Vector v) {
     return x;
 }
 
+static inline int32_t hvx_vec_get_i32(HVX_Vector v) {
+    int32_t __attribute__((aligned(128))) x;
+    hvx_vec_store_a(&x, 4, v);
+    return x;
+}
+
 static inline HVX_Vector hvx_vec_abs_f16(HVX_Vector v) {
     // abs by clearing the fp16 sign bit
     HVX_Vector mask = Q6_Vh_vsplat_R(0x7fff);
diff --git a/ggml/src/ggml-hexagon/htp/hvx-copy.h b/ggml/src/ggml-hexagon/htp/hvx-copy.h
index 6b617b7617..ae0dbed030 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-copy.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-copy.h
@@ -136,8 +136,6 @@ static inline void hvx_copy_f32_uu(uint8_t * restrict dst, const uint8_t * restr
         dst_type * restrict vdst = (dst_type *) dst;                                \
         src_type * restrict vsrc = (src_type *) src;                                \
                                                                                     \
-        const HVX_Vector zero = Q6_V_vsplat_R(0);                                   \
-                                                                                    \
         const uint32_t elem_size = sizeof(__fp16);                                  \
         const uint32_t epv  = 128 / elem_size;                                      \
         const uint32_t nvec = n / epv;                                              \
diff --git a/ggml/src/ggml-hexagon/htp/hvx-div.h b/ggml/src/ggml-hexagon/htp/hvx-div.h
new file mode 100644
index 0000000000..7dae012e0e
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hvx-div.h
@@ -0,0 +1,116 @@
+#ifndef HVX_DIV_H
+#define HVX_DIV_H
+
+#include 
+
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include "hvx-base.h"
+#include "hex-utils.h"
+#include "hvx-inverse.h"
+#include "hvx-arith.h"
+
+#if __HVX_ARCH__ < 79
+#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
+#define hvx_div_f32_loop_body(dst_type, src0_type, src1_type, vec_store)             \
+    do {                                                                             \
+        dst_type * restrict vdst = (dst_type *) dst;                                 \
+        src0_type * restrict vsrc0 = (src0_type *) src0;                             \
+        src1_type * restrict vsrc1 = (src1_type *) src1;                             \
+                                                                                     \
+        const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(0x7f800000);                   \
+                                                                                     \
+        const uint32_t nvec = n / VLEN_FP32;                                         \
+        const uint32_t nloe = n % VLEN_FP32;                                         \
+                                                                                     \
+        uint32_t i = 0;                                                              \
+                                                                                     \
+        _Pragma("unroll(4)")                                                         \
+        for (; i < nvec; i++) {                                                      \
+            HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \
+            HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1);                         \
+            vdst[i] = res;                                                           \
+        }                                                                            \
+        if (nloe) {                                                                  \
+            HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \
+            HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1);                         \
+            vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, res);                   \
+        }                                                                            \
+    } while(0)
+
+// 3-letter suffix variants
+static inline void hvx_div_f32_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src0 % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src0 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) dst % 128 == 0);
+    hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_div_f32_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src0 % 128 == 0);
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src0 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    assert((uintptr_t) src1 % 128 == 0);
+    hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
+    hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_div_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) {
+    if (hex_is_aligned((void *) dst, 128)) {
+        if (hex_is_aligned((void *) src0, 128)) {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aaa(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_aau(dst, src0, src1, num_elems);
+        } else {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aua(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_auu(dst, src0, src1, num_elems);
+        }
+    } else {
+        if (hex_is_aligned((void *) src0, 128)) {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uaa(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_uau(dst, src0, src1, num_elems);
+        } else {
+            if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uua(dst, src0, src1, num_elems);
+            else                                    hvx_div_f32_uuu(dst, src0, src1, num_elems);
+        }
+    }
+}
+
+#undef HVX_OP_MUL
+
+#endif // HVX_DIV_H
diff --git a/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h b/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
index 1b4aaff0c9..095193277e 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
@@ -91,6 +91,27 @@ static inline HVX_Vector hvx_vec_tanh_f32(HVX_Vector x) {
         }                                                       \
     } while(0)
 
+#define hvx_tanh_loop_body(dst_type, src_type, vec_store)       \
+    do {                                                        \
+        dst_type * restrict vdst = (dst_type *) dst;            \
+        src_type * restrict vsrc = (src_type *) src;            \
+                                                                \
+        const uint32_t epv  = 128 / sizeof(float);              \
+        const uint32_t nvec = n / epv;                          \
+        const uint32_t nloe = n % epv;                          \
+                                                                \
+        uint32_t i = 0;                                         \
+                                                                \
+        _Pragma("unroll(4)")                                    \
+        for (; i < nvec; i++) {                                 \
+             vdst[i] = hvx_vec_tanh_f32(vsrc[i]);               \
+        }                                                       \
+        if (nloe) {                                             \
+             HVX_Vector tmp = hvx_vec_tanh_f32(vsrc[i]);        \
+             vec_store((void *) &vdst[i], nloe * sizeof(float), tmp); \
+        }                                                       \
+    } while(0)
+
 static inline void hvx_sigmoid_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
     assert((unsigned long) dst % 128 == 0);
     assert((unsigned long) src % 128 == 0);
@@ -111,4 +132,10 @@ static inline void hvx_sigmoid_f32_uu(uint8_t * restrict dst, const uint8_t * re
     hvx_sigmoid_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
 }
 
+static inline void hvx_tanh_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_tanh_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
 #endif /* HVX_SIGMOID_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-sqrt.h b/ggml/src/ggml-hexagon/htp/hvx-sqrt.h
index 28ee9f68d3..e31a1006d2 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-sqrt.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-sqrt.h
@@ -12,11 +12,17 @@
 #define RSQRT_ONE_HALF     0x3f000000  // 0.5
 #define RSQRT_THREE_HALVES 0x3fc00000  // 1.5
 
+#if __HVX_ARCH__ < 79
+#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
 static inline HVX_Vector hvx_vec_rsqrt_f32(HVX_Vector in_vec) {
     //Algorithm :
     //  x2 = input*0.5
     //  y  = * (long *) &input
-    //  y  = 0x5f3759df - (y>>2)
+    //  y  = 0x5f3759df - (y>>1)
     //  y  = y*(threehalfs - x2*y*y)
 
     HVX_Vector rsqrtconst = Q6_V_vsplat_R(RSQRT_CONST);
@@ -57,4 +63,64 @@ static inline HVX_Vector hvx_vec_rsqrt_f32(HVX_Vector in_vec) {
     return Q6_Vsf_equals_Vqf32(temp);
 }
 
+// Compute sqrt(x) as x*inv_sqrt(x)
+#define hvx_sqrt_f32_loop_body(dst_type, src_type, vec_store)                \
+    do {                                                                     \
+        dst_type * restrict vdst = (dst_type *) dst;                         \
+        src_type * restrict vsrc = (src_type *) src;                         \
+                                                                             \
+        const uint32_t nvec = n / VLEN_FP32;                                 \
+        const uint32_t nloe = n % VLEN_FP32;                                 \
+                                                                             \
+        uint32_t i = 0;                                                      \
+                                                                             \
+        _Pragma("unroll(4)")                                                 \
+        for (; i < nvec; i++) {                                              \
+            HVX_Vector inv_sqrt = hvx_vec_rsqrt_f32(vsrc[i]);                \
+            HVX_Vector sqrt_res = HVX_OP_MUL(inv_sqrt, vsrc[i]);             \
+            vdst[i] = sqrt_res;                                              \
+        }                                                                    \
+        if (nloe) {                                                          \
+            HVX_Vector inv_sqrt = hvx_vec_rsqrt_f32(vsrc[i]);                \
+            HVX_Vector sqrt_res = HVX_OP_MUL(inv_sqrt, vsrc[i]);             \
+            vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, sqrt_res);      \
+        }                                                                    \
+    } while(0)
+
+static inline void hvx_sqrt_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqrt_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a);
+}
+
+static inline void hvx_sqrt_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) src % 128 == 0);
+    hvx_sqrt_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqrt_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    hvx_sqrt_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u);
+}
+
+static inline void hvx_sqrt_f32(uint8_t * restrict dst, const uint8_t * restrict src, const int num_elems) {
+    if ((unsigned long) dst % 128 == 0) {
+        if ((unsigned long) src % 128 == 0) {
+            hvx_sqrt_f32_aa(dst, src, num_elems);
+        } else {
+            hvx_sqrt_f32_au(dst, src, num_elems);
+        }
+    } else {
+        if ((unsigned long) src % 128 == 0) {
+            hvx_sqrt_f32_ua(dst, src, num_elems);
+        } else {
+            hvx_sqrt_f32_uu(dst, src, num_elems);
+        }
+    }
+}
+
 #endif /* HVX_SQRT_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h
index 7b79a5ea32..a518ad3733 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h
@@ -12,6 +12,7 @@
 #include "hvx-sigmoid.h"
 #include "hvx-sqrt.h"
 #include "hvx-arith.h"
+#include "hvx-div.h"
 #include "hvx-base.h"
 
 #endif /* HVX_UTILS_H */
diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index e28a67a95d..62708eee5c 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -440,6 +440,45 @@ static void proc_matmul_req(struct htp_context *     ctx,
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
+static void proc_argsort_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
+    struct dspqueue_buffer rsp_bufs[1];
+
+    // We had written to the output buffer, we'd also need to flush it
+    rsp_bufs[0].fd     = bufs[1].fd;
+    rsp_bufs[0].ptr    = bufs[1].ptr;
+    rsp_bufs[0].offset = bufs[1].offset;
+    rsp_bufs[0].size   = bufs[1].size;
+    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
+                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
+
+    // Setup Op context
+    struct htp_ops_context octx = { 0 };
+    octx.ctx                    = ctx;
+    octx.src0                   = req->src0;
+    octx.dst                    = req->dst;
+    octx.flags                  = req->flags;
+    octx.op                     = req->op;
+
+    memcpy(octx.op_params, req->op_params, sizeof(octx.op_params));
+
+    // Update data pointers
+    octx.src0.data = (uint32_t) bufs[0].ptr;
+    octx.dst.data  = (uint32_t) bufs[1].ptr;
+    octx.n_threads = ctx->n_threads;
+
+    struct profile_data prof;
+    profile_start(&prof);
+
+    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
+    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
+        rsp_status = op_argsort(&octx);
+        vtcm_release(ctx);
+    }
+
+    profile_stop(&prof);
+    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
+}
+
 static void proc_cpy_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
     struct dspqueue_buffer rsp_bufs[1];
 
@@ -679,6 +718,45 @@ static void proc_unary_req(struct htp_context * ctx, struct htp_general_req * re
     send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
 }
 
+static void proc_sum_rows_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) {
+    struct dspqueue_buffer rsp_bufs[HTP_MAX_PACKET_BUFFERS];
+
+    // We had written to the output buffer, we'd also need to flush it
+    rsp_bufs[0].fd     = bufs[1].fd;
+    rsp_bufs[0].ptr    = bufs[1].ptr;
+    rsp_bufs[0].offset = bufs[1].offset;
+    rsp_bufs[0].size   = bufs[1].size;
+    rsp_bufs[0].flags  = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER |         // Flush HTP
+                         DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT);  // Invalidate CPU
+
+    // Setup Op context
+    struct htp_ops_context octx = { 0 };
+    octx.ctx                    = ctx;
+    octx.src0                   = req->src0;
+    octx.dst                    = req->dst;
+    octx.flags                  = req->flags;
+    octx.op                     = req->op;
+
+    memcpy(octx.op_params, req->op_params, sizeof(octx.op_params));
+
+    // Update data pointers
+    octx.src0.data = (uint32_t) bufs[0].ptr;
+    octx.dst.data  = (uint32_t) bufs[1].ptr;
+    octx.n_threads = ctx->n_threads;
+
+    struct profile_data prof;
+    profile_start(&prof);
+
+    uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR;
+    if (vtcm_acquire(ctx) == AEE_SUCCESS) {
+        rsp_status = op_sum_rows(&octx);
+        vtcm_release(ctx);
+    }
+
+    profile_stop(&prof);
+    send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof);
+}
+
 static void proc_activations_req(struct htp_context *     ctx,
                                  struct htp_general_req * req,
                                  struct dspqueue_buffer * bufs,
@@ -951,6 +1029,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             case HTP_OP_MUL:
             case HTP_OP_ADD:
             case HTP_OP_SUB:
+            case HTP_OP_DIV:
                 if (n_bufs != 3) {
                     FARF(ERROR, "Bad binary-req buffer list");
                     continue;
@@ -968,6 +1047,25 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_unary_req(ctx, &req, bufs);
                 break;
 
+            case HTP_OP_SQR:
+            case HTP_OP_SQRT:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad unary-req buffer list");
+                    continue;
+                }
+
+                proc_unary_req(ctx, &req, bufs);
+                break;
+
+            case HTP_OP_SUM_ROWS:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad unary-req buffer list");
+                    continue;
+                }
+
+                proc_sum_rows_req(ctx, &req, bufs);
+                break;
+
             case HTP_OP_UNARY_SILU:
             case HTP_OP_UNARY_GELU:
                 if (n_bufs != 2) {
@@ -980,6 +1078,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             case HTP_OP_GLU_SWIGLU:
             case HTP_OP_GLU_SWIGLU_OAI:
             case HTP_OP_SOFTMAX:
+            case HTP_OP_GLU_GEGLU:
                 if ((n_bufs != 2) && (n_bufs != 3)) {
                     FARF(ERROR, "Bad act-req buffer list");
                     continue;
@@ -1035,6 +1134,14 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
                 proc_cpy_req(ctx, &req, bufs);
                 break;
 
+            case HTP_OP_ARGSORT:
+                if (n_bufs != 2) {
+                    FARF(ERROR, "Bad argsort-req buffer list");
+                    continue;
+                }
+                proc_argsort_req(ctx, &req, bufs);
+                break;
+
             default:
                 FARF(ERROR, "Unknown Op %u", req.op);
                 break;
diff --git a/ggml/src/ggml-hexagon/htp/sum-rows-ops.c b/ggml/src/ggml-hexagon/htp/sum-rows-ops.c
new file mode 100644
index 0000000000..62e45da2b3
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/sum-rows-ops.c
@@ -0,0 +1,115 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include 
+#include 
+
+#include 
+#include 
+
+#include "hex-dma.h"
+#include "hvx-utils.h"
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-msg.h"
+#include "htp-ops.h"
+
+
+#define sum_rows_preamble                       \
+    struct htp_tensor *src0 =  &octx->src0;\
+    struct htp_tensor *dst  = &octx->dst;  \
+                                           \
+    const uint32_t ne00 = src0->ne[0];     \
+    const uint32_t ne01 = src0->ne[1];     \
+    const uint32_t ne02 = src0->ne[2];     \
+    const uint32_t ne03 = src0->ne[3];     \
+                                           \
+    const uint32_t nb00 = src0->nb[0];     \
+    const uint32_t nb01 = src0->nb[1];     \
+    const uint32_t nb02 = src0->nb[2];     \
+    const uint32_t nb03 = src0->nb[3];     \
+                                           \
+    const uint32_t  ne0 = dst->ne[0];      \
+    const uint32_t  ne1 = dst->ne[1];      \
+    const uint32_t  ne2 = dst->ne[2];      \
+    const uint32_t  ne3 = dst->ne[3];      \
+                                           \
+    const uint32_t  nb0 = dst->nb[0];      \
+    const uint32_t  nb1 = dst->nb[1];      \
+    const uint32_t  nb2 = dst->nb[2];      \
+    const uint32_t  nb3 = dst->nb[3];      \
+
+static int sum_rows_thread_f32(struct htp_ops_context * octx, const int nth, const int ith) {
+    sum_rows_preamble;
+
+    const uint32_t src0_nrows_per_thread  = octx->src0_nrows_per_thread;
+    const size_t src0_row_size = nb01;
+    const size_t dst_row_size  = nb1;
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
+
+    const uint32_t src0_start_row = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row   = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+
+    // no work for this thread
+    if (src0_start_row >= src0_end_row) {
+        return HTP_STATUS_OK;
+    }
+
+    int opt_path   = 0;
+    if ((0 == hex_is_aligned((void *) src0->data, VLEN)) && !(nb01 & (VLEN - 1))) {
+        opt_path = 1;
+    }
+
+    const uint8_t * restrict data_src = (const uint8_t *) src0->data;
+    uint8_t * restrict data_dst       = (uint8_t *) dst->data;
+
+    const float * restrict src_th = (float *) (data_src + (src0_start_row * src0_row_size));
+    float * restrict dst_th       = (float *) (data_dst + (src0_start_row * dst_row_size));
+
+    for (uint32_t ir = 0; ir < src0_nrows_per_thread; ir++) {
+        const float * restrict src_local = src_th + (ir * ne00);
+
+        if (ir + 1 < src0_nrows_per_thread) {
+            hex_l2fetch(src_local + ne00, src0_row_size, src0_row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            dst_th[ir] = hvx_reduce_sum_f32_a((const uint8_t *) src_local, ne00);
+        } else {
+            dst_th[ir] = hvx_reduce_sum_f32((const uint8_t *) src_local, ne00);
+        }
+    }
+
+    return HTP_STATUS_OK;
+}
+
+static void sum_rows_work_f32(unsigned int n, unsigned int i, void *data) {
+    sum_rows_thread_f32((struct htp_ops_context *) data, n, i);
+}
+
+int op_sum_rows(struct htp_ops_context * octx) {
+    sum_rows_preamble;
+
+    if (octx->src0.type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    const int      n_threads  = octx->n_threads;
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;
+
+    uint32_t n_jobs = MIN(n_threads, src0_nrows);
+    octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
+
+    worker_pool_run_func(octx->ctx->worker_pool, sum_rows_work_f32, octx, n_jobs);
+
+    return HTP_STATUS_OK;
+}
+
diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c
index 1a27cb6e63..ce879bf037 100644
--- a/ggml/src/ggml-hexagon/htp/unary-ops.c
+++ b/ggml/src/ggml-hexagon/htp/unary-ops.c
@@ -132,6 +132,56 @@ static void rms_norm_htp_f32(const float * restrict src,
     }
 }
 
+static void sqr_htp_f32(const float * restrict src,
+                          float * restrict dst,
+                          uint8_t * restrict spad,
+                          const uint32_t num_rows,
+                          const uint32_t row_elems,
+                          const size_t   row_size,
+                          int32_t *      op_params,
+                          int            opt_path) {
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const float * restrict src_local = src + (ir * row_elems);
+        float * restrict dst_local       = dst + (ir * row_elems);
+
+        if (ir + 1 < num_rows) {
+            hex_l2fetch(src_local + row_elems, row_size, row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            hvx_sqr_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        } else {
+            hvx_sqr_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        }
+    }
+}
+
+static void sqrt_htp_f32(const float * restrict src,
+                          float * restrict dst,
+                          uint8_t * restrict spad,
+                          const uint32_t num_rows,
+                          const uint32_t row_elems,
+                          const size_t   row_size,
+                          int32_t *      op_params,
+                          int            opt_path) {
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const float * restrict src_local = src + (ir * row_elems);
+        float * restrict dst_local       = dst + (ir * row_elems);
+
+        if (ir + 1 < num_rows) {
+            hex_l2fetch(src_local + row_elems, row_size, row_size, 1);
+        }
+
+        if (1 == opt_path) {
+            hvx_sqrt_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        } else {
+            hvx_sqrt_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems);
+        }
+    }
+}
+
 static void unary_job_f32_per_thread(const struct htp_tensor * src,
                                      struct htp_tensor *       dst,
                                      uint8_t *                 spad,
@@ -181,6 +231,12 @@ static void unary_job_f32_per_thread(const struct htp_tensor * src,
         case HTP_OP_SCALE:
             scale_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
             break;
+        case HTP_OP_SQR:
+            sqr_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
+            break;
+        case HTP_OP_SQRT:
+            sqrt_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path);
+            break;
 
         default:
             break;
@@ -218,6 +274,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
             unary_op_func = unary_job_dispatcher_f32;
             op_type       = "scale-f32";
             break;
+        case HTP_OP_SQR:
+            unary_op_func = unary_job_dispatcher_f32;
+            op_type       = "sqr-f32";
+            break;
+        case HTP_OP_SQRT:
+            unary_op_func = unary_job_dispatcher_f32;
+            op_type       = "sqrt-f32";
+            break;
 
         default:
             FARF(ERROR, "Unsupported unary Op %u\n", octx->op);

From 0c1f39a9ae68d502bc73b2901e6985ae9f4557a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= 
Date: Wed, 11 Feb 2026 09:27:55 +0100
Subject: [PATCH 183/206] common : improve download error reporting (#19491)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët 
---
 common/download.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/common/download.cpp b/common/download.cpp
index 57f29a23ba..8710438aa4 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -305,7 +305,10 @@ static bool common_pull_file(httplib::Client & cli,
     );
 
     if (!res) {
-        LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
+        LOG_ERR("%s: download failed: %s (status: %d)\n",
+                __func__,
+                httplib::to_string(res.error()).c_str(),
+                res ? res->status : -1);
         return false;
     }
 

From ada90bf2ba9a440883a8bfcd6506329c412d4b51 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= 
Date: Wed, 11 Feb 2026 12:49:40 +0100
Subject: [PATCH 184/206] docs: ban AI for issues and discussions [no CI]
 (#19512)

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

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c928bc39ce..7545e790f8 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -20,7 +20,7 @@ If AI is used to generate any portion of the code, contributors must adhere to t
 1. Explicitly disclose the manner in which AI was employed.
 2. Perform a comprehensive manual review prior to submitting the pull request.
 3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
+4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
 
 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 

From 9ab072ebbe8ae679f6a35090979fe58c8659dc11 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 11 Feb 2026 14:53:19 +0200
Subject: [PATCH 185/206] metal : extend l2_norm support for non-cont src0
 (#19502)

---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 11 ++++--
 ggml/src/ggml-metal/ggml-metal-device.m   |  3 +-
 ggml/src/ggml-metal/ggml-metal-impl.h     | 15 +++++++-
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 46 ++++++++++++++++-------
 ggml/src/ggml-metal/ggml-metal.metal      | 30 ++++++++++-----
 5 files changed, 75 insertions(+), 30 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 949e344cc8..517559d12a 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -1480,13 +1480,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_met
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
     assert(op->op == GGML_OP_L2_NORM);
 
-    GGML_ASSERT(op->src[0]->ne[0] % 4 == 0);
-    GGML_ASSERT(ggml_is_contiguous_1(op->src[0]));
-
     char base[256];
     char name[256];
 
-    snprintf(base, 256, "kernel_l2_norm_f32");
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
+
+    snprintf(base, 256, "kernel_l2_norm_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
     snprintf(name, 256, "%s", base);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
@@ -1494,6 +1496,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_met
         res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
     }
 
+    res.c4   = is_c4;
     res.smem = 32*sizeof(float);
 
     return res;
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 50a2a3e7f7..c714ef3add 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1086,9 +1086,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_MEAN:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_GROUP_NORM:
-            return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_L2_NORM:
-            return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
+            return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_COUNT_EQUAL:
             return has_simdgroup_reduction &&
                 op->src[0]->type == GGML_TYPE_I32 &&
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 44141f8e3d..952e1be076 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -539,8 +539,21 @@ typedef struct {
 
 typedef struct {
     int32_t  ne00;
-    int32_t  ne00_4;
+    int32_t  ne01;
+    int32_t  ne02;
+    int32_t  ne03;
+    uint64_t nb00;
     uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  ne0;
+    int32_t  ne1;
+    int32_t  ne2;
+    int32_t  ne3;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
     float    eps;
 } ggml_metal_kargs_l2_norm;
 
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index b159a8e7fd..7db95d1c84 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -2979,39 +2979,59 @@ int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
+
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
     float eps;
     memcpy(&eps, op->op_params, sizeof(float));
 
-    int nth = 32; // SIMD width
-
     ggml_metal_kargs_l2_norm args = {
-        /*.ne00   =*/ ne00,
-        /*.ne00_4 =*/ ne00/4,
-        /*.nb01   =*/ nb01,
-        /*.eps    =*/ eps,
+        /*.ne00  =*/ ne00,
+        /*.ne01  =*/ ne01,
+        /*.ne02  =*/ ne02,
+        /*.ne03  =*/ ne03,
+        /*.nb00  =*/ nb00,
+        /*.nb01  =*/ nb01,
+        /*.nb02  =*/ nb02,
+        /*.nb03  =*/ nb03,
+        /*.ne0   =*/ ne0,
+        /*.ne1   =*/ ne1,
+        /*.ne2   =*/ ne2,
+        /*.ne3   =*/ ne3,
+        /*.nb0   =*/ nb0,
+        /*.nb1   =*/ nb1,
+        /*.nb2   =*/ nb2,
+        /*.nb3   =*/ nb3,
+        /*.eps   =*/ eps,
     };
 
     auto pipeline = ggml_metal_library_get_pipeline_l2_norm(lib, op);
 
-    while (nth < ne00/4 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
+
+    int nth = 32; // SIMD width
+
+    while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
         nth *= 2;
     }
 
     nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    nth = std::min(nth, ne00/4);
 
     const size_t smem = pipeline.smem;
 
-    const int64_t nrows = ggml_nrows(op->src[0]);
-
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
 
     ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, nrows, 1, 1, nth, 1, 1);
+    ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
 
     return 1;
 }
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 7d841341a1..a385a50b94 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -2706,26 +2706,32 @@ template [[host_name("kernel_rms_norm_f32_4")]]         kernel kernel_rms_norm_f
 template [[host_name("kernel_rms_norm_mul_f32_4")]]     kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl;
 template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl;
 
-kernel void kernel_l2_norm_f32(
+template 
+kernel void kernel_l2_norm_impl(
         constant ggml_metal_kargs_l2_norm & args,
         device const char * src0,
         device       char * dst,
         threadgroup float * shmem_f32 [[threadgroup(0)]],
-        uint   tgpig[[threadgroup_position_in_grid]],
-        ushort tpitg[[thread_position_in_threadgroup]],
-        ushort sgitg[[simdgroup_index_in_threadgroup]],
-        ushort tiisg[[thread_index_in_simdgroup]],
-        ushort   ntg[[threads_per_threadgroup]]) {
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    const int i03 = tgpig.z;
+    const int i02 = tgpig.y;
+    const int i01 = tgpig.x;
+
     if (sgitg == 0) {
         shmem_f32[tiisg] = 0.0f;
     }
 
-    device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01);
+    device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
+    device       T  * y = (device       T  *) (dst  + i03*args.nb3  + i02*args.nb2  + i01*args.nb1);
 
     float sumf = 0.0f;
 
     // parallel sum
-    for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
+    for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
         sumf += dot(x[i00], x[i00]);
     }
     sumf = simd_sum(sumf);
@@ -2743,12 +2749,16 @@ kernel void kernel_l2_norm_f32(
 
     const float scale = 1.0f/sqrt(max(sumf, args.eps));
 
-    device float4 * y = (device float4 *) dst + tgpig*args.ne00_4;
-    for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
+    for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
         y[i00] = x[i00] * scale;
     }
 }
 
+typedef decltype(kernel_l2_norm_impl) kernel_l2_norm_t;
+
+template [[host_name("kernel_l2_norm_f32_f32")]]   kernel kernel_l2_norm_t kernel_l2_norm_impl;
+template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl;
+
 kernel void kernel_group_norm_f32(
         constant ggml_metal_kargs_group_norm & args,
         device const float * src0,

From 53de59f67dfd17fe9df32bf48e0df757e833419f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 11 Feb 2026 14:02:29 +0100
Subject: [PATCH 186/206] build : fix case in dSYMs path for build-macos [no
 ci] (#19515)

This commit updates an incorrect dSYMs where the the 's' was uppercase
by mistake.

The motivation for fixing this is that this can cause issues on case
sensitive operating systems.

Refs: https://github.com/ggml-org/whisper.cpp/pull/3630
---
 build-xcframework.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build-xcframework.sh b/build-xcframework.sh
index 0eec871139..e8af16211f 100755
--- a/build-xcframework.sh
+++ b/build-xcframework.sh
@@ -534,7 +534,7 @@ xcodebuild -create-xcframework \
     -framework $(pwd)/build-ios-device/framework/llama.framework \
     -debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \
     -framework $(pwd)/build-macos/framework/llama.framework \
-    -debug-symbols $(pwd)/build-macos/dSYMS/llama.dSYM \
+    -debug-symbols $(pwd)/build-macos/dSYMs/llama.dSYM \
     -framework $(pwd)/build-visionos/framework/llama.framework \
     -debug-symbols $(pwd)/build-visionos/dSYMs/llama.dSYM \
     -framework $(pwd)/build-visionos-sim/framework/llama.framework \

From e463bbdf659fed30b0fba822257ec7d8b373d1c2 Mon Sep 17 00:00:00 2001
From: AesSedai <7980540+AesSedai@users.noreply.github.com>
Date: Wed, 11 Feb 2026 07:47:30 -0800
Subject: [PATCH 187/206] model: Add Kimi-K2.5 support (#19170)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Move dequant_model to after the text_config merge
Add new kimi-k2.5 keys to mtmd convert
Update V_MMPROJ tensor mapping for new mm_projector.proj keys
Update V_M_IMP_NORM for new mm_projector.pre_norm key

* Fix a couple of oversights

* Add image support for Kimi-K2.5

* Revert changes to KimiVLForConditionalGeneration

* Fix an assert crash

* Fix permute swapping w / h on accident

* Kimi-K2.5: Use merged QKV for vision

* Kimi-K2.5: pre-convert vision QK to use build_rope_2d

* Kimi-K2.5: support non-interleaved rope for vision

* Kimi-K2.5: fix min / max pixel

* Kimi-K2.5: remove v/o permutes, unnecessary

* Kimi-K2.5: update permute name to match

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret 

* Kimi-K2.5: replace build_rope_2d ggml_cont with ggml_view_3d pointers

---------

Co-authored-by: Sigbjørn Skjæret 
---
 convert_hf_to_gguf.py          | 124 ++++++++++++++++++++++++++++++---
 gguf-py/gguf/constants.py      |   1 +
 gguf-py/gguf/tensor_mapping.py |   3 +
 tools/mtmd/CMakeLists.txt      |   1 +
 tools/mtmd/clip-impl.h         |   2 +
 tools/mtmd/clip.cpp            |  90 ++++++++++++++++++++++--
 tools/mtmd/models/kimik25.cpp  | 101 +++++++++++++++++++++++++++
 tools/mtmd/models/models.h     |   7 ++
 8 files changed, 316 insertions(+), 13 deletions(-)
 create mode 100644 tools/mtmd/models/kimik25.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 61eb9839e0..2afaf85fb8 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -160,8 +160,6 @@ class ModelBase:
                 self.ftype = gguf.LlamaFileType.MOSTLY_F16
                 logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")
 
-        self.dequant_model()
-
         # Configure GGUF Writer
         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)
@@ -527,6 +525,8 @@ class ModelBase:
         return ()
 
     def prepare_tensors(self):
+        self.dequant_model()
+
         # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
         if self.tensor_map.mapping:
             max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
@@ -1815,7 +1815,7 @@ class MmprojModel(ModelBase):
     preprocessor_config: dict[str, Any]
     global_config: dict[str, Any]
 
-    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"]
+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"]
 
     has_vision_encoder: bool = True # by default
     has_audio_encoder: bool = False
@@ -1870,7 +1870,15 @@ class MmprojModel(ModelBase):
         preprocessor_config_path = self.dir_model / "preprocessor_config.json"
         if preprocessor_config_path.is_file():
             with open(preprocessor_config_path, "r", encoding="utf-8") as f:
-                self.preprocessor_config = json.load(f)
+                cfg = json.load(f)
+                # move media_proc_cfg to root level for compat
+                if "media_proc_cfg" in cfg:
+                    cfg = {
+                        **cfg,
+                        **cfg["media_proc_cfg"],
+                    }
+                # merge configs
+                self.preprocessor_config = {**self.preprocessor_config, **cfg}
 
         # prefer processor_config.json if possible
         processor_config_path = self.dir_model / "processor_config.json"
@@ -1919,10 +1927,10 @@ class MmprojModel(ModelBase):
             self.image_size = self.find_vparam(["image_size"])
             self.gguf_writer.add_vision_image_size(self.image_size)
             self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
-            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
-            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "vt_hidden_size"]))
+            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
             self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
-            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads"]))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "vt_num_attention_heads"]))
 
             # preprocessor config
             image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
@@ -7695,6 +7703,7 @@ class DeepseekModel(TextModel):
     "DeepseekV2ForCausalLM",
     "DeepseekV3ForCausalLM",
     "KimiVLForConditionalGeneration",
+    "KimiK25ForConditionalGeneration",
     "YoutuForCausalLM",
     "YoutuVLForConditionalGeneration",
 )
@@ -7813,8 +7822,8 @@ class DeepseekV2Model(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # skip vision tensors and remove "language_model." for Kimi-VL
-        if "vision_tower" in name or "multi_modal_projector" in name:
+        # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5
+        if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name:
             return
         if name.startswith("siglip2.") or name.startswith("merger."):
             return
@@ -11176,6 +11185,103 @@ class KimiVLModel(MmprojModel):
                 yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("KimiK25ForConditionalGeneration")
+class KimiK25Model(MmprojModel):
+    """Kimi-K2.5 with MoonViT3d vision encoder"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config"
+
+        self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2]))
+        self.patch_size = self.hparams_vision.get("patch_size", 14)
+
+        # Set image_size for compatibility with base class
+        # Use position embedding dimensions as image_size reference
+        pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64)
+        self.hparams_vision["image_size"] = pos_emb_h * self.patch_size
+
+    def set_gguf_parameters(self):
+        # Base class MmprojModel.set_gguf_parameters() already writes:
+        # - vision_block_count, vision_head_count, vision_embedding_length
+        # - vision_feed_forward_length, vision_patch_size, image_mean, image_std
+        # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25)
+
+        # Position embedding parameters (for interpolation)
+        self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4))
+
+        # Projector parameters
+        self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu")
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5))
+        self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0])
+
+        # Image size limits
+        # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet)
+        in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384)
+        min_patches = 8  # reasonable minimum
+        pixels_per_patch = self.patch_size ** 2
+        self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch)
+        self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int) -> Tensor:
+        out_dim, in_dim = weights.shape
+        head_dim = out_dim // n_head
+        w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim)
+        w = w.permute(0, 2, 1, 3, 4)
+        return w.reshape(out_dim, in_dim)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Only process vision and projector tensors
+        is_vision = any(x in name for x in ["vision_tower", "mm_projector"])
+
+        if not is_vision:
+            return
+
+        assert self.hparams_vision is not None
+        n_head = self.hparams_vision.get("num_attention_heads", 16)
+
+        # Permute Q/K weights/biases from interleaved to split RoPE format
+        # This allows using build_rope_2d at runtime without post-permutation.
+        if "wqkv" in name:
+            out_dim = data_torch.shape[0]
+            qkv_dim = out_dim // 3
+            head_dim = qkv_dim // n_head
+
+            if "weight" in name:
+                wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2 * qkv_dim, :], data_torch[2 * qkv_dim:, :]
+                wq = self.permute(wq, n_head)
+                wk = self.permute(wk, n_head)
+                data_torch = torch.cat([wq, wk, wv], dim=0)
+            elif "bias" in name:
+                bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]
+                bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                data_torch = torch.cat([bq, bk, bv], dim=0)
+
+        # Temporal embeddings: (T, 1, C) → (T, C)
+        if "pos_emb.time_weight" in name:
+            T, _, C = data_torch.shape
+            data_torch = data_torch.reshape(T, C)
+
+        # PatchMergerMLP tensor name mapping
+        # proj.0.weight → proj.linear_1.weight
+        # proj.2.weight → proj.linear_2.weight
+        if "mm_projector.proj.0." in name:
+            name = name.replace(".proj.0.", ".proj.linear_1.")
+        elif "mm_projector.proj.2." in name:
+            name = name.replace(".proj.2.", ".proj.linear_2.")
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("CogVLMForCausalLM")
 class CogVLMVisionModel(MmprojModel):
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index ec36b6292a..9dab0df08a 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -3766,6 +3766,7 @@ class VisionProjectorType:
     VOXTRAL = "voxtral"
     LFM2 = "lfm2"
     KIMIVL = "kimivl"
+    KIMIK25 = "kimik25"
     LIGHTONOCR = "lightonocr"
     COGVLM = "cogvlm"
     JANUS_PRO = "janus_pro"
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 71dd7e887b..43647904b4 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1303,6 +1303,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MMPROJ: (
             "multi_modal_projector.linear_{bid}",
+            "mm_projector.proj.linear_{bid}", # Kimi-K2.5
             "visual.merger.mlp.{bid}", # qwen2vl
             "merger.mlp.{bid}",
         ),
@@ -1364,6 +1365,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_ATTN_QKV: (
             "visual.blocks.{bid}.attn.qkv", # qwen3vl
             "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm
+            "vision_tower.encoder.blocks.{bid}.wqkv" # Kimi-K2.5
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
@@ -1538,6 +1540,7 @@ class TensorNameMap:
             "multi_modal_projector.norm",
             "multi_modal_projector.layer_norm",
             "multi_modal_projector.pre_norm",
+            "mm_projector.pre_norm", # Kimi-K2.5
             "pre_mm_projector_norm",
             "model.vision.linear_proj.norm1", # cogvlm
             "merger.ln_q",
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 751440af32..02d71f224e 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -19,6 +19,7 @@ add_library(mtmd
             models/glm4v.cpp
             models/internvl.cpp
             models/kimivl.cpp
+            models/kimik25.cpp
             models/llama4.cpp
             models/llava.cpp
             models/minicpmv.cpp
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index ad232178bf..3bc93ead86 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -235,6 +235,7 @@ enum projector_type {
     PROJECTOR_TYPE_LFM2A,
     PROJECTOR_TYPE_GLM4V,
     PROJECTOR_TYPE_YOUTUVL,
+    PROJECTOR_TYPE_KIMIK25,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -268,6 +269,7 @@ static std::map PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LFM2A,     "lfm2a"},
     { PROJECTOR_TYPE_GLM4V,     "glm4v"},
     { PROJECTOR_TYPE_YOUTUVL,   "youtuvl"},
+    { PROJECTOR_TYPE_KIMIK25,   "kimik25"},
 };
 
 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 614fe66fde..eeccb4cda0 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -673,8 +673,8 @@ ggml_tensor * clip_graph::build_rope_2d(
     {
         first = ggml_view_3d(ctx0, cur,
             n_dim/2, n_head, n_pos,
-            ggml_row_size(cur->type, n_dim),
-            ggml_row_size(cur->type, n_dim*n_head),
+            cur->nb[1],
+            cur->nb[2],
             0);
         first = ggml_rope_ext(
             ctx0,
@@ -692,8 +692,8 @@ ggml_tensor * clip_graph::build_rope_2d(
     {
         second = ggml_view_3d(ctx0, cur,
             n_dim/2, n_head, n_pos,
-            ggml_row_size(cur->type, n_dim),
-            ggml_row_size(cur->type, n_dim*n_head),
+            cur->nb[1],
+            cur->nb[2],
             n_dim/2 * ggml_element_size(cur));
         second = ggml_rope_ext(
             ctx0,
@@ -826,6 +826,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique(ctx, img);
             } break;
+        case PROJECTOR_TYPE_KIMIK25:
+            {
+                builder = std::make_unique(ctx, img);
+            } break;
         case PROJECTOR_TYPE_COGVLM:
             {
                 builder = std::make_unique(ctx, img);
@@ -1139,6 +1143,22 @@ struct clip_model_loader {
                         hparams.set_limit_image_tokens(8, 1024);
                         hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
                     } break;
+                case PROJECTOR_TYPE_KIMIK25:
+                    {
+                        hparams.rope_theta = 10000.0f;
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+
+                        int min_pixels = 0, max_pixels = 0;
+                        get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false);
+                        get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false);
+                        if (min_pixels > 0 && max_pixels > 0) {
+                            hparams.image_min_pixels = min_pixels;
+                            hparams.image_max_pixels = max_pixels;
+                            hparams.warmup_image_size = static_cast(std::sqrt(max_pixels));
+                        } else {
+                            hparams.set_limit_image_tokens(2, 4096);
+                        }
+                    } break;
                 case PROJECTOR_TYPE_GEMMA3:
                     {
                         // default value (used by all model sizes in gemma 3 family)
@@ -1668,6 +1688,7 @@ struct clip_model_loader {
                     model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
                 } break;
             case PROJECTOR_TYPE_KIMIVL:
+            case PROJECTOR_TYPE_KIMIK25:
                 {
                     model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
                     model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B);
@@ -3165,6 +3186,23 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
                 res_imgs->entries.push_back(std::move(res));
             } break;
 
+        case PROJECTOR_TYPE_KIMIK25:
+            {
+                GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
+                const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
+                    original_size,
+                    params.patch_size * params.n_merge,
+                    params.image_min_pixels,
+                    params.image_max_pixels);
+                const std::array pad_color = {0, 0, 0};
+
+                clip_image_u8 resized_img;
+                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BICUBIC, true, 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));
+            } break;
+
         case PROJECTOR_TYPE_MLP:
         case PROJECTOR_TYPE_MLP_NORM:
         case PROJECTOR_TYPE_LDP:
@@ -3373,6 +3411,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
             } break;
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_KIMIVL:
+        case PROJECTOR_TYPE_KIMIK25:
             {
                 // dynamic size
                 int out_patch_size = params.patch_size * ctx->model.hparams.n_merge;
@@ -3714,6 +3753,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             } break;
         case PROJECTOR_TYPE_PIXTRAL:
         case PROJECTOR_TYPE_KIMIVL:
+        case PROJECTOR_TYPE_KIMIK25:
         case PROJECTOR_TYPE_LIGHTONOCR:
             {
                 // set the 2D positions
@@ -3850,6 +3890,47 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
     }
 
+    // Debug: dump final embeddings if MTMD_DEBUG_EMBEDDINGS is set
+    if (std::getenv("MTMD_DEBUG_EMBEDDINGS") != nullptr) {
+        const int64_t n_embd = embeddings->ne[0];
+        const int64_t n_tokens = embeddings->ne[1];
+        std::vector emb_data(n_embd * n_tokens);
+        ggml_backend_tensor_get(embeddings, emb_data.data(), 0, ggml_nbytes(embeddings));
+
+        LOG_INF("\n=== MTMD_DEBUG_EMBEDDINGS ===\n");
+        LOG_INF("Shape: [%lld, %lld]\n", (long long)n_embd, (long long)n_tokens);
+
+        // Print first few values of first token
+        LOG_INF("Token 0 (first 16 values): ");
+        for (int i = 0; i < std::min((int64_t)16, n_embd); i++) {
+            LOG_INF("%.6f ", emb_data[i]);
+        }
+        LOG_INF("\n");
+
+        // Print last few values of first token
+        if (n_embd > 16) {
+            LOG_INF("Token 0 (last 16 values):  ");
+            for (int64_t i = n_embd - 16; i < n_embd; i++) {
+                LOG_INF("%.6f ", emb_data[i]);
+            }
+            LOG_INF("\n");
+        }
+
+        // Compute and print statistics
+        float sum = 0.0f, sum_sq = 0.0f, min_val = emb_data[0], max_val = emb_data[0];
+        for (size_t i = 0; i < emb_data.size(); i++) {
+            sum += emb_data[i];
+            sum_sq += emb_data[i] * emb_data[i];
+            min_val = std::min(min_val, emb_data[i]);
+            max_val = std::max(max_val, emb_data[i]);
+        }
+        float mean = sum / emb_data.size();
+        float variance = (sum_sq / emb_data.size()) - (mean * mean);
+        LOG_INF("Stats: mean=%.6f, std=%.6f, min=%.6f, max=%.6f, sum=%.6f\n",
+                mean, sqrtf(variance), min_val, max_val, sum);
+        LOG_INF("=== END MTMD_DEBUG_EMBEDDINGS ===\n\n");
+    }
+
     return true;
 }
 
@@ -3896,6 +3977,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_KIMIVL:
+        case PROJECTOR_TYPE_KIMIK25:
             return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_COGVLM:
             return ctx->model.mm_4h_to_h_w->ne[1];
diff --git a/tools/mtmd/models/kimik25.cpp b/tools/mtmd/models/kimik25.cpp
new file mode 100644
index 0000000000..cf9f27f63a
--- /dev/null
+++ b/tools/mtmd/models/kimik25.cpp
@@ -0,0 +1,101 @@
+#include "models.h"
+#include 
+#include 
+
+// note: this is similar to clip_graph::resize_position_embeddings, major difference is having
+// the w/h in ne[1] and ne[2] instead of assuming with sqrt. Could try storing the tensor in 2D instead
+// with a w*h? Also the permute is a bit different at (2, 1, 0, 3) instead of (2, 0, 1, 3).
+ggml_tensor * clip_graph_kimik25::resize_position_embeddings_3d(uint32_t interpolation_mode) {
+    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    = interpolation_mode;
+
+    GGML_ASSERT(pos_embd);
+
+    const int64_t stored_c = pos_embd->ne[0];  // C = 1152
+    const int64_t orig_w = pos_embd->ne[1];    // W = 64
+    const int64_t orig_h = pos_embd->ne[2];    // H = 64
+
+    GGML_ASSERT(stored_c == n_embd);
+
+    if (height == (int)orig_h && width == (int)orig_w) {
+        // No interpolation needed, just flatten to [C, H*W]
+        return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);
+    }
+
+    pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3);
+    pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, mode);
+    pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3);
+    pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);
+    return pos_embd;
+}
+
+ggml_cgraph * clip_graph_kimik25::build() {
+    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_3d(GGML_SCALE_MODE_BICUBIC);
+
+    // Kimi-K2.5 uses interleaved 2D RoPE pattern natively, but
+    // Q / K are permuted during conversion to use split format.
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        cur = build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
+        return cur;
+    };
+
+    ggml_tensor * inp = build_inp();
+
+    // I don't know why, but doing this in the build_vit lead to the ggml_add not occurring?
+    // Doing it manually here does work.
+    inp = ggml_add(ctx0, inp, learned_pos_embd);
+
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            nullptr,
+                            add_pos);
+
+    cb(cur, "vit_out", -1);
+
+    {
+        // patch_merger
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+
+        // projection norm
+        int proj_inp_dim = cur->ne[0];
+        int n_merged_patches = cur->ne[1];
+        cur = ggml_view_2d(ctx0, cur,
+            n_embd, n_merged_patches * scale_factor * scale_factor,
+            ggml_row_size(cur->type, n_embd), 0);
+        cur = ggml_norm(ctx0, cur, hparams.eps);
+        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, n_merged_patches,
+            ggml_row_size(cur->type, proj_inp_dim), 0);
+        cb(cur, "proj_inp_normed", -1);
+
+        // projection mlp
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU,
+            -1);
+
+        cb(cur, "proj_out", -1);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
index 9970980c7b..c4c67ace62 100644
--- a/tools/mtmd/models/models.h
+++ b/tools/mtmd/models/models.h
@@ -109,3 +109,10 @@ struct clip_graph_mobilenetv5 : clip_graph {
         ggml_tensor * inp,
         const mobilenetv5_block & block);
 };
+
+struct clip_graph_kimik25 : clip_graph {
+    clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+
+    ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
+};

From 3136a849dba799490d863ba03ef56b92ff052caf Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Wed, 11 Feb 2026 17:41:35 +0100
Subject: [PATCH 188/206] common : remove unused token util functions (#19506)

This commit removes two unused functions `common_lcp` and `common_lcs`.
The last usage of these functions was removed in
Commit 33eff4024084d1f0c8441b79f7208a52fad79858 ("server : vision support
via libmtmd") and are no longer used anywhere in the codebase.
---
 common/common.cpp | 60 -----------------------------------------------
 common/common.h   | 10 --------
 2 files changed, 70 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 3aa396127c..93474f88c7 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1469,66 +1469,6 @@ void common_batch_add(
     batch.n_tokens++;
 }
 
-//
-// Token utils
-//
-
-size_t common_lcp(const llama_tokens & a, const llama_tokens & b) {
-    size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
-
-    return i;
-}
-
-size_t common_lcs(const llama_tokens & a, const llama_tokens & b) {
-    // check for empty sequences
-    if (a.empty() || b.empty()) {
-        return 0;
-    }
-
-    // get the lengths of the input sequences
-    size_t a_len = a.size();
-    size_t b_len = b.size();
-
-    // initialize the maximum length of the longest common subsequence (LCS)
-    size_t max_length = 0;
-
-    // use two rows instead of a 2D matrix to optimize space
-    std::vector prev_row(b_len + 1, 0);
-    std::vector curr_row(b_len + 1, 0);
-
-    // iterate through the elements of a
-    for (size_t i = 1; i <= a_len; i++) {
-        // iterate through the elements of b
-        for (size_t j = 1; j <= b_len; j++) {
-            // if elements at the current positions match
-            if (a[i - 1] == b[j - 1]) {
-                // if it's the first element of either sequences, set LCS length to 1
-                if (i == 1 || j == 1) {
-                    curr_row[j] = 1;
-                } else {
-                    // increment LCS length by 1 compared to the previous element
-                    curr_row[j] = prev_row[j - 1] + 1;
-                }
-
-                // update max_length if necessary
-                if (curr_row[j] > max_length) {
-                    max_length = curr_row[j];
-                }
-            } else {
-                // reset LCS length if elements don't match
-                curr_row[j] = 0;
-            }
-        }
-
-        // update the previous row for the next iteration
-        prev_row = curr_row;
-    }
-
-    // return the maximum length of the LCS
-    return max_length;
-}
-
 //
 // Vocab utils
 //
diff --git a/common/common.h b/common/common.h
index b284244530..804485fb19 100644
--- a/common/common.h
+++ b/common/common.h
@@ -779,16 +779,6 @@ void common_batch_add(
     const std::vector & seq_ids,
                                bool   logits);
 
-//
-// Token utils
-//
-
-// longest common prefix
-size_t common_lcp(const llama_tokens & a, const llama_tokens & b);
-
-// longet common subsequence
-size_t common_lcs(const llama_tokens & a, const llama_tokens & b);
-
 //
 // Vocab utils
 //

From 914dde72babffc8aa2e04c97dafcec029a671b2e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Wed, 11 Feb 2026 18:58:43 +0200
Subject: [PATCH 189/206] ggml : unary ops support non-cont src0 + metal F16
 unary ops (#19511)

* ggml : unary ops support non-cont src0

* metal : support F16 unary ops + fix ELU
---
 ggml/src/ggml-cpu/ops.cpp               | 144 +++++++++++++++++-------
 ggml/src/ggml-cpu/unary-ops.cpp         |   2 +-
 ggml/src/ggml-metal/ggml-metal-device.m |   4 +-
 ggml/src/ggml-metal/ggml-metal.metal    |  84 ++++++++------
 ggml/src/ggml.c                         |   2 +-
 tests/test-backend-ops.cpp              |   6 +-
 6 files changed, 164 insertions(+), 78 deletions(-)

diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index ed45350207..4352e13280 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -2096,10 +2096,14 @@ static void ggml_compute_forward_gelu_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2113,10 +2117,14 @@ static void ggml_compute_forward_gelu_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2135,10 +2143,14 @@ static void ggml_compute_forward_gelu_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2152,10 +2164,14 @@ static void ggml_compute_forward_gelu_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2276,10 +2292,14 @@ static void ggml_compute_forward_gelu_erf_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2293,10 +2313,14 @@ static void ggml_compute_forward_gelu_erf_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_erf_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2315,10 +2339,14 @@ static void ggml_compute_forward_gelu_erf_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2332,10 +2360,14 @@ static void ggml_compute_forward_gelu_erf_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_erf_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2379,10 +2411,14 @@ static void ggml_compute_forward_gelu_quick_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2396,10 +2432,14 @@ static void ggml_compute_forward_gelu_quick_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_quick_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2418,10 +2458,14 @@ static void ggml_compute_forward_gelu_quick_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2435,10 +2479,14 @@ static void ggml_compute_forward_gelu_quick_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_gelu_quick_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2482,10 +2530,14 @@ static void ggml_compute_forward_silu_f32(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2499,10 +2551,14 @@ static void ggml_compute_forward_silu_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_silu_f32(nc,
-                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
@@ -2521,10 +2577,14 @@ static void ggml_compute_forward_silu_f16(
 
     const ggml_tensor * src0 = dst->src[0];
 
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_is_contiguous_rows(src0));
     assert(ggml_are_same_shape(src0, dst));
 
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -2538,10 +2598,14 @@ static void ggml_compute_forward_silu_f16(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    for (int i1 = ir0; i1 < ir1; i1++) {
+    for (int ir = ir0; ir < ir1; ++ir) {
+        const int i3 = ir/(ne02*ne01);
+        const int i2 = (ir - i3*ne02*ne01)/ne01;
+        const int i1 = (ir - i3*ne02*ne01 - i2*ne01);
+
         ggml_vec_silu_f16(nc,
-                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
-                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+                (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1),
+                (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01));
 
 #ifndef NDEBUG
         for (int k = 0; k < nc; k++) {
diff --git a/ggml/src/ggml-cpu/unary-ops.cpp b/ggml/src/ggml-cpu/unary-ops.cpp
index 1d9873ad0f..1d8344436f 100644
--- a/ggml/src/ggml-cpu/unary-ops.cpp
+++ b/ggml/src/ggml-cpu/unary-ops.cpp
@@ -111,7 +111,7 @@ template 
 static void apply_unary_op(const ggml_compute_params * params, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_1(src0) && ggml_is_contiguous_1(dst) && ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_is_contiguous_rows(src0) && ggml_is_contiguous_rows(dst) && ggml_are_same_shape(src0, dst));
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index c714ef3add..4ea0bfb94d 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1019,7 +1019,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_LOG:
-            return ggml_is_contiguous_rows(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+            return ggml_is_contiguous_rows(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_TANH:
@@ -1039,7 +1039,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_EXPM1:
-                    return ggml_is_contiguous_rows(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                    return ggml_is_contiguous_rows(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
                 default:
                     return false;
             }
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index a385a50b94..0036ba90ec 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -910,7 +910,7 @@ constant float a4_erf = -1.453152027f;
 constant float a5_erf = 1.061405429f;
 
 template
-T erf_approx(T x) {
+inline T erf_approx(T x) {
     T sign_x = sign(x);
     x = fabs(x);
     T t = 1.0f / (1.0f + p_erf * x);
@@ -918,10 +918,27 @@ T erf_approx(T x) {
     return sign_x * y;
 }
 
+template T elu_approx(T x);
+
+template<> inline float elu_approx(float x) {
+    return (x > 0.f) ? x : (exp(x) - 1);
+}
+
+template<> inline float4 elu_approx(float4 x) {
+    float4 res;
+
+    res[0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
+    res[1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
+    res[2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
+    res[3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
+
+    return res;
+}
+
 constant short FC_unary_op [[function_constant(FC_UNARY + 0)]];
 constant bool  FC_unary_cnt[[function_constant(FC_UNARY + 1)]];
 
-template 
+template 
 kernel void kernel_unary_impl(
         constant ggml_metal_kargs_unary & args,
         device const char * src0,
@@ -963,111 +980,111 @@ kernel void kernel_unary_impl(
             }
         }
 
-        device const T0 & x = src0_ptr[i0];
+        const TC x = (TC) src0_ptr[i0];
 
         if (FC_OP == OP_UNARY_NUM_SCALE) {
-            dst_ptr[i0] = args.scale * x + args.bias;
+            dst_ptr[i0] = (T) (args.scale * x + args.bias);
         }
 
         if (FC_OP == OP_UNARY_NUM_FILL) {
-            dst_ptr[i0] = args.val;
+            dst_ptr[i0] = (T) args.val;
         }
 
         if (FC_OP == OP_UNARY_NUM_CLAMP) {
-            dst_ptr[i0] = clamp(x, args.min, args.max);
+            dst_ptr[i0] = (T) clamp(x, args.min, args.max);
         }
 
         if (FC_OP == OP_UNARY_NUM_SQR) {
-            dst_ptr[i0] = x * x;
+            dst_ptr[i0] = (T) (x * x);
         }
 
         if (FC_OP == OP_UNARY_NUM_SQRT) {
-            dst_ptr[i0] = sqrt(x);
+            dst_ptr[i0] = (T) sqrt(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_SIN) {
-            dst_ptr[i0] = sin(x);
+            dst_ptr[i0] = (T) sin(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_COS) {
-            dst_ptr[i0] = cos(x);
+            dst_ptr[i0] = (T) cos(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_LOG) {
-            dst_ptr[i0] = log(x);
+            dst_ptr[i0] = (T) log(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_LEAKY_RELU) {
-            dst_ptr[i0] = T(x > 0.0f)*x + T(x <= 0.0f)*(x * args.slope);
+            dst_ptr[i0] = (T) (TC(x > 0)*x + TC(x <= 0)*(x * args.slope));
         }
 
         if (FC_OP == OP_UNARY_NUM_TANH) {
-            dst_ptr[i0] = precise::tanh(x);
+            dst_ptr[i0] = (T) precise::tanh(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_RELU) {
-            dst_ptr[i0] = fmax(0.0f, x);
+            dst_ptr[i0] = (T) fmax(0, x);
         }
 
         if (FC_OP == OP_UNARY_NUM_SIGMOID) {
-            dst_ptr[i0] = 1.0f / (1.0f + exp(-x));
+            dst_ptr[i0] = (T) (1 / (1 + exp(-x)));
         }
 
         if (FC_OP == OP_UNARY_NUM_GELU) {
-            dst_ptr[i0] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+            dst_ptr[i0] = (T) (0.5*x*(1 + precise::tanh(SQRT_2_OVER_PI*x*(1 + GELU_COEF_A*x*x))));
         }
 
         if (FC_OP == OP_UNARY_NUM_GELU_ERF) {
-            dst_ptr[i0] = 0.5f*x*(1.0f + erf_approx(SQRT_2_INV*x));
+            dst_ptr[i0] = (T) (0.5*x*(1 + erf_approx(SQRT_2_INV*x)));
         }
 
         if (FC_OP == OP_UNARY_NUM_GELU_QUICK) {
-            dst_ptr[i0] = x * (1.0f/(1.0f + exp(GELU_QUICK_COEF*x)));
+            dst_ptr[i0] = (T) (x * (1/(1 + exp(GELU_QUICK_COEF*x))));
         }
 
         if (FC_OP == OP_UNARY_NUM_SILU) {
-            dst_ptr[i0] = x / (1.0f + exp(-x));
+            dst_ptr[i0] = (T) (x / (1 + exp(-x)));
         }
 
         if (FC_OP == OP_UNARY_NUM_ELU) {
-            dst_ptr[i0] = T(x > 0.0f)*x + T(x <= 0.0f)*(exp(x) - 1.0f);
+            dst_ptr[i0] = (T) elu_approx(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_NEG) {
-            dst_ptr[i0] = -x;
+            dst_ptr[i0] = (T) -x;
         }
 
         if (FC_OP == OP_UNARY_NUM_ABS) {
-            dst_ptr[i0] = fabs(x);
+            dst_ptr[i0] = (T) fabs(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_SGN) {
-            dst_ptr[i0] = T(x > 0.0f) - T(x < 0.0f);
+            dst_ptr[i0] = T(x > 0) - T(x < 0);
         }
 
         if (FC_OP == OP_UNARY_NUM_STEP) {
-            dst_ptr[i0] = T(x > 0.0f);
+            dst_ptr[i0] = T(x > 0);
         }
 
         if (FC_OP == OP_UNARY_NUM_HARDSWISH) {
-            dst_ptr[i0] = x * fmax(0.0f, fmin(1.0f, x/6.0f + 0.5f));
+            dst_ptr[i0] = (T) (x * fmax(0, fmin(1, x/6 + 0.5)));
         }
 
         if (FC_OP == OP_UNARY_NUM_HARDSIGMOID) {
-            dst_ptr[i0] = fmax(0.0f, fmin(1.0f, x/6.0f + 0.5f));
+            dst_ptr[i0] = (T) fmax(0, fmin(1, x/6 + 0.5));
         }
 
         if (FC_OP == OP_UNARY_NUM_EXP) {
-            dst_ptr[i0] = exp(x);
+            dst_ptr[i0] = (T) exp(x);
         }
 
         if (FC_OP == OP_UNARY_NUM_SOFTPLUS) {
-            dst_ptr[i0] = select(log(1.0f + exp(x)), x, x > 20.0f);
+            dst_ptr[i0] = (T) select(log(1 + exp(x)), x, x > 20);
         }
 
         if (FC_OP == OP_UNARY_NUM_EXPM1) {
             // TODO: precise implementation
-            dst_ptr[i0] = exp(x) - 1.0f;
+            dst_ptr[i0] = (T) (exp(x) - 1);
         }
     }
 
@@ -1075,11 +1092,12 @@ kernel void kernel_unary_impl(
 #undef FC_CNT
 }
 
-typedef decltype(kernel_unary_impl) kernel_unary_t;
-
-template [[host_name("kernel_unary_f32_f32")]]   kernel kernel_unary_t kernel_unary_impl;
-template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl;
+typedef decltype(kernel_unary_impl) kernel_unary_t;
 
+template [[host_name("kernel_unary_f32_f32")]]   kernel kernel_unary_t kernel_unary_impl;
+template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl;
+template [[host_name("kernel_unary_f16_f16")]]   kernel kernel_unary_t kernel_unary_impl;
+template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl;
 
 // OP: 0 - add, 1 - sub, 2 - mul, 3 - div
 constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 500cb6b72f..e2a6ff67be 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -5749,7 +5749,7 @@ static struct ggml_tensor * ggml_unary_impl(
         struct ggml_tensor  * a,
         enum ggml_unary_op    op,
         bool                  inplace) {
-    GGML_ASSERT(ggml_is_contiguous_1(a));
+    GGML_ASSERT(ggml_is_contiguous_rows(a));
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 5d5e44a0c7..ed99c24516 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1943,7 +1943,11 @@ struct test_unary : public test_case {
 
         ggml_tensor * a;
         if (v & 1) {
-            auto ne = ne_a; ne[0] *= 3;
+            auto ne = ne_a;
+            ne[0] *= 3;
+            ne[1] *= 2;
+            ne[2] *= 5;
+            ne[3] *= 4;
             a = ggml_new_tensor(ctx, type, 4, ne.data());
             if (grad_supported) {
                 ggml_set_param(a);

From 4d3daf80f8834e0eb5148efc7610513f1e263653 Mon Sep 17 00:00:00 2001
From: lhez 
Date: Wed, 11 Feb 2026 10:33:13 -0800
Subject: [PATCH 190/206] opencl: add general Q6_K mm and Q4_K mv (#19347)

* opencl: add general q6_k mm

* opencl: refine condition for q6_K mm

* opencl: add general q4_K mv

* opencl: fix whitespace
---
 ggml/src/ggml-opencl/CMakeLists.txt           |   2 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 123 +++++++++++-
 .../kernels/mul_mm_q6_k_f32_l4_lm.cl          | 158 +++++++++++++++
 .../ggml-opencl/kernels/mul_mv_q4_k_f32.cl    | 180 ++++++++++++++++++
 4 files changed, 461 insertions(+), 2 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mm_q6_k_f32_l4_lm.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index fa5fadd112..b6094fb68b 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -85,6 +85,7 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_8x_flat
     mul_mv_q4_0_f32_1d_8x_flat
     mul_mv_q4_0_f32_1d_16x_flat
+    mul_mv_q4_k_f32
     mul_mv_q6_k_f32
     mul_mv_q6_k_f32_flat
     mul_mv_q8_0_f32
@@ -101,6 +102,7 @@ set(GGML_OPENCL_KERNELS
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
     mul_mm_q8_0_f32_l4_lm
+    mul_mm_q6_k_f32_l4_lm
     mul_mm_q8_0_f32_8x4
     gemv_noshuffle_general_q8_0_f32
     mul
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 508b2b8f03..40474c193b 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -532,6 +532,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_restore_block_q4_0_noshuffle;
     cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
     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_q4_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32_flat;
     cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
@@ -564,6 +565,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
     cl_kernel kernel_mul_mm_q8_0_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
 
     std::vector profiling_info;
 
@@ -1117,6 +1119,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_q4_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_k_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_q6_k_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1358,6 +1377,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_q6_k_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q6_k_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q6_k_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q6_k_f32_l4_lm", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_f16_f32_kq_kqv
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3364,6 +3400,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             } 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 || op->src[0]->type == GGML_TYPE_MXFP4 ||
+                       op->src[0]->type == GGML_TYPE_Q4_K ||
                        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]);
             } else if (op->src[0]->type == GGML_TYPE_Q8_0) {
@@ -8927,6 +8964,50 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_Q6_K: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q6_K->ql));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q6_K->qh));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q6_K->s));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra0_q6_K->d));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset1));
+                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),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             default:
                 break;
         }
@@ -9262,7 +9343,42 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
         }
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q4_K: {
+            kernel = backend_ctx->kernel_mul_mv_q4_K_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),     &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(int),        &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),     &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(int),        &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),     &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),        &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),        &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),        &ne01));
+            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),   &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),        &ne12));
+            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(int),        &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),        &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),        &r2));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),        &r3));
+            break;
+        }
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
 #ifdef GGML_OPENCL_SOA_Q
@@ -9424,7 +9540,10 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
         backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
     } else if (src0t == GGML_TYPE_Q4_K) {
-        GGML_ASSERT(false && "not implemented");
+        size_t global_work_size[] = {(size_t)(ne01+ndst*nth1-1)/(ndst*nth1)*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
+        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
     } else if (src0t == GGML_TYPE_Q3_K) {
         GGML_ASSERT(false && "not implemented");
     } else if (src0t == GGML_TYPE_Q5_K) {
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q6_k_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q6_k_f32_l4_lm.cl
new file mode 100644
index 0000000000..3602c92fef
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_q6_k_f32_l4_lm.cl
@@ -0,0 +1,158 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 2
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q6_k_f32_l4_lm(
+    global uchar * src0_ql,
+    global uchar * src0_qh,
+    global char  * src0_s,
+    global half  * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+
+                int ib = idx / 128;                  // 2 values per idx
+                int iqs = idx % 128;                 // 0..127
+
+                int n = iqs / 64;                    // 0,1
+                int b = (iqs % 64) / 32;             // 0,1
+                int is_b = (iqs % 16) / 8;           // 0,1
+                int qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+                int is = 8 * n + qhshift + is_b;     // 0..15
+                int qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+                int qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+                float dscale = (float)src0_d[ib] * (float)src0_s[ib*16 + is];
+
+                buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = dscale * convert_float(convert_char(((src0_ql[128*ib + qsi + 0] >> (b * 4)) & 0xF) | (((src0_qh[64*ib + qhi + 0] >> qhshift) & 3) << 4)) - 32);
+                buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = dscale * convert_float(convert_char(((src0_ql[128*ib + qsi + 1] >> (b * 4)) & 0xF) | (((src0_qh[64*ib + qhi + 1] >> qhshift) & 3) << 4)) - 32);
+            } else {
+                buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl
new file mode 100644
index 0000000000..71ab989821
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl
@@ -0,0 +1,180 @@
+#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
+
+//------------------------------------------------------------------------------
+// block_q4_K
+//------------------------------------------------------------------------------
+#define QK_K            256
+#define K_SCALE_SIZE    12
+
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+typedef struct {
+    half d;    // super-block scale for quantized scales
+    half dmin; // super-block scale for quantized mins
+
+    uchar scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uchar qs[QK_K/2];           // 4-bit quants
+} block_q4_K;
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // number of rows each SIMD group works on
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // SIMD group size
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+#undef  BLOCK_STRIDE
+// number of (super) blocks each subgroup processes
+// each thread in a subgroup processes a block (32 weights)
+#define BLOCK_STRIDE (N_SIMDWIDTH/8)
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_K_f32(
+        global char * src0,
+        int offset0,
+        global char * src1,
+        int offset1,
+        global char * dst,
+        int offsetd,
+        int ne00,
+        int ne01,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne12,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    ushort kmask1 = 0x3f3f;
+    ushort kmask2 = 0x0f0f;
+    ushort kmask3 = 0xc0c0;
+
+    int ix = get_sub_group_local_id()/8;  // super block index
+    int it = get_sub_group_local_id()%8;  // block index (inside super block)
+    int iq = it/4;     // 0 or 1 - first or second half of the super block
+    int ir = it%4;     // 0...3 - block index in the half super block
+
+    int nb = ne00/QK_K;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    int offset_src0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    int offset_src1 =        r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global block_q4_K * x = (global block_q4_K *) (src0 + offset_src0);
+    global float      * y = (global float      *) (src1 + offset_src1);
+
+    float yl[16];
+    float yh[16];
+    float sumf[N_DST] = {0.f};
+    float all_sum;
+
+    global float * y4 = y + ix * QK_K + 64 * iq + 8 * ir;
+
+    ushort  sc16[4];
+    uchar * sc8 = (uchar *)sc16;
+
+    for (int ib = ix; ib < nb; ib += BLOCK_STRIDE) {
+        float4 sumy = {0.f, 0.f, 0.f, 0.f};
+        for (int i = 0; i < 8; ++i) {
+            yl[i+0] = y4[i+0];
+            sumy.s0 += yl[i+0];
+
+            yl[i+8] = y4[i+32];
+            sumy.s1 += yl[i+8];
+
+            yh[i+0] = y4[i+128];
+            sumy.s2 += yh[i+0];
+
+            yh[i+8] = y4[i+160];
+            sumy.s3 += yh[i+8];
+        }
+
+        global ushort * sc = (global ushort *)x[ib].scales + iq;
+        global ushort * q1 = (global ushort *)x[ib].qs + 16 * iq + 4 * ir;
+        global half     * dh = &x[ib].d;
+
+        for (int row = 0; row < N_DST; row++) {
+            sc16[0] = sc[0] & kmask1;
+            sc16[1] = sc[2] & kmask1;
+            sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2);
+            sc16[3] = ((sc[4] >> 4) & kmask2) | ((sc[2] & kmask3) >> 2);
+
+            global ushort * q2 = q1 + 32;
+
+            float4 acc1 = {0.f, 0.f, 0.f, 0.f};
+            float4 acc2 = {0.f, 0.f, 0.f, 0.f};
+            for (int i = 0; i < 8; i += 2) {
+                acc1.s0 += yl[i+0] * (q1[i/2] & 0x000F);
+                acc1.s1 += yl[i+1] * (q1[i/2] & 0x0F00);
+                acc1.s2 += yl[i+8] * (q1[i/2] & 0x00F0);
+                acc1.s3 += yl[i+9] * (q1[i/2] & 0xF000);
+                acc2.s0 += yh[i+0] * (q2[i/2] & 0x000F);
+                acc2.s1 += yh[i+1] * (q2[i/2] & 0x0F00);
+                acc2.s2 += yh[i+8] * (q2[i/2] & 0x00F0);
+                acc2.s3 += yh[i+9] * (q2[i/2] & 0xF000);
+            }
+
+            float dall = dh[0];
+            float dmin = dh[1];
+            sumf[row] += dall * ((acc1.s0 + 1.f/256.f * acc1.s1) * sc8[0] +
+                                 (acc1.s2 + 1.f/256.f * acc1.s3) * sc8[1] * 1.f/16.f +
+                                 (acc2.s0 + 1.f/256.f * acc2.s1) * sc8[4] +
+                                 (acc2.s2 + 1.f/256.f * acc2.s3) * sc8[5] * 1.f/16.f) -
+                         dmin * (sumy.s0 * sc8[2] + sumy.s1 * sc8[3] + sumy.s2 * sc8[6] + sumy.s3 * sc8[7]);
+
+            q1 += nb01/2;
+            sc += nb01/2;
+            dh += nb01/2;
+        }
+
+        y4 += BLOCK_STRIDE * QK_K;
+    }
+
+    global float * dst_f32 = (global float *) dst + im*ne0*ne1 + r1*ne0;
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = sub_group_reduce_add(sumf[row]);
+        if (first_row + row < ne01) {
+            if (get_sub_group_local_id() == 0) {
+                dst_f32[first_row + row] = all_sum;
+            }
+        }
+    }
+}

From 4ae1b7517a787f5b37776b0598ed2b69e6caf5bd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= 
Date: Thu, 12 Feb 2026 07:27:52 +0100
Subject: [PATCH 191/206] common : replace deprecated codecvt using
 parse_utf8_codepoint (#19517)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët 
---
 common/common.cpp | 61 ++++++++++++++++-------------------------------
 1 file changed, 21 insertions(+), 40 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 93474f88c7..ec15804c91 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,7 +1,3 @@
-#if defined(_MSC_VER)
-#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
-#endif
-
 #include "ggml.h"
 #include "gguf.h"
 
@@ -9,12 +5,12 @@
 #include "log.h"
 #include "llama.h"
 #include "sampling.h"
+#include "unicode.h"
 
 #include 
 #include 
 #include 
 #include 
-#include 
 #include 
 #include 
 #include 
@@ -706,45 +702,28 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
         return false;
     }
 
-    std::u32string filename_utf32;
-    try {
-#if defined(__clang__)
-        // disable C++17 deprecation warning for std::codecvt_utf8
-#    pragma clang diagnostic push
-#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
-#elif defined(__GNUC__)
-#    pragma GCC diagnostic push
-#    pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
+    size_t offset = 0;
+    while (offset < filename.size()) {
+        utf8_parse_result result = parse_utf8_codepoint(filename, offset);
 
-        std::wstring_convert, char32_t> converter;
-
-#if defined(__clang__)
-#    pragma clang diagnostic pop
-#elif defined(__GNUC__)
-#    pragma GCC diagnostic pop
-#endif
-
-        filename_utf32 = converter.from_bytes(filename);
-
-        // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
-        // or invalid encodings were encountered. Reject such attempts
-        std::string filename_reencoded = converter.to_bytes(filename_utf32);
-        if (filename_reencoded != filename) {
+        if (result.status != utf8_parse_result::SUCCESS) {
             return false;
         }
-    } catch (const std::exception &) {
-        return false;
-    }
+        uint32_t c = result.codepoint;
 
-    // Check for forbidden codepoints:
-    // - Control characters
-    // - Unicode equivalents of illegal characters
-    // - UTF-16 surrogate pairs
-    // - UTF-8 replacement character
-    // - Byte order mark (BOM)
-    // - Illegal characters: / \ : * ? " < > |
-    for (char32_t c : filename_utf32) {
+        if ((result.bytes_consumed == 2 && c < 0x80) ||
+            (result.bytes_consumed == 3 && c < 0x800) ||
+            (result.bytes_consumed == 4 && c < 0x10000)) {
+            return false;
+        }
+
+        // Check for forbidden codepoints:
+        // - Control characters
+        // - Unicode equivalents of illegal characters
+        // - UTF-16 surrogate pairs
+        // - UTF-8 replacement character
+        // - Byte order mark (BOM)
+        // - Illegal characters: / \ : * ? " < > |
         if (c <= 0x1F // Control characters (C0)
             || c == 0x7F // Control characters (DEL)
             || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
@@ -752,6 +731,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
             || c == 0x2215 // Division Slash (forward slash equivalent)
             || c == 0x2216 // Set Minus (backslash equivalent)
             || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
+            || c > 0x10FFFF // Max Unicode limit
             || c == 0xFFFD // Replacement Character (UTF-8)
             || c == 0xFEFF // Byte Order Mark (BOM)
             || c == ':' || c == '*' // Illegal characters
@@ -762,6 +742,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
             // Subdirectories not allowed, reject path separators
             return false;
         }
+        offset += result.bytes_consumed;
     }
 
     // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename

From b1ff83bbb08bd946f79b89bc7adbd7f08ae61303 Mon Sep 17 00:00:00 2001
From: Max Krasnyansky 
Date: Wed, 11 Feb 2026 23:04:27 -0800
Subject: [PATCH 192/206] hexagon: further optimization and tuning of matmul
 and dot kernels (#19407)

* ggml-hexagon: implement 2x2 matmul kernel

* hexmm: implement vec_dot_rx2x2 for Q8_0 and MXFP4

* hexagon: fix editor config failures

* hexagon: refactor matmul ops to use context struct and remove wrappers

Also implement vec_dot_f16 2x2

* hexagon: refactor dyn quantizers to use mmctx

* hexagon: remove mm fastdiv from op_ctx

* hexagon: refactor matmul entry point to reduce code duplication

---------

Co-authored-by: Trivikram Reddy 
---
 ggml/src/ggml-hexagon/htp/htp-ops.h    |   13 -
 ggml/src/ggml-hexagon/htp/matmul-ops.c | 1519 +++++++++++++-----------
 2 files changed, 854 insertions(+), 678 deletions(-)

diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index c0d72587ce..f1ad24dbfa 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -64,25 +64,12 @@ struct htp_ops_context {
     struct fastdiv_values broadcast_rv2;
     struct fastdiv_values broadcast_rv3;
 
-    struct fastdiv_values mm_div_ne12_ne1; // fastdiv values for ne12 * ne1
-    struct fastdiv_values mm_div_ne1;      // fastdiv values for ne1
-    struct fastdiv_values mm_div_r2;       // fastdiv values for ne12 / ne02
-    struct fastdiv_values mm_div_r3;       // fastdiv values for ne13 / ne03
-
     struct fastdiv_values set_rows_div_ne12; // fastdiv values for ne12
     struct fastdiv_values set_rows_div_ne11; // fastdiv values for ne11
 
     struct fastdiv_values get_rows_div_ne10;      // fastdiv values for ne10
     struct fastdiv_values get_rows_div_ne10_ne11; // fastdiv values for ne10 * ne11
 
-    struct fastdiv_values cpy_div_ne01; // fastdiv values for ne01
-    struct fastdiv_values cpy_div_ne02; // fastdiv values for ne02
-    struct fastdiv_values cpy_div_ne03; // fastdiv values for ne03
-
-    struct fastdiv_values cpy_rshp_div_n0;       // fastdiv values for ne00
-    struct fastdiv_values cpy_rshp_div_n1n0;     // fastdiv values for ne00*ne01
-    struct fastdiv_values cpy_rshp_div_n2n1n0;   // fastdiv values for ne00*ne01*ne02
-
     uint32_t flags;
 };
 
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index d251eeed33..c360abe8da 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -23,10 +23,30 @@
 #define MM_SPAD_SRC1_NROWS 16
 #define MM_SPAD_DST_NROWS  2
 
-struct htp_matmul_type {
+struct htp_matmul_context {
     const char * type;
-    void (*vec_dot)(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-    void (*vec_dot_rx2)(const int n, float * restrict s, const void * restrict vx, uint32_t vx_row_size, const void * restrict vy);
+    struct htp_ops_context * octx;
+
+    void (*vec_dot_1x1)(const int n, float * restrict s0,
+         const void * restrict vx0,
+         const void * restrict vy0);
+
+    void (*vec_dot_2x1)(const int n, float * restrict s0,
+         const void * restrict vx0, const void * restrict vx1,
+         const void * restrict vy0);
+
+    void (*vec_dot_2x2)(const int n, float * restrict s0, float * restrict s1,
+         const void * restrict vx0, const void * restrict vx1,
+         const void * restrict vy0, const void * restrict vy1);
+
+    // Precomputed values
+    uint32_t src0_nrows_per_thread;
+    uint32_t src1_nrows_per_thread;
+
+    struct fastdiv_values mm_div_ne12_ne1;
+    struct fastdiv_values mm_div_ne1;
+    struct fastdiv_values mm_div_r2;
+    struct fastdiv_values mm_div_r3;
 };
 
 // vdelta control to replicate first 4x fp32 values across lanes
@@ -122,6 +142,7 @@ static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) {
     HVX_Vector v6_7 = vptr[3];  // ...
 
     const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
 
     HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F
     HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4
@@ -133,15 +154,14 @@ static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) {
     HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
 
     // Convert uint4 to int4 (i.e. x - 8)
-    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
-    v0                  = Q6_Vb_vsub_VbVb(v0, i8);
-    v1                  = Q6_Vb_vsub_VbVb(v1, i8);
-    v2                  = Q6_Vb_vsub_VbVb(v2, i8);
-    v3                  = Q6_Vb_vsub_VbVb(v3, i8);
-    v4                  = Q6_Vb_vsub_VbVb(v4, i8);
-    v5                  = Q6_Vb_vsub_VbVb(v5, i8);
-    v6                  = Q6_Vb_vsub_VbVb(v6, i8);
-    v7                  = Q6_Vb_vsub_VbVb(v7, i8);
+    v0 = Q6_Vb_vsub_VbVb(v0, i8);
+    v1 = Q6_Vb_vsub_VbVb(v1, i8);
+    v2 = Q6_Vb_vsub_VbVb(v2, i8);
+    v3 = Q6_Vb_vsub_VbVb(v3, i8);
+    v4 = Q6_Vb_vsub_VbVb(v4, i8);
+    v5 = Q6_Vb_vsub_VbVb(v5, i8);
+    v6 = Q6_Vb_vsub_VbVb(v6, i8);
+    v7 = Q6_Vb_vsub_VbVb(v7, i8);
 
     HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
     return r;
@@ -156,6 +176,7 @@ static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8(const uint8_t * restrict ptr)
     HVX_Vector v6_7 = vptr[3];  // ...
 
     const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
 
     HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F
     HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4
@@ -166,15 +187,14 @@ static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8(const uint8_t * restrict ptr)
     HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
     HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
 
-    HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
-    v0             = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
-    v1             = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
-    v2             = Q6_Vb_vlut32_VbVbI(v2, lut, 0);
-    v3             = Q6_Vb_vlut32_VbVbI(v3, lut, 0);
-    v4             = Q6_Vb_vlut32_VbVbI(v4, lut, 0);
-    v5             = Q6_Vb_vlut32_VbVbI(v5, lut, 0);
-    v6             = Q6_Vb_vlut32_VbVbI(v6, lut, 0);
-    v7             = Q6_Vb_vlut32_VbVbI(v7, lut, 0);
+    v0 = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
+    v1 = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
+    v2 = Q6_Vb_vlut32_VbVbI(v2, lut, 0);
+    v3 = Q6_Vb_vlut32_VbVbI(v3, lut, 0);
+    v4 = Q6_Vb_vlut32_VbVbI(v4, lut, 0);
+    v5 = Q6_Vb_vlut32_VbVbI(v5, lut, 0);
+    v6 = Q6_Vb_vlut32_VbVbI(v6, lut, 0);
+    v7 = Q6_Vb_vlut32_VbVbI(v7, lut, 0);
 
     HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
     return r;
@@ -196,46 +216,6 @@ static inline HVX_Vector_x8 hvx_vec_load_q8x4x8(const uint8_t * restrict ptr) {
     return r;
 }
 
-static inline HVX_Vector_x4 hvx_vec_load_x4_f16(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0 = vptr[0];  // first  64 vals
-    HVX_Vector v1 = vptr[1];  // second 64 vals
-    HVX_Vector v2 = vptr[2];  // third  64 vals
-    HVX_Vector v3 = vptr[3];  // forth  64 vals
-
-    HVX_Vector_x4 r = { v0, v1, v2, v3 };
-    return r;
-}
-
-static inline HVX_Vector_x4 hvx_vec_load_x4_f32_as_f16(const uint8_t * restrict ptr) {
-    const HVX_VectorPair * restrict vptr = (const HVX_VectorPair *) ptr;
-
-    HVX_VectorPair v0 = vptr[0];  // first  64 vals
-    HVX_VectorPair v1 = vptr[1];  // second 64 vals
-    HVX_VectorPair v2 = vptr[2];  // third  64 vals
-    HVX_VectorPair v3 = vptr[3];  // forth  64 vals
-
-    HVX_Vector vq0_lo = Q6_Vqf32_vsub_VsfVsf(Q6_V_lo_W(v0), Q6_V_vzero());
-    HVX_Vector vq0_hi = Q6_Vqf32_vsub_VsfVsf(Q6_V_hi_W(v0), Q6_V_vzero());
-    HVX_Vector vq1_lo = Q6_Vqf32_vsub_VsfVsf(Q6_V_lo_W(v1), Q6_V_vzero());
-    HVX_Vector vq1_hi = Q6_Vqf32_vsub_VsfVsf(Q6_V_hi_W(v1), Q6_V_vzero());
-    HVX_Vector vq2_lo = Q6_Vqf32_vsub_VsfVsf(Q6_V_lo_W(v2), Q6_V_vzero());
-    HVX_Vector vq2_hi = Q6_Vqf32_vsub_VsfVsf(Q6_V_hi_W(v2), Q6_V_vzero());
-    HVX_Vector vq3_lo = Q6_Vqf32_vsub_VsfVsf(Q6_V_lo_W(v3), Q6_V_vzero());
-    HVX_Vector vq3_hi = Q6_Vqf32_vsub_VsfVsf(Q6_V_hi_W(v3), Q6_V_vzero());
-
-    HVX_Vector vh0 = Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vq0_hi, vq0_lo));
-    HVX_Vector vh1 = Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vq1_hi, vq1_lo));
-    HVX_Vector vh2 = Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vq2_hi, vq2_lo));
-    HVX_Vector vh3 = Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vq3_hi, vq3_lo));
-
-    // vcombine does a shuffle, use vdeal to undo
-
-    HVX_Vector_x4 r = { Q6_Vh_vdeal_Vh(vh0), Q6_Vh_vdeal_Vh(vh1), Q6_Vh_vdeal_Vh(vh2), Q6_Vh_vdeal_Vh(vh3) };
-    return r;
-}
-
 // Reduce multiply 1024 x 1024 int8 elements (32x q4/8 blocks in 8x HVX vectors).
 // Accumulate each block into a single int32 value.
 // Return a single HVX vector with 32x int32 accumulators.
@@ -300,26 +280,26 @@ static inline HVX_Vector hvx_vec_rmpy_x8_nloe(HVX_Vector_x8 x, HVX_Vector_x8 y,
     return hvx_vec_rmpy_x8_n(x, y, 1024);
 }
 
-static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_Q4_0x4x2 * 4;
 
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                  // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                     // int4
-    const uint32_t x_qrow_size = n / 2;                                      // int4 (not padded)
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;                                      // int4
+    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
 
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                  // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                         // int8
-    const uint32_t y_qrow_size = n;                                          // int8 (not padded)
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size);  // then scales
 
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);     // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
 
     // Row sum (sf)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -372,36 +352,34 @@ static void vec_dot_q4x4x2_q8x4x2(const int n, float * restrict s, const void *
 
     r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
 
-    hvx_vec_store_u(&s[0], 4, r0_sum);
+    hvx_vec_store_u(s0, 4, r0_sum);
 }
 
-static void vec_dot_q4x4x2_q8x4x2_rx2(const int n,
-                                      float * restrict s,
-                                      const void * restrict vx,
-                                      uint32_t vx_row_size,
-                                      const void * restrict vy) {
+static void vec_dot_q4x4x2_q8x4x2_2x1(const int n, float * restrict s0,
+                                      const void * restrict vx0, const void * restrict vx1,
+                                      const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_Q4_0x4x2 * 4;
 
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                                        // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                                           // int4
-    const uint32_t x_qrow_size = n / 2;                                                            // int4 (not padded)
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;                                      // int4
+    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
 
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                                        // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                                               // int8
-    const uint32_t y_qrow_size = n;                                                                // int8 (not padded)
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) (vx + (0 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) (vx + (0 * vx_row_size)) + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
 
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) (vx + (1 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) (vx + (1 * vx_row_size)) + x_qrow_size);  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);                                     // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);                           // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
 
     // Row sum (sf)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -468,13 +446,143 @@ static void vec_dot_q4x4x2_q8x4x2_rx2(const int n,
     }
 
     HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(&s[0], 8, rsum);
+    hvx_vec_store_u(s0, 8, rsum);
 }
 
-static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
+                                        const void * restrict vx0, const void * restrict vx1,
+                                        const void * restrict vy0, const void * restrict vy1) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+    assert((unsigned long) vy1 % 128 == 0);
+
+    const uint32_t qk = QK_Q4_0x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;                                      // int4
+    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
+
+    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
+    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
+    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
+    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
+
+    // Row sums (sf) - 4 accumulators for 2×2 tile
+    HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0);
+
+    const uint32_t nb   = n / qk;  // num full blocks
+    const uint32_t nloe = n % qk;  // num leftover elements
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        // Load src1 columns (reused across both src0 rows)
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+
+        // Load src0 rows (reused across both src1 columns)
+        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size);
+
+        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
+
+        // Load scales
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        // Compute combined scales
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        // Apply scales and accumulate
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Process leftovers
+    if (nloe) {
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe));
+
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        // Zero out unused scales
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
+        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
+        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
+        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
+        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
+        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
+        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
+        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
+
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(s0, 8, r0_r1_c0_sum);  // row0,col0 row1,col0
+    hvx_vec_store_u(s1, 8, r0_r1_c1_sum);  // row0,col1 row1,col1
+}
+
+static void vec_dot_q8x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_Q4_0x4x2 * 4;
 
@@ -486,11 +594,11 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void *
     const uint32_t y_qblk_size = qk;                                         // int8
     const uint32_t y_qrow_size = n;                                          // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);           // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size); // then scales
 
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);     // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);              // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);    // then scales
 
     // Row sum (sf)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -543,36 +651,34 @@ static void vec_dot_q8x4x2_q8x4x2(const int n, float * restrict s, const void *
 
     r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
 
-    hvx_vec_store_u(&s[0], 4, r0_sum);
+    hvx_vec_store_u(s0, 4, r0_sum);
 }
 
-static void vec_dot_q8x4x2_q8x4x2_rx2(const int n,
-                                      float * restrict s,
-                                      const void * restrict vx,
-                                      uint32_t vx_row_size,
-                                      const void * restrict vy) {
+static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0,
+                                      const void * restrict vx0, const void * restrict vx1,
+                                      const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_Q4_0x4x2 * 4;
 
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                                        // 32x __fp16
-    const uint32_t x_qblk_size = qk;                                                               // int8
-    const uint32_t x_qrow_size = n;                                                                // int8 (not padded)
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk;                                          // int8
+    const uint32_t x_qrow_size = n;                                           // int8 (not padded)
 
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                                        // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                                               // int8
-    const uint32_t y_qrow_size = n;                                                                // int8 (not padded)
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) (vx + (0 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) (vx + (0 * vx_row_size)) + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
 
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) (vx + (1 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) (vx + (1 * vx_row_size)) + x_qrow_size);  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);                                     // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);                           // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
 
     // Row sum (qf32)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -639,16 +745,143 @@ static void vec_dot_q8x4x2_q8x4x2_rx2(const int n,
     }
 
     HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(&s[0], 8, rsum);
+    hvx_vec_store_u(s0, 8, rsum);
 }
 
-static void vec_dot_mxfp4x4x2_q8x4x2(const int n,
-                                     float * restrict s,
-                                     const void * restrict vx,
-                                     const void * restrict vy) {
+static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
+                                        const void * restrict vx0, const void * restrict vx1,
+                                        const void * restrict vy0, const void * restrict vy1) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+    assert((unsigned long) vy1 % 128 == 0);
+
+    const uint32_t qk = QK_Q8_0x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk;                                          // int8
+    const uint32_t x_qrow_size = n;                                           // int8 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
+
+    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
+    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
+    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
+    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
+
+    // Row sums (sf) - 4 accumulators for 2×2 tile
+    HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0);
+
+    const uint32_t nb   = n / qk;  // num full blocks
+    const uint32_t nloe = n % qk;  // num leftover elements
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        // Load src1 columns (reused across both src0 rows)
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+
+        // Load src0 rows (reused across both src1 columns)
+        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size);
+
+        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
+
+        // Load scales
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        // Compute combined scales
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        // Apply scales and accumulate
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Process leftovers
+    if (nloe) {
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q  = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q  = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe));
+
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        // Zero out unused scales
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
+        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
+        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
+        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
+        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
+        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
+        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
+        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
+
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
+    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
+}
+
+static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_MXFP4x4x2 * 4;
 
@@ -660,11 +893,11 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n,
     const uint32_t y_qblk_size = qk;                                         // int8
     const uint32_t y_qrow_size = n;                                          // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);           // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size); // then scales
 
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);     // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);              // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);    // then scales
 
     // Row sum (sf)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -747,36 +980,34 @@ static void vec_dot_mxfp4x4x2_q8x4x2(const int n,
 
     r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
 
-    hvx_vec_store_u(&s[0], 4, r0_sum);
+    hvx_vec_store_u(s0, 4, r0_sum);
 }
 
-static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n,
-                                         float * restrict s,
-                                         const void * restrict vx,
-                                         uint32_t vx_row_size,
-                                         const void * restrict vy) {
+static void vec_dot_mxfp4x4x2_q8x4x2_2x1(const int n, float * restrict s0,
+                                      const void * restrict vx0, const void * restrict vx1,
+                                      const void * restrict vy0) {
     assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx % 128 == 0);
-    assert((unsigned long) vy % 128 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
 
     const uint32_t qk = QK_MXFP4x4x2 * 4;
 
-    const uint32_t x_dblk_size = 8 * 4 * 1;                                                        // 32x e8m0
-    const uint32_t x_qblk_size = qk / 2;                                                           // fp4
-    const uint32_t x_qrow_size = n / 2;                                                            // fp4 (not padded)
+    const uint32_t x_dblk_size = 8 * 4 * 1;                                   // 32x e8m0
+    const uint32_t x_qblk_size = qk / 2;                                      // fp4
+    const uint32_t x_qrow_size = n / 2;                                       // fp4 (not padded)
 
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                                        // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                                               // int8
-    const uint32_t y_qrow_size = n;                                                                // int8 (not padded)
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
 
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) (vx + (0 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) (vx + (0 * vx_row_size)) + x_qrow_size);  // then scales
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
 
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) (vx + (1 * vx_row_size)) + 0);            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) (vx + (1 * vx_row_size)) + x_qrow_size);  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy + 0);                                     // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy + y_qrow_size);                           // then scales
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0) + 0;               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0) + y_qrow_size;     // then scales
 
     // Row sum (sf)
     HVX_Vector r0_sum = Q6_V_vsplat_R(0);
@@ -879,10 +1110,180 @@ static void vec_dot_mxfp4x4x2_q8x4x2_rx2(const int n,
     }
 
     HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(&s[0], 8, rsum);
+    hvx_vec_store_u(s0, 8, rsum);
 }
 
-static void vec_dot_f16_f16_aa(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
+                                        const void * restrict vx0, const void * restrict vx1,
+                                        const void * restrict vy0, const void * restrict vy1) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+    assert((unsigned long) vy1 % 128 == 0);
+
+    const uint32_t qk = QK_MXFP4x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 1;                                   // 32x e8m0
+    const uint32_t x_qblk_size = qk / 2;                                      // fp4
+    const uint32_t x_qrow_size = n / 2;                                       // fp4 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
+
+    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
+    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
+    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
+    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
+
+    // Row sums (sf) - 4 accumulators for 2×2 tile
+    HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0);
+
+    const uint32_t nb   = n / qk;  // num full blocks
+    const uint32_t nloe = n % qk;  // num leftover elements
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        // Load src1 columns (reused across both src0 rows)
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+
+        // Load src0 rows (reused across both src1 columns)
+        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size);
+
+        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
+
+        // Load scales
+        HVX_Vector vy0_d = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
+        HVX_Vector vy1_d = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
+        HVX_Vector r0_d  = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
+        HVX_Vector r1_d  = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
+
+        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
+        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
+        vy0_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy0_d), half));
+        vy0_d           = Q6_Vsf_equals_Vqf32(vy0_d);
+        vy1_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy1_d), half));
+        vy1_d           = Q6_Vsf_equals_Vqf32(vy1_d);
+
+        // Convert rX_d scales from e8m0 to fp32
+        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
+        // Left shift with zero fill to create FP32
+        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
+        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
+        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
+        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
+        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
+        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
+        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
+        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
+
+        // Compute combined scales
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy0_d));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy1_d));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy0_d));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy1_d));
+
+        // Apply scales and accumulate
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Process leftovers
+    if (nloe) {
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q  = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q  = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe));
+
+        HVX_Vector vy0_d = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
+        HVX_Vector vy1_d = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
+        HVX_Vector r0_d  = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
+        HVX_Vector r1_d  = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
+
+        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
+        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
+        vy0_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy0_d), half));
+        vy0_d           = Q6_Vsf_equals_Vqf32(vy0_d);
+        vy1_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy1_d), half));
+        vy1_d           = Q6_Vsf_equals_Vqf32(vy1_d);
+
+        // Convert rX_d scales from e8m0 to fp32
+        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
+        // Left shift with zero fill to create FP32
+        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
+        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
+        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
+        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
+        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
+        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
+        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
+        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
+
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy0_d));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy1_d));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy0_d));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy1_d));
+
+        // Zero out unused scales
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
+        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
+        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
+        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
+        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
+        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
+        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
+        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
+
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
+    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
+}
+
+static void vec_dot_f16_f16_aa_1x1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
     const HVX_Vector * restrict x = (const HVX_Vector *) vx;
     const HVX_Vector * restrict y = (const HVX_Vector *) vy;
 
@@ -912,14 +1313,12 @@ static void vec_dot_f16_f16_aa(const int n, float * restrict s, const void * res
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-static void vec_dot_f16_f16_aa_rx2(const int n,
-                                float * restrict s,
-                                const void * restrict vx,
-                                uint32_t vx_row_size,
-                                const void * restrict vy) {
-    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx;
-    const HVX_Vector * restrict x1 = (const HVX_Vector *) ((const uint8_t *) vx + vx_row_size);
-    const HVX_Vector * restrict y  = (const HVX_Vector *) vy;
+static void vec_dot_f16_f16_aa_2x1(const int n, float * restrict s0,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y  = (const HVX_Vector *) vy0;
 
     uint32_t nvec = n / VLEN_FP16;
     uint32_t nloe = n % VLEN_FP16;
@@ -953,10 +1352,86 @@ static void vec_dot_f16_f16_aa_rx2(const int n,
     }
 
     HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(Q6_Vsf_equals_Vqf32(rsum0), Q6_Vsf_equals_Vqf32(rsum1));
-    hvx_vec_store_u(&s[0], 8, rsum);
+    hvx_vec_store_u(s0, 8, rsum);
 }
 
-static void vec_dot_f16_f16_uu(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * restrict s1,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0, const void * restrict vy1) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y0 = (const HVX_Vector *) vy0;
+    const HVX_Vector * restrict y1 = (const HVX_Vector *) vy1;
+
+    uint32_t nvec = n / VLEN_FP16;
+    uint32_t nloe = n % VLEN_FP16;
+
+    // Row sums (sf) - 4 accumulators for 2×2 tile
+    HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0);
+    HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0);
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector r0_hf = x0[i];
+        HVX_Vector r1_hf = x1[i];
+        HVX_Vector c0_hf = y0[i];
+        HVX_Vector c1_hf = y1[i];
+
+        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
+        HVX_VectorPair r0_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c0_hf);
+        HVX_VectorPair r0_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c1_hf);
+        HVX_VectorPair r1_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c0_hf);
+        HVX_VectorPair r1_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c1_hf);
+
+        HVX_Vector r0_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c0_qf_p), Q6_V_hi_W(r0_c0_qf_p));
+        HVX_Vector r0_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c1_qf_p), Q6_V_hi_W(r0_c1_qf_p));
+        HVX_Vector r1_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c0_qf_p), Q6_V_hi_W(r1_c0_qf_p));
+        HVX_Vector r1_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c1_qf_p), Q6_V_hi_W(r1_c1_qf_p));
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_qf, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_qf, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_qf, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_qf, r1_c1_sum));
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+
+        HVX_Vector r0_hf = Q6_V_vand_QV(bmask, x0[i]);
+        HVX_Vector r1_hf = Q6_V_vand_QV(bmask, x1[i]);
+        HVX_Vector c0_hf = Q6_V_vand_QV(bmask, y0[i]);
+        HVX_Vector c1_hf = Q6_V_vand_QV(bmask, y1[i]);
+
+        HVX_VectorPair r0_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c0_hf);
+        HVX_VectorPair r0_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c1_hf);
+        HVX_VectorPair r1_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c0_hf);
+        HVX_VectorPair r1_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c1_hf);
+
+        HVX_Vector r0_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c0_qf_p), Q6_V_hi_W(r0_c0_qf_p));
+        HVX_Vector r0_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c1_qf_p), Q6_V_hi_W(r0_c1_qf_p));
+        HVX_Vector r1_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c0_qf_p), Q6_V_hi_W(r1_c0_qf_p));
+        HVX_Vector r1_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c1_qf_p), Q6_V_hi_W(r1_c1_qf_p));
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_qf, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_qf, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_qf, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_qf, r1_c1_sum));
+
+    }
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
+    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
+}
+
+static void vec_dot_f16_f16_uu_1x1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
     const HVX_UVector * restrict x = (const HVX_UVector *) vx;
     const HVX_UVector * restrict y = (const HVX_UVector *) vy;
 
@@ -986,7 +1461,7 @@ static void vec_dot_f16_f16_uu(const int n, float * restrict s, const void * res
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-static void vec_dot_f16_f32_uu(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
+static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
     const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
     const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
 
@@ -1083,14 +1558,16 @@ static void vec_dot_f16_f32_uu(const int n, float * restrict s, const void * res
     const uint32_t nb2 = dst->nb[2];   \
     const uint32_t nb3 = dst->nb[3];
 
-#define htp_matmul_preamble            \
-    htp_matmul_tensors_preamble;       \
-    dma_queue *dma_queue           = octx->ctx->dma[ith];         \
-    uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread;
+#define htp_matmul_preamble                                     \
+    struct htp_matmul_context * mmctx = data;                   \
+    struct htp_ops_context * octx  = mmctx->octx;               \
+    htp_matmul_tensors_preamble;                                \
+    dma_queue *dma_queue           = octx->ctx->dma[ith];       \
+    uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;
 
 // *** matmul with support for 4d tensors and full broadcasting
 
-static void matmul_4d(struct htp_matmul_type * mt, struct htp_ops_context * octx, uint32_t nth, uint32_t ith) {
+static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
     uint64_t t1, t2;
@@ -1136,13 +1613,13 @@ static void matmul_4d(struct htp_matmul_type * mt, struct htp_ops_context * octx
     for (uint32_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
         for (uint32_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
             for (uint32_t ir1 = iir1; ir1 < MIN(iir1 + blck_1, ir1_end); ir1++) {
-                const uint32_t i13 = fastdiv(ir1, &octx->mm_div_ne12_ne1);
-                const uint32_t i12 = fastdiv(ir1 - i13 * ne12 * ne1, &octx->mm_div_ne1);
+                const uint32_t i13 = fastdiv(ir1, &mmctx->mm_div_ne12_ne1);
+                const uint32_t i12 = fastdiv(ir1 - i13 * ne12 * ne1, &mmctx->mm_div_ne1);
                 const uint32_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1);
 
                 // broadcast src0 into src1
-                const uint32_t i03 = fastdiv(i13, &octx->mm_div_r3);
-                const uint32_t i02 = fastdiv(i12, &octx->mm_div_r2);
+                const uint32_t i03 = fastdiv(i13, &mmctx->mm_div_r3);
+                const uint32_t i02 = fastdiv(i12, &mmctx->mm_div_r2);
 
                 const uint32_t i1 = i11;
                 const uint32_t i2 = i12;
@@ -1155,7 +1632,7 @@ static void matmul_4d(struct htp_matmul_type * mt, struct htp_ops_context * octx
                 const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end);
                 for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) {
                     const uint8_t * restrict src0_row = src0_base + ir0 * nb01;
-                    mt->vec_dot(ne00, &dst_col[ir0], src0_row, src1_col);
+                    mmctx->vec_dot_1x1(ne00, &dst_col[ir0], src0_row, src1_col);
                 }
             }
         }
@@ -1170,7 +1647,7 @@ static void matmul_4d(struct htp_matmul_type * mt, struct htp_ops_context * octx
 }
 
 // src1 tensor is already in VTCM spad
-static void matmul_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx, uint32_t nth, uint32_t ith) {
+static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
@@ -1195,7 +1672,7 @@ static void matmul_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
     // Per-thread VTCM scratchpads for all tensors
     // Note that the entire src1 tensor is already in VTCM
     // For other tensors we allocate N rows per thread, padded to HVX vector size
-    uint8_t * restrict spad_dst  = dst_spad->data + dst_spad->size_per_thread * ith;
+    uint8_t * restrict spad_dst  = dst_spad->data  + dst_spad->size_per_thread  * ith;
     uint8_t * restrict spad_src0 = src0_spad->data + src0_spad->size_per_thread * ith;
     uint8_t * restrict src1_data = src1_spad->data;
 
@@ -1219,11 +1696,21 @@ static void matmul_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
     for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
-        #pragma unroll(2)
-        for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) {
+        // Process src1 columns in pairs (2×2 tiling)
+        uint32_t ir1 = 0;
+        for (; ir1 + 1 < src1_nrows; ir1 += 2) {
+            const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);
+            const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);
+            float * restrict dst_row0 = (float *) (dst->data + ((ir1+0) * dst_row_size));
+            float * restrict dst_row1 = (float *) (dst->data + ((ir1+1) * dst_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0[ir0], &dst_row1[ir0], ss0, ss0 + src0_stride, src1_col0, src1_col1);
+        }
+
+        // Handle remaining src1 rows (fallback to 2×1)
+        for (; ir1 < src1_nrows; ++ir1) {
             const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
             float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));
-            mt->vec_dot_rx2(ne00, &dst_row[ir0], ss0, src0_stride, src1_col);
+            mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_stride, src1_col);
         }
 
         // Prefetch next (n + spad_nrows) row
@@ -1247,20 +1734,20 @@ static void matmul_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
         for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) {
             const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
             float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));
-            mt->vec_dot(ne00, &dst_row[ir0], ss0, src1_col);
+            mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
         }
     }
 
     t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "matmul-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mt->type, ith, nth,
+    FARF(HIGH, "matmul-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mmctx->type, ith, nth,
          src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1],
          src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
          (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
 // q8x4x2 src1 tensor is already in VTCM spad
-static void matvec_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx, uint32_t nth, uint32_t ith) {
+static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
     const uint32_t src0_nrows = ne01;
@@ -1311,7 +1798,7 @@ static void matvec_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
     // Process src0 rows
     for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-        mt->vec_dot_rx2(ne00, &tmp[ir0 - src0_start_row], ss0, src0_stride, src1_col);
+        mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
 
         // Prefetch next (n + spad_nrows) row
         const uint32_t pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -1329,14 +1816,14 @@ static void matvec_2d(struct htp_matmul_type * mt, struct htp_ops_context * octx
         dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
                        src0_stride, src0_row_size, 1);
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-        mt->vec_dot(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
+        mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
     }
 
     hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, src0_end_row - src0_start_row);
 
     t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "matvec-%s %u/%u: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mt->type, ith, nth,
+    FARF(HIGH, "matvec-%s %u/%u: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mmctx->type, ith, nth,
          src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1],
          src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
          (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
@@ -1350,7 +1837,7 @@ struct mmid_row_mapping {
 };
 
 // src1 tensor is already in VTCM spad
-static void matmul_id(struct htp_matmul_type * mt, struct htp_ops_context * octx, uint32_t nth, uint32_t ith) {
+static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
     struct htp_tensor * restrict     ids = &octx->src2;
@@ -1423,11 +1910,10 @@ static void matmul_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
                 const int               rm2         = row_mapping.i2;  // token idx
 
                 const uint32_t ir1 = src1_nrows == 1 ? 0 : rm1;        // src1 row idx
-                const uint8_t * restrict src1_col =
-                    (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
+                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
                 float * dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
 
-                mt->vec_dot_rx2(ne00, &dst_row[ir0], ss0, src0_row_size_padded, src1_col);
+                mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
             }
 
             // Prefetch next (n + spad_nrows) row
@@ -1453,25 +1939,24 @@ static void matmul_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
                 const int               rm2         = row_mapping.i2;  // token idx
 
                 const uint32_t ir1 = src1_nrows == 1 ? 0 : rm1;        // src1 row idx
-                const uint8_t * restrict src1_col =
-                    (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
+                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
                 float * dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
 
-                mt->vec_dot(ne00, &dst_row[ir0], ss0, src1_col);
+                mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
             }
         }
     }
 
     t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "matmul-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mt->type,
+    FARF(HIGH, "matmul-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mmctx->type,
          ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0],
          src1->ne[1], src1->ne[2], src1->ne[3], ids->ne[0], ids->ne[1], ids->ne[2], ids->ne[3], dst->ne[0], dst->ne[1],
          dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
 // src1 tensor is already in VTCM spad
-static void matvec_id(struct htp_matmul_type * mt, struct htp_ops_context * octx, uint32_t nth, uint32_t ith) {
+static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
     struct htp_tensor * restrict     ids = &octx->src2;
@@ -1531,7 +2016,7 @@ static void matvec_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
         // Process src0 rows
         for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            mt->vec_dot_rx2(ne00, &dst_row[ir0], ss0, src0_row_size_padded, src1_col);
+            mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
 
             // Prefetch next (n + spad_nrows) row
             const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -1549,13 +2034,13 @@ static void matvec_id(struct htp_matmul_type * mt, struct htp_ops_context * octx
             dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
                            src0_row_size_padded, src0_row_size, 1);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            mt->vec_dot(ne00, &dst_row[ir0], ss0, src1_col);
+            mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
         }
     }
 
     t2 = HAP_perf_get_qtimer_count();
 
-    FARF(HIGH, "matvec-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mt->type,
+    FARF(HIGH, "matvec-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mmctx->type,
          ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0],
          src1->ne[1], src1->ne[2], src1->ne[3], src2->ne[0], src2->ne[1], src2->ne[2], src2->ne[3], dst->ne[0],
          dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
@@ -1754,12 +2239,14 @@ static void quantize_row_f32_q8x4x2(float * restrict x, uint8_t * restrict y, ui
     hvx_copy_f16_ua(y_d, t_d, nb * 8);
 }
 
-static void quantize_f32_q8x4x2(const struct htp_tensor * src,
-                                 uint8_t * restrict dst,
-                                 struct htp_spad * spad,
-                                 uint32_t          nth,
-                                 uint32_t          ith,
-                                 uint32_t          nrows_per_thread) {
+static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_matmul_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+
+    const struct htp_tensor * src = &octx->src1;
+    uint8_t * restrict dst = octx->src1_spad.data;
+    struct htp_spad * spad = &octx->src0_spad;
+    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
 
     uint64_t t1 = HAP_perf_get_qtimer_count();
 
@@ -1799,8 +2286,14 @@ static void quantize_f32_q8x4x2(const struct htp_tensor * src,
          ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void quantize_f32_f16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
-                              uint32_t nrows_per_thread, uint32_t dst_stride) {
+static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_matmul_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+
+    const struct htp_tensor * src = &octx->src1;
+    uint8_t * restrict dst = octx->src1_spad.data;
+    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
+    uint32_t dst_stride = octx->src1_spad.stride;
 
     uint64_t t1 = HAP_perf_get_qtimer_count();
 
@@ -1835,8 +2328,14 @@ static void quantize_f32_f16(const struct htp_tensor * src, uint8_t * restrict d
 }
 
 // TODO just a plain copy that should be done via the DMA during the Op setup
-static void quantize_f16_f16(const struct htp_tensor * src, uint8_t * restrict dst, uint32_t nth, uint32_t ith,
-                              uint32_t nrows_per_thread, uint32_t dst_stride) {
+static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_matmul_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+
+    const struct htp_tensor * src = &octx->src1;
+    uint8_t * restrict dst = octx->src1_spad.data;
+    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
+    uint32_t dst_stride = octx->src1_spad.stride;
 
     uint64_t t1 = HAP_perf_get_qtimer_count();
 
@@ -1870,213 +2369,76 @@ static void quantize_f16_f16(const struct htp_tensor * src, uint8_t * restrict d
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void htp_quantize_f32_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-    quantize_f32_q8x4x2(&octx->src1, octx->src1_spad.data, &octx->src0_spad, n, i, octx->src1_nrows_per_thread);
-}
-
-static void htp_quantize_f32_f16(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-    quantize_f32_f16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
-}
-
-static void htp_quantize_f16_f16(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-    quantize_f16_f16(&octx->src1, octx->src1_spad.data, n, i, octx->src1_nrows_per_thread, octx->src1_spad.stride);
-}
-
-// ** matmul/matvec callbacks for worker_pool
-
-static void htp_matvec_2d_q4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q4x4x2_q8x4x2_rx2;
-
-    matvec_2d(&mt, octx, n, i);
-}
-
-static void htp_matmul_2d_q4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q4x4x2_q8x4x2_rx2;
-
-    matmul_2d(&mt, octx, n, i);
-}
-
-static void htp_matvec_2d_q8x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q8x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q8x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q8x4x2_q8x4x2_rx2;
-
-    matvec_2d(&mt, octx, n, i);
-}
-
-static void htp_matmul_2d_q8x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q8x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q8x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q8x4x2_q8x4x2_rx2;
-
-    matmul_2d(&mt, octx, n, i);
-}
-
-static void htp_matvec_2d_mxfp4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "mxfp4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_mxfp4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_mxfp4x4x2_q8x4x2_rx2;
-
-    matvec_2d(&mt, octx, n, i);
-}
-
-static void htp_matmul_2d_mxfp4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "mxfp4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_mxfp4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_mxfp4x4x2_q8x4x2_rx2;
-
-    matmul_2d(&mt, octx, n, i);
-}
-
-static void htp_matvec_2d_f16_f16(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "f16-f16";
-    mt.vec_dot     = vec_dot_f16_f16_aa;
-    mt.vec_dot_rx2 = vec_dot_f16_f16_aa_rx2;
-
-    matvec_2d(&mt, octx, n, i);
-}
-
-static void htp_matmul_2d_f16_f16(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "f16-f16";
-    mt.vec_dot     = vec_dot_f16_f16_aa;
-    mt.vec_dot_rx2 = vec_dot_f16_f16_aa_rx2;
-
-    matmul_2d(&mt, octx, n, i);
-}
-
-static void htp_matmul_4d_f16_f32(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "f16-f32";
-    mt.vec_dot     = vec_dot_f16_f32_uu;
-
-    matmul_4d(&mt, octx, n, i);
-}
-
-static void htp_matmul_4d_f16_f16(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "f16-f16";
-    mt.vec_dot     = vec_dot_f16_f16_uu;
-
-    matmul_4d(&mt, octx, n, i);
-}
-
-// ** matmul-id callbacks for worker_pool
-
-static void htp_matvec_id_q4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q4x4x2_q8x4x2_rx2;
-
-    matvec_id(&mt, octx, n, i);
-}
-
-static void htp_matmul_id_q4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q4x4x2_q8x4x2_rx2;
-
-    matmul_id(&mt, octx, n, i);
-}
-
-static void htp_matvec_id_q8x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q8x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q8x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q8x4x2_q8x4x2_rx2;
-
-    matvec_id(&mt, octx, n, i);
-}
-
-static void htp_matmul_id_q8x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "q8x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_q8x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_q8x4x2_q8x4x2_rx2;
-
-    matmul_id(&mt, octx, n, i);
-}
-
-static void htp_matvec_id_mxfp4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "mxfp4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_mxfp4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_mxfp4x4x2_q8x4x2_rx2;
-
-    matvec_id(&mt, octx, n, i);
-}
-
-static void htp_matmul_id_mxfp4x4x2_q8x4x2(unsigned int n, unsigned int i, void * data) {
-    struct htp_ops_context * octx = data;
-
-    struct htp_matmul_type mt;
-    mt.type        = "mxfp4x4x2-q8x4x2";
-    mt.vec_dot     = vec_dot_mxfp4x4x2_q8x4x2;
-    mt.vec_dot_rx2 = vec_dot_mxfp4x4x2_q8x4x2_rx2;
-
-    matmul_id(&mt, octx, n, i);
-}
-
-// ** main matmul entry point
 
 static inline bool htp_is_permuted(const struct htp_tensor * t) {
     return t->nb[0] > t->nb[1] || t->nb[1] > t->nb[2] || t->nb[2] > t->nb[3];
 }
 
+static int htp_mminit_vec_dot(struct htp_matmul_context * mmctx, enum htp_data_type type) {
+    switch (type) {
+        case HTP_TYPE_Q4_0:
+            mmctx->type        = "q4x4x2-f32";
+            mmctx->vec_dot_1x1 = vec_dot_q4x4x2_q8x4x2_1x1;
+            mmctx->vec_dot_2x1 = vec_dot_q4x4x2_q8x4x2_2x1;
+            mmctx->vec_dot_2x2 = vec_dot_q4x4x2_q8x4x2_2x2;
+            return 0;
+        case HTP_TYPE_Q8_0:
+            mmctx->type        = "q8x4x2-f32";
+            mmctx->vec_dot_1x1 = vec_dot_q8x4x2_q8x4x2_1x1;
+            mmctx->vec_dot_2x1 = vec_dot_q8x4x2_q8x4x2_2x1;
+            mmctx->vec_dot_2x2 = vec_dot_q8x4x2_q8x4x2_2x2;
+            return 0;
+        case HTP_TYPE_MXFP4:
+            mmctx->type        = "mxfp4x4x2-f32";
+            mmctx->vec_dot_1x1 = vec_dot_mxfp4x4x2_q8x4x2_1x1;
+            mmctx->vec_dot_2x1 = vec_dot_mxfp4x4x2_q8x4x2_2x1;
+            mmctx->vec_dot_2x2 = vec_dot_mxfp4x4x2_q8x4x2_2x2;
+            return 0;
+        default:
+            return -1;
+    }
+}
+
+static void htp_mminit_spad(struct htp_ops_context * octx,
+                                 size_t dst_row_size,
+                                 size_t src0_row_size_padded,
+                                 size_t src1_row_size,
+                                 uint32_t src1_nrows,
+                                 size_t src2_spad_size_per_thread) {
+    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
+    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
+    octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+
+    if (src2_spad_size_per_thread > 0) {
+        octx->src2_spad.size_per_thread = src2_spad_size_per_thread;
+        octx->src2_spad.size            = octx->src2_spad.size_per_thread;
+    }
+
+    // src0 spad is also used in dynamic quantizer to store padded src1 rows
+    size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
+    if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
+        octx->src0_spad.size_per_thread = src1_row_size_padded;
+    }
+
+    octx->src1_spad.size = octx->src1_spad.size_per_thread;
+    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
+    octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
+}
+
 int op_matmul(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
-    const char * op_type;
+    struct htp_matmul_context mmctx_struct = {0};
+    struct htp_matmul_context * mmctx = &mmctx_struct;
+    mmctx->octx = octx;
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
 
+    // Compute src0_nrows_per_thread
+    mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
+    mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
     size_t       src1_row_size = nb11;
@@ -2085,181 +2447,95 @@ int op_matmul(struct htp_ops_context * octx) {
     size_t       src1_row_size_padded;
 
     worker_callback_t quant_job_func;
-    worker_callback_t matmul_job_func;
+    worker_callback_t matmul_job_func = src1_nrows > 1 ? matmul_2d : matvec_2d;
 
     bool need_quant = !(octx->flags & HTP_OPFLAGS_SKIP_QUANTIZE);
 
-    switch (src0->type) {
-        case HTP_TYPE_Q4_0:
-            op_type        = "q4x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            if (src1_nrows > 1) {
-                matmul_job_func = htp_matmul_2d_q4x4x2_q8x4x2;
-            } else {
-                matmul_job_func = htp_matvec_2d_q4x4x2_q8x4x2;
-            }
+    if (src0->type == HTP_TYPE_F16) {
+        // Try optimized f16-f16 path first (src1 in VTCM)
+        const size_t f16_src1_row_size  = hex_round_up(ne10 * 2, 128);
+        const size_t f16_src1_spad_size = hex_round_up(f16_src1_row_size * src1_nrows, 256);
+        const size_t f16_src0_spad_size = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
+        const size_t f16_dst_spad_size  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256) * octx->n_threads;
 
-            src1_row_size = q8x4x2_row_size(ne10);  // row size post quantization
+        const size_t f16_total_size = f16_src1_spad_size + f16_src0_spad_size + f16_dst_spad_size;
 
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
+        // Default matmul implementation does not support multi-batch src0 (N-vs-N broadcasting).
+        // It only supports 1-vs-N broadcasting (src0 is 2D) or standard 2D matmul.
+        const bool is_batched  = (ne02 > 1) || (ne03 > 1);
+        const bool is_permuted = htp_is_permuted(&octx->src0) || htp_is_permuted(&octx->src1);
+
+        if (!is_batched && !is_permuted && f16_total_size <= octx->ctx->vtcm_size) {
+            // Optimized path
+            quant_job_func     = (src1->type == HTP_TYPE_F32) ? quantize_f32_f16 : quantize_f16_f16;
+            mmctx->type        = "f16-f16";
+            mmctx->vec_dot_1x1 = vec_dot_f16_f16_aa_1x1;
+            mmctx->vec_dot_2x1 = vec_dot_f16_f16_aa_2x1;
+            mmctx->vec_dot_2x2 = vec_dot_f16_f16_aa_2x2;
+
+            src1_row_size = f16_src1_row_size;  // row size post quantization
 
             octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
             octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
             octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
 
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
             octx->src1_spad.size = octx->src1_spad.size_per_thread;
             octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
             octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
-
-        case HTP_TYPE_Q8_0:
-            op_type        = "q8x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            if (src1_nrows > 1) {
-                matmul_job_func = htp_matmul_2d_q8x4x2_q8x4x2;
+        } else {
+            // Fallback to f16/f32 (DDR) if src1 doesn't fit in VTCM or broadcasting is required
+            quant_job_func = NULL;
+            if (src1->type == HTP_TYPE_F32) {
+                mmctx->type        = "f16-f32";
+                mmctx->vec_dot_1x1 = vec_dot_f16_f32_uu_1x1;
+                matmul_job_func    = matmul_4d;
             } else {
-                matmul_job_func = htp_matvec_2d_q8x4x2_q8x4x2;
+                mmctx->type        = "f16-f16";
+                mmctx->vec_dot_1x1 = vec_dot_f16_f16_uu_1x1;
+                matmul_job_func    = matmul_4d;
             }
 
-            src1_row_size = q8x4x2_row_size(ne10);  // row size post quantization
-
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
+            src1_row_size = nb11;  // original row size in DDR
 
             octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
+            octx->src1_spad.size_per_thread = hex_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
 
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
             octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
+            octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
             octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
 
-        case HTP_TYPE_MXFP4:
-            op_type        = "mxfp4x4x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            if (src1_nrows > 1) {
-                matmul_job_func = htp_matmul_2d_mxfp4x4x2_q8x4x2;
-            } else {
-                matmul_job_func = htp_matvec_2d_mxfp4x4x2_q8x4x2;
-            }
+            // Init fastdiv for matmul_4d (supports broadcasting)
+            mmctx->mm_div_ne12_ne1 = init_fastdiv_values(src1->ne[2] * dst->ne[1]);
+            mmctx->mm_div_ne1      = init_fastdiv_values(dst->ne[1]);
+            mmctx->mm_div_r2       = init_fastdiv_values(src1->ne[2] / src0->ne[2]);
+            mmctx->mm_div_r3       = init_fastdiv_values(src1->ne[3] / src0->ne[3]);
 
-            src1_row_size = q8x4x2_row_size(ne10);  // row size post quantization
-
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
-
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
-
-        case HTP_TYPE_F16:
-            {
-                // Try optimized f16-f16 path first (src1 in VTCM)
-                const size_t f16_src1_row_size  = hex_round_up(ne10 * 2, 128);
-                const size_t f16_src1_spad_size = hex_round_up(f16_src1_row_size * src1_nrows, 256);
-                const size_t f16_src0_spad_size = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
-                const size_t f16_dst_spad_size  = hex_round_up(MM_SPAD_DST_NROWS  * dst_row_size, 256) * octx->n_threads;
-
-                const size_t f16_total_size = f16_src1_spad_size + f16_src0_spad_size + f16_dst_spad_size;
-
-                // Default matmul implementation does not support multi-batch src0 (N-vs-N broadcasting).
-                // It only supports 1-vs-N broadcasting (src0 is 2D) or standard 2D matmul.
-                const bool is_batched  = (ne02 > 1) || (ne03 > 1);
-                const bool is_permuted = htp_is_permuted(&octx->src0) || htp_is_permuted(&octx->src1);
-
-                if (!is_batched && !is_permuted && f16_total_size <= octx->ctx->vtcm_size) {
-                    // Optimized path
-                    op_type        = "f16-f16";
-                    quant_job_func = (src1->type == HTP_TYPE_F32) ? htp_quantize_f32_f16 : htp_quantize_f16_f16;
-                    if (src1_nrows > 1) {
-                        matmul_job_func = htp_matmul_2d_f16_f16;
-                    } else {
-                        matmul_job_func = htp_matvec_2d_f16_f16;
-                    }
-
-                    src1_row_size = f16_src1_row_size; // row size post quantization
-
-                    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-                    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-                    octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-
-                    octx->src1_spad.size = octx->src1_spad.size_per_thread;
-                    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-                    octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-                } else {
-                    // Fallback to f16/f32 (DDR) if src1 doesn't fit in VTCM or broadcasting is required
-                    quant_job_func  = NULL;
-                    if (src1->type == HTP_TYPE_F32) {
-                        op_type         = "f16-f32";
-                        matmul_job_func = htp_matmul_4d_f16_f32;
-                    } else {
-                        op_type         = "f16-f16";
-                        matmul_job_func = htp_matmul_4d_f16_f16;
-                    }
-
-                    src1_row_size = nb11; // original row size in DDR
-
-                    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-                    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
-                    octx->src1_spad.size_per_thread = hex_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
-
-                    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-                    octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
-                    octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-
-                    // Init fastdiv for matmul_4d (supports broadcasting)
-                    octx->mm_div_ne12_ne1 = init_fastdiv_values(src1->ne[2] * dst->ne[1]);
-                    octx->mm_div_ne1      = init_fastdiv_values(dst->ne[1]);
-                    octx->mm_div_r2       = init_fastdiv_values(src1->ne[2] / src0->ne[2]);
-                    octx->mm_div_r3       = init_fastdiv_values(src1->ne[3] / src0->ne[3]);
-
-                    need_quant = false;
-                }
-            }
-            break;
-
-        default:
+            need_quant = false;
+        }
+    } else {
+        if (htp_mminit_vec_dot(mmctx, src0->type) != 0) {
             return HTP_STATUS_NO_SUPPORT;
+        }
+
+        quant_job_func = quantize_f32_q8x4x2;
+        src1_row_size  = q8x4x2_row_size(ne10);
+        htp_mminit_spad(octx, dst_row_size, src0_row_size_padded, src1_row_size, src1_nrows, 0);
     }
 
     // VTCM scratchpads for all tensors
     size_t spad_size = octx->src1_spad.size + octx->src0_spad.size + octx->dst_spad.size;
 
-    FARF(HIGH, "matmul-%s : src0-spad-size %u src1-spad-size %u dst-spad-size %u (%zu)\n", op_type,
+    FARF(HIGH, "matmul-%s : src0-spad-size %u src1-spad-size %u dst-spad-size %u (%zu)\n", mmctx->type,
          octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size, spad_size);
 
-    FARF(HIGH, "matmul-%s : %ux%ux%ux%u * %ux%ux%ux%u-> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", op_type, src0->ne[0],
+    FARF(HIGH, "matmul-%s : %ux%ux%ux%u * %ux%ux%ux%u-> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", mmctx->type, src0->ne[0],
          src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0],
          dst->ne[1], dst->ne[2], dst->ne[3], src0->data, src1->data, dst->data);
 
     // Make sure the reserved vtcm size is sufficient
     if (octx->ctx->vtcm_size < spad_size) {
-        FARF(ERROR, "matmul-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type,
+        FARF(ERROR, "matmul-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type,
              octx->ctx->vtcm_size, spad_size);
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
@@ -2268,40 +2544,32 @@ int op_matmul(struct htp_ops_context * octx) {
     octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
     octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
 
-    octx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
-    octx->src0_nrows_per_thread += (octx->src0_nrows_per_thread & 1);  // round up to even
-
     octx->src0_spad.stride = src0_row_size_padded;
     octx->src1_spad.stride = src1_row_size;
 
     if (need_quant) {
-        // Run quant jobs
-        const uint32_t n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        octx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, octx, n_quant_jobs);
+        const uint32_t n_quant_jobs  = MIN(src1_nrows, octx->n_threads);
+        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
+        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
     }
 
     if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
-        // Run matmul jobs
         const uint32_t n_matmul_jobs = octx->n_threads;
-        worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, octx, n_matmul_jobs);
+        worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs);
     }
 
     return HTP_STATUS_OK;
 }
 
-// ** main matmul-id entry point
-
 int op_matmul_id(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
+    struct htp_matmul_context mmctx_struct = {0};
+    struct htp_matmul_context * mmctx = &mmctx_struct;
+    mmctx->octx = octx;
+
     struct htp_tensor * restrict ids = &octx->src2;
 
-    const char * op_type;
-
-    worker_callback_t quant_job_func;
-    worker_callback_t matmul_id_job_func;
-
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
 
@@ -2310,6 +2578,13 @@ int op_matmul_id(struct htp_ops_context * octx) {
     const uint32_t src0_nrows = ne01;  // per expert
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
 
+    worker_callback_t quant_job_func;
+    worker_callback_t matmul_id_job_func = src1_nrows > 1 ? matmul_id : matvec_id;
+
+    // Compute src0_nrows_per_thread
+    mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
+    mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+
     size_t src1_row_size;
     size_t src1_row_size_padded;
 
@@ -2320,112 +2595,29 @@ int op_matmul_id(struct htp_ops_context * octx) {
     size_t matrix_row_counts_size = n_as * sizeof(uint32_t);
     size_t matrix_row_map_size    = n_as * ids->ne[0] * ids->ne[1] * sizeof(struct mmid_row_mapping);
 
-    switch (src0->type) {
-        case HTP_TYPE_Q4_0:
-            op_type        = "q4x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
-            if (src1_nrows > 1) {
-                matmul_id_job_func = htp_matmul_id_q4x4x2_q8x4x2;
-            } else {
-                matmul_id_job_func = htp_matvec_id_q4x4x2_q8x4x2;
-            }
-
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
-
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
-            octx->src2_spad.size = octx->src2_spad.size_per_thread;
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
-
-        case HTP_TYPE_Q8_0:
-            op_type        = "q8x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
-            if (src1_nrows > 1) {
-                matmul_id_job_func = htp_matmul_id_q8x4x2_q8x4x2;
-            } else {
-                matmul_id_job_func = htp_matvec_id_q8x4x2_q8x4x2;
-            }
-
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
-
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
-            octx->src2_spad.size = octx->src2_spad.size_per_thread;
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
-
-        case HTP_TYPE_MXFP4:
-            op_type        = "mxfp4x2x2-f32";
-            quant_job_func = htp_quantize_f32_q8x4x2;
-            src1_row_size  = q8x4x2_row_size(ne10);  // row size post quantization
-            if (src1_nrows > 1) {
-                matmul_id_job_func = htp_matmul_id_mxfp4x4x2_q8x4x2;
-            } else {
-                matmul_id_job_func = htp_matvec_id_mxfp4x4x2_q8x4x2;
-            }
-
-            // Entire src1 tensor is placed into the VTCM
-            // For other tensors we allocate N rows per thread, padded to HVX vector size
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-            octx->src2_spad.size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
-
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-            if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-                octx->src0_spad.size_per_thread = src1_row_size_padded;
-            }
-
-            octx->src2_spad.size = octx->src2_spad.size_per_thread;
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-            break;
-
-        default:
-            return HTP_STATUS_NO_SUPPORT;
+    if (htp_mminit_vec_dot(mmctx, src0->type) != 0) {
+        return HTP_STATUS_NO_SUPPORT;
     }
 
+    quant_job_func = quantize_f32_q8x4x2;
+    src1_row_size  = q8x4x2_row_size(ne10);
+
+    const size_t src2_spad_size_per_thread = hex_round_up(matrix_row_counts_size + matrix_row_map_size, 256);
+    htp_mminit_spad(octx, dst_row_size, src0_row_size_padded, src1_row_size, src1_nrows, src2_spad_size_per_thread);
+
     size_t spad_size = octx->src2_spad.size + octx->src1_spad.size + octx->src0_spad.size + octx->dst_spad.size;
 
-    FARF(HIGH, "matmul-id-%s : src0-spad-size %u src1-spad-size %u src2-spad-size %u dst-spad-size %u (%zu)\n", op_type,
+    FARF(HIGH, "matmul-id-%s : src0-spad-size %u src1-spad-size %u src2-spad-size %u dst-spad-size %u (%zu)\n", mmctx->type,
          octx->src0_spad.size, octx->src1_spad.size, octx->src2_spad.size, octx->dst_spad.size, spad_size);
 
-    FARF(HIGH, "matmul-id-%s : %ux%ux%ux%u * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", op_type,
+    FARF(HIGH, "matmul-id-%s : %ux%ux%ux%u * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", mmctx->type,
          src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
          ids->ne[0], ids->ne[1], ids->ne[2], ids->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], src0->data,
          src1->data, dst->data);
 
     // Make sure the reserved vtcm size is sufficient
     if (octx->ctx->vtcm_size < spad_size) {
-        FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type,
-             octx->ctx->vtcm_size, spad_size);
+        FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type, octx->ctx->vtcm_size, spad_size);
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
@@ -2434,8 +2626,8 @@ int op_matmul_id(struct htp_ops_context * octx) {
     octx->src2_spad.data = octx->src1_spad.data + octx->src1_spad.size;
     octx->dst_spad.data  = octx->src2_spad.data + octx->src2_spad.size;
 
-    octx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
-    octx->src0_nrows_per_thread += (octx->src0_nrows_per_thread & 1);  // round up to even
+    octx->src0_spad.stride = src0_row_size_padded;
+    octx->src1_spad.stride = src1_row_size;
 
     if (src1_nrows > 1) {
         // initialize matrix_row_counts and map
@@ -2447,8 +2639,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
         // group rows by src0 matrix
         for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {  // token idx
             for (uint32_t id = 0; id < n_ids; ++id) {         // expert idx
-                const uint32_t i02 =
-                    *(const uint32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
+                const uint32_t i02 = *(const uint32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
 
                 assert(i02 >= 0 && i02 < n_as);
 
@@ -2460,16 +2651,14 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
     // Setup worker pool callbacks
     if (!(octx->flags & HTP_OPFLAGS_SKIP_QUANTIZE)) {
-        // Run quant jobs
         const uint32_t n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        octx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, octx, n_quant_jobs);
+        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
+        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
     }
 
     if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
-        // Run matmul-id jobs
         const uint32_t n_matmul_jobs = octx->n_threads;
-        worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, octx, n_matmul_jobs);
+        worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
     }
 
     return HTP_STATUS_OK;

From 313493de533845150316b9be11bd95511b9f3d4c Mon Sep 17 00:00:00 2001
From: TriDefender 
Date: Thu, 12 Feb 2026 15:13:51 +0800
Subject: [PATCH 193/206] docs : update path in snapdragon README.md (#19533)

paths changed so original example didn't work
---
 docs/backend/snapdragon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/backend/snapdragon/README.md b/docs/backend/snapdragon/README.md
index 8e1f37b206..2c3f88e91a 100644
--- a/docs/backend/snapdragon/README.md
+++ b/docs/backend/snapdragon/README.md
@@ -35,7 +35,7 @@ Adapt below build commands accordingly.
 Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
 
 ```
-[d]/workspace> cp docs/backend/hexagon/CMakeUserPresets.json .
+[d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json .
 
 [d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
 Preset CMake variables:

From fa16e517a3bcc15818d0813993693e1c92ce6c76 Mon Sep 17 00:00:00 2001
From: RichardScottOZ 
Date: Thu, 12 Feb 2026 18:26:25 +1030
Subject: [PATCH 194/206] server : fix typo in README.md for features list
 (#19510)

extra l for full
---
 tools/server/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index d132830171..0b56ca1e27 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -19,7 +19,7 @@ Set of LLM REST APIs and a web UI to interact with llama.cpp.
  * Speculative decoding
  * Easy-to-use web UI
 
-For the ful list of features, please refer to [server's changelog](https://github.com/ggml-org/llama.cpp/issues/9291)
+For the full list of features, please refer to [server's changelog](https://github.com/ggml-org/llama.cpp/issues/9291)
 
 ## Usage
 

From 6845f7f87f4ece1ac13db2e7d0388090bc1c8d3c Mon Sep 17 00:00:00 2001
From: Mario Limonciello 
Date: Thu, 12 Feb 2026 02:38:35 -0600
Subject: [PATCH 195/206] Add a workaround for compilation with ROCWMMA_FATTN
 and gfx9 (#19461)

There is an upstream problem [1] with AMD's LLVM 22 fork and
rocWMMA 2.2.0 causing compilation issues on devices without
native fp16 support (CDNA devices).

The specialized types aren't resolved properly:
```
/opt/rocm/include/rocwmma/internal/mfma_impl.hpp:2549:37: error: ambiguous partial specializations of 'amdgcn_mfma<__half, __half, __half, 16, 16, 16>'
 2549 |             using ARegsT = typename Impl::ARegsT;
```

Add a workaround to explicitly declare the types and cast when
compiling with HIP and ROCWMMA_FATTN [2].  When this is actually
fixed upstream some guards can be used to detect and wrap the
version that has the fix to only apply when necessary.

Link: https://github.com/ROCm/rocm-libraries/issues/4398 [1]
Link: https://github.com/ggml-org/llama.cpp/issues/19269 [2]

Signed-off-by: Mario Limonciello 
---
 ggml/src/ggml-cuda/fattn-wmma-f16.cu | 31 +++++++++++++++++++++++-----
 1 file changed, 26 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
index 8694fd06c7..35735d48b2 100644
--- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
@@ -63,11 +63,19 @@ static __global__ void flash_attn_ext_f16(
     constexpr int frag_m = ncols == 8 ? 32 : 16;
     constexpr int frag_n = ncols == 8 ?  8 : 16;
     static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+#if defined(GGML_USE_HIP)
+    typedef wmma::fragment frag_a_K;
+    typedef wmma::fragment frag_a_V;
+    typedef wmma::fragment frag_b;
+    typedef wmma::fragment                      frag_c_KQ;
+    typedef wmma::fragment                          frag_c_VKQ;
+#else
     typedef wmma::fragment frag_a_K;
     typedef wmma::fragment frag_a_V;
     typedef wmma::fragment frag_b;
     typedef wmma::fragment                      frag_c_KQ;
     typedef wmma::fragment                          frag_c_VKQ;
+#endif
 
     constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
     constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
@@ -126,6 +134,19 @@ static __global__ void flash_attn_ext_f16(
 
     __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
     half2 * VKQ2 = (half2 *) VKQ;
+
+#if defined(GGML_USE_HIP)
+    const _Float16 * K_h_f16  = reinterpret_cast(K_h);
+    const _Float16 * V_h_f16  = reinterpret_cast(V_h);
+    _Float16       * KQ_f16   = reinterpret_cast<_Float16 *>(KQ);
+    _Float16       * VKQ_f16  = reinterpret_cast<_Float16 *>(VKQ);
+#else
+    const half * K_h_f16  = K_h;
+    const half * V_h_f16  = V_h;
+    half       * KQ_f16   = KQ;
+    half       * VKQ_f16  = VKQ;
+#endif
+
 #pragma unroll
     for (int j0 = 0; j0 < ncols; j0 += nwarps) {
         const int j = j0 + threadIdx.y;
@@ -160,7 +181,7 @@ static __global__ void flash_attn_ext_f16(
     for (int i0 = 0; i0 < D; i0 += 16) {
 #pragma unroll
         for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-            wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+            wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ_f16 + j0*D_padded + i0, D_padded);
         }
     }
 
@@ -180,7 +201,7 @@ static __global__ void flash_attn_ext_f16(
 #pragma unroll
             for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
                 frag_a_K K_a;
-                wmma::load_matrix_sync(K_a, K_h + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+                wmma::load_matrix_sync(K_a, K_h_f16 + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
 #pragma unroll
                 for (int j = 0; j < ncols/frag_n; ++j) {
                     wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
@@ -310,7 +331,7 @@ static __global__ void flash_attn_ext_f16(
                 const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
                 wmma::load_matrix_sync(
                     KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
-                    KQ + j0*(kqar*kqs_padded) + k,
+                    KQ_f16 + j0*(kqar*kqs_padded) + k,
                     kqar*kqs_padded);
             }
         }
@@ -328,7 +349,7 @@ static __global__ void flash_attn_ext_f16(
                 const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
 
                 frag_a_V v_a;
-                wmma::load_matrix_sync(v_a, V_h + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+                wmma::load_matrix_sync(v_a, V_h_f16 + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
 #pragma unroll
                 for (int j = 0; j < ncols/frag_n; ++j) {
                     wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
@@ -344,7 +365,7 @@ static __global__ void flash_attn_ext_f16(
 #pragma unroll
             for (int j0 = 0; j0 < ncols; j0 += frag_n) {
                 wmma::store_matrix_sync(
-                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+                    KQ_f16 + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
                     VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
                     D_padded, wmma::mem_col_major);
             }

From 3b3a9481341ce475089efaa25151508a961fe217 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Thu, 12 Feb 2026 11:35:28 +0200
Subject: [PATCH 196/206] metal : update sum_rows kernel to support float4
 (#19524)

---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 33 +++++++---
 ggml/src/ggml-metal/ggml-metal-impl.h     |  3 +
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 18 ++++--
 ggml/src/ggml-metal/ggml-metal.metal      | 79 ++++++++++++++---------
 tests/test-backend-ops.cpp                | 24 ++++---
 5 files changed, 106 insertions(+), 51 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 517559d12a..06f3d80459 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -328,31 +328,46 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum(ggml_metal_l
 }
 
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum_rows(ggml_metal_library_t lib, const ggml_tensor * op) {
-    GGML_ASSERT(op->src[0]->nb[0] == ggml_type_size(op->src[0]->type));
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
 
     char base[256];
     char name[256];
 
-    const char * op_str = "undefined";
+    int op_num = -1;
+
     switch (op->op) {
-        case GGML_OP_SUM_ROWS:
-            op_str = "sum_rows"; break;
-        case GGML_OP_MEAN:
-            op_str = "mean"; break;
+        case GGML_OP_SUM_ROWS: op_num = OP_SUM_ROWS_NUM_SUM_ROWS; break;
+        case GGML_OP_MEAN:     op_num = OP_SUM_ROWS_NUM_MEAN;     break;
         default: GGML_ABORT("fatal error");
     };
 
-    snprintf(base, 256, "kernel_%s_%s", op_str, ggml_type_name(op->src[0]->type));
+    const char * t0_str = ggml_type_name(op->src[0]->type);
+    const char * t_str  = ggml_type_name(op->type);
 
-    snprintf(name, 256, "%s", base);
+    const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
+
+    snprintf(base, 256, "kernel_sum_rows_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
+    snprintf(name, 256, "%s_op=%d", base, op_num);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, op_num, FC_SUM_ROWS + 0);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
     }
 
     res.smem = 32*sizeof(float);
 
+    if (is_c4) {
+        res.smem *= 4;
+    }
+
+    res.c4  = is_c4;
+
     return res;
 }
 
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 952e1be076..383e0d6e93 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -82,6 +82,7 @@
 #define FC_COUNT_EQUAL                 1100
 #define FC_UNARY                       1200
 #define FC_BIN                         1300
+#define FC_SUM_ROWS                    1400
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPSG 8
@@ -118,6 +119,8 @@
 #define OP_UNARY_NUM_SOFTPLUS    115
 #define OP_UNARY_NUM_EXPM1       116
 
+#define OP_SUM_ROWS_NUM_SUM_ROWS 10
+#define OP_SUM_ROWS_NUM_MEAN     11
 
 // kernel argument structs
 //
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 7db95d1c84..20880d9551 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -904,6 +904,11 @@ int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
+
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
     ggml_metal_kargs_sum_rows args = {
         /*.ne00 =*/ ne00,
         /*.ne01 =*/ ne01,
@@ -925,21 +930,26 @@ int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) {
 
     auto pipeline = ggml_metal_library_get_pipeline_sum_rows(lib, op);
 
+    if (pipeline.c4) {
+        args.ne00 = ne00/4;
+        args.ne0  = ne0/4;
+    }
+
     int nth = 32; // SIMD width
 
-    while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+    while (nth < args.ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
         nth *= 2;
     }
 
     nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    nth = std::min(nth, ne00);
+    nth = std::min(nth, (int) args.ne00);
 
     const size_t smem = pipeline.smem;
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
 
     ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 0036ba90ec..6c349aa0c9 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -77,6 +77,14 @@ static inline float dot(float x, float y) {
     return x*y;
 }
 
+static inline float sum(float x) {
+    return x;
+}
+
+static inline float sum(float4 x) {
+    return x[0] + x[1] + x[2] + x[3];
+}
+
 // NOTE: this is not dequantizing - we are simply fitting the template
 template 
 void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
@@ -1501,33 +1509,35 @@ kernel void kernel_op_sum_f32(
     }
 }
 
-template 
-kernel void kernel_sum_rows(
+constant short FC_sum_rows_op [[function_constant(FC_SUM_ROWS + 0)]];
+
+template 
+kernel void kernel_sum_rows_impl(
         constant ggml_metal_kargs_sum_rows & args,
-        device const float * src0,
-        device       float * dst,
-        threadgroup  float * shmem_f32 [[threadgroup(0)]],
+        device const char * src0,
+        device       char * dst,
+        threadgroup  char * shmem [[threadgroup(0)]],
         uint3   tgpig[[threadgroup_position_in_grid]],
         ushort3 tpitg[[thread_position_in_threadgroup]],
         ushort  sgitg[[simdgroup_index_in_threadgroup]],
         ushort  tiisg[[thread_index_in_simdgroup]],
         ushort3   ntg[[threads_per_threadgroup]]) {
-    int64_t i3 = tgpig.z;
-    int64_t i2 = tgpig.y;
-    int64_t i1 = tgpig.x;
+#define FC_OP  FC_sum_rows_op
 
-    if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
-        return;
-    }
+    const int i3 = tgpig.z;
+    const int i2 = tgpig.y;
+    const int i1 = tgpig.x;
+
+    threadgroup T0 * shmem_t = (threadgroup T0 *) shmem;
 
     if (sgitg == 0) {
-        shmem_f32[tiisg] = 0.0f;
+        shmem_t[tiisg] = 0.0f;
     }
 
-    device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
-    device       float * dst_row = (device       float *) ((device       char *) dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
+    device const T0 * src_row = (device const T0 *) (src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
+    device       T  * dst_row = (device       T  *) (dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
 
-    float sumf = 0;
+    T0 sumf = T0(0.0f);
 
     for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
         sumf += src_row[i0];
@@ -1538,23 +1548,33 @@ kernel void kernel_sum_rows(
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     if (tiisg == 0) {
-        shmem_f32[sgitg] = sumf;
+        shmem_t[sgitg] = sumf;
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    sumf = shmem_f32[tiisg];
+    sumf = shmem_t[tiisg];
     sumf = simd_sum(sumf);
 
     if (tpitg.x == 0) {
-        dst_row[0] = norm ? sumf / args.ne00 : sumf;
+        if (FC_OP == OP_SUM_ROWS_NUM_MEAN) {
+            if (is_same::value) {
+                dst_row[0] = sum(sumf) / (4*args.ne00);
+            } else {
+                dst_row[0] = sum(sumf) / args.ne00;
+            }
+        } else {
+            dst_row[0] = sum(sumf);
+        }
     }
+
+#undef FC_OP
 }
 
-typedef decltype(kernel_sum_rows) kernel_sum_rows_t;
+typedef decltype(kernel_sum_rows_impl) kernel_sum_rows_t;
 
-template [[host_name("kernel_sum_rows_f32")]] kernel kernel_sum_rows_t kernel_sum_rows;
-template [[host_name("kernel_mean_f32")]]     kernel kernel_sum_rows_t kernel_sum_rows;
+template [[host_name("kernel_sum_rows_f32_f32")]]   kernel kernel_sum_rows_t kernel_sum_rows_impl;
+template [[host_name("kernel_sum_rows_f32_f32_4")]] kernel kernel_sum_rows_t kernel_sum_rows_impl;
 
 template
 kernel void kernel_cumsum_blk(
@@ -2435,9 +2455,6 @@ kernel void kernel_solve_tri_f32(
     const short K   = FC_solve_tri_k;
     const short NP  = PAD2(N, NW);
 
-    const int32_t ne02 = args.ne02;
-    const int32_t ne03 = args.ne03;
-
     const int32_t i03 = tgpig.z;
     const int32_t i02 = tgpig.y;
     const int32_t i01 = tgpig.x*NSG + sgitg;
@@ -5949,7 +5966,7 @@ kernel void kernel_flash_attn_ext_vec(
     static_assert(DK4 % NL == 0, "DK4 must be divisible by NL");
     static_assert(DV4 % NL == 0, "DV4 must be divisible by NL");
 
-    const short T = PK + NSG*SH; // shared memory size per query in (half)
+  //const short T = PK + NSG*SH; // shared memory size per query in (half)
 
   //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                      0*PK); // holds the query data
     threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                      0*PK); // same as above but in q4_t
@@ -8537,7 +8554,9 @@ kernel void kernel_mul_mm(
     threadgroup S0 * sa = (threadgroup S0 *)(shmem);
     threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
 
+#ifdef GGML_METAL_HAS_TENSOR
     threadgroup float * sc = (threadgroup float *)(shmem);
+#endif
 
     constexpr int NR0 = 64;
     constexpr int NR1 = 32;
@@ -8660,8 +8679,8 @@ kernel void kernel_mul_mm(
             const short sx = (tiitg%NL1);
             const short sy = (tiitg/NL1)/8;
 
-            const short dx = sx;
-            const short dy = sy;
+          //const short dx = sx;
+          //const short dy = sy;
 
             const short ly = (tiitg/NL1)%8;
 
@@ -8910,7 +8929,9 @@ kernel void kernel_mul_mm_id(
     threadgroup S0 * sa = (threadgroup S0 *)(shmem);
     threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
 
+#ifdef GGML_METAL_HAS_TENSOR
     threadgroup float * sc = (threadgroup float *)(shmem);
+#endif
 
     constexpr int NR0 = 64;
     constexpr int NR1 = 32;
@@ -9045,8 +9066,8 @@ kernel void kernel_mul_mm_id(
             const short sx = (tiitg%NL1);
             const short sy = (tiitg/NL1)/8;
 
-            const short dx = sx;
-            const short dy = sy;
+          //const short dx = sx;
+          //const short dy = sy;
 
             const short ly = (tiitg/NL1)%8;
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index ed99c24516..222b935841 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8132,24 +8132,30 @@ static std::vector> 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(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 2, 1, 3}));  // row-contiguous but non-contiguous
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 3, 2, 1}));
     test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 1, 3, 2}));
+    test_cases.emplace_back(new test_mean());
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 1, 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_mean(GGML_TYPE_F32, { 32, 1, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32, 256, 1, 1 }));
+    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32768, 1, 1, 1 }));
+    test_cases.emplace_back(new test_sum(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(GGML_TYPE_F32, { 33, 256, 1, 1 }));
+    test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }, { 1, 0, 2, 3 })); // sum dst not-contiguous
+    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, { 16, 5, 6, 3 }, true, false));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 16, 5, 6, 3 }, false, true));
+    test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 16, 5, 6, 3 }, true, true));
     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(GGML_TYPE_F32, { 33, 256, 1, 1 }, { 1, 0, 2, 3 })); // sum dst not-contiguous
     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_group_norm_mul_add(GGML_TYPE_F32, {64, 64, 320, 1}));

From 38adc7d469baaf8c1bbfbf60bdbefb288a6ab1c5 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier 
Date: Thu, 12 Feb 2026 11:22:27 +0100
Subject: [PATCH 197/206] WebUI Architecture Cleanup (#19541)

* webui: architecture foundation (non-MCP core refactors)

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1453103 -> 1453494 bytes
 .../src/lib/constants/binary-detection.ts     |   3 -
 .../webui/src/lib/constants/chat-form.ts      |   3 +
 .../webui/src/lib/constants/code-blocks.ts    |   8 +
 tools/server/webui/src/lib/constants/code.ts  |   7 +
 .../webui/src/lib/constants/css-classes.ts    |  10 +
 .../webui/src/lib/constants/formatters.ts     |   8 +
 .../webui/src/lib/constants/markdown.ts       |   4 +
 .../src/lib/constants/processing-info.ts      |   7 +
 .../src/lib/constants/settings-fields.ts      |  33 ++
 .../webui/src/lib/constants/tooltip-config.ts |   2 +-
 tools/server/webui/src/lib/constants/ui.ts    |   1 +
 .../src/lib/contexts/chat-actions.context.ts  |  34 ++
 tools/server/webui/src/lib/contexts/index.ts  |  13 +
 .../src/lib/contexts/message-edit.context.ts  |  39 ++
 tools/server/webui/src/lib/enums/chat.ts      |  49 ++-
 tools/server/webui/src/lib/enums/keyboard.ts  |  15 +
 tools/server/webui/src/lib/enums/settings.ts  |  26 ++
 .../src/lib/hooks/use-auto-scroll.svelte.ts   | 165 ++++++++
 .../lib/hooks/use-processing-state.svelte.ts  |  85 +++-
 .../src/lib/markdown/enhance-code-blocks.ts   |  33 +-
 .../src/lib/services/database.service.ts      | 368 ++++++++++++++++
 .../webui/src/lib/services/models.service.ts  |  99 +++++
 .../services/parameter-sync.service.spec.ts   | 148 +++++++
 .../lib/services/parameter-sync.service.ts    | 400 ++++++++++++++++++
 .../webui/src/lib/services/props.service.ts   |  47 ++
 tools/server/webui/src/lib/types/api.d.ts     |  26 +-
 tools/server/webui/src/lib/types/models.d.ts  |  12 +-
 tools/server/webui/src/lib/utils/abort.ts     | 151 +++++++
 tools/server/webui/src/lib/utils/api-fetch.ts | 155 +++++++
 tools/server/webui/src/lib/utils/branching.ts |  11 +-
 .../webui/src/lib/utils/browser-only.ts       |   2 +-
 tools/server/webui/src/lib/utils/cache-ttl.ts | 293 +++++++++++++
 tools/server/webui/src/lib/utils/code.ts      |  85 ++++
 tools/server/webui/src/lib/utils/data-url.ts  |  10 +
 tools/server/webui/src/lib/utils/debounce.ts  |  22 +
 .../src/lib/utils/image-error-fallback.ts     |  10 +
 tools/server/webui/src/lib/utils/index.ts     |   4 +-
 .../src/lib/utils/modality-file-validation.ts |   7 +-
 .../server/webui/src/lib/utils/text-files.ts  |   6 +-
 40 files changed, 2361 insertions(+), 40 deletions(-)
 create mode 100644 tools/server/webui/src/lib/constants/chat-form.ts
 create mode 100644 tools/server/webui/src/lib/constants/code-blocks.ts
 create mode 100644 tools/server/webui/src/lib/constants/code.ts
 create mode 100644 tools/server/webui/src/lib/constants/css-classes.ts
 create mode 100644 tools/server/webui/src/lib/constants/formatters.ts
 create mode 100644 tools/server/webui/src/lib/constants/markdown.ts
 create mode 100644 tools/server/webui/src/lib/constants/settings-fields.ts
 create mode 100644 tools/server/webui/src/lib/constants/ui.ts
 create mode 100644 tools/server/webui/src/lib/contexts/chat-actions.context.ts
 create mode 100644 tools/server/webui/src/lib/contexts/index.ts
 create mode 100644 tools/server/webui/src/lib/contexts/message-edit.context.ts
 create mode 100644 tools/server/webui/src/lib/enums/keyboard.ts
 create mode 100644 tools/server/webui/src/lib/enums/settings.ts
 create mode 100644 tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts
 create mode 100644 tools/server/webui/src/lib/services/database.service.ts
 create mode 100644 tools/server/webui/src/lib/services/models.service.ts
 create mode 100644 tools/server/webui/src/lib/services/parameter-sync.service.spec.ts
 create mode 100644 tools/server/webui/src/lib/services/parameter-sync.service.ts
 create mode 100644 tools/server/webui/src/lib/services/props.service.ts
 create mode 100644 tools/server/webui/src/lib/utils/abort.ts
 create mode 100644 tools/server/webui/src/lib/utils/api-fetch.ts
 create mode 100644 tools/server/webui/src/lib/utils/cache-ttl.ts
 create mode 100644 tools/server/webui/src/lib/utils/code.ts
 create mode 100644 tools/server/webui/src/lib/utils/data-url.ts
 create mode 100644 tools/server/webui/src/lib/utils/debounce.ts
 create mode 100644 tools/server/webui/src/lib/utils/image-error-fallback.ts

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index e3b06f490103a30ef41f127d35275f11b1255e02..327386f413a633359ac0fe4fd25a7a42f15fe749 100644
GIT binary patch
delta 1378164
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+		Image cannot be displayed
+		(open link)
+	
        `;
+}
diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/server/webui/src/lib/utils/index.ts
index 588167b8ca..38e809f2d3 100644
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/server/webui/src/lib/utils/index.ts
@@ -9,6 +9,7 @@
 
 // API utilities
 export { getAuthHeaders, getJsonHeaders } from './api-headers';
+export { apiFetch, apiFetchWithParams, apiPost, type ApiFetchOptions } from './api-fetch';
 export { validateApiKey } from './api-key-validation';
 
 // Attachment utilities
@@ -75,8 +76,7 @@ export { maskInlineLaTeX, preprocessLaTeX } from './latex-protection';
 export {
 	isFileTypeSupportedByModel,
 	filterFilesByModalities,
-	generateModalityErrorMessage,
-	type ModalityCapabilities
+	generateModalityErrorMessage
 } from './modality-file-validation';
 
 // Model name utilities
diff --git a/tools/server/webui/src/lib/utils/modality-file-validation.ts b/tools/server/webui/src/lib/utils/modality-file-validation.ts
index 136c084146..02fb4e4a36 100644
--- a/tools/server/webui/src/lib/utils/modality-file-validation.ts
+++ b/tools/server/webui/src/lib/utils/modality-file-validation.ts
@@ -5,12 +5,7 @@
 
 import { getFileTypeCategory } from '$lib/utils';
 import { FileTypeCategory } from '$lib/enums';
-
-/** Modality capabilities for file validation */
-export interface ModalityCapabilities {
-	hasVision: boolean;
-	hasAudio: boolean;
-}
+import type { ModalityCapabilities } from '$lib/types/models';
 
 /**
  * Check if a file type is supported by the given modalities
diff --git a/tools/server/webui/src/lib/utils/text-files.ts b/tools/server/webui/src/lib/utils/text-files.ts
index e8006de64d..2f1a575d1d 100644
--- a/tools/server/webui/src/lib/utils/text-files.ts
+++ b/tools/server/webui/src/lib/utils/text-files.ts
@@ -3,10 +3,8 @@
  * Handles text file detection, reading, and validation
  */
 
-import {
-	DEFAULT_BINARY_DETECTION_OPTIONS,
-	type BinaryDetectionOptions
-} from '$lib/constants/binary-detection';
+import { DEFAULT_BINARY_DETECTION_OPTIONS } from '$lib/constants/binary-detection';
+import type { BinaryDetectionOptions } from '$lib/constants/binary-detection';
 import { FileExtensionText } from '$lib/enums';
 
 /**

From f486ce9f30528dbbbf5591e0eeb0caa15d883a20 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier 
Date: Thu, 12 Feb 2026 12:21:00 +0100
Subject: [PATCH 198/206] (webui) REFACTOR: UI primitives and polish (#19551)

* webui: UI primitives and polish (non-MCP)

* chore: update webui build output
---
 tools/server/public/index.html.gz             |  Bin 1453494 -> 1454104 bytes
 tools/server/webui/src/app.css                |   58 +-
 .../components/app/actions/ActionIcon.svelte  |   48 +
 .../actions/ActionIconCopyToClipboard.svelte  |   18 +
 .../app/actions/ActionIconRemove.svelte       |   26 +
 .../app/actions/ActionIconsCodeBlock.svelte   |   46 +
 .../src/lib/components/app/actions/index.ts   |   19 +
 .../app/badges/BadgeChatStatistic.svelte      |   44 +
 .../components/app/badges/BadgeInfo.svelte    |   27 +
 .../app/badges/BadgeModality.svelte           |   39 +
 .../src/lib/components/app/badges/index.ts    |   16 +
 .../content/CollapsibleContentBlock.svelte    |   97 ++
 .../app/content/MarkdownContent.svelte        | 1201 +++++++++++++++++
 .../app/content/SyntaxHighlightedCode.svelte  |   95 ++
 .../src/lib/components/app/content/index.ts   |   79 ++
 .../app/misc/ConversationSelection.svelte     |   10 +-
 .../app/misc/HorizontalScrollCarousel.svelte  |   93 ++
 .../app/misc/KeyboardShortcutInfo.svelte      |    4 +-
 .../components/app/misc/TruncatedText.svelte  |   48 +
 .../src/lib/components/app/misc/index.ts      |   45 +
 .../app/navigation/DropdownMenuActions.svelte |   86 ++
 .../navigation/DropdownMenuSearchable.svelte  |   50 +
 .../lib/components/app/navigation/index.ts    |   65 +
 .../app/server/ServerErrorSplash.svelte       |    3 +-
 .../components/app/server/ServerStatus.svelte |    2 +-
 .../src/lib/components/app/server/index.ts    |   80 ++
 .../src/lib/components/ui/badge/badge.svelte  |    2 +-
 .../lib/components/ui/button/button.svelte    |    5 +-
 .../src/lib/components/ui/card/card.svelte    |    4 +-
 .../dropdown-menu-content.svelte              |    2 +-
 .../src/lib/components/ui/input/input.svelte  |    1 +
 .../ui/sidebar/sidebar-trigger.svelte         |    3 +-
 .../lib/components/ui/switch/switch.svelte    |    2 +-
 .../ui/tooltip/tooltip-content.svelte         |   26 +-
 tools/server/webui/src/lib/utils/api-fetch.ts |    3 +-
 tools/server/webui/src/lib/utils/index.ts     |    3 +
 36 files changed, 2324 insertions(+), 26 deletions(-)
 create mode 100644 tools/server/webui/src/lib/components/app/actions/ActionIcon.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/actions/index.ts
 create mode 100644 tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/badges/index.ts
 create mode 100644 tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/content/index.ts
 create mode 100644 tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/misc/index.ts
 create mode 100644 tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
 create mode 100644 tools/server/webui/src/lib/components/app/navigation/index.ts
 create mode 100644 tools/server/webui/src/lib/components/app/server/index.ts

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 327386f413a633359ac0fe4fd25a7a42f15fe749..64a6402fc9dc94d4792dbe950aee98cff702ef85 100644
GIT binary patch
delta 1390774
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z?;{y0&a&Ahqo`bSe(aj5S0F9pH*1Whpw*L}ZXC*!Cb%gE61aPAaAE?V^PKg2N^+Zt
zG8?w!w2C~9w|9xUt0GGgqM>=-2#QxGxk5sb?I%09?u&yyHg$BU@7D*BJKo!v$bam$
zU~9S{LW(bcqn-4_q!>P*N$WLMD6gRTovmy*+WTj_Y#81Vi4*w`)A{c1Q7Ux=n8Rzr
zjEuNru+fvqR-E@)?8vQK$7O)WUNfBJLlr&pGK)q55|f9igpiT`ry`zcJQr73%*gbN
ze}!O-ghvP)6_sXV6-og&%?y%>ihqgX7aGuT6@X%`Ivi`UUn2<%u|Mr<`7sGUUzZN&
zk9Ho9c4O>Z|E`W8ZH!T=ZiygL3ZzfE&%mH*w-&~c@DH3<5YGTmA$X|zXG2g0t1O@O
zPNz^Xk4Hn&BS48K-IO5$F
zSzojoLsHZT@|+o7amZ>0=n3O{mr+pA0aoW{F)$;b^-Wyg8t14(1~teBK|-G-twz#X
zN^R`C=>n+
z&2^!1(ja4RF4HFOxYSOcXgVz^=pdzzP~tFou9fyO`92?nXM7X8hV7-Y3o
zC9`%zOGPkmrD>#N{FB;M6lA1MF}gk8-MV3ag{_dutsT9=-5%+}mVfvx`a#$f#uMsA
z;EzKtZNsBU?Ll!u)p0t9s!kk1kQ~!ZNY>wbcZX}Z3nu#ihU?V2Ial?d78{H2f`K@S
zUm@a1IK^tu5d<4c28Sf}GQ0bh#pEAzoyMK3mJ{V-8-8gROUKLKE_yF7PY
+	import { Button } from '$lib/components/ui/button';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import type { Component } from 'svelte';
+
+	interface Props {
+		icon: Component;
+		tooltip: string;
+		variant?: 'default' | 'destructive' | 'outline' | 'secondary' | 'ghost' | 'link';
+		size?: 'default' | 'sm' | 'lg' | 'icon';
+		class?: string;
+		disabled?: boolean;
+		onclick: () => void;
+		'aria-label'?: string;
+	}
+
+	let {
+		icon,
+		tooltip,
+		variant = 'ghost',
+		size = 'sm',
+		class: className = '',
+		disabled = false,
+		onclick,
+		'aria-label': ariaLabel
+	}: Props = $props();
+
+
+
+	
+		
+	
+
+	
+		
        {tooltip}
        
+	
+
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
new file mode 100644
index 0000000000..bf6cd4fb28
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
@@ -0,0 +1,18 @@
+
+
+ canCopy && copyToClipboard(text)}
+/>
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte
new file mode 100644
index 0000000000..1ae3d21774
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte
@@ -0,0 +1,26 @@
+
+
+
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte
new file mode 100644
index 0000000000..54ff0af1a0
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte
@@ -0,0 +1,46 @@
+
+
+
        
+	
        
+		
+	
        
+
+	{#if showPreview}
+		
+	{/if}
+
        
diff --git a/tools/server/webui/src/lib/components/app/actions/index.ts b/tools/server/webui/src/lib/components/app/actions/index.ts
new file mode 100644
index 0000000000..43485c7b7e
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/actions/index.ts
@@ -0,0 +1,19 @@
+/**
+ *
+ * ACTIONS
+ *
+ * Small interactive components for user actions.
+ *
+ */
+
+/** Styled icon button for action triggers with tooltip. */
+export { default as ActionIcon } from './ActionIcon.svelte';
+
+/** Code block actions component (copy, preview). */
+export { default as ActionIconsCodeBlock } from './ActionIconsCodeBlock.svelte';
+
+/** Copy-to-clipboard icon button with click handler. */
+export { default as ActionIconCopyToClipboard } from './ActionIconCopyToClipboard.svelte';
+
+/** Remove/delete icon button with X icon. */
+export { default as ActionIconRemove } from './ActionIconRemove.svelte';
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte b/tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte
new file mode 100644
index 0000000000..a2b28d2057
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte
@@ -0,0 +1,44 @@
+
+
+{#if tooltipLabel}
+	
+		
+			
+				{#snippet icon()}
+					
+				{/snippet}
+
+				{value}
+			
+		
+		
+			
        {tooltipLabel}
        
+		        
+	        
+{:else}
+	
+		{#snippet icon()}
+			
+		{/snippet}
+
+		{value}
+	
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte b/tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte
new file mode 100644
index 0000000000..c70af6f423
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte
@@ -0,0 +1,27 @@
+
+
+
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte b/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte
new file mode 100644
index 0000000000..a0d5e863c2
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte
@@ -0,0 +1,39 @@
+
+
+{#each displayableModalities as modality, index (index)}
+	{@const IconComponent = MODALITY_ICONS[modality]}
+	{@const label = MODALITY_LABELS[modality]}
+
+	
+		{#if IconComponent}
+			
+		{/if}
+
+		{label}
+	
+{/each}
diff --git a/tools/server/webui/src/lib/components/app/badges/index.ts b/tools/server/webui/src/lib/components/app/badges/index.ts
new file mode 100644
index 0000000000..860afe3084
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/badges/index.ts
@@ -0,0 +1,16 @@
+/**
+ *
+ * BADGES & INDICATORS
+ *
+ * Small visual indicators for status and metadata.
+ *
+ */
+
+/** Badge displaying chat statistics (tokens, timing). */
+export { default as BadgeChatStatistic } from './BadgeChatStatistic.svelte';
+
+/** Generic info badge with optional tooltip and click handler. */
+export { default as BadgeInfo } from './BadgeInfo.svelte';
+
+/** Badge indicating model modality (vision, audio, tools). */
+export { default as BadgeModality } from './BadgeModality.svelte';
diff --git a/tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte b/tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte
new file mode 100644
index 0000000000..082738da57
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte
@@ -0,0 +1,97 @@
+
+
+ {
+		open = value;
+		onToggle?.();
+	}}
+	class={className}
+>
+	
+		
+			
        
+				{#if Icon}
+					
+				{/if}
+
+				{title}
+
+				{#if subtitle}
+					{subtitle}
+				{/if}
+			
        
+
+			
        
+				
+
+				Toggle content
+			
        
+		        
+
+		
+			
        
+				{@render children()}
+			
        
+		        
+	        
+        
diff --git a/tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte
new file mode 100644
index 0000000000..ef6c7e064f
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte
@@ -0,0 +1,1201 @@
+
+
+
        
+	{#each renderedBlocks as block (block.id)}
+		
        
+			
+			{@html block.html}
+		
        
+	{/each}
+
+	{#if unstableBlockHtml}
+		
        
+			
+			{@html unstableBlockHtml}
+		
        
+	{/if}
+
+	{#if incompleteCodeBlock}
+		
        
+			
        
+				{incompleteCodeBlock.language || 'text'}
+				 {
+						previewCode = code;
+						previewLanguage = lang;
+						previewDialogOpen = true;
+					}}
+				/>
+			
        
+			
         streamingAutoScroll.handleScroll()}
+			>
+				
        {@html highlightCode(
+							incompleteCodeBlock.code,
+							incompleteCodeBlock.language || 'text'
+						)}
        
+			
        
+		
        
+	{/if}
+
        
+
+
+
+
diff --git a/tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte b/tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
new file mode 100644
index 0000000000..625fdc7b1b
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
@@ -0,0 +1,95 @@
+
+
+
        
+	
+	
        {@html highlightedHtml}
        
+
        
+
+
diff --git a/tools/server/webui/src/lib/components/app/content/index.ts b/tools/server/webui/src/lib/components/app/content/index.ts
new file mode 100644
index 0000000000..bca1c9f4c2
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/content/index.ts
@@ -0,0 +1,79 @@
+/**
+ *
+ * CONTENT RENDERING
+ *
+ * Components for rendering rich content: markdown, code, and previews.
+ *
+ */
+
+/**
+ * **MarkdownContent** - Rich markdown renderer
+ *
+ * Renders markdown content with syntax highlighting, LaTeX math,
+ * tables, links, and code blocks. Optimized for streaming with
+ * incremental block-based rendering.
+ *
+ * **Features:**
+ * - GFM (GitHub Flavored Markdown): tables, task lists, strikethrough
+ * - LaTeX math via KaTeX (`$inline$` and `$$block$$`)
+ * - Syntax highlighting (highlight.js) with language detection
+ * - Code copy buttons with click feedback
+ * - External links open in new tab with security attrs
+ * - Image attachment resolution from message extras
+ * - Dark/light theme support (auto-switching)
+ * - Streaming-optimized incremental rendering
+ * - Code preview dialog for large blocks
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as MarkdownContent } from './MarkdownContent.svelte';
+
+/**
+ * **SyntaxHighlightedCode** - Code syntax highlighting
+ *
+ * Renders code with syntax highlighting using highlight.js.
+ * Supports theme switching and scrollable containers.
+ *
+ * **Features:**
+ * - Auto language detection with fallback
+ * - Dark/light theme auto-switching
+ * - Scrollable container with configurable max dimensions
+ * - Monospace font styling
+ * - Preserves whitespace and formatting
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as SyntaxHighlightedCode } from './SyntaxHighlightedCode.svelte';
+
+/**
+ * **CollapsibleContentBlock** - Expandable content card
+ *
+ * Reusable collapsible card with header, icon, and auto-scroll.
+ * Used for tool calls and reasoning blocks in chat messages.
+ *
+ * **Features:**
+ * - Collapsible content with smooth animation
+ * - Custom icon and title display
+ * - Optional subtitle/status text
+ * - Auto-scroll during streaming (pauses on user scroll)
+ * - Configurable max height with overflow scroll
+ *
+ * @example
+ * ```svelte
+ * 
+ *   {reasoningContent}
+ * 
+ * ```
+ */
+export { default as CollapsibleContentBlock } from './CollapsibleContentBlock.svelte';
diff --git a/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte b/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte
index e2095e0876..21412f47e5 100644
--- a/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte
@@ -17,9 +17,13 @@
 	let { conversations, messageCountMap = new Map(), mode, onCancel, onConfirm }: Props = $props();
 
 	let searchQuery = $state('');
-	let selectedIds = $state.raw>(new SvelteSet(conversations.map((c) => c.id)));
+	let selectedIds = $state.raw>(getInitialSelectedIds());
 	let lastClickedId = $state(null);
 
+	function getInitialSelectedIds(): SvelteSet {
+		return new SvelteSet(conversations.map((c) => c.id));
+	}
+
 	let filteredConversations = $derived(
 		conversations.filter((conv) => {
 			const name = conv.name || 'Untitled conversation';
@@ -92,7 +96,7 @@
 	}
 
 	function handleCancel() {
-		selectedIds = new SvelteSet(conversations.map((c) => c.id));
+		selectedIds = getInitialSelectedIds();
 		searchQuery = '';
 		lastClickedId = null;
 
@@ -100,7 +104,7 @@
 	}
 
 	export function reset() {
-		selectedIds = new SvelteSet(conversations.map((c) => c.id));
+		selectedIds = getInitialSelectedIds();
 		searchQuery = '';
 		lastClickedId = null;
 	}
diff --git a/tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte b/tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
new file mode 100644
index 0000000000..e302f83e11
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
@@ -0,0 +1,93 @@
+
+
+
        
+	
+
+	
        
+		{@render children?.()}
+	
        
+
+	
+
        
diff --git a/tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte b/tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
index 5b7522fe1b..da55abda02 100644
--- a/tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
@@ -11,7 +11,9 @@
 
 	let baseClasses =
 		'px-1 pointer-events-none inline-flex select-none items-center gap-0.5 font-sans text-md font-medium opacity-0 transition-opacity -my-1';
-	let variantClasses = variant === 'destructive' ? 'text-destructive' : 'text-muted-foreground';
+	let variantClasses = $derived(
+		variant === 'destructive' ? 'text-destructive' : 'text-muted-foreground'
+	);
 
 
 
diff --git a/tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte b/tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte
new file mode 100644
index 0000000000..9a8731fc78
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte
@@ -0,0 +1,48 @@
+
+
+{#if isTruncated}
+	
+		
+			
+				{text}
+			
+		
+
+		
+			
        {text}
        
+		        
+	        
+{:else}
+	
+		{text}
+	
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/misc/index.ts b/tools/server/webui/src/lib/components/app/misc/index.ts
new file mode 100644
index 0000000000..02bd70b24f
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/misc/index.ts
@@ -0,0 +1,45 @@
+/**
+ *
+ * MISC
+ *
+ * Miscellaneous utility components.
+ *
+ */
+
+/**
+ * **ConversationSelection** - Multi-select conversation picker
+ *
+ * List of conversations with checkboxes for multi-selection.
+ * Used in import/export dialogs for selecting conversations.
+ *
+ * **Features:**
+ * - Search/filter conversations by name
+ * - Select all / deselect all controls
+ * - Shift-click for range selection
+ * - Message count display per conversation
+ * - Mode-specific UI (export vs import)
+ */
+export { default as ConversationSelection } from './ConversationSelection.svelte';
+
+/**
+ * Horizontal scrollable carousel with navigation arrows.
+ * Used for displaying items in a horizontally scrollable container
+ * with left/right navigation buttons that appear on hover.
+ */
+export { default as HorizontalScrollCarousel } from './HorizontalScrollCarousel.svelte';
+
+/**
+ * **TruncatedText** - Text with ellipsis and tooltip
+ *
+ * Displays text with automatic truncation and full content in tooltip.
+ * Useful for long names or paths in constrained spaces.
+ */
+export { default as TruncatedText } from './TruncatedText.svelte';
+
+/**
+ * **KeyboardShortcutInfo** - Keyboard shortcut hint display
+ *
+ * Displays keyboard shortcut hints (e.g., "⌘ + Enter").
+ * Supports special keys like shift, cmd, and custom text.
+ */
+export { default as KeyboardShortcutInfo } from './KeyboardShortcutInfo.svelte';
diff --git a/tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte b/tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte
new file mode 100644
index 0000000000..83d856d10e
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte
@@ -0,0 +1,86 @@
+
+
+
+	 e.stopPropagation()}
+	>
+		{#if triggerTooltip}
+			
+				
+					{@render iconComponent(triggerIcon, 'h-3 w-3')}
+					{triggerTooltip}
+				
+				
+					
        {triggerTooltip}
        
+				        
+			        
+		{:else}
+			{@render iconComponent(triggerIcon, 'h-3 w-3')}
+		{/if}
+	        
+
+	
+		{#each actions as action, index (action.label)}
+			{#if action.separator && index > 0}
+				
+			{/if}
+
+			
+				
        
+					{@render iconComponent(
+						action.icon,
+						`h-4 w-4 ${action.variant === 'destructive' ? 'text-destructive' : ''}`
+					)}
+					{action.label}
+				
        
+
+				{#if action.shortcut}
+					
+				{/if}
+			        
+		{/each}
+	        
+        
+
+{#snippet iconComponent(IconComponent: Component, className: string)}
+	
+{/snippet}
diff --git a/tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte b/tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
new file mode 100644
index 0000000000..3bd68d3bd6
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
@@ -0,0 +1,50 @@
+
+
+
        
+	
+
        
+
+
        
+	{@render children()}
+
+	{#if isEmpty}
+		
        {emptyMessage}
        
+	{/if}
+
        
+
+{#if footer}
+	
+
+	{@render footer()}
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/navigation/index.ts b/tools/server/webui/src/lib/components/app/navigation/index.ts
new file mode 100644
index 0000000000..051491b866
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/navigation/index.ts
@@ -0,0 +1,65 @@
+/**
+ *
+ * NAVIGATION & MENUS
+ *
+ * Components for dropdown menus and action selection.
+ *
+ */
+
+/**
+ * **DropdownMenuSearchable** - Searchable content for dropdown menus
+ *
+ * Renders a search input with filtered content area, empty state, and optional footer.
+ * Designed to be injected into any dropdown container (DropdownMenu.Content,
+ * DropdownMenu.SubContent, etc.) without providing its own Root.
+ *
+ * **Features:**
+ * - Search/filter input
+ * - Keyboard navigation support
+ * - Custom content and footer via snippets
+ * - Empty state message
+ *
+ * @example
+ * ```svelte
+ * 
+ *   ...
+ *   
+ *     
+ *       {#each items as item}{/each}
+ *     
+ *   
+ * 
+ * ```
+ */
+export { default as DropdownMenuSearchable } from './DropdownMenuSearchable.svelte';
+
+/**
+ * **DropdownMenuActions** - Multi-action dropdown menu
+ *
+ * Dropdown menu for multiple action options with icons and shortcuts.
+ * Supports destructive variants and keyboard shortcut hints.
+ *
+ * **Features:**
+ * - Configurable trigger icon with tooltip
+ * - Action items with icons and labels
+ * - Destructive variant styling
+ * - Keyboard shortcut display
+ * - Separator support between groups
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as DropdownMenuActions } from './DropdownMenuActions.svelte';
diff --git a/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte b/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte
index fa4c2842cc..520e5bf56f 100644
--- a/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte
+++ b/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte
@@ -8,6 +8,7 @@
 	import { serverStore, serverLoading } from '$lib/stores/server.svelte';
 	import { config, settingsStore } from '$lib/stores/settings.svelte';
 	import { fade, fly, scale } from 'svelte/transition';
+	import { KeyboardKey } from '$lib/enums/keyboard';
 
 	interface Props {
 		class?: string;
@@ -117,7 +118,7 @@
 	}
 
 	function handleApiKeyKeydown(event: KeyboardEvent) {
-		if (event.key === 'Enter') {
+		if (event.key === KeyboardKey.ENTER) {
 			handleSaveApiKey();
 		}
 	}
diff --git a/tools/server/webui/src/lib/components/app/server/ServerStatus.svelte b/tools/server/webui/src/lib/components/app/server/ServerStatus.svelte
index d9f6d4a32a..86a962de12 100644
--- a/tools/server/webui/src/lib/components/app/server/ServerStatus.svelte
+++ b/tools/server/webui/src/lib/components/app/server/ServerStatus.svelte
@@ -48,7 +48,7 @@
 			{model || 'Unknown Model'}
 		
 
-		{#if serverData.default_generation_settings.n_ctx}
+		{#if serverData?.default_generation_settings?.n_ctx}
 			
 				ctx: {serverData.default_generation_settings.n_ctx.toLocaleString()}
 			
diff --git a/tools/server/webui/src/lib/components/app/server/index.ts b/tools/server/webui/src/lib/components/app/server/index.ts
new file mode 100644
index 0000000000..39ac5b482d
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/server/index.ts
@@ -0,0 +1,80 @@
+/**
+ *
+ * SERVER
+ *
+ * Components for displaying server connection state and handling
+ * connection errors. Integrates with serverStore for state management.
+ *
+ */
+
+/**
+ * **ServerStatus** - Server connection status indicator
+ *
+ * Compact status display showing connection state, model name,
+ * and context size. Used in headers and loading screens.
+ *
+ * **Architecture:**
+ * - Reads state from serverStore (props, loading, error)
+ * - Displays model name from modelsStore
+ *
+ * **Features:**
+ * - Status dot: green (connected), yellow (connecting), red (error), gray (unknown)
+ * - Status text label
+ * - Model name badge with icon
+ * - Context size badge
+ * - Optional error action button
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as ServerStatus } from './ServerStatus.svelte';
+
+/**
+ * **ServerErrorSplash** - Full-screen connection error display
+ *
+ * Blocking error screen shown when server connection fails.
+ * Provides retry options and API key input for authentication errors.
+ *
+ * **Architecture:**
+ * - Detects access denied errors for API key flow
+ * - Validates API key against server before saving
+ * - Integrates with settingsStore for API key persistence
+ *
+ * **Features:**
+ * - Error message display with icon
+ * - Retry connection button with loading state
+ * - API key input for authentication errors
+ * - API key validation with success/error feedback
+ * - Troubleshooting section with server start commands
+ * - Animated transitions for UI elements
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as ServerErrorSplash } from './ServerErrorSplash.svelte';
+
+/**
+ * **ServerLoadingSplash** - Full-screen loading display
+ *
+ * Shown during initial server connection. Displays loading animation
+ * with ServerStatus component for real-time connection state.
+ *
+ * **Features:**
+ * - Animated server icon
+ * - Customizable loading message
+ * - Embedded ServerStatus for live updates
+ *
+ * @example
+ * ```svelte
+ * 
+ * ```
+ */
+export { default as ServerLoadingSplash } from './ServerLoadingSplash.svelte';
diff --git a/tools/server/webui/src/lib/components/ui/badge/badge.svelte b/tools/server/webui/src/lib/components/ui/badge/badge.svelte
index 4d15145493..c3e6ac0720 100644
--- a/tools/server/webui/src/lib/components/ui/badge/badge.svelte
+++ b/tools/server/webui/src/lib/components/ui/badge/badge.svelte
@@ -42,7 +42,7 @@
 	bind:this={ref}
 	data-slot="badge"
 	{href}
-	class={cn(badgeVariants({ variant }), className)}
+	class={cn(badgeVariants({ variant }), className, 'backdrop-blur-sm')}
 	{...restProps}
 >
 	{@render children?.()}
diff --git a/tools/server/webui/src/lib/components/ui/button/button.svelte b/tools/server/webui/src/lib/components/ui/button/button.svelte
index d12c8de147..d29358c8e0 100644
--- a/tools/server/webui/src/lib/components/ui/button/button.svelte
+++ b/tools/server/webui/src/lib/components/ui/button/button.svelte
@@ -12,8 +12,9 @@
 					'bg-destructive shadow-xs hover:bg-destructive/90 focus-visible:ring-destructive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/60 text-white',
 				outline:
 					'bg-background shadow-xs hover:bg-accent hover:text-accent-foreground dark:bg-input/30 dark:border-input dark:hover:bg-input/50 border',
-				secondary: 'bg-secondary text-secondary-foreground shadow-xs hover:bg-secondary/80',
-				ghost: 'hover:bg-accent hover:text-accent-foreground dark:hover:bg-accent/50',
+				secondary:
+					'dark:bg-secondary dark:text-secondary-foreground bg-background shadow-sm text-foreground hover:bg-muted-foreground/20',
+				ghost: 'hover:text-accent-foreground hover:bg-muted-foreground/10',
 				link: 'text-primary underline-offset-4 hover:underline'
 			},
 			size: {
diff --git a/tools/server/webui/src/lib/components/ui/card/card.svelte b/tools/server/webui/src/lib/components/ui/card/card.svelte
index c40d14309f..b9dcd2de6f 100644
--- a/tools/server/webui/src/lib/components/ui/card/card.svelte
+++ b/tools/server/webui/src/lib/components/ui/card/card.svelte
@@ -1,6 +1,7 @@
 
 
-
+{#snippet tooltipContent()}
 	
 		{@render children?.()}
@@ -44,4 +50,12 @@
 			{/snippet}
 		
 	
-
+{/snippet}
+
+{#if noPortal}
+	{@render tooltipContent()}
+{:else}
+	
+		{@render tooltipContent()}
+	
+{/if}
diff --git a/tools/server/webui/src/lib/utils/api-fetch.ts b/tools/server/webui/src/lib/utils/api-fetch.ts
index 8081ef2ec2..28757a966f 100644
--- a/tools/server/webui/src/lib/utils/api-fetch.ts
+++ b/tools/server/webui/src/lib/utils/api-fetch.ts
@@ -48,8 +48,7 @@ export async function apiFetch(path: string, options: ApiFetchOptions = {}):
 	const baseHeaders = authOnly ? getAuthHeaders() : getJsonHeaders();
 	const headers = { ...baseHeaders, ...customHeaders };
 
-	const url =
-		path.startsWith('http://') || path.startsWith('https://') ? path : `${base}${path}`;
+	const url = path.startsWith('http://') || path.startsWith('https://') ? path : `${base}${path}`;
 
 	const response = await fetch(url, {
 		...fetchOptions,
diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/server/webui/src/lib/utils/index.ts
index 38e809f2d3..5eb2bbaea1 100644
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/server/webui/src/lib/utils/index.ts
@@ -93,3 +93,6 @@ export { getLanguageFromFilename } from './syntax-highlight-language';
 
 // Text file utilities
 export { isTextFileByName, readFileAsText, isLikelyTextFile } from './text-files';
+
+// Image error fallback utilities
+export { getImageErrorFallbackHtml } from './image-error-fallback';

From ff599039a9f54fc75c0d4202941079058a26de2c Mon Sep 17 00:00:00 2001
From: Daniel Bevenius 
Date: Thu, 12 Feb 2026 13:14:28 +0100
Subject: [PATCH 199/206] scripts : add support for forks in pr2wt.sh (#19540)

This commit adds support for using the pr2wt.sh (pull request to
workspace) script with forks of upstream llama.cpp.
---
 scripts/pr2wt.sh | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/scripts/pr2wt.sh b/scripts/pr2wt.sh
index bd635f3b9d..067f5d466b 100755
--- a/scripts/pr2wt.sh
+++ b/scripts/pr2wt.sh
@@ -30,12 +30,18 @@ fi
 PR=$1
 [[ "$PR" =~ ^[0-9]+$ ]] || { echo "error: PR number must be numeric"; exit 1; }
 
+url_origin=$(git config --get remote.upstream.url 2>/dev/null) || \
 url_origin=$(git config --get remote.origin.url) || {
-    echo "error: no remote named 'origin' in this repository"
+    echo "error: no remote named 'upstream' or 'origin' in this repository"
     exit 1
 }
 
-org_repo=$(echo $url_origin | cut -d/ -f4-)
+# Extract org/repo from either https or ssh format.
+if [[ $url_origin =~ ^git@ ]]; then
+    org_repo=$(echo $url_origin | cut -d: -f2)
+else
+    org_repo=$(echo $url_origin | cut -d/ -f4-)
+fi
 org_repo=${org_repo%.git}
 
 echo "org/repo: $org_repo"

From 4d688f9ebba03a597dbaf9df1d0a4375d4947780 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier 
Date: Thu, 12 Feb 2026 13:56:08 +0100
Subject: [PATCH 200/206] (webui) FEATURE: Enable adding or injecting System
 Message into chat (#19556)

* feat: Enable adding System Prompt per-chat

* fix: Save draft message in Chat Form when adding System Prompt from new chat view

* fix: Proper system message deletion logic

* chore: Formatting

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1454104 -> 1455432 bytes
 .../app/chat/ChatForm/ChatForm.svelte         |  20 +-
 .../ChatFormActionFileAttachments.svelte      |  22 +-
 .../ChatFormActions/ChatFormActions.svelte    |   5 +-
 .../app/chat/ChatMessages/ChatMessage.svelte  |  76 +++++-
 .../ChatMessages/ChatMessageSystem.svelte     |   2 +-
 .../app/chat/ChatScreen/ChatScreen.svelte     |  20 ++
 .../webui/src/lib/stores/chat.svelte.ts       | 216 ++++++++++++++++++
 8 files changed, 351 insertions(+), 10 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 64a6402fc9dc94d4792dbe950aee98cff702ef85..ae2c8f77a7c83a47f1a1183ece27be472e03e84f 100644
GIT binary patch
delta 1173697
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
index 27ab975cbd..95645295fb 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
@@ -27,11 +27,13 @@
 	interface Props {
 		class?: string;
 		disabled?: boolean;
+		initialMessage?: string;
 		isLoading?: boolean;
 		onFileRemove?: (fileId: string) => void;
 		onFileUpload?: (files: File[]) => void;
 		onSend?: (message: string, files?: ChatUploadedFile[]) => Promise;
 		onStop?: () => void;
+		onSystemPromptAdd?: (draft: { message: string; files: ChatUploadedFile[] }) => void;
 		showHelperText?: boolean;
 		uploadedFiles?: ChatUploadedFile[];
 	}
@@ -39,11 +41,13 @@
 	let {
 		class: className,
 		disabled = false,
+		initialMessage = '',
 		isLoading = false,
 		onFileRemove,
 		onFileUpload,
 		onSend,
 		onStop,
+		onSystemPromptAdd,
 		showHelperText = true,
 		uploadedFiles = $bindable([])
 	}: Props = $props();
@@ -53,15 +57,28 @@
 	let currentConfig = $derived(config());
 	let fileInputRef: ChatFormFileInputInvisible | undefined = $state(undefined);
 	let isRecording = $state(false);
-	let message = $state('');
+	let message = $state(initialMessage);
 	let pasteLongTextToFileLength = $derived.by(() => {
 		const n = Number(currentConfig.pasteLongTextToFileLen);
 		return Number.isNaN(n) ? Number(SETTING_CONFIG_DEFAULT.pasteLongTextToFileLen) : n;
 	});
 	let previousIsLoading = $state(isLoading);
+	let previousInitialMessage = $state(initialMessage);
 	let recordingSupported = $state(false);
 	let textareaRef: ChatFormTextarea | undefined = $state(undefined);
 
+	// Sync message when initialMessage prop changes (e.g., after draft restoration)
+	$effect(() => {
+		if (initialMessage !== previousInitialMessage) {
+			message = initialMessage;
+			previousInitialMessage = initialMessage;
+		}
+	});
+
+	function handleSystemPromptClick() {
+		onSystemPromptAdd?.({ message, files: uploadedFiles });
+	}
+
 	// Check if model is selected (in ROUTER mode)
 	let conversationModel = $derived(
 		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
@@ -308,6 +325,7 @@
 			onFileUpload={handleFileUpload}
 			onMicClick={handleMicClick}
 			onStop={handleStop}
+			onSystemPromptClick={handleSystemPromptClick}
 		/>
 	
 
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
index dd37268096..3545b4aebf 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
@@ -1,5 +1,6 @@
 
 
-
        
+
        
 	
        
@@ -81,6 +88,16 @@
 			
 		
        
 	
        
+
+	{#if showRawOutputSwitch}
+		
        
+			Show raw output
+			 onRawOutputToggle?.(checked)}
+			/>
+		
        
+	{/if}
 
        
 
  {
@@ -238,7 +241,7 @@
 			
 		
 	{:else if message.role === 'assistant'}
-		{#if config().disableReasoningFormat}
+		{#if showRawOutput}
 			
        {messageContent || ''}
        
 		{:else}
 			
@@ -352,6 +355,9 @@
 			{onConfirmDelete}
 			{onNavigateToSibling}
 			{onShowDeleteDialogChange}
+			showRawOutputSwitch={currentConfig.showRawOutputSwitch}
+			rawOutputEnabled={showRawOutput}
+			onRawOutputToggle={(enabled) => (showRawOutput = enabled)}
 		/>
 	{/if}
 
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
index 6e26d510cd..a5450e6af8 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
@@ -21,6 +21,7 @@
 		chatStore,
 		errorDialog,
 		isLoading,
+		isChatStreaming,
 		isEditing,
 		getAddFilesHandler
 	} from '$lib/stores/chat.svelte';
@@ -81,7 +82,7 @@
 	let isServerLoading = $derived(serverLoading());
 	let hasPropsError = $derived(!!serverError());
 
-	let isCurrentConversationLoading = $derived(isLoading());
+	let isCurrentConversationLoading = $derived(isLoading() || isChatStreaming());
 
 	let isRouter = $derived(isRouterMode());
 
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
index 5a668aa300..967f19bbce 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
@@ -254,8 +254,13 @@
 					type: 'checkbox'
 				},
 				{
-					key: 'disableReasoningFormat',
-					label: 'Show raw LLM output',
+					key: 'disableReasoningParsing',
+					label: 'Disable reasoning content parsing',
+					type: 'checkbox'
+				},
+				{
+					key: 'showRawOutputSwitch',
+					label: 'Enable raw output toggle',
 					type: 'checkbox'
 				},
 				{
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index cac48a557c..1b959f3b69 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -7,7 +7,8 @@ export const SETTING_CONFIG_DEFAULT: Record =
 	theme: 'system',
 	showThoughtInProgress: false,
 	showToolCalls: false,
-	disableReasoningFormat: false,
+	disableReasoningParsing: false,
+	showRawOutputSwitch: false,
 	keepStatsVisible: false,
 	showMessageStats: true,
 	askForTitleConfirmation: false,
@@ -92,8 +93,10 @@ export const SETTING_CONFIG_INFO: Record = {
 	showThoughtInProgress: 'Expand thought process by default when generating messages.',
 	showToolCalls:
 		'Display tool call labels and payloads from Harmony-compatible delta.tool_calls data below assistant messages.',
-	disableReasoningFormat:
-		'Show raw LLM output without backend parsing and frontend Markdown rendering to inspect streaming across different models.',
+	disableReasoningParsing:
+		'Send reasoning_format=none to prevent server-side extraction of reasoning tokens into separate field',
+	showRawOutputSwitch:
+		'Show toggle button to display messages as plain text instead of Markdown-formatted content',
 	keepStatsVisible: 'Keep processing statistics visible after generation finishes.',
 	showMessageStats:
 		'Display generation statistics (tokens/second, token count, duration) below each assistant message.',
diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts
index 02fc6381c0..55af0ce816 100644
--- a/tools/server/webui/src/lib/services/chat.ts
+++ b/tools/server/webui/src/lib/services/chat.ts
@@ -90,7 +90,7 @@ export class ChatService {
 			custom,
 			timings_per_token,
 			// Config options
-			disableReasoningFormat
+			disableReasoningParsing
 		} = options;
 
 		const normalizedMessages: ApiChatMessageData[] = messages
@@ -127,7 +127,7 @@ export class ChatService {
 			requestBody.model = options.model;
 		}
 
-		requestBody.reasoning_format = disableReasoningFormat ? 'none' : 'auto';
+		requestBody.reasoning_format = disableReasoningParsing ? 'none' : 'auto';
 
 		if (temperature !== undefined) requestBody.temperature = temperature;
 		if (max_tokens !== undefined) {
diff --git a/tools/server/webui/src/lib/services/parameter-sync.ts b/tools/server/webui/src/lib/services/parameter-sync.ts
index d124cf5c8d..333260701f 100644
--- a/tools/server/webui/src/lib/services/parameter-sync.ts
+++ b/tools/server/webui/src/lib/services/parameter-sync.ts
@@ -70,12 +70,6 @@ export const SYNCABLE_PARAMETERS: SyncableParameter[] = [
 		canSync: true
 	},
 	{ key: 'showToolCalls', serverKey: 'showToolCalls', type: 'boolean', canSync: true },
-	{
-		key: 'disableReasoningFormat',
-		serverKey: 'disableReasoningFormat',
-		type: 'boolean',
-		canSync: true
-	},
 	{ key: 'keepStatsVisible', serverKey: 'keepStatsVisible', type: 'boolean', canSync: true },
 	{ key: 'showMessageStats', serverKey: 'showMessageStats', type: 'boolean', canSync: true },
 	{
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index 89de4f080c..f00f418b4c 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -118,6 +118,16 @@ class ChatStore {
 		this.isLoading = this.isChatLoading(convId);
 		const streamingState = this.getChatStreaming(convId);
 		this.currentResponse = streamingState?.response || '';
+		this.isStreamingActive = streamingState !== undefined;
+		this.setActiveProcessingConversation(convId);
+
+		// Sync streaming content to activeMessages so UI displays current content
+		if (streamingState?.response && streamingState?.messageId) {
+			const idx = conversationsStore.findMessageIndex(streamingState.messageId);
+			if (idx !== -1) {
+				conversationsStore.updateMessageAtIndex(idx, { content: streamingState.response });
+			}
+		}
 	}
 
 	/**
@@ -1639,7 +1649,7 @@ class ChatStore {
 
 		// Config options needed by ChatService
 		if (currentConfig.systemMessage) apiOptions.systemMessage = currentConfig.systemMessage;
-		if (currentConfig.disableReasoningFormat) apiOptions.disableReasoningFormat = true;
+		if (currentConfig.disableReasoningParsing) apiOptions.disableReasoningParsing = true;
 
 		if (hasValue(currentConfig.temperature))
 			apiOptions.temperature = Number(currentConfig.temperature);
diff --git a/tools/server/webui/src/lib/types/settings.d.ts b/tools/server/webui/src/lib/types/settings.d.ts
index 38b3047dd0..d894245ec3 100644
--- a/tools/server/webui/src/lib/types/settings.d.ts
+++ b/tools/server/webui/src/lib/types/settings.d.ts
@@ -18,8 +18,8 @@ export interface SettingsChatServiceOptions {
 	model?: string;
 	// System message to inject
 	systemMessage?: string;
-	// Disable reasoning format (use 'none' instead of 'auto')
-	disableReasoningFormat?: boolean;
+	// Disable reasoning parsing (use 'none' instead of 'auto')
+	disableReasoningParsing?: boolean;
 	// Generation parameters
 	temperature?: number;
 	max_tokens?: number;
diff --git a/tools/server/webui/tests/stories/ChatMessage.stories.svelte b/tools/server/webui/tests/stories/ChatMessage.stories.svelte
index 5f4de7d476..a3579cf04e 100644
--- a/tools/server/webui/tests/stories/ChatMessage.stories.svelte
+++ b/tools/server/webui/tests/stories/ChatMessage.stories.svelte
@@ -93,7 +93,7 @@
 	}}
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', false);
+		settingsStore.updateConfig('showRawOutputSwitch', false);
 	}}
 />
 
@@ -105,7 +105,7 @@
 	}}
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', false);
+		settingsStore.updateConfig('showRawOutputSwitch', false);
 	}}
 />
 
@@ -117,7 +117,7 @@
 	}}
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', false);
+		settingsStore.updateConfig('showRawOutputSwitch', false);
 	}}
 />
 
@@ -129,7 +129,7 @@
 	}}
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', true);
+		settingsStore.updateConfig('showRawOutputSwitch', true);
 	}}
 />
 
@@ -141,7 +141,7 @@
 	asChild
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', false);
+		settingsStore.updateConfig('showRawOutputSwitch', false);
 		// Phase 1: Stream reasoning content in chunks
 		let reasoningText =
 			'I need to think about this carefully. Let me break down the problem:\n\n1. The user is asking for help with something complex\n2. I should provide a thorough and helpful response\n3. I need to consider multiple approaches\n4. The best solution would be to explain step by step\n\nThis approach will ensure clarity and understanding.';
@@ -193,7 +193,7 @@
 	}}
 	play={async () => {
 		const { settingsStore } = await import('$lib/stores/settings.svelte');
-		settingsStore.updateConfig('disableReasoningFormat', false);
+		settingsStore.updateConfig('showRawOutputSwitch', false);
 		// Import the chat store to simulate loading state
 		const { chatStore } = await import('$lib/stores/chat.svelte');
 

From 338085c69e486b7155e5b03d7b5087e02c0e2528 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov 
Date: Thu, 12 Feb 2026 21:52:41 +0200
Subject: [PATCH 204/206] args : add -kvu to llama-parallel (#19577)

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

diff --git a/common/arg.cpp b/common/arg.cpp
index 9c85696ebd..18f953a38e 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1301,7 +1301,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.kv_unified = value;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},

From 79cc0f2dafbc7cbbfc37a8d99b399b816e141b90 Mon Sep 17 00:00:00 2001
From: lhez 
Date: Thu, 12 Feb 2026 14:52:37 -0800
Subject: [PATCH 205/206] opencl: add basic support for q4_1 (#19534)

* opencl: add q4_1 mv

* opencl: clean up

* opencl: add flattened q4_1 mv

* opencl: clean up

* opencl: add basic q4_1 mm

* opencl: fix whitespace

* opencl: add general q4_0 mm
---
 ggml/src/ggml-opencl/CMakeLists.txt           |   4 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 404 +++++++++++++++++-
 ggml/src/ggml-opencl/kernels/cvt.cl           |  51 +++
 .../kernels/mul_mm_q4_0_f32_l4_lm.cl          | 163 +++++++
 .../kernels/mul_mm_q4_1_f32_l4_lm.cl          | 165 +++++++
 .../ggml-opencl/kernels/mul_mv_q4_1_f32.cl    | 219 ++++++++++
 .../kernels/mul_mv_q4_1_f32_flat.cl           | 229 ++++++++++
 7 files changed, 1231 insertions(+), 4 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index b6094fb68b..f389193691 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -85,6 +85,8 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_8x_flat
     mul_mv_q4_0_f32_1d_8x_flat
     mul_mv_q4_0_f32_1d_16x_flat
+    mul_mv_q4_1_f32
+    mul_mv_q4_1_f32_flat
     mul_mv_q4_k_f32
     mul_mv_q6_k_f32
     mul_mv_q6_k_f32_flat
@@ -101,6 +103,8 @@ set(GGML_OPENCL_KERNELS
     gemv_moe_mxfp4_f32
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
+    mul_mm_q4_0_f32_l4_lm
+    mul_mm_q4_1_f32_l4_lm
     mul_mm_q8_0_f32_l4_lm
     mul_mm_q6_k_f32_l4_lm
     mul_mm_q8_0_f32_8x4
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 40474c193b..ae3f79fd0d 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -525,6 +525,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mm_f16_f32_kq;
     cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
     cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
+    cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
     cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
     cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
     cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
@@ -532,6 +533,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_restore_block_q4_0_noshuffle;
     cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
     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_q4_1_f32;
+    cl_kernel kernel_mul_mv_q4_1_f32_flat;
     cl_kernel kernel_mul_mv_q4_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32;
     cl_kernel kernel_mul_mv_q6_K_f32_flat;
@@ -564,6 +567,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q4_0_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q4_1_f32_l4_lm;
     cl_kernel kernel_mul_mm_q8_0_f32_l4_lm;
     cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
 
@@ -888,6 +893,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans", &err), err));
@@ -1119,6 +1126,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_q4_1_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_1_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_1_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mv_q4_1_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q4_1_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q4_1_f32_flat.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32_flat", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_q4_k_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1361,6 +1402,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_q4_0_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q4_0_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q4_0_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_0_f32_l4_lm", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mm_q4_1_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q4_1_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q4_1_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_1_f32_l4_lm", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_q8_0_f32_l4_lm
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2923,6 +2996,59 @@ struct ggml_tensor_extra_cl_q4_0 {
     }
 };
 
+struct ggml_tensor_extra_cl_q4_1 {
+    // Quantized values.
+    cl_mem q = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem q_img = nullptr;
+    // Scales.
+    cl_mem d = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem d_img = nullptr;
+    // Min
+    cl_mem m = nullptr;
+    // Min in image1d_buffer_t.
+    cl_mem m_img = nullptr;
+    // Size of quantized values.
+    size_t size_q = 0;
+    // Size of scales.
+    size_t size_d = 0;
+    // Size of min values.
+    size_t size_m = 0;
+
+    ~ggml_tensor_extra_cl_q4_1() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q));
+            q = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        if (m != nullptr) {
+            CL_CHECK(clReleaseMemObject(m));
+            m = nullptr;
+        }
+        // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
+        // enabled. They point to the images in ggml_backend_opencl_buffer_context.
+        // So, there is no need to release them here.
+        // TODO: initialize them for non SMALL_PATH path, or remove them.
+        q_img = nullptr;
+        d_img = nullptr;
+        m_img = nullptr;
+        size_q = 0;
+        size_d = 0;
+        size_m = 0;
+    }
+};
+
 struct ggml_tensor_extra_cl_mxfp4 {
     // Quantized values.
     cl_mem q = nullptr;
@@ -3399,8 +3525,9 @@ 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 || op->src[0]->type == GGML_TYPE_MXFP4 ||
-                       op->src[0]->type == GGML_TYPE_Q4_K ||
+            } else if (op->src[0]->type == GGML_TYPE_Q4_0  || op->src[0]->type == GGML_TYPE_Q4_1 ||
+                       op->src[0]->type == GGML_TYPE_MXFP4 ||
+                       op->src[0]->type == GGML_TYPE_Q4_K  ||
                        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]);
             } else if (op->src[0]->type == GGML_TYPE_Q8_0) {
@@ -3629,6 +3756,21 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_q4_1 * ggml_opencl_alloc_temp_tensor_extra_q4_1() {
+        ggml_tensor_extra_cl_q4_1 * extra;
+        if (temp_tensor_extras_q4_1.empty()) {
+            extra = new ggml_tensor_extra_cl_q4_1();
+        } else {
+            extra = temp_tensor_extras_q4_1.back();
+            temp_tensor_extras_q4_1.pop_back();
+        }
+
+        temp_tensor_extras_q4_1_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     ggml_tensor_extra_cl_mxfp4 * ggml_opencl_alloc_temp_tensor_extra_mxfp4() {
         ggml_tensor_extra_cl_mxfp4 * extra;
         if (temp_tensor_extras_mxfp4.empty()) {
@@ -3685,6 +3827,11 @@ struct ggml_backend_opencl_buffer_context {
         }
         temp_tensor_extras_q4_0_in_use.clear();
 
+        for (ggml_tensor_extra_cl_q4_1 * e : temp_tensor_extras_q4_1_in_use) {
+            temp_tensor_extras_q4_1.push_back(e);
+        }
+        temp_tensor_extras_q4_1_in_use.clear();
+
         for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
             temp_tensor_extras_mxfp4.push_back(e);
         }
@@ -3710,6 +3857,8 @@ struct ggml_backend_opencl_buffer_context {
     std::vector temp_tensor_extras_in_use;
     std::vector temp_tensor_extras_q4_0;
     std::vector temp_tensor_extras_q4_0_in_use;
+    std::vector temp_tensor_extras_q4_1;
+    std::vector temp_tensor_extras_q4_1_in_use;
     std::vector temp_tensor_extras_mxfp4;
     std::vector temp_tensor_extras_mxfp4_in_use;
     std::vector temp_tensor_extras_q8_0;
@@ -4079,6 +4228,75 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         return;
 
     }
+    if (tensor->type == GGML_TYPE_Q4_1) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_q4_1 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_1();
+
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_m = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        GGML_ASSERT(size_d + size_m + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        cl_buffer_region region;
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, mins, then quants.
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for mins.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_m;
+        extra->m = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err);
+        CL_CHECK(err);
+        previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_m, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->m));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+
+        return;
+    }
     if (tensor->type == GGML_TYPE_MXFP4) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
         GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
@@ -4581,7 +4799,35 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             size, data, 0, NULL, NULL));
         CL_CHECK(clReleaseMemObject(data_device));
         return;
-    } else if (tensor->type == GGML_TYPE_MXFP4) {
+    }
+    if (tensor->type == GGML_TYPE_Q4_1) {
+        ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra;
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_restore_block_q4_1;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->m));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &data_device));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+            global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(
+            queue, data_device, CL_TRUE, offset,
+            size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    }
+    if (tensor->type == GGML_TYPE_MXFP4) {
         ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *)tensor->extra;
 
         cl_int err;
@@ -8409,6 +8655,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
 #ifdef GGML_OPENCL_SOA_Q
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
     ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
     ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
     ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
@@ -8922,6 +9169,91 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_Q4_0: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+                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),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
+            case GGML_TYPE_Q4_1: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_1->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_1->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q4_1->m));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             case GGML_TYPE_Q8_0: {
                 if (ne11 < 32) {
                     break;
@@ -9262,7 +9594,71 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
 #endif // GGML_OPENCL_SOA_Q
             break;
-        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q4_1: {
+#ifdef GGML_OPENCL_SOA_Q
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            kernel = backend_ctx->kernel_mul_mv_q4_1_f32_flat;
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_1->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_1->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q4_1->m));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &r3));
+#else
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            kernel = backend_ctx->kernel_mul_mv_q4_1_f32;
+
+            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),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+#endif // GGML_OPENCL_SOA_Q
+            break;
+        }
         case GGML_TYPE_Q8_0: {
 #ifdef GGML_OPENCL_SOA_Q
             kernel = backend_ctx->kernel_mul_mv_q8_0_f32_flat;
diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl
index 9fb434713d..2c244ce321 100644
--- a/ggml/src/ggml-opencl/kernels/cvt.cl
+++ b/ggml/src/ggml-opencl/kernels/cvt.cl
@@ -46,6 +46,15 @@ struct block_q4_0
     uint8_t qs[QK4_0 / 2];
 };
 
+//------------------------------------------------------------------------------
+// block_q4_1
+//------------------------------------------------------------------------------
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
 //------------------------------------------------------------------------------
 // block_q6_K
 //------------------------------------------------------------------------------
@@ -148,6 +157,48 @@ kernel void kernel_restore_block_q4_0_noshuffle(
     }
 }
 
+//------------------------------------------------------------------------------
+// kernel_convert_block_q4_1
+// Convert the block_q4_1 format to 2 separate arrays (AOS -> SOA).
+// This kernel does not deshuffle the bits.
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_q4_1(
+    global struct block_q4_1 * src0,
+    global uchar * dst_q,
+    global half  * dst_d,
+    global half  * dst_m
+) {
+    global struct block_q4_1 * b = (global struct block_q4_1 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_1/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+    global half  * m = (global half *) dst_m + get_global_id(0);
+
+    *d = b->d;
+    *m = b->m;
+
+    for (int i = 0; i < QK4_1/2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_q4_1(
+    global uchar * src_q,
+    global half  * src_d,
+    global half  * src_m,
+    global struct block_q4_1 * dst
+) {
+    global struct block_q4_1 * b = (global struct block_q4_1 *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_1/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+    global half  * m = (global half *) src_m + get_global_id(0);
+
+    b->d = *d;
+    b->m = *m;
+    for (int i = 0; i < QK4_1/2; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
 //------------------------------------------------------------------------------
 // block_mxfp4
 //------------------------------------------------------------------------------
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl
new file mode 100644
index 0000000000..4100e3080a
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl
@@ -0,0 +1,163 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q4_0_f32_l4_lm(
+    global uchar4 * src0_q,
+    global half   * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 4;
+                int iqs = idx % 4;
+
+                float d = (float)src0_d[ib];
+                global uchar4 * qs = src0_q + ib*4 + iqs;
+                uchar4 q = *qs;
+                float4 v1 = (convert_float4((uchar4)((q.s0   )&0x0F, (q.s1   )&0x0F, (q.s2   )&0x0F, (q.s3   )&0x0F)) - 8.0f)*d;
+                float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)) - 8.0f)*d;
+
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = v1.s0;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = v1.s1;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = v1.s2;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = v1.s3;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
+            } else {
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl
new file mode 100644
index 0000000000..d0d2f08361
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl
@@ -0,0 +1,165 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q4_1_f32_l4_lm(
+    global uchar4 * src0_q,
+    global half   * src0_d,
+    global half   * src0_m,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 4;
+                int iqs = idx % 4;
+
+                float d = (float)src0_d[ib];
+                float m = (float)src0_m[ib];
+                global uchar4 * qs = src0_q + ib*4 + iqs;
+                uchar4 q = *qs;
+                float4 v1 = (convert_float4((uchar4)((q.s0   )&0x0F, (q.s1   )&0x0F, (q.s2   )&0x0F, (q.s3   )&0x0F)))*d + m;
+                float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)))*d + m;
+
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = v1.s0;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = v1.s1;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = v1.s2;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = v1.s3;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
+            } else {
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl
new file mode 100644
index 0000000000..6fe828f20e
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl
@@ -0,0 +1,219 @@
+#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 QK4_1                   32
+
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
+inline float block_q4_1_dot_y(
+    global const struct block_q4_1 * qb_curr,
+    float sumy,
+    float16 yl,
+    int il
+) {
+    float d = qb_curr->d;
+    float m = qb_curr->m;
+
+    float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
+
+    global const ushort * qs = ((global const ushort *) qb_curr + 2 + il/2);
+
+    acc.s0 += yl.s0 * (qs[0] & 0x000F);
+    acc.s0 += yl.s1 * (qs[0] & 0x0F00);
+    acc.s0 += yl.s8 * (qs[0] & 0x00F0);
+    acc.s3 += yl.s9 * (qs[0] & 0xF000);
+
+    acc.s0 += yl.s2 * (qs[1] & 0x000F);
+    acc.s1 += yl.s3 * (qs[1] & 0x0F00);
+    acc.s2 += yl.sa * (qs[1] & 0x00F0);
+    acc.s3 += yl.sb * (qs[1] & 0xF000);
+
+    acc.s0 += yl.s4 * (qs[2] & 0x000F);
+    acc.s1 += yl.s5 * (qs[2] & 0x0F00);
+    acc.s2 += yl.sc * (qs[2] & 0x00F0);
+    acc.s3 += yl.sd * (qs[2] & 0xF000);
+
+    acc.s0 += yl.s6 * (qs[3] & 0x000F);
+    acc.s1 += yl.s7 * (qs[3] & 0x0F00);
+    acc.s2 += yl.se * (qs[3] & 0x00F0);
+    acc.s3 += yl.sf * (qs[3] & 0xF000);
+
+    return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each subgroup works on 4 rows
+#define N_SIMDGROUP 1 // number of subgroups in a thread group
+#define N_SIMDWIDTH 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_1;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_1 * x = (global struct block_q4_1 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_1 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q4_1_dot_y(x+ib+0*nb, sumy, yl, il);
+        sumf.s1 += block_q4_1_dot_y(x+ib+1*nb, sumy, yl, il);
+        sumf.s2 += block_q4_1_dot_y(x+ib+2*nb, sumy, yl, il);
+        sumf.s3 += block_q4_1_dot_y(x+ib+3*nb, sumy, yl, il);
+
+        yb += QK4_1 * (N_SIMDWIDTH/2);
+    }
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_1_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl
new file mode 100644
index 0000000000..d7c4645d67
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl
@@ -0,0 +1,229 @@
+#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 QK4_1                   32
+
+struct block_q4_1 {
+    half d; // delta
+    half m; // min
+    uchar qs[QK4_1 / 2]; // nibbles / quants
+};
+
+inline float block_q4_1_dot_y_flat(
+    global const uchar * x,
+    global const half  * dh,
+    global const half  * mh,
+    float sumy,
+    float16 yl,
+    int il
+) {
+    float                 d   = *dh;
+    float                 m   = *mh;
+    global const ushort * qs = ((global const ushort *) x + il/2);
+
+    float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
+
+    acc.s0 += yl.s0 * (qs[0] & 0x000F);
+    acc.s0 += yl.s1 * (qs[0] & 0x0F00);
+    acc.s0 += yl.s8 * (qs[0] & 0x00F0);
+    acc.s3 += yl.s9 * (qs[0] & 0xF000);
+
+    acc.s0 += yl.s2 * (qs[1] & 0x000F);
+    acc.s1 += yl.s3 * (qs[1] & 0x0F00);
+    acc.s2 += yl.sa * (qs[1] & 0x00F0);
+    acc.s3 += yl.sb * (qs[1] & 0xF000);
+
+    acc.s0 += yl.s4 * (qs[2] & 0x000F);
+    acc.s1 += yl.s5 * (qs[2] & 0x0F00);
+    acc.s2 += yl.sc * (qs[2] & 0x00F0);
+    acc.s3 += yl.sd * (qs[2] & 0xF000);
+
+    acc.s0 += yl.s6 * (qs[3] & 0x000F);
+    acc.s1 += yl.s7 * (qs[3] & 0x0F00);
+    acc.s2 += yl.se * (qs[3] & 0x00F0);
+    acc.s3 += yl.sf * (qs[3] & 0xF000);
+
+    return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each subgroup works on 4 rows
+#define N_SIMDGROUP 1 // number of subgroups in a thread group
+#define N_SIMDWIDTH 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_flat(
+        global void * src0_q,
+        global void * src0_d,
+        global void * src0_m,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_1;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    // The number of scales/mins is the same as the number of blocks.
+    ulong offset0_dm = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02));
+    // Each block contains QK4_1/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q  = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_1/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_dm;
+    global half  * m = (global half  *) src0_m + offset0_dm;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_1 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 0*nb*QK4_1/2, d + ib + 0*nb, m + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 1*nb*QK4_1/2, d + ib + 1*nb, m + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 2*nb*QK4_1/2, d + ib + 2*nb, m + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 3*nb*QK4_1/2, d + ib + 3*nb, m + ib + 3*nb, sumy, yl, il);
+
+        yb += QK4_1 * (N_SIMDWIDTH/2);
+    }
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q4_1_f32_flat(
+        global void * src0_q,
+        global void * src0_d,
+        global void * src0_m,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_flat(src0_q, src0_d, src0_m, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

From 3bb78133abd7c7d341b9b65fa6f1cf406cba82b8 Mon Sep 17 00:00:00 2001
From: Shupei Fan 
Date: Fri, 13 Feb 2026 07:07:49 +0800
Subject: [PATCH 206/206] hexagon: fix typo in vtcm_needs_release (#19545)

---
 ggml/src/ggml-hexagon/htp/main.c | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index 62708eee5c..92a1422896 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -189,7 +189,7 @@ static int vtcm_release_callback(unsigned int rctx, void * state) {
     // otherwise we'll release it once we're done with the current Op.
 
     if (ctx->vtcm_inuse) {
-        ctx->vtcm_needs_release = false;
+        ctx->vtcm_needs_release = true;
         return 0;
     }
 

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z/g;
+export const FENCE_PATTERN = /^```|\n```/g;
diff --git a/tools/server/webui/src/lib/constants/css-classes.ts b/tools/server/webui/src/lib/constants/css-classes.ts
new file mode 100644
index 0000000000..46076e55f6
--- /dev/null
+++ b/tools/server/webui/src/lib/constants/css-classes.ts
@@ -0,0 +1,10 @@
+export const BOX_BORDER =
+	'border border-border/30 focus-within:border-border  dark:border-border/20 dark:focus-within:border-border';
+
+export const INPUT_CLASSES = `
+    bg-muted/60 dark:bg-muted/75
+    ${BOX_BORDER}
+    shadow-sm
+    outline-none
+    text-foreground
+`;
diff --git a/tools/server/webui/src/lib/constants/formatters.ts b/tools/server/webui/src/lib/constants/formatters.ts
new file mode 100644
index 0000000000..d6d1b883ff
--- /dev/null
+++ b/tools/server/webui/src/lib/constants/formatters.ts
@@ -0,0 +1,8 @@
+export const MS_PER_SECOND = 1000;
+export const SECONDS_PER_MINUTE = 60;
+export const SECONDS_PER_HOUR = 3600;
+export const SHORT_DURATION_THRESHOLD = 1;
+export const MEDIUM_DURATION_THRESHOLD = 10;
+
+/** Default display value when no performance time is available */
+export const DEFAULT_PERFORMANCE_TIME = '0s';
diff --git a/tools/server/webui/src/lib/constants/markdown.ts b/tools/server/webui/src/lib/constants/markdown.ts
new file mode 100644
index 0000000000..783d31a22c
--- /dev/null
+++ b/tools/server/webui/src/lib/constants/markdown.ts
@@ -0,0 +1,4 @@
+export const IMAGE_NOT_ERROR_BOUND_SELECTOR = 'img:not([data-error-bound])';
+export const DATA_ERROR_BOUND_ATTR = 'errorBound';
+export const DATA_ERROR_HANDLED_ATTR = 'errorHandled';
+export const BOOL_TRUE_STRING = 'true';
diff --git a/tools/server/webui/src/lib/constants/processing-info.ts b/tools/server/webui/src/lib/constants/processing-info.ts
index 726439211b..2c3f7dc534 100644
--- a/tools/server/webui/src/lib/constants/processing-info.ts
+++ b/tools/server/webui/src/lib/constants/processing-info.ts
@@ -1 +1,8 @@
 export const PROCESSING_INFO_TIMEOUT = 2000;
+
+/**
+ * Statistics units labels
+ */
+export const STATS_UNITS = {
+	TOKENS_PER_SECOND: 't/s'
+} as const;
diff --git a/tools/server/webui/src/lib/constants/settings-fields.ts b/tools/server/webui/src/lib/constants/settings-fields.ts
new file mode 100644
index 0000000000..79a6e92870
--- /dev/null
+++ b/tools/server/webui/src/lib/constants/settings-fields.ts
@@ -0,0 +1,33 @@
+/**
+ * List of all numeric fields in settings configuration.
+ * These fields will be converted from strings to numbers during save.
+ */
+export const NUMERIC_FIELDS = [
+	'temperature',
+	'top_k',
+	'top_p',
+	'min_p',
+	'max_tokens',
+	'pasteLongTextToFileLen',
+	'dynatemp_range',
+	'dynatemp_exponent',
+	'typ_p',
+	'xtc_probability',
+	'xtc_threshold',
+	'repeat_last_n',
+	'repeat_penalty',
+	'presence_penalty',
+	'frequency_penalty',
+	'dry_multiplier',
+	'dry_base',
+	'dry_allowed_length',
+	'dry_penalty_last_n',
+	'agenticMaxTurns',
+	'agenticMaxToolPreviewLines'
+] as const;
+
+/**
+ * Fields that must be positive integers (>= 1).
+ * These will be clamped to minimum 1 and rounded during save.
+ */
+export const POSITIVE_INTEGER_FIELDS = ['agenticMaxTurns', 'agenticMaxToolPreviewLines'] as const;
diff --git a/tools/server/webui/src/lib/constants/tooltip-config.ts b/tools/server/webui/src/lib/constants/tooltip-config.ts
index 3c30c8c072..ad76ab3522 100644
--- a/tools/server/webui/src/lib/constants/tooltip-config.ts
+++ b/tools/server/webui/src/lib/constants/tooltip-config.ts
@@ -1 +1 @@
-export const TOOLTIP_DELAY_DURATION = 100;
+export const TOOLTIP_DELAY_DURATION = 500;
diff --git a/tools/server/webui/src/lib/constants/ui.ts b/tools/server/webui/src/lib/constants/ui.ts
new file mode 100644
index 0000000000..a75b30f2f8
--- /dev/null
+++ b/tools/server/webui/src/lib/constants/ui.ts
@@ -0,0 +1 @@
+export const SYSTEM_MESSAGE_PLACEHOLDER = 'System message';
diff --git a/tools/server/webui/src/lib/contexts/chat-actions.context.ts b/tools/server/webui/src/lib/contexts/chat-actions.context.ts
new file mode 100644
index 0000000000..eba0fec027
--- /dev/null
+++ b/tools/server/webui/src/lib/contexts/chat-actions.context.ts
@@ -0,0 +1,34 @@
+import { getContext, setContext } from 'svelte';
+
+export interface ChatActionsContext {
+	copy: (message: DatabaseMessage) => void;
+	delete: (message: DatabaseMessage) => void;
+	navigateToSibling: (siblingId: string) => void;
+	editWithBranching: (
+		message: DatabaseMessage,
+		newContent: string,
+		newExtras?: DatabaseMessageExtra[]
+	) => void;
+	editWithReplacement: (
+		message: DatabaseMessage,
+		newContent: string,
+		shouldBranch: boolean
+	) => void;
+	editUserMessagePreserveResponses: (
+		message: DatabaseMessage,
+		newContent: string,
+		newExtras?: DatabaseMessageExtra[]
+	) => void;
+	regenerateWithBranching: (message: DatabaseMessage, modelOverride?: string) => void;
+	continueAssistantMessage: (message: DatabaseMessage) => void;
+}
+
+const CHAT_ACTIONS_KEY = Symbol.for('chat-actions');
+
+export function setChatActionsContext(ctx: ChatActionsContext): ChatActionsContext {
+	return setContext(CHAT_ACTIONS_KEY, ctx);
+}
+
+export function getChatActionsContext(): ChatActionsContext {
+	return getContext(CHAT_ACTIONS_KEY);
+}
diff --git a/tools/server/webui/src/lib/contexts/index.ts b/tools/server/webui/src/lib/contexts/index.ts
new file mode 100644
index 0000000000..73ff6f96fa
--- /dev/null
+++ b/tools/server/webui/src/lib/contexts/index.ts
@@ -0,0 +1,13 @@
+export {
+	getMessageEditContext,
+	setMessageEditContext,
+	type MessageEditContext,
+	type MessageEditState,
+	type MessageEditActions
+} from './message-edit.context';
+
+export {
+	getChatActionsContext,
+	setChatActionsContext,
+	type ChatActionsContext
+} from './chat-actions.context';
diff --git a/tools/server/webui/src/lib/contexts/message-edit.context.ts b/tools/server/webui/src/lib/contexts/message-edit.context.ts
new file mode 100644
index 0000000000..7af116daa5
--- /dev/null
+++ b/tools/server/webui/src/lib/contexts/message-edit.context.ts
@@ -0,0 +1,39 @@
+import { getContext, setContext } from 'svelte';
+
+export interface MessageEditState {
+	readonly isEditing: boolean;
+	readonly editedContent: string;
+	readonly editedExtras: DatabaseMessageExtra[];
+	readonly editedUploadedFiles: ChatUploadedFile[];
+	readonly originalContent: string;
+	readonly originalExtras: DatabaseMessageExtra[];
+	readonly showSaveOnlyOption: boolean;
+}
+
+export interface MessageEditActions {
+	setContent: (content: string) => void;
+	setExtras: (extras: DatabaseMessageExtra[]) => void;
+	setUploadedFiles: (files: ChatUploadedFile[]) => void;
+	save: () => void;
+	saveOnly: () => void;
+	cancel: () => void;
+	startEdit: () => void;
+}
+
+export type MessageEditContext = MessageEditState & MessageEditActions;
+
+const MESSAGE_EDIT_KEY = Symbol.for('chat-message-edit');
+
+/**
+ * Sets the message edit context. Call this in the parent component (ChatMessage.svelte).
+ */
+export function setMessageEditContext(ctx: MessageEditContext): MessageEditContext {
+	return setContext(MESSAGE_EDIT_KEY, ctx);
+}
+
+/**
+ * Gets the message edit context. Call this in child components.
+ */
+export function getMessageEditContext(): MessageEditContext {
+	return getContext(MESSAGE_EDIT_KEY);
+}
diff --git a/tools/server/webui/src/lib/enums/chat.ts b/tools/server/webui/src/lib/enums/chat.ts
index 2b9eb7bc2e..0b6f357d9a 100644
--- a/tools/server/webui/src/lib/enums/chat.ts
+++ b/tools/server/webui/src/lib/enums/chat.ts
@@ -1,4 +1,51 @@
 export enum ChatMessageStatsView {
 	GENERATION = 'generation',
-	READING = 'reading'
+	READING = 'reading',
+	TOOLS = 'tools',
+	SUMMARY = 'summary'
+}
+
+/**
+ * Reasoning format options for API requests.
+ */
+export enum ReasoningFormat {
+	NONE = 'none',
+	AUTO = 'auto'
+}
+
+/**
+ * Message roles for chat messages.
+ */
+export enum MessageRole {
+	USER = 'user',
+	ASSISTANT = 'assistant',
+	SYSTEM = 'system',
+	TOOL = 'tool'
+}
+
+/**
+ * Message types for different content kinds.
+ */
+export enum MessageType {
+	ROOT = 'root',
+	TEXT = 'text',
+	THINK = 'think',
+	SYSTEM = 'system'
+}
+
+/**
+ * Content part types for API chat message content.
+ */
+export enum ContentPartType {
+	TEXT = 'text',
+	IMAGE_URL = 'image_url',
+	INPUT_AUDIO = 'input_audio'
+}
+
+/**
+ * Error dialog types for displaying server/timeout errors.
+ */
+export enum ErrorDialogType {
+	TIMEOUT = 'timeout',
+	SERVER = 'server'
 }
diff --git a/tools/server/webui/src/lib/enums/keyboard.ts b/tools/server/webui/src/lib/enums/keyboard.ts
new file mode 100644
index 0000000000..b8f6d5f7a2
--- /dev/null
+++ b/tools/server/webui/src/lib/enums/keyboard.ts
@@ -0,0 +1,15 @@
+/**
+ * Keyboard key names for event handling
+ */
+export enum KeyboardKey {
+	ENTER = 'Enter',
+	ESCAPE = 'Escape',
+	ARROW_UP = 'ArrowUp',
+	ARROW_DOWN = 'ArrowDown',
+	TAB = 'Tab',
+	D_LOWER = 'd',
+	D_UPPER = 'D',
+	E_UPPER = 'E',
+	K_LOWER = 'k',
+	O_UPPER = 'O'
+}
diff --git a/tools/server/webui/src/lib/enums/settings.ts b/tools/server/webui/src/lib/enums/settings.ts
new file mode 100644
index 0000000000..f17f219762
--- /dev/null
+++ b/tools/server/webui/src/lib/enums/settings.ts
@@ -0,0 +1,26 @@
+/**
+ * Parameter source - indicates whether a parameter uses default or custom value
+ */
+export enum ParameterSource {
+	DEFAULT = 'default',
+	CUSTOM = 'custom'
+}
+
+/**
+ * Syncable parameter type - data types for parameters that can be synced with server
+ */
+export enum SyncableParameterType {
+	NUMBER = 'number',
+	STRING = 'string',
+	BOOLEAN = 'boolean'
+}
+
+/**
+ * Settings field type - defines the input type for settings fields
+ */
+export enum SettingsFieldType {
+	INPUT = 'input',
+	TEXTAREA = 'textarea',
+	CHECKBOX = 'checkbox',
+	SELECT = 'select'
+}
diff --git a/tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts b/tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts
new file mode 100644
index 0000000000..bbaa5d1362
--- /dev/null
+++ b/tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts
@@ -0,0 +1,165 @@
+import { AUTO_SCROLL_AT_BOTTOM_THRESHOLD, AUTO_SCROLL_INTERVAL } from '$lib/constants/auto-scroll';
+
+export interface AutoScrollOptions {
+	/** Whether auto-scroll is disabled globally (e.g., from settings) */
+	disabled?: boolean;
+}
+
+/**
+ * Creates an auto-scroll controller for a scrollable container.
+ *
+ * Features:
+ * - Auto-scrolls to bottom during streaming/loading
+ * - Stops auto-scroll when user manually scrolls up
+ * - Resumes auto-scroll when user scrolls back to bottom
+ */
+export class AutoScrollController {
+	private _autoScrollEnabled = $state(true);
+	private _userScrolledUp = $state(false);
+	private _lastScrollTop = $state(0);
+	private _scrollInterval: ReturnType | undefined;
+	private _scrollTimeout: ReturnType | undefined;
+	private _container: HTMLElement | undefined;
+	private _disabled: boolean;
+
+	constructor(options: AutoScrollOptions = {}) {
+		this._disabled = options.disabled ?? false;
+	}
+
+	get autoScrollEnabled(): boolean {
+		return this._autoScrollEnabled;
+	}
+
+	get userScrolledUp(): boolean {
+		return this._userScrolledUp;
+	}
+
+	/**
+	 * Binds the controller to a scrollable container element.
+	 */
+	setContainer(container: HTMLElement | undefined): void {
+		this._container = container;
+	}
+
+	/**
+	 * Updates the disabled state.
+	 */
+	setDisabled(disabled: boolean): void {
+		this._disabled = disabled;
+		if (disabled) {
+			this._autoScrollEnabled = false;
+			this.stopInterval();
+		}
+	}
+
+	/**
+	 * Handles scroll events to detect user scroll direction and toggle auto-scroll.
+	 */
+	handleScroll(): void {
+		if (this._disabled || !this._container) return;
+
+		const { scrollTop, scrollHeight, clientHeight } = this._container;
+		const distanceFromBottom = scrollHeight - scrollTop - clientHeight;
+		const isAtBottom = distanceFromBottom < AUTO_SCROLL_AT_BOTTOM_THRESHOLD;
+
+		if (scrollTop < this._lastScrollTop && !isAtBottom) {
+			this._userScrolledUp = true;
+			this._autoScrollEnabled = false;
+		} else if (isAtBottom && this._userScrolledUp) {
+			this._userScrolledUp = false;
+			this._autoScrollEnabled = true;
+		}
+
+		if (this._scrollTimeout) {
+			clearTimeout(this._scrollTimeout);
+		}
+
+		this._scrollTimeout = setTimeout(() => {
+			if (isAtBottom) {
+				this._userScrolledUp = false;
+				this._autoScrollEnabled = true;
+			}
+		}, AUTO_SCROLL_INTERVAL);
+
+		this._lastScrollTop = scrollTop;
+	}
+
+	/**
+	 * Scrolls the container to the bottom.
+	 */
+	scrollToBottom(behavior: ScrollBehavior = 'smooth'): void {
+		if (this._disabled || !this._container) return;
+
+		this._container.scrollTo({
+			top: this._container.scrollHeight,
+			behavior
+		});
+	}
+
+	/**
+	 * Enables auto-scroll (e.g., when user sends a message).
+	 */
+	enable(): void {
+		if (this._disabled) return;
+		this._userScrolledUp = false;
+		this._autoScrollEnabled = true;
+	}
+
+	/**
+	 * Starts the auto-scroll interval for continuous scrolling during streaming.
+	 */
+	startInterval(): void {
+		if (this._disabled || this._scrollInterval) return;
+
+		this._scrollInterval = setInterval(() => {
+			this.scrollToBottom();
+		}, AUTO_SCROLL_INTERVAL);
+	}
+
+	/**
+	 * Stops the auto-scroll interval.
+	 */
+	stopInterval(): void {
+		if (this._scrollInterval) {
+			clearInterval(this._scrollInterval);
+			this._scrollInterval = undefined;
+		}
+	}
+
+	/**
+	 * Updates the auto-scroll interval based on streaming state.
+	 * Call this in a $effect to automatically manage the interval.
+	 */
+	updateInterval(isStreaming: boolean): void {
+		if (this._disabled) {
+			this.stopInterval();
+			return;
+		}
+
+		if (isStreaming && this._autoScrollEnabled) {
+			if (!this._scrollInterval) {
+				this.startInterval();
+			}
+		} else {
+			this.stopInterval();
+		}
+	}
+
+	/**
+	 * Cleans up resources. Call this in onDestroy or when the component unmounts.
+	 */
+	destroy(): void {
+		this.stopInterval();
+		if (this._scrollTimeout) {
+			clearTimeout(this._scrollTimeout);
+			this._scrollTimeout = undefined;
+		}
+	}
+}
+
+/**
+ * Creates a new AutoScrollController instance.
+ */
+export function createAutoScrollController(options: AutoScrollOptions = {}): AutoScrollController {
+	return new AutoScrollController(options);
+}
diff --git a/tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts b/tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts
index c06cf28864..068440cdc0 100644
--- a/tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts
+++ b/tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts
@@ -1,7 +1,9 @@
 import { activeProcessingState } from '$lib/stores/chat.svelte';
 import { config } from '$lib/stores/settings.svelte';
+import { STATS_UNITS } from '$lib/constants/processing-info';
+import type { ApiProcessingState } from '$lib/types';
 
-export interface LiveProcessingStats {
+interface LiveProcessingStats {
 	tokensProcessed: number;
 	totalTokens: number;
 	timeMs: number;
@@ -9,7 +11,7 @@ export interface LiveProcessingStats {
 	etaSecs?: number;
 }
 
-export interface LiveGenerationStats {
+interface LiveGenerationStats {
 	tokensGenerated: number;
 	timeMs: number;
 	tokensPerSecond: number;
@@ -18,6 +20,7 @@ export interface LiveGenerationStats {
 export interface UseProcessingStateReturn {
 	readonly processingState: ApiProcessingState | null;
 	getProcessingDetails(): string[];
+	getTechnicalDetails(): string[];
 	getProcessingMessage(): string;
 	getPromptProgressText(): string | null;
 	getLiveProcessingStats(): LiveProcessingStats | null;
@@ -138,8 +141,31 @@ export function useProcessingState(): UseProcessingStateReturn {
 
 		const details: string[] = [];
 
+		// Show prompt processing progress with ETA during preparation phase
+		if (stateToUse.promptProgress) {
+			const { processed, total, time_ms, cache } = stateToUse.promptProgress;
+			const actualProcessed = processed - cache;
+			const actualTotal = total - cache;
+
+			if (actualProcessed < actualTotal && actualProcessed > 0) {
+				const percent = Math.round((actualProcessed / actualTotal) * 100);
+				const eta = getETASecs(actualProcessed, actualTotal, time_ms);
+
+				if (eta !== undefined) {
+					const etaSecs = Math.ceil(eta);
+					details.push(`Processing ${percent}% (ETA: ${etaSecs}s)`);
+				} else {
+					details.push(`Processing ${percent}%`);
+				}
+			}
+		}
+
 		// Always show context info when we have valid data
-		if (stateToUse.contextUsed >= 0 && stateToUse.contextTotal > 0) {
+		if (
+			typeof stateToUse.contextTotal === 'number' &&
+			stateToUse.contextUsed >= 0 &&
+			stateToUse.contextTotal > 0
+		) {
 			const contextPercent = Math.round((stateToUse.contextUsed / stateToUse.contextTotal) * 100);
 
 			details.push(
@@ -163,7 +189,57 @@ export function useProcessingState(): UseProcessingStateReturn {
 		}
 
 		if (stateToUse.tokensPerSecond && stateToUse.tokensPerSecond > 0) {
-			details.push(`${stateToUse.tokensPerSecond.toFixed(1)} tokens/sec`);
+			details.push(`${stateToUse.tokensPerSecond.toFixed(1)} ${STATS_UNITS.TOKENS_PER_SECOND}`);
+		}
+
+		if (stateToUse.speculative) {
+			details.push('Speculative decoding enabled');
+		}
+
+		return details;
+	}
+
+	/**
+	 * Returns technical details without the progress message (for bottom bar)
+	 */
+	function getTechnicalDetails(): string[] {
+		const stateToUse = processingState || lastKnownState;
+		if (!stateToUse) {
+			return [];
+		}
+
+		const details: string[] = [];
+
+		// Always show context info when we have valid data
+		if (
+			typeof stateToUse.contextTotal === 'number' &&
+			stateToUse.contextUsed >= 0 &&
+			stateToUse.contextTotal > 0
+		) {
+			const contextPercent = Math.round((stateToUse.contextUsed / stateToUse.contextTotal) * 100);
+
+			details.push(
+				`Context: ${stateToUse.contextUsed}/${stateToUse.contextTotal} (${contextPercent}%)`
+			);
+		}
+
+		if (stateToUse.outputTokensUsed > 0) {
+			// Handle infinite max_tokens (-1) case
+			if (stateToUse.outputTokensMax <= 0) {
+				details.push(`Output: ${stateToUse.outputTokensUsed}/∞`);
+			} else {
+				const outputPercent = Math.round(
+					(stateToUse.outputTokensUsed / stateToUse.outputTokensMax) * 100
+				);
+
+				details.push(
+					`Output: ${stateToUse.outputTokensUsed}/${stateToUse.outputTokensMax} (${outputPercent}%)`
+				);
+			}
+		}
+
+		if (stateToUse.tokensPerSecond && stateToUse.tokensPerSecond > 0) {
+			details.push(`${stateToUse.tokensPerSecond.toFixed(1)} ${STATS_UNITS.TOKENS_PER_SECOND}`);
 		}
 
 		if (stateToUse.speculative) {
@@ -251,6 +327,7 @@ export function useProcessingState(): UseProcessingStateReturn {
 			return processingState;
 		},
 		getProcessingDetails,
+		getTechnicalDetails,
 		getProcessingMessage,
 		getPromptProgressText,
 		getLiveProcessingStats,
diff --git a/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts b/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts
index 6f0e03e211..168de97403 100644
--- a/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts
+++ b/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts
@@ -13,6 +13,16 @@
 import type { Plugin } from 'unified';
 import type { Root, Element, ElementContent } from 'hast';
 import { visit } from 'unist-util-visit';
+import {
+	CODE_BLOCK_SCROLL_CONTAINER_CLASS,
+	CODE_BLOCK_WRAPPER_CLASS,
+	CODE_BLOCK_HEADER_CLASS,
+	CODE_BLOCK_ACTIONS_CLASS,
+	CODE_LANGUAGE_CLASS,
+	COPY_CODE_BTN_CLASS,
+	PREVIEW_CODE_BTN_CLASS,
+	RELATIVE_CLASS
+} from '$lib/constants/code-blocks';
 
 declare global {
 	interface Window {
@@ -42,7 +52,7 @@ function createCopyButton(codeId: string): Element {
 		type: 'element',
 		tagName: 'button',
 		properties: {
-			className: ['copy-code-btn'],
+			className: [COPY_CODE_BTN_CLASS],
 			'data-code-id': codeId,
 			title: 'Copy code',
 			type: 'button'
@@ -56,7 +66,7 @@ function createPreviewButton(codeId: string): Element {
 		type: 'element',
 		tagName: 'button',
 		properties: {
-			className: ['preview-code-btn'],
+			className: [PREVIEW_CODE_BTN_CLASS],
 			'data-code-id': codeId,
 			title: 'Preview code',
 			type: 'button'
@@ -75,30 +85,39 @@ function createHeader(language: string, codeId: string): Element {
 	return {
 		type: 'element',
 		tagName: 'div',
-		properties: { className: ['code-block-header'] },
+		properties: { className: [CODE_BLOCK_HEADER_CLASS] },
 		children: [
 			{
 				type: 'element',
 				tagName: 'span',
-				properties: { className: ['code-language'] },
+				properties: { className: [CODE_LANGUAGE_CLASS] },
 				children: [{ type: 'text', value: language }]
 			},
 			{
 				type: 'element',
 				tagName: 'div',
-				properties: { className: ['code-block-actions'] },
+				properties: { className: [CODE_BLOCK_ACTIONS_CLASS] },
 				children: actions
 			}
 		]
 	};
 }
 
+function createScrollContainer(preElement: Element): Element {
+	return {
+		type: 'element',
+		tagName: 'div',
+		properties: { className: [CODE_BLOCK_SCROLL_CONTAINER_CLASS] },
+		children: [preElement]
+	};
+}
+
 function createWrapper(header: Element, preElement: Element): Element {
 	return {
 		type: 'element',
 		tagName: 'div',
-		properties: { className: ['code-block-wrapper'] },
-		children: [header, preElement]
+		properties: { className: [CODE_BLOCK_WRAPPER_CLASS, RELATIVE_CLASS] },
+		children: [header, createScrollContainer(preElement)]
 	};
 }
 
diff --git a/tools/server/webui/src/lib/services/database.service.ts b/tools/server/webui/src/lib/services/database.service.ts
new file mode 100644
index 0000000000..0d5a9c1b99
--- /dev/null
+++ b/tools/server/webui/src/lib/services/database.service.ts
@@ -0,0 +1,368 @@
+import Dexie, { type EntityTable } from 'dexie';
+import { findDescendantMessages } from '$lib/utils';
+
+class LlamacppDatabase extends Dexie {
+	conversations!: EntityTable;
+	messages!: EntityTable;
+
+	constructor() {
+		super('LlamacppWebui');
+
+		this.version(1).stores({
+			conversations: 'id, lastModified, currNode, name',
+			messages: 'id, convId, type, role, timestamp, parent, children'
+		});
+	}
+}
+
+const db = new LlamacppDatabase();
+import { v4 as uuid } from 'uuid';
+import { MessageRole } from '$lib/enums/chat';
+
+export class DatabaseService {
+	/**
+	 *
+	 *
+	 * Conversations
+	 *
+	 *
+	 */
+
+	/**
+	 * Creates a new conversation.
+	 *
+	 * @param name - Name of the conversation
+	 * @returns The created conversation
+	 */
+	static async createConversation(name: string): Promise {
+		const conversation: DatabaseConversation = {
+			id: uuid(),
+			name,
+			lastModified: Date.now(),
+			currNode: ''
+		};
+
+		await db.conversations.add(conversation);
+		return conversation;
+	}
+
+	/**
+	 *
+	 *
+	 * Messages
+	 *
+	 *
+	 */
+
+	/**
+	 * Creates a new message branch by adding a message and updating parent/child relationships.
+	 * Also updates the conversation's currNode to point to the new message.
+	 *
+	 * @param message - Message to add (without id)
+	 * @param parentId - Parent message ID to attach to
+	 * @returns The created message
+	 */
+	static async createMessageBranch(
+		message: Omit,
+		parentId: string | null
+	): Promise {
+		return await db.transaction('rw', [db.conversations, db.messages], async () => {
+			// Handle null parent (root message case)
+			if (parentId !== null) {
+				const parentMessage = await db.messages.get(parentId);
+				if (!parentMessage) {
+					throw new Error(`Parent message ${parentId} not found`);
+				}
+			}
+
+			const newMessage: DatabaseMessage = {
+				...message,
+				id: uuid(),
+				parent: parentId,
+				toolCalls: message.toolCalls ?? '',
+				children: []
+			};
+
+			await db.messages.add(newMessage);
+
+			// Update parent's children array if parent exists
+			if (parentId !== null) {
+				const parentMessage = await db.messages.get(parentId);
+				if (parentMessage) {
+					await db.messages.update(parentId, {
+						children: [...parentMessage.children, newMessage.id]
+					});
+				}
+			}
+
+			await this.updateConversation(message.convId, {
+				currNode: newMessage.id
+			});
+
+			return newMessage;
+		});
+	}
+
+	/**
+	 * Creates a root message for a new conversation.
+	 * Root messages are not displayed but serve as the tree root for branching.
+	 *
+	 * @param convId - Conversation ID
+	 * @returns The created root message
+	 */
+	static async createRootMessage(convId: string): Promise {
+		const rootMessage: DatabaseMessage = {
+			id: uuid(),
+			convId,
+			type: 'root',
+			timestamp: Date.now(),
+			role: MessageRole.SYSTEM,
+			content: '',
+			parent: null,
+			toolCalls: '',
+			children: []
+		};
+
+		await db.messages.add(rootMessage);
+		return rootMessage.id;
+	}
+
+	/**
+	 * Creates a system prompt message for a conversation.
+	 *
+	 * @param convId - Conversation ID
+	 * @param systemPrompt - The system prompt content (must be non-empty)
+	 * @param parentId - Parent message ID (typically the root message)
+	 * @returns The created system message
+	 * @throws Error if systemPrompt is empty
+	 */
+	static async createSystemMessage(
+		convId: string,
+		systemPrompt: string,
+		parentId: string
+	): Promise {
+		const trimmedPrompt = systemPrompt.trim();
+		if (!trimmedPrompt) {
+			throw new Error('Cannot create system message with empty content');
+		}
+
+		const systemMessage: DatabaseMessage = {
+			id: uuid(),
+			convId,
+			type: MessageRole.SYSTEM,
+			timestamp: Date.now(),
+			role: MessageRole.SYSTEM,
+			content: trimmedPrompt,
+			parent: parentId,
+			children: []
+		};
+
+		await db.messages.add(systemMessage);
+
+		const parentMessage = await db.messages.get(parentId);
+		if (parentMessage) {
+			await db.messages.update(parentId, {
+				children: [...parentMessage.children, systemMessage.id]
+			});
+		}
+
+		return systemMessage;
+	}
+
+	/**
+	 * Deletes a conversation and all its messages.
+	 *
+	 * @param id - Conversation ID
+	 */
+	static async deleteConversation(id: string): Promise {
+		await db.transaction('rw', [db.conversations, db.messages], async () => {
+			await db.conversations.delete(id);
+			await db.messages.where('convId').equals(id).delete();
+		});
+	}
+
+	/**
+	 * Deletes a message and removes it from its parent's children array.
+	 *
+	 * @param messageId - ID of the message to delete
+	 */
+	static async deleteMessage(messageId: string): Promise {
+		await db.transaction('rw', db.messages, async () => {
+			const message = await db.messages.get(messageId);
+			if (!message) return;
+
+			// Remove this message from its parent's children array
+			if (message.parent) {
+				const parent = await db.messages.get(message.parent);
+				if (parent) {
+					parent.children = parent.children.filter((childId: string) => childId !== messageId);
+					await db.messages.put(parent);
+				}
+			}
+
+			// Delete the message
+			await db.messages.delete(messageId);
+		});
+	}
+
+	/**
+	 * Deletes a message and all its descendant messages (cascading deletion).
+	 * This removes the entire branch starting from the specified message.
+	 *
+	 * @param conversationId - ID of the conversation containing the message
+	 * @param messageId - ID of the root message to delete (along with all descendants)
+	 * @returns Array of all deleted message IDs
+	 */
+	static async deleteMessageCascading(
+		conversationId: string,
+		messageId: string
+	): Promise {
+		return await db.transaction('rw', db.messages, async () => {
+			// Get all messages in the conversation to find descendants
+			const allMessages = await db.messages.where('convId').equals(conversationId).toArray();
+
+			// Find all descendant messages
+			const descendants = findDescendantMessages(allMessages, messageId);
+			const allToDelete = [messageId, ...descendants];
+
+			// Get the message to delete for parent cleanup
+			const message = await db.messages.get(messageId);
+			if (message && message.parent) {
+				const parent = await db.messages.get(message.parent);
+				if (parent) {
+					parent.children = parent.children.filter((childId: string) => childId !== messageId);
+					await db.messages.put(parent);
+				}
+			}
+
+			// Delete all messages in the branch
+			await db.messages.bulkDelete(allToDelete);
+
+			return allToDelete;
+		});
+	}
+
+	/**
+	 * Gets all conversations, sorted by last modified time (newest first).
+	 *
+	 * @returns Array of conversations
+	 */
+	static async getAllConversations(): Promise {
+		return await db.conversations.orderBy('lastModified').reverse().toArray();
+	}
+
+	/**
+	 * Gets a conversation by ID.
+	 *
+	 * @param id - Conversation ID
+	 * @returns The conversation if found, otherwise undefined
+	 */
+	static async getConversation(id: string): Promise {
+		return await db.conversations.get(id);
+	}
+
+	/**
+	 * Gets all messages in a conversation, sorted by timestamp (oldest first).
+	 *
+	 * @param convId - Conversation ID
+	 * @returns Array of messages in the conversation
+	 */
+	static async getConversationMessages(convId: string): Promise {
+		return await db.messages.where('convId').equals(convId).sortBy('timestamp');
+	}
+
+	/**
+	 * Updates a conversation.
+	 *
+	 * @param id - Conversation ID
+	 * @param updates - Partial updates to apply
+	 * @returns Promise that resolves when the conversation is updated
+	 */
+	static async updateConversation(
+		id: string,
+		updates: Partial>
+	): Promise {
+		await db.conversations.update(id, {
+			...updates,
+			lastModified: Date.now()
+		});
+	}
+
+	/**
+	 *
+	 *
+	 * Navigation
+	 *
+	 *
+	 */
+
+	/**
+	 * Updates the conversation's current node (active branch).
+	 * This determines which conversation path is currently being viewed.
+	 *
+	 * @param convId - Conversation ID
+	 * @param nodeId - Message ID to set as current node
+	 */
+	static async updateCurrentNode(convId: string, nodeId: string): Promise {
+		await this.updateConversation(convId, {
+			currNode: nodeId
+		});
+	}
+
+	/**
+	 * Updates a message.
+	 *
+	 * @param id - Message ID
+	 * @param updates - Partial updates to apply
+	 * @returns Promise that resolves when the message is updated
+	 */
+	static async updateMessage(
+		id: string,
+		updates: Partial>
+	): Promise {
+		await db.messages.update(id, updates);
+	}
+
+	/**
+	 *
+	 *
+	 * Import
+	 *
+	 *
+	 */
+
+	/**
+	 * Imports multiple conversations and their messages.
+	 * Skips conversations that already exist.
+	 *
+	 * @param data - Array of { conv, messages } objects
+	 */
+	static async importConversations(
+		data: { conv: DatabaseConversation; messages: DatabaseMessage[] }[]
+	): Promise<{ imported: number; skipped: number }> {
+		let importedCount = 0;
+		let skippedCount = 0;
+
+		return await db.transaction('rw', [db.conversations, db.messages], async () => {
+			for (const item of data) {
+				const { conv, messages } = item;
+
+				const existing = await db.conversations.get(conv.id);
+				if (existing) {
+					console.warn(`Conversation "${conv.name}" already exists, skipping...`);
+					skippedCount++;
+					continue;
+				}
+
+				await db.conversations.add(conv);
+				for (const msg of messages) {
+					await db.messages.put(msg);
+				}
+
+				importedCount++;
+			}
+
+			return { imported: importedCount, skipped: skippedCount };
+		});
+	}
+}
diff --git a/tools/server/webui/src/lib/services/models.service.ts b/tools/server/webui/src/lib/services/models.service.ts
new file mode 100644
index 0000000000..7357c3f400
--- /dev/null
+++ b/tools/server/webui/src/lib/services/models.service.ts
@@ -0,0 +1,99 @@
+import { ServerModelStatus } from '$lib/enums';
+import { apiFetch, apiPost } from '$lib/utils/api-fetch';
+
+export class ModelsService {
+	/**
+	 *
+	 *
+	 * Listing
+	 *
+	 *
+	 */
+
+	/**
+	 * Fetch list of models from OpenAI-compatible endpoint.
+	 * Works in both MODEL and ROUTER modes.
+	 *
+	 * @returns List of available models with basic metadata
+	 */
+	static async list(): Promise {
+		return apiFetch('/v1/models');
+	}
+
+	/**
+	 * Fetch list of all models with detailed metadata (ROUTER mode).
+	 * Returns models with load status, paths, and other metadata
+	 * beyond what the OpenAI-compatible endpoint provides.
+	 *
+	 * @returns List of models with detailed status and configuration info
+	 */
+	static async listRouter(): Promise {
+		return apiFetch('/v1/models');
+	}
+
+	/**
+	 *
+	 *
+	 * Load/Unload
+	 *
+	 *
+	 */
+
+	/**
+	 * Load a model (ROUTER mode only).
+	 * Sends POST request to `/models/load`. Note: the endpoint returns success
+	 * before loading completes — use polling to await actual load status.
+	 *
+	 * @param modelId - Model identifier to load
+	 * @param extraArgs - Optional additional arguments to pass to the model instance
+	 * @returns Load response from the server
+	 */
+	static async load(modelId: string, extraArgs?: string[]): Promise {
+		const payload: { model: string; extra_args?: string[] } = { model: modelId };
+		if (extraArgs && extraArgs.length > 0) {
+			payload.extra_args = extraArgs;
+		}
+
+		return apiPost('/models/load', payload);
+	}
+
+	/**
+	 * Unload a model (ROUTER mode only).
+	 * Sends POST request to `/models/unload`. Note: the endpoint returns success
+	 * before unloading completes — use polling to await actual unload status.
+	 *
+	 * @param modelId - Model identifier to unload
+	 * @returns Unload response from the server
+	 */
+	static async unload(modelId: string): Promise {
+		return apiPost('/models/unload', { model: modelId });
+	}
+
+	/**
+	 *
+	 *
+	 * Status
+	 *
+	 *
+	 */
+
+	/**
+	 * Check if a model is loaded based on its metadata.
+	 *
+	 * @param model - Model data entry from the API response
+	 * @returns True if the model status is LOADED
+	 */
+	static isModelLoaded(model: ApiModelDataEntry): boolean {
+		return model.status.value === ServerModelStatus.LOADED;
+	}
+
+	/**
+	 * Check if a model is currently loading.
+	 *
+	 * @param model - Model data entry from the API response
+	 * @returns True if the model status is LOADING
+	 */
+	static isModelLoading(model: ApiModelDataEntry): boolean {
+		return model.status.value === ServerModelStatus.LOADING;
+	}
+}
diff --git a/tools/server/webui/src/lib/services/parameter-sync.service.spec.ts b/tools/server/webui/src/lib/services/parameter-sync.service.spec.ts
new file mode 100644
index 0000000000..46cce5e7cb
--- /dev/null
+++ b/tools/server/webui/src/lib/services/parameter-sync.service.spec.ts
@@ -0,0 +1,148 @@
+import { describe, it, expect } from 'vitest';
+import { ParameterSyncService } from './parameter-sync.service';
+
+describe('ParameterSyncService', () => {
+	describe('roundFloatingPoint', () => {
+		it('should fix JavaScript floating-point precision issues', () => {
+			// Test the specific values from the screenshot
+			const mockServerParams = {
+				top_p: 0.949999988079071,
+				min_p: 0.009999999776482582,
+				temperature: 0.800000011920929,
+				top_k: 40,
+				samplers: ['top_k', 'typ_p', 'top_p', 'min_p', 'temperature']
+			};
+
+			const result = ParameterSyncService.extractServerDefaults({
+				...mockServerParams,
+				// Add other required fields to match the API type
+				n_predict: 512,
+				seed: -1,
+				dynatemp_range: 0.0,
+				dynatemp_exponent: 1.0,
+				xtc_probability: 0.0,
+				xtc_threshold: 0.1,
+				typ_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: -1,
+				mirostat: 0,
+				mirostat_tau: 5.0,
+				mirostat_eta: 0.1,
+				stop: [],
+				max_tokens: -1,
+				n_keep: 0,
+				n_discard: 0,
+				ignore_eos: false,
+				stream: true,
+				logit_bias: [],
+				n_probs: 0,
+				min_keep: 0,
+				grammar: '',
+				grammar_lazy: false,
+				grammar_triggers: [],
+				preserved_tokens: [],
+				chat_format: '',
+				reasoning_format: '',
+				reasoning_in_content: false,
+				thinking_forced_open: false,
+				'speculative.n_max': 0,
+				'speculative.n_min': 0,
+				'speculative.p_min': 0.0,
+				timings_per_token: false,
+				post_sampling_probs: false,
+				lora: [],
+				top_n_sigma: 0.0,
+				dry_sequence_breakers: []
+			} as ApiLlamaCppServerProps['default_generation_settings']['params']);
+
+			// Check that the problematic floating-point values are rounded correctly
+			expect(result.top_p).toBe(0.95);
+			expect(result.min_p).toBe(0.01);
+			expect(result.temperature).toBe(0.8);
+			expect(result.top_k).toBe(40); // Integer should remain unchanged
+			expect(result.samplers).toBe('top_k;typ_p;top_p;min_p;temperature');
+		});
+
+		it('should preserve non-numeric values', () => {
+			const mockServerParams = {
+				samplers: ['top_k', 'temperature'],
+				max_tokens: -1,
+				temperature: 0.7
+			};
+
+			const result = ParameterSyncService.extractServerDefaults({
+				...mockServerParams,
+				// Minimal required fields
+				n_predict: 512,
+				seed: -1,
+				dynatemp_range: 0.0,
+				dynatemp_exponent: 1.0,
+				top_k: 40,
+				top_p: 0.95,
+				min_p: 0.05,
+				xtc_probability: 0.0,
+				xtc_threshold: 0.1,
+				typ_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: -1,
+				mirostat: 0,
+				mirostat_tau: 5.0,
+				mirostat_eta: 0.1,
+				stop: [],
+				n_keep: 0,
+				n_discard: 0,
+				ignore_eos: false,
+				stream: true,
+				logit_bias: [],
+				n_probs: 0,
+				min_keep: 0,
+				grammar: '',
+				grammar_lazy: false,
+				grammar_triggers: [],
+				preserved_tokens: [],
+				chat_format: '',
+				reasoning_format: '',
+				reasoning_in_content: false,
+				thinking_forced_open: false,
+				'speculative.n_max': 0,
+				'speculative.n_min': 0,
+				'speculative.p_min': 0.0,
+				timings_per_token: false,
+				post_sampling_probs: false,
+				lora: [],
+				top_n_sigma: 0.0,
+				dry_sequence_breakers: []
+			} as ApiLlamaCppServerProps['default_generation_settings']['params']);
+
+			expect(result.samplers).toBe('top_k;temperature');
+			expect(result.max_tokens).toBe(-1);
+			expect(result.temperature).toBe(0.7);
+		});
+
+		it('should merge webui settings from props when provided', () => {
+			const result = ParameterSyncService.extractServerDefaults(null, {
+				pasteLongTextToFileLen: 0,
+				pdfAsImage: true,
+				renderUserContentAsMarkdown: false,
+				theme: 'dark'
+			});
+
+			expect(result.pasteLongTextToFileLen).toBe(0);
+			expect(result.pdfAsImage).toBe(true);
+			expect(result.renderUserContentAsMarkdown).toBe(false);
+			expect(result.theme).toBeUndefined();
+		});
+	});
+});
diff --git a/tools/server/webui/src/lib/services/parameter-sync.service.ts b/tools/server/webui/src/lib/services/parameter-sync.service.ts
new file mode 100644
index 0000000000..6cb53d12d1
--- /dev/null
+++ b/tools/server/webui/src/lib/services/parameter-sync.service.ts
@@ -0,0 +1,400 @@
+import { normalizeFloatingPoint } from '$lib/utils';
+import { SyncableParameterType, ParameterSource } from '$lib/enums/settings';
+
+type ParameterValue = string | number | boolean;
+type ParameterRecord = Record;
+
+interface ParameterInfo {
+	value: string | number | boolean;
+	source: ParameterSource;
+	serverDefault?: string | number | boolean;
+	userOverride?: string | number | boolean;
+}
+
+interface SyncableParameter {
+	key: string;
+	serverKey: string;
+	type: SyncableParameterType;
+	canSync: boolean;
+}
+
+/**
+ * Mapping of webui setting keys to server parameter keys.
+ * Only parameters listed here can be synced from the server `/props` endpoint.
+ * Each entry defines the webui key, corresponding server key, value type,
+ * and whether sync is enabled.
+ */
+export const SYNCABLE_PARAMETERS: SyncableParameter[] = [
+	{
+		key: 'temperature',
+		serverKey: 'temperature',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{ key: 'top_k', serverKey: 'top_k', type: SyncableParameterType.NUMBER, canSync: true },
+	{ key: 'top_p', serverKey: 'top_p', type: SyncableParameterType.NUMBER, canSync: true },
+	{ key: 'min_p', serverKey: 'min_p', type: SyncableParameterType.NUMBER, canSync: true },
+	{
+		key: 'dynatemp_range',
+		serverKey: 'dynatemp_range',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'dynatemp_exponent',
+		serverKey: 'dynatemp_exponent',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'xtc_probability',
+		serverKey: 'xtc_probability',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'xtc_threshold',
+		serverKey: 'xtc_threshold',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{ key: 'typ_p', serverKey: 'typ_p', type: SyncableParameterType.NUMBER, canSync: true },
+	{
+		key: 'repeat_last_n',
+		serverKey: 'repeat_last_n',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'repeat_penalty',
+		serverKey: 'repeat_penalty',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'presence_penalty',
+		serverKey: 'presence_penalty',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'frequency_penalty',
+		serverKey: 'frequency_penalty',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'dry_multiplier',
+		serverKey: 'dry_multiplier',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{ key: 'dry_base', serverKey: 'dry_base', type: SyncableParameterType.NUMBER, canSync: true },
+	{
+		key: 'dry_allowed_length',
+		serverKey: 'dry_allowed_length',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'dry_penalty_last_n',
+		serverKey: 'dry_penalty_last_n',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{ key: 'max_tokens', serverKey: 'max_tokens', type: SyncableParameterType.NUMBER, canSync: true },
+	{ key: 'samplers', serverKey: 'samplers', type: SyncableParameterType.STRING, canSync: true },
+	{
+		key: 'pasteLongTextToFileLen',
+		serverKey: 'pasteLongTextToFileLen',
+		type: SyncableParameterType.NUMBER,
+		canSync: true
+	},
+	{
+		key: 'pdfAsImage',
+		serverKey: 'pdfAsImage',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'showThoughtInProgress',
+		serverKey: 'showThoughtInProgress',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'keepStatsVisible',
+		serverKey: 'keepStatsVisible',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'showMessageStats',
+		serverKey: 'showMessageStats',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'askForTitleConfirmation',
+		serverKey: 'askForTitleConfirmation',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'disableAutoScroll',
+		serverKey: 'disableAutoScroll',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'renderUserContentAsMarkdown',
+		serverKey: 'renderUserContentAsMarkdown',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'autoMicOnEmpty',
+		serverKey: 'autoMicOnEmpty',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'pyInterpreterEnabled',
+		serverKey: 'pyInterpreterEnabled',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	},
+	{
+		key: 'enableContinueGeneration',
+		serverKey: 'enableContinueGeneration',
+		type: SyncableParameterType.BOOLEAN,
+		canSync: true
+	}
+];
+
+export class ParameterSyncService {
+	/**
+	 *
+	 *
+	 * Extraction
+	 *
+	 *
+	 */
+
+	/**
+	 * Round floating-point numbers to avoid JavaScript precision issues.
+	 * E.g., 0.1 + 0.2 = 0.30000000000000004 → 0.3
+	 *
+	 * @param value - Parameter value to normalize
+	 * @returns Precision-normalized value
+	 */
+	private static roundFloatingPoint(value: ParameterValue): ParameterValue {
+		return normalizeFloatingPoint(value) as ParameterValue;
+	}
+
+	/**
+	 * Extract server default parameters that can be synced from `/props` response.
+	 * Handles both generation settings parameters and webui-specific settings.
+	 * Converts samplers array to semicolon-delimited string for UI display.
+	 *
+	 * @param serverParams - Raw generation settings from server `/props` endpoint
+	 * @param webuiSettings - Optional webui-specific settings from server
+	 * @returns Record of extracted parameter key-value pairs with normalized precision
+	 */
+	static extractServerDefaults(
+		serverParams: ApiLlamaCppServerProps['default_generation_settings']['params'] | null,
+		webuiSettings?: Record
+	): ParameterRecord {
+		const extracted: ParameterRecord = {};
+
+		if (serverParams) {
+			for (const param of SYNCABLE_PARAMETERS) {
+				if (param.canSync && param.serverKey in serverParams) {
+					const value = (serverParams as unknown as Record)[
+						param.serverKey
+					];
+					if (value !== undefined) {
+						// Apply precision rounding to avoid JavaScript floating-point issues
+						extracted[param.key] = this.roundFloatingPoint(value);
+					}
+				}
+			}
+
+			// Handle samplers array conversion to string
+			if (serverParams.samplers && Array.isArray(serverParams.samplers)) {
+				extracted.samplers = serverParams.samplers.join(';');
+			}
+		}
+
+		if (webuiSettings) {
+			for (const param of SYNCABLE_PARAMETERS) {
+				if (param.canSync && param.serverKey in webuiSettings) {
+					const value = webuiSettings[param.serverKey];
+					if (value !== undefined) {
+						extracted[param.key] = this.roundFloatingPoint(value);
+					}
+				}
+			}
+		}
+
+		return extracted;
+	}
+
+	/**
+	 *
+	 *
+	 * Merging
+	 *
+	 *
+	 */
+
+	/**
+	 * Merge server defaults with current user settings.
+	 * User overrides always take priority — only parameters not in `userOverrides`
+	 * set will be updated from server defaults.
+	 *
+	 * @param currentSettings - Current parameter values in the settings store
+	 * @param serverDefaults - Default values extracted from server props
+	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
+	 * @returns Merged parameter record with user overrides preserved
+	 */
+	static mergeWithServerDefaults(
+		currentSettings: ParameterRecord,
+		serverDefaults: ParameterRecord,
+		userOverrides: Set = new Set()
+	): ParameterRecord {
+		const merged = { ...currentSettings };
+
+		for (const [key, serverValue] of Object.entries(serverDefaults)) {
+			// Only update if user hasn't explicitly overridden this parameter
+			if (!userOverrides.has(key)) {
+				merged[key] = this.roundFloatingPoint(serverValue);
+			}
+		}
+
+		return merged;
+	}
+
+	/**
+	 *
+	 *
+	 * Info
+	 *
+	 *
+	 */
+
+	/**
+	 * Get parameter information including source and values.
+	 * Used by ChatSettingsParameterSourceIndicator to display the correct badge
+	 * (Custom vs Default) for each parameter in the settings UI.
+	 *
+	 * @param key - The parameter key to get info for
+	 * @param currentValue - The current value of the parameter
+	 * @param propsDefaults - Server default values from `/props`
+	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
+	 * @returns Parameter info with source, server default, and user override values
+	 */
+	static getParameterInfo(
+		key: string,
+		currentValue: ParameterValue,
+		propsDefaults: ParameterRecord,
+		userOverrides: Set
+	): ParameterInfo {
+		const hasPropsDefault = propsDefaults[key] !== undefined;
+		const isUserOverride = userOverrides.has(key);
+
+		// Simple logic: either using default (from props) or custom (user override)
+		const source = isUserOverride ? ParameterSource.CUSTOM : ParameterSource.DEFAULT;
+
+		return {
+			value: currentValue,
+			source,
+			serverDefault: hasPropsDefault ? propsDefaults[key] : undefined, // Keep same field name for compatibility
+			userOverride: isUserOverride ? currentValue : undefined
+		};
+	}
+
+	/**
+	 * Check if a parameter can be synced from server.
+	 *
+	 * @param key - The parameter key to check
+	 * @returns True if the parameter is in the syncable parameters list
+	 */
+	static canSyncParameter(key: string): boolean {
+		return SYNCABLE_PARAMETERS.some((param) => param.key === key && param.canSync);
+	}
+
+	/**
+	 * Get all syncable parameter keys.
+	 *
+	 * @returns Array of parameter keys that can be synced from server
+	 */
+	static getSyncableParameterKeys(): string[] {
+		return SYNCABLE_PARAMETERS.filter((param) => param.canSync).map((param) => param.key);
+	}
+
+	/**
+	 * Validate a server parameter value against its expected type.
+	 *
+	 * @param key - The parameter key to validate
+	 * @param value - The value to validate
+	 * @returns True if value matches the expected type for this parameter
+	 */
+	static validateServerParameter(key: string, value: ParameterValue): boolean {
+		const param = SYNCABLE_PARAMETERS.find((p) => p.key === key);
+		if (!param) return false;
+
+		switch (param.type) {
+			case SyncableParameterType.NUMBER:
+				return typeof value === 'number' && !isNaN(value);
+			case SyncableParameterType.STRING:
+				return typeof value === 'string';
+			case SyncableParameterType.BOOLEAN:
+				return typeof value === 'boolean';
+			default:
+				return false;
+		}
+	}
+
+	/**
+	 *
+	 *
+	 * Diff
+	 *
+	 *
+	 */
+
+	/**
+	 * Create a diff between current settings and server defaults.
+	 * Shows which parameters differ from server values, useful for debugging
+	 * and for the "Reset to defaults" functionality.
+	 *
+	 * @param currentSettings - Current parameter values in the settings store
+	 * @param serverDefaults - Default values extracted from server props
+	 * @returns Record of parameter diffs with current value, server value, and whether they differ
+	 */
+	static createParameterDiff(
+		currentSettings: ParameterRecord,
+		serverDefaults: ParameterRecord
+	): Record {
+		const diff: Record<
+			string,
+			{ current: ParameterValue; server: ParameterValue; differs: boolean }
+		> = {};
+
+		for (const key of this.getSyncableParameterKeys()) {
+			const currentValue = currentSettings[key];
+			const serverValue = serverDefaults[key];
+
+			if (serverValue !== undefined) {
+				diff[key] = {
+					current: currentValue,
+					server: serverValue,
+					differs: currentValue !== serverValue
+				};
+			}
+		}
+
+		return diff;
+	}
+}
diff --git a/tools/server/webui/src/lib/services/props.service.ts b/tools/server/webui/src/lib/services/props.service.ts
new file mode 100644
index 0000000000..7373b7e016
--- /dev/null
+++ b/tools/server/webui/src/lib/services/props.service.ts
@@ -0,0 +1,47 @@
+import { apiFetchWithParams } from '$lib/utils/api-fetch';
+
+export class PropsService {
+	/**
+	 *
+	 *
+	 * Fetching
+	 *
+	 *
+	 */
+
+	/**
+	 * Fetches global server properties from the `/props` endpoint.
+	 * In MODEL mode, returns modalities for the single loaded model.
+	 * In ROUTER mode, returns server-wide settings without model-specific modalities.
+	 *
+	 * @param autoload - If false, prevents automatic model loading (default: false)
+	 * @returns Server properties including default generation settings and capabilities
+	 * @throws {Error} If the request fails or returns invalid data
+	 */
+	static async fetch(autoload = false): Promise {
+		const params: Record = {};
+		if (!autoload) {
+			params.autoload = 'false';
+		}
+
+		return apiFetchWithParams('./props', params, { authOnly: true });
+	}
+
+	/**
+	 * Fetches server properties for a specific model (ROUTER mode only).
+	 * Required in ROUTER mode because global `/props` does not include per-model modalities.
+	 *
+	 * @param modelId - The model ID to fetch properties for
+	 * @param autoload - If false, prevents automatic model loading (default: false)
+	 * @returns Server properties specific to the requested model
+	 * @throws {Error} If the request fails, model not found, or model not loaded
+	 */
+	static async fetchForModel(modelId: string, autoload = false): Promise {
+		const params: Record = { model: modelId };
+		if (!autoload) {
+			params.autoload = 'false';
+		}
+
+		return apiFetchWithParams('./props', params, { authOnly: true });
+	}
+}
diff --git a/tools/server/webui/src/lib/types/api.d.ts b/tools/server/webui/src/lib/types/api.d.ts
index 714509f024..307e3b71d9 100644
--- a/tools/server/webui/src/lib/types/api.d.ts
+++ b/tools/server/webui/src/lib/types/api.d.ts
@@ -1,8 +1,19 @@
-import type { ServerModelStatus, ServerRole } from '$lib/enums';
-import type { ChatMessagePromptProgress } from './chat';
+import type { ContentPartType, ServerModelStatus, ServerRole } from '$lib/enums';
+import type { ChatMessagePromptProgress, ChatRole } from './chat';
+
+export interface ApiChatCompletionToolFunction {
+	name: string;
+	description?: string;
+	parameters: Record;
+}
+
+export interface ApiChatCompletionTool {
+	type: 'function';
+	function: ApiChatCompletionToolFunction;
+}
 
 export interface ApiChatMessageContentPart {
-	type: 'text' | 'image_url' | 'input_audio';
+	type: ContentPartType;
 	text?: string;
 	image_url?: {
 		url: string;
@@ -34,6 +45,8 @@ export interface ApiErrorResponse {
 export interface ApiChatMessageData {
 	role: ChatRole;
 	content: string | ApiChatMessageContentPart[];
+	tool_calls?: ApiChatCompletionToolCall[];
+	tool_call_id?: string;
 	timestamp?: number;
 }
 
@@ -188,6 +201,7 @@ export interface ApiChatCompletionRequest {
 	stream?: boolean;
 	model?: string;
 	return_progress?: boolean;
+	tools?: ApiChatCompletionTool[];
 	// Reasoning parameters
 	reasoning_format?: string;
 	// Generation parameters
@@ -247,6 +261,7 @@ export interface ApiChatCompletionStreamChunk {
 			model?: string;
 			tool_calls?: ApiChatCompletionToolCallDelta[];
 		};
+		finish_reason?: string | null;
 	}>;
 	timings?: {
 		prompt_n?: number;
@@ -267,8 +282,9 @@ export interface ApiChatCompletionResponse {
 			content: string;
 			reasoning_content?: string;
 			model?: string;
-			tool_calls?: ApiChatCompletionToolCallDelta[];
+			tool_calls?: ApiChatCompletionToolCall[];
 		};
+		finish_reason?: string | null;
 	}>;
 }
 
@@ -335,7 +351,7 @@ export interface ApiProcessingState {
 	tokensDecoded: number;
 	tokensRemaining: number;
 	contextUsed: number;
-	contextTotal: number;
+	contextTotal: number | null;
 	outputTokensUsed: number; // Total output tokens (thinking + regular content)
 	outputTokensMax: number; // Max output tokens allowed
 	temperature: number;
diff --git a/tools/server/webui/src/lib/types/models.d.ts b/tools/server/webui/src/lib/types/models.d.ts
index ef44a2cb6d..505867a1f0 100644
--- a/tools/server/webui/src/lib/types/models.d.ts
+++ b/tools/server/webui/src/lib/types/models.d.ts
@@ -1,8 +1,5 @@
 import type { ApiModelDataEntry, ApiModelDetails } from '$lib/types/api';
 
-/**
- * Model modalities - vision and audio capabilities
- */
 export interface ModelModalities {
 	vision: boolean;
 	audio: boolean;
@@ -14,8 +11,15 @@ export interface ModelOption {
 	model: string;
 	description?: string;
 	capabilities: string[];
-	/** Model modalities from /props endpoint */
 	modalities?: ModelModalities;
 	details?: ApiModelDetails['details'];
 	meta?: ApiModelDataEntry['meta'];
 }
+
+/**
+ * Modality capabilities for file validation
+ */
+export interface ModalityCapabilities {
+	hasVision: boolean;
+	hasAudio: boolean;
+}
diff --git a/tools/server/webui/src/lib/utils/abort.ts b/tools/server/webui/src/lib/utils/abort.ts
new file mode 100644
index 0000000000..fc4f31ec69
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/abort.ts
@@ -0,0 +1,151 @@
+/**
+ * Abort Signal Utilities
+ *
+ * Provides utilities for consistent AbortSignal propagation across the application.
+ * These utilities help ensure that async operations can be properly cancelled
+ * when needed (e.g., user stops generation, navigates away, etc.).
+ */
+
+/**
+ * Throws an AbortError if the signal is aborted.
+ * Use this at the start of async operations to fail fast.
+ *
+ * @param signal - Optional AbortSignal to check
+ * @throws DOMException with name 'AbortError' if signal is aborted
+ *
+ * @example
+ * ```ts
+ * async function fetchData(signal?: AbortSignal) {
+ *   throwIfAborted(signal);
+ *   // ... proceed with operation
+ * }
+ * ```
+ */
+export function throwIfAborted(signal?: AbortSignal): void {
+	if (signal?.aborted) {
+		throw new DOMException('Operation was aborted', 'AbortError');
+	}
+}
+
+/**
+ * Checks if an error is an AbortError.
+ * Use this to distinguish between user-initiated cancellation and actual errors.
+ *
+ * @param error - Error to check
+ * @returns true if the error is an AbortError
+ *
+ * @example
+ * ```ts
+ * try {
+ *   await fetchData(signal);
+ * } catch (error) {
+ *   if (isAbortError(error)) {
+ *     // User cancelled - no error dialog needed
+ *     return;
+ *   }
+ *   // Handle actual error
+ * }
+ * ```
+ */
+export function isAbortError(error: unknown): boolean {
+	if (error instanceof DOMException && error.name === 'AbortError') {
+		return true;
+	}
+	if (error instanceof Error && error.name === 'AbortError') {
+		return true;
+	}
+	return false;
+}
+
+/**
+ * Creates a new AbortController that is linked to one or more parent signals.
+ * When any parent signal aborts, the returned controller also aborts.
+ *
+ * Useful for creating child operations that should be cancelled when
+ * either the parent operation or their own timeout/condition triggers.
+ *
+ * @param signals - Parent signals to link to (undefined signals are ignored)
+ * @returns A new AbortController linked to all provided signals
+ *
+ * @example
+ * ```ts
+ * // Link to user's abort signal and add a timeout
+ * const linked = createLinkedController(userSignal, timeoutSignal);
+ * await fetch(url, { signal: linked.signal });
+ * ```
+ */
+export function createLinkedController(...signals: (AbortSignal | undefined)[]): AbortController {
+	const controller = new AbortController();
+
+	for (const signal of signals) {
+		if (!signal) continue;
+
+		// If already aborted, abort immediately
+		if (signal.aborted) {
+			controller.abort(signal.reason);
+			return controller;
+		}
+
+		// Link to parent signal
+		signal.addEventListener('abort', () => controller.abort(signal.reason), { once: true });
+	}
+
+	return controller;
+}
+
+/**
+ * Creates an AbortSignal that times out after the specified duration.
+ *
+ * @param ms - Timeout duration in milliseconds
+ * @returns AbortSignal that will abort after the timeout
+ *
+ * @example
+ * ```ts
+ * const signal = createTimeoutSignal(5000); // 5 second timeout
+ * await fetch(url, { signal });
+ * ```
+ */
+export function createTimeoutSignal(ms: number): AbortSignal {
+	return AbortSignal.timeout(ms);
+}
+
+/**
+ * Wraps a promise to reject if the signal is aborted.
+ * Useful for making non-abortable promises respect an AbortSignal.
+ *
+ * @param promise - Promise to wrap
+ * @param signal - AbortSignal to respect
+ * @returns Promise that rejects with AbortError if signal aborts
+ *
+ * @example
+ * ```ts
+ * // Make a non-abortable operation respect abort signal
+ * const result = await withAbortSignal(
+ *   someNonAbortableOperation(),
+ *   signal
+ * );
+ * ```
+ */
+export async function withAbortSignal(promise: Promise, signal?: AbortSignal): Promise {
+	if (!signal) return promise;
+
+	throwIfAborted(signal);
+
+	return new Promise((resolve, reject) => {
+		const abortHandler = () => {
+			reject(new DOMException('Operation was aborted', 'AbortError'));
+		};
+
+		signal.addEventListener('abort', abortHandler, { once: true });
+
+		promise
+			.then((value) => {
+				signal.removeEventListener('abort', abortHandler);
+				resolve(value);
+			})
+			.catch((error) => {
+				signal.removeEventListener('abort', abortHandler);
+				reject(error);
+			});
+	});
+}
diff --git a/tools/server/webui/src/lib/utils/api-fetch.ts b/tools/server/webui/src/lib/utils/api-fetch.ts
new file mode 100644
index 0000000000..8081ef2ec2
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/api-fetch.ts
@@ -0,0 +1,155 @@
+import { base } from '$app/paths';
+import { getJsonHeaders, getAuthHeaders } from './api-headers';
+
+/**
+ * API Fetch Utilities
+ *
+ * Provides common fetch patterns used across services:
+ * - Automatic JSON headers
+ * - Error handling with proper error messages
+ * - Base path resolution
+ */
+
+export interface ApiFetchOptions extends Omit {
+	/**
+	 * Use auth-only headers (no Content-Type).
+	 * Default: false (uses JSON headers with Content-Type: application/json)
+	 */
+	authOnly?: boolean;
+	/**
+	 * Additional headers to merge with default headers.
+	 */
+	headers?: Record;
+}
+
+/**
+ * Fetch JSON data from an API endpoint with standard headers and error handling.
+ *
+ * @param path - API path (will be prefixed with base path)
+ * @param options - Fetch options with additional authOnly flag
+ * @returns Parsed JSON response
+ * @throws Error with formatted message on failure
+ *
+ * @example
+ * ```typescript
+ * // GET request
+ * const models = await apiFetch('/v1/models');
+ *
+ * // POST request
+ * const result = await apiFetch('/models/load', {
+ *   method: 'POST',
+ *   body: JSON.stringify({ model: 'gpt-4' })
+ * });
+ * ```
+ */
+export async function apiFetch(path: string, options: ApiFetchOptions = {}): Promise {
+	const { authOnly = false, headers: customHeaders, ...fetchOptions } = options;
+
+	const baseHeaders = authOnly ? getAuthHeaders() : getJsonHeaders();
+	const headers = { ...baseHeaders, ...customHeaders };
+
+	const url =
+		path.startsWith('http://') || path.startsWith('https://') ? path : `${base}${path}`;
+
+	const response = await fetch(url, {
+		...fetchOptions,
+		headers
+	});
+
+	if (!response.ok) {
+		const errorMessage = await parseErrorMessage(response);
+		throw new Error(errorMessage);
+	}
+
+	return response.json() as Promise;
+}
+
+/**
+ * Fetch with URL constructed from base URL and query parameters.
+ *
+ * @param basePath - Base API path
+ * @param params - Query parameters to append
+ * @param options - Fetch options
+ * @returns Parsed JSON response
+ *
+ * @example
+ * ```typescript
+ * const props = await apiFetchWithParams('./props', {
+ *   model: 'gpt-4',
+ *   autoload: 'false'
+ * });
+ * ```
+ */
+export async function apiFetchWithParams(
+	basePath: string,
+	params: Record,
+	options: ApiFetchOptions = {}
+): Promise {
+	const url = new URL(basePath, window.location.href);
+
+	for (const [key, value] of Object.entries(params)) {
+		if (value !== undefined && value !== null) {
+			url.searchParams.set(key, value);
+		}
+	}
+
+	const { authOnly = false, headers: customHeaders, ...fetchOptions } = options;
+
+	const baseHeaders = authOnly ? getAuthHeaders() : getJsonHeaders();
+	const headers = { ...baseHeaders, ...customHeaders };
+
+	const response = await fetch(url.toString(), {
+		...fetchOptions,
+		headers
+	});
+
+	if (!response.ok) {
+		const errorMessage = await parseErrorMessage(response);
+		throw new Error(errorMessage);
+	}
+
+	return response.json() as Promise;
+}
+
+/**
+ * POST JSON data to an API endpoint.
+ *
+ * @param path - API path
+ * @param body - Request body (will be JSON stringified)
+ * @param options - Additional fetch options
+ * @returns Parsed JSON response
+ */
+export async function apiPost(
+	path: string,
+	body: B,
+	options: ApiFetchOptions = {}
+): Promise {
+	return apiFetch(path, {
+		method: 'POST',
+		body: JSON.stringify(body),
+		...options
+	});
+}
+
+/**
+ * Parse error message from a failed response.
+ * Tries to extract error message from JSON body, falls back to status text.
+ */
+async function parseErrorMessage(response: Response): Promise {
+	try {
+		const errorData = await response.json();
+		if (errorData?.error?.message) {
+			return errorData.error.message;
+		}
+		if (errorData?.error && typeof errorData.error === 'string') {
+			return errorData.error;
+		}
+		if (errorData?.message) {
+			return errorData.message;
+		}
+	} catch {
+		// JSON parsing failed, use status text
+	}
+
+	return `Request failed: ${response.status} ${response.statusText}`;
+}
diff --git a/tools/server/webui/src/lib/utils/branching.ts b/tools/server/webui/src/lib/utils/branching.ts
index 3be56047a5..ee3a505eed 100644
--- a/tools/server/webui/src/lib/utils/branching.ts
+++ b/tools/server/webui/src/lib/utils/branching.ts
@@ -15,6 +15,8 @@
  *        └── message 5 (assistant)
  */
 
+import { MessageRole } from '$lib/enums/chat';
+
 /**
  * Filters messages to get the conversation path from root to a specific leaf node.
  * If the leafNodeId doesn't exist, returns the path with the latest timestamp.
@@ -65,8 +67,13 @@ export function filterByLeafNodeId(
 		currentNode = nodeMap.get(currentNode.parent);
 	}
 
-	// Sort by timestamp to get chronological order (root to leaf)
-	result.sort((a, b) => a.timestamp - b.timestamp);
+	// Sort: system messages first, then by timestamp
+	result.sort((a, b) => {
+		if (a.role === MessageRole.SYSTEM && b.role !== MessageRole.SYSTEM) return -1;
+		if (a.role !== MessageRole.SYSTEM && b.role === MessageRole.SYSTEM) return 1;
+
+		return a.timestamp - b.timestamp;
+	});
 	return result;
 }
 
diff --git a/tools/server/webui/src/lib/utils/browser-only.ts b/tools/server/webui/src/lib/utils/browser-only.ts
index 0af800638b..27d2be4aaa 100644
--- a/tools/server/webui/src/lib/utils/browser-only.ts
+++ b/tools/server/webui/src/lib/utils/browser-only.ts
@@ -23,7 +23,7 @@ export {
 } from './pdf-processing';
 
 // File conversion utilities (depends on pdf-processing)
-export { parseFilesToMessageExtras, type FileProcessingResult } from './convert-files-to-extra';
+export { parseFilesToMessageExtras } from './convert-files-to-extra';
 
 // File upload processing utilities (depends on pdf-processing, svg-to-png, webp-to-png)
 export { processFilesToChatUploaded } from './process-uploaded-files';
diff --git a/tools/server/webui/src/lib/utils/cache-ttl.ts b/tools/server/webui/src/lib/utils/cache-ttl.ts
new file mode 100644
index 0000000000..9d1f005822
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/cache-ttl.ts
@@ -0,0 +1,293 @@
+const DEFAULT_CACHE_TTL_MS = 5 * 60 * 1000;
+const DEFAULT_CACHE_MAX_ENTRIES = 100;
+
+/**
+ * TTL Cache - Time-To-Live cache implementation for memory optimization
+ *
+ * Provides automatic expiration of cached entries to prevent memory bloat
+ * in long-running sessions.
+ *
+ * @example
+ * ```ts
+ * const cache = new TTLCache({ ttlMs: 5 * 60 * 1000 }); // 5 minutes
+ * cache.set('key', data);
+ * const value = cache.get('key'); // null if expired
+ * ```
+ */
+
+export interface TTLCacheOptions {
+	/** Time-to-live in milliseconds. Default: 5 minutes */
+	ttlMs?: number;
+	/** Maximum number of entries. Oldest entries are evicted when exceeded. Default: 100 */
+	maxEntries?: number;
+	/** Callback when an entry expires or is evicted */
+	onEvict?: (key: string, value: unknown) => void;
+}
+
+interface CacheEntry {
+	value: T;
+	expiresAt: number;
+	lastAccessed: number;
+}
+
+export class TTLCache {
+	private cache = new Map>();
+	private readonly ttlMs: number;
+	private readonly maxEntries: number;
+	private readonly onEvict?: (key: string, value: unknown) => void;
+
+	constructor(options: TTLCacheOptions = {}) {
+		this.ttlMs = options.ttlMs ?? DEFAULT_CACHE_TTL_MS;
+		this.maxEntries = options.maxEntries ?? DEFAULT_CACHE_MAX_ENTRIES;
+		this.onEvict = options.onEvict;
+	}
+
+	/**
+	 * Get a value from cache. Returns null if expired or not found.
+	 */
+	get(key: K): V | null {
+		const entry = this.cache.get(key);
+		if (!entry) return null;
+
+		if (Date.now() > entry.expiresAt) {
+			this.delete(key);
+			return null;
+		}
+
+		// Update last accessed time for LRU-like behavior
+		entry.lastAccessed = Date.now();
+		return entry.value;
+	}
+
+	/**
+	 * Set a value in cache with TTL.
+	 */
+	set(key: K, value: V, customTtlMs?: number): void {
+		// Evict oldest entries if at capacity
+		if (this.cache.size >= this.maxEntries && !this.cache.has(key)) {
+			this.evictOldest();
+		}
+
+		const ttl = customTtlMs ?? this.ttlMs;
+		const now = Date.now();
+
+		this.cache.set(key, {
+			value,
+			expiresAt: now + ttl,
+			lastAccessed: now
+		});
+	}
+
+	/**
+	 * Check if key exists and is not expired.
+	 */
+	has(key: K): boolean {
+		const entry = this.cache.get(key);
+		if (!entry) return false;
+
+		if (Date.now() > entry.expiresAt) {
+			this.delete(key);
+			return false;
+		}
+
+		return true;
+	}
+
+	/**
+	 * Delete a specific key from cache.
+	 */
+	delete(key: K): boolean {
+		const entry = this.cache.get(key);
+		if (entry && this.onEvict) {
+			this.onEvict(key, entry.value);
+		}
+		return this.cache.delete(key);
+	}
+
+	/**
+	 * Clear all entries from cache.
+	 */
+	clear(): void {
+		if (this.onEvict) {
+			for (const [key, entry] of this.cache) {
+				this.onEvict(key, entry.value);
+			}
+		}
+		this.cache.clear();
+	}
+
+	/**
+	 * Get the number of entries (including potentially expired ones).
+	 */
+	get size(): number {
+		return this.cache.size;
+	}
+
+	/**
+	 * Remove all expired entries from cache.
+	 * Call periodically for proactive cleanup.
+	 */
+	prune(): number {
+		const now = Date.now();
+		let pruned = 0;
+
+		for (const [key, entry] of this.cache) {
+			if (now > entry.expiresAt) {
+				this.delete(key);
+				pruned++;
+			}
+		}
+
+		return pruned;
+	}
+
+	/**
+	 * Get all valid (non-expired) keys.
+	 */
+	keys(): K[] {
+		const now = Date.now();
+		const validKeys: K[] = [];
+
+		for (const [key, entry] of this.cache) {
+			if (now <= entry.expiresAt) {
+				validKeys.push(key);
+			}
+		}
+
+		return validKeys;
+	}
+
+	/**
+	 * Evict the oldest (least recently accessed) entry.
+	 */
+	private evictOldest(): void {
+		let oldestKey: K | null = null;
+		let oldestTime = Infinity;
+
+		for (const [key, entry] of this.cache) {
+			if (entry.lastAccessed < oldestTime) {
+				oldestTime = entry.lastAccessed;
+				oldestKey = key;
+			}
+		}
+
+		if (oldestKey !== null) {
+			this.delete(oldestKey);
+		}
+	}
+
+	/**
+	 * Refresh TTL for an existing entry without changing the value.
+	 */
+	touch(key: K): boolean {
+		const entry = this.cache.get(key);
+		if (!entry) return false;
+
+		const now = Date.now();
+		if (now > entry.expiresAt) {
+			this.delete(key);
+			return false;
+		}
+
+		entry.expiresAt = now + this.ttlMs;
+		entry.lastAccessed = now;
+		return true;
+	}
+}
+
+/**
+ * Reactive TTL Map for Svelte stores
+ * Wraps SvelteMap with TTL functionality
+ */
+export class ReactiveTTLMap {
+	private entries = $state>>(new Map());
+	private readonly ttlMs: number;
+	private readonly maxEntries: number;
+
+	constructor(options: TTLCacheOptions = {}) {
+		this.ttlMs = options.ttlMs ?? DEFAULT_CACHE_TTL_MS;
+		this.maxEntries = options.maxEntries ?? DEFAULT_CACHE_MAX_ENTRIES;
+	}
+
+	get(key: K): V | null {
+		const entry = this.entries.get(key);
+		if (!entry) return null;
+
+		if (Date.now() > entry.expiresAt) {
+			this.entries.delete(key);
+			return null;
+		}
+
+		entry.lastAccessed = Date.now();
+		return entry.value;
+	}
+
+	set(key: K, value: V, customTtlMs?: number): void {
+		if (this.entries.size >= this.maxEntries && !this.entries.has(key)) {
+			this.evictOldest();
+		}
+
+		const ttl = customTtlMs ?? this.ttlMs;
+		const now = Date.now();
+
+		this.entries.set(key, {
+			value,
+			expiresAt: now + ttl,
+			lastAccessed: now
+		});
+	}
+
+	has(key: K): boolean {
+		const entry = this.entries.get(key);
+		if (!entry) return false;
+
+		if (Date.now() > entry.expiresAt) {
+			this.entries.delete(key);
+			return false;
+		}
+
+		return true;
+	}
+
+	delete(key: K): boolean {
+		return this.entries.delete(key);
+	}
+
+	clear(): void {
+		this.entries.clear();
+	}
+
+	get size(): number {
+		return this.entries.size;
+	}
+
+	prune(): number {
+		const now = Date.now();
+		let pruned = 0;
+
+		for (const [key, entry] of this.entries) {
+			if (now > entry.expiresAt) {
+				this.entries.delete(key);
+				pruned++;
+			}
+		}
+
+		return pruned;
+	}
+
+	private evictOldest(): void {
+		let oldestKey: K | null = null;
+		let oldestTime = Infinity;
+
+		for (const [key, entry] of this.entries) {
+			if (entry.lastAccessed < oldestTime) {
+				oldestTime = entry.lastAccessed;
+				oldestKey = key;
+			}
+		}
+
+		if (oldestKey !== null) {
+			this.entries.delete(oldestKey);
+		}
+	}
+}
diff --git a/tools/server/webui/src/lib/utils/code.ts b/tools/server/webui/src/lib/utils/code.ts
new file mode 100644
index 0000000000..67efc6b27e
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/code.ts
@@ -0,0 +1,85 @@
+import hljs from 'highlight.js';
+import {
+	NEWLINE,
+	DEFAULT_LANGUAGE,
+	LANG_PATTERN,
+	AMPERSAND_REGEX,
+	LT_REGEX,
+	GT_REGEX,
+	FENCE_PATTERN
+} from '$lib/constants/code';
+
+export interface IncompleteCodeBlock {
+	language: string;
+	code: string;
+	openingIndex: number;
+}
+
+/**
+ * Highlights code using highlight.js
+ * @param code - The code to highlight
+ * @param language - The programming language
+ * @returns HTML string with syntax highlighting
+ */
+export function highlightCode(code: string, language: string): string {
+	if (!code) return '';
+
+	try {
+		const lang = language.toLowerCase();
+		const isSupported = hljs.getLanguage(lang);
+
+		if (isSupported) {
+			return hljs.highlight(code, { language: lang }).value;
+		} else {
+			return hljs.highlightAuto(code).value;
+		}
+	} catch {
+		// Fallback to escaped plain text
+		return code
+			.replace(AMPERSAND_REGEX, '&')
+			.replace(LT_REGEX, '<')
+			.replace(GT_REGEX, '>');
+	}
+}
+
+/**
+ * Detects if markdown ends with an incomplete code block (opened but not closed).
+ * Returns the code block info if found, null otherwise.
+ * @param markdown - The raw markdown string to check
+ * @returns IncompleteCodeBlock info or null
+ */
+export function detectIncompleteCodeBlock(markdown: string): IncompleteCodeBlock | null {
+	// Count all code fences in the markdown
+	// A code block is incomplete if there's an odd number of ``` fences
+	const fencePattern = new RegExp(FENCE_PATTERN.source, FENCE_PATTERN.flags);
+	const fences: number[] = [];
+	let fenceMatch;
+
+	while ((fenceMatch = fencePattern.exec(markdown)) !== null) {
+		// Store the position after the ```
+		const pos = fenceMatch[0].startsWith(NEWLINE) ? fenceMatch.index + 1 : fenceMatch.index;
+		fences.push(pos);
+	}
+
+	// If even number of fences (including 0), all code blocks are closed
+	if (fences.length % 2 === 0) {
+		return null;
+	}
+
+	// Odd number means last code block is incomplete
+	// The last fence is the opening of the incomplete block
+	const openingIndex = fences[fences.length - 1];
+	const afterOpening = markdown.slice(openingIndex + 3);
+
+	// Extract language and code content
+	const langMatch = afterOpening.match(LANG_PATTERN);
+	const language = langMatch?.[1] || DEFAULT_LANGUAGE;
+	const codeStartIndex = openingIndex + 3 + (langMatch?.[0]?.length ?? 0);
+	const code = markdown.slice(codeStartIndex);
+
+	return {
+		language,
+		code,
+		openingIndex
+	};
+}
diff --git a/tools/server/webui/src/lib/utils/data-url.ts b/tools/server/webui/src/lib/utils/data-url.ts
new file mode 100644
index 0000000000..6f55be793d
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/data-url.ts
@@ -0,0 +1,10 @@
+/**
+ * Creates a base64 data URL from MIME type and base64-encoded data.
+ *
+ * @param mimeType - The MIME type (e.g., 'image/png', 'audio/mp3')
+ * @param base64Data - The base64-encoded data
+ * @returns A data URL string in format 'data:{mimeType};base64,{data}'
+ */
+export function createBase64DataUrl(mimeType: string, base64Data: string): string {
+	return `data:${mimeType};base64,${base64Data}`;
+}
diff --git a/tools/server/webui/src/lib/utils/debounce.ts b/tools/server/webui/src/lib/utils/debounce.ts
new file mode 100644
index 0000000000..90a5a01783
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/debounce.ts
@@ -0,0 +1,22 @@
+/**
+ * @param fn - The function to debounce
+ * @param delay - The delay in milliseconds
+ * @returns A debounced version of the function
+ */
+export function debounce) => void>(
+	fn: T,
+	delay: number
+): (...args: Parameters) => void {
+	let timeoutId: ReturnType | null = null;
+
+	return (...args: Parameters) => {
+		if (timeoutId) {
+			clearTimeout(timeoutId);
+		}
+
+		timeoutId = setTimeout(() => {
+			fn(...args);
+			timeoutId = null;
+		}, delay);
+	};
+}
diff --git a/tools/server/webui/src/lib/utils/image-error-fallback.ts b/tools/server/webui/src/lib/utils/image-error-fallback.ts
new file mode 100644
index 0000000000..6e3260f4ae
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/image-error-fallback.ts
@@ -0,0 +1,10 @@
+/**
+ * Simplified HTML fallback for external images that fail to load.
+ * Displays a centered message with a link to open the image in a new tab.
+ */
+export function getImageErrorFallbackHtml(src: string): string {
+	return `
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