From b7feacf7f39d79518e6ca48a9fcf697c5244b585 Mon Sep 17 00:00:00 2001 From: Kevin Pouget Date: Wed, 28 Jan 2026 10:49:40 +0100 Subject: [PATCH 01/49] 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 02/49] 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 03/49] 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 04/49] 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 05/49] 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 06/49] 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 07/49] =?UTF-8?q?spec=20:=20add=20self=E2=80=91speculative?= =?UTF-8?q?=20decoding=20(no=20draft=20model=20required)=20+=20refactor=20?= =?UTF-8?q?(#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 08/49] 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 09/49] 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 10/49] 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 11/49] 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 12/49] 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 13/49] 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 14/49] 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 15/49] 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 16/49] 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 17/49] 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 18/49] 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 19/49] 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 20/49] 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 21/49] 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 22/49] 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 23/49] 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 24/49] 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 25/49] 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 26/49] 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 27/49] 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 28/49] 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 29/49] 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 30/49] 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 31/49] 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 32/49] 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 33/49] 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 34/49] 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 35/49] 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 36/49] 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 37/49] 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 38/49] 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 39/49] 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 40/49] 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 41/49] 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 42/49] 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 43/49] 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 44/49] 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 45/49] 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 46/49] 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 47/49] 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 48/49] 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 49/49] 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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