diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md index 044ac606b..1bce56cd8 100644 --- a/docs/backend/OPENCL.md +++ b/docs/backend/OPENCL.md @@ -1,16 +1,26 @@ # llama.cpp for OpenCL -- [Background](#background) -- [OS](#os) -- [Hardware](#hardware) -- [DataType Supports](#datatype-supports) -- [Model Preparation](#model-preparation) -- [CMake Options](#cmake-options) -- [Android](#android) -- [Windows 11 Arm64](#windows-11-arm64) -- [Linux](#Linux) -- [Known Issue](#known-issues) -- [TODO](#todo) +- [llama.cpp for OpenCL](#llamacpp-for-opencl) + - [Background](#background) + - [Llama.cpp + OpenCL](#llamacpp--opencl) + - [OS](#os) + - [Hardware](#hardware) + - [Adreno GPU](#adreno-gpu) + - [DataType Supports](#datatype-supports) + - [Model Preparation](#model-preparation) + - [Binary Kernel Library](#binary-kernel-library) + - [CMake Options](#cmake-options) + - [Android](#android) + - [I. Setup Environment](#i-setup-environment) + - [II. Build llama.cpp](#ii-build-llamacpp) + - [Windows 11 Arm64](#windows-11-arm64) + - [I. Setup Environment](#i-setup-environment-1) + - [II. Build llama.cpp](#ii-build-llamacpp-1) + - [Linux](#linux) + - [I. Setup Environment](#i-setup-environment-2) + - [II. Build llama.cpp](#ii-build-llamacpp-2) + - [Known Issues](#known-issues) + - [TODO](#todo) ## Background @@ -34,11 +44,13 @@ The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adren **Verified devices** -| Adreno GPU | Status | -|:------------------------------------:|:-------:| -| Adreno 750 (Snapdragon 8 Gen 3) | Support | -| Adreno 830 (Snapdragon 8 Elite) | Support | -| Adreno X85 (Snapdragon X Elite) | Support | +| Adreno GPU | Status | +|:-------------------------------------:|:-------:| +| Adreno 750 (Snapdragon 8 Gen 3) | Support | +| Adreno 830 (Snapdragon 8 Elite) | Support | +| Adreno 840 (Snapdragon 8 Elite Gen 5) | Support | +| Adreno X1-85 (Snapdragon X Elite) | Support | +| Adreno X2-90 (Snapdragon X2 Elite) | Support | > A6x GPUs with a recent driver and compiler are supported; they are usually found in IoT platforms. However, A6x GPUs in phones are likely not supported due to the outdated driver and compiler. @@ -47,42 +59,43 @@ However, A6x GPUs in phones are likely not supported due to the outdated driver | DataType | Status | |:----------------------:|:--------------------------:| +| Q1_0 | Support | | Q4_0 | Support | -| Q6_K | Support, but not optimized | +| Q4_1 | Support | +| Q5_0 | Support | +| Q5_1 | Support | | Q8_0 | Support | +| Q4_K | Support | +| Q5_K | Support | +| Q6_K | Support | | MXFP4 | Support | +| IQ4_NL | Support | ## Model Preparation -You can refer to the general [llama-quantize tool](/tools/quantize/README.md) for steps to convert a model in Hugging Face safetensor format to GGUF with quantization. +Since common quantizations are supported now, it is recommanded to download GGUF models directly from Huggingface. -Currently we support `Q4_0` quantization and have optimized for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize` (i.e., make all weights in `Q4_0`). For example, +## Binary Kernel Library -```sh -./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0 -``` +A prebuilt binary kernel library has been introduced for Adreno GPUs. +It currently targets X2 GPUs (X2-90, X2-85 and X2-45) found in Snapdragon X2 SoC. +The library currently contains kernels for MUL_MAT_ID with Q4_0, Q4_1, Q4_K, MXFP4. +The library must be manually downloaded from https://softwarecenter.qualcomm.com/catalog/item/Adreno_Kernel_Library_GGML. -Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization. +To allow using the kernel library, add `-DGGML_OPENCL_USE_ADRENO_BIN_KERNELS=ON` when configuring with CMake. +Then, extract `adreno-opencl-kernels.dll` from the zip file downloaded from the above URL and put it alongside the executables. +If kernels compatible with the current GPU are found in the library, they will be loaded and used. -### `MXFP4` MoE Models - -OpenAI gpt-oss models are MoE models in `MXFP4`. The quantized model will be in `MXFP4_MOE`, a mixture of `MXFP4` and `Q8_0`. -For this quantization, there is no need to specify `--pure`. -For gpt-oss-20b model, you can directly [download](https://huggingface.co/ggml-org/gpt-oss-20b-GGUF) the quantized GGUF file in `MXFP4_MOE` from Hugging Face. - -Although it is possible to quantize gpt-oss-20b model in pure `Q4_0` (all weights in `Q4_0`), it is not recommended since `MXFP4` has been optimized for MoE while `Q4_0` is not. In addition, accuracy should degrade with such pure `Q4_0` quantization. -Hence, using the default `MXFP4_MOE` quantization (see the link above) is recommended for this model. - -> Note that the `Q4_0` model found [here](https://huggingface.co/unsloth/gpt-oss-20b-GGUF/blob/main/gpt-oss-20b-Q4_0.gguf) is a mixture of `Q4_0`, `Q8_0` and `MXFP4` and gives better performance than `MXFP4_MOE` quantization. ## CMake Options The OpenCL backend has the following CMake options that control the behavior of the backend. -| CMake options | Default value | Description | -|:---------------------------------:|:--------------:|:------------------------------------------| -| `GGML_OPENCL_EMBED_KERNELS` | `ON` | Embed OpenCL kernels into the executable. | -| `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | +| CMake options | Default value | Description | +|:------------------------------------:|:--------------:|:------------------------------------------| +| `GGML_OPENCL_EMBED_KERNELS` | `ON` | Embed OpenCL kernels into the executable. | +| `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | +| `GGML_OPENCL_USE_ADRENO_BIN_KERNELS` | `OFF` | Allow using binary kernel lib for Adreno. | ## Android @@ -277,6 +290,5 @@ ninja ## TODO -- Optimization for Q6_K -- Support and optimization for Q4_K - Improve flash attention +- Improve OpenCL C kernels performance diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 52f48d122..9ec3268b7 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -31,6 +31,11 @@ if (GGML_OPENCL_EMBED_KERNELS) target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/autogenerated") endif () +if (GGML_OPENCL_USE_ADRENO_BIN_KERNELS) + message(STATUS "OpenCL will use precompiled binary kernels for Adreno (improved performance on some platforms)") + add_compile_definitions(GGML_OPENCL_USE_ADRENO_BIN_KERNELS) +endif () + function(ggml_opencl_add_kernel KNAME) set(KERN_HDR ${CMAKE_CURRENT_BINARY_DIR}/autogenerated/${KNAME}.cl.h) set(KERN_SRC ${CMAKE_CURRENT_SOURCE_DIR}/kernels/${KNAME}.cl) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 215aa5079..10f9cc22d 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -13,6 +13,22 @@ #include "ggml-backend-impl.h" #include "ggml.h" +#ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS +#include "libdl.h" +#ifdef _WIN32 +#define KERNEL_LIB_NAME "adreno-opencl-kernels.dll" +#else +#define KERNEL_LIB_NAME "libadreno-opencl-kernels.so" +#endif // _WIN32 +#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS + +typedef const void * (*get_adreno_bin_kernel_func_t)( + const char * name, + const char * gpu_name, + const char * compiler_ver, + size_t * out_size +); + #include #include @@ -476,6 +492,8 @@ struct ggml_backend_opencl_context { bool adreno_has_large_buffer; bool adreno_use_large_buffer; + bool adreno_use_bin_kernels; + get_adreno_bin_kernel_func_t get_adreno_bin_kernel_func = nullptr; ggml_cl_compiler_version adreno_cl_compiler_version; std::string kernel_compile_opts; // cached for lazy-compiled kernels. @@ -718,15 +736,15 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gated_delta_net_f32[4][2][2] = {}; cl_kernel kernel_timestep_embedding; - cl_kernel kernel_gemv_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns; - cl_kernel kernel_gemv_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns; + cl_kernel kernel_gemv_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns_bin; + cl_kernel kernel_gemv_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns_bin; cl_kernel kernel_gemv_moe_q5_0_f32_ns, kernel_gemm_moe_q5_0_f32_ns; cl_kernel kernel_gemv_moe_q5_1_f32_ns, kernel_gemm_moe_q5_1_f32_ns; - cl_kernel kernel_gemv_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns; + cl_kernel kernel_gemv_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns_bin; cl_kernel kernel_gemv_moe_q5_k_f32_ns, kernel_gemm_moe_q5_k_f32_ns; cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns; cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32; - cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns; + cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns_bin; cl_kernel kernel_moe_reorder_b; cl_kernel kernel_moe_histogram, kernel_moe_scan, kernel_moe_fill, kernel_moe_scatter; cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat; @@ -870,6 +888,20 @@ struct ggml_backend_opencl_context { #endif } + const void * get_adreno_bin_kernel(const std::string &kernel_name, size_t *bin_size) const { + if (!get_adreno_bin_kernel_func) { + return nullptr; + } + + size_t sz; + const void * kernel_bin = get_adreno_bin_kernel_func( + kernel_name.c_str(), device_name.c_str(), driver_version.c_str(), &sz); + if (bin_size) { + *bin_size = sz; + } + return kernel_bin; + } + #ifdef GGML_OPENCL_USE_ADRENO_KERNELS // Transpose kernels cl_program program_transpose; @@ -891,7 +923,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemv_noshuffle_q4_0_f32_32000_1_4096; cl_kernel kernel_gemv_noshuffle_q4_1_f32; cl_kernel kernel_gemm_noshuffle_q4_1_f32; - cl_kernel kernel_gemm_noshuffle_q8_0_f32; + cl_kernel kernel_gemm_noshuffle_q8_0_f32, kernel_gemm_noshuffle_q8_0_f32_bin; cl_kernel kernel_gemv_noshuffle_q8_0_f32; cl_kernel kernel_gemm_noshuffle_q1_0_f32; cl_kernel kernel_gemv_noshuffle_q1_0_f32; @@ -988,6 +1020,32 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co return build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true); } +static cl_program build_program_from_binary(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts, size_t bin_size = 0) { + cl_program p; + char *program_log; + size_t log_size; + int err; + + p = clCreateProgramWithBinary(ctx, 1, &dev, &bin_size, (const unsigned char**)&program_buffer, NULL, &err); + if(err < 0) { + GGML_LOG_ERROR("OpenCL error creating program from binary"); + exit(1); + } + + err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL); + if(err < 0) { + clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size); + program_log = (char*) malloc(log_size + 1); + program_log[log_size] = '\0'; + clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL); + GGML_LOG_ERROR("ggml_opencl: kernel compile error:\n\n%s\n", program_log); + free(program_log); + exit(1); + } + + return p; +} + static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) { // compiler options for general kernels auto opencl_c_std = @@ -1014,6 +1072,17 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) { } } +static bool use_adreno_bin_kernels(ggml_backend_opencl_context * backend_ctx) { +#ifndef GGML_OPENCL_USE_ADRENO_BIN_KERNELS + return false; +#else + if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) { + return false; + } + return backend_ctx->adreno_use_bin_kernels; +#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS +} + static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { if (backend_ctx->kernels_loaded) { return; @@ -3323,6 +3392,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // gemm_noshuffle_q8_0_f32_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_noshuffle_q8_0_f32_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_f32_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemv_noshuffle_general_q8_0_f32 { std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std + @@ -3424,6 +3511,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // gemm_moe_q4_1_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_1_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_1_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemv_moe_mxfp4_f32 { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -3490,6 +3595,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // gemm_moe_q4_0_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_0_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_0_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemv_moe_q5_0_f32_ns { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -3592,6 +3715,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // gemm_moe_q4_k_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_k_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_k_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemv_moe_q5_k_f32_ns { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -3689,9 +3830,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); - CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err)); - CL_CHECK(clReleaseProgram(prog)); - GGML_LOG_CONT("."); + CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + + // gemm_moe_mxfp4_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_mxfp4_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } } // moe_reorder_b @@ -4770,6 +4929,27 @@ static ggml_backend_opencl_context * ggml_cl_init(ggml_backend_dev_t dev) { backend_ctx->adreno_use_large_buffer = getenv("GGML_OPENCL_ADRENO_USE_LARGE_BUFFER") != nullptr && backend_ctx->gpu_family == GPU_FAMILY::ADRENO; +#ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS + // try loading adreno binary kernels if enabled + // if fails to load, builtin kernels will be used + { + dl_handle * kernel_lib_handle = dl_load_library(KERNEL_LIB_NAME); + backend_ctx->adreno_use_bin_kernels = false; + + if (kernel_lib_handle) { + backend_ctx->get_adreno_bin_kernel_func = (get_adreno_bin_kernel_func_t)dl_get_sym(kernel_lib_handle, "get_adreno_kernels"); + if (backend_ctx->get_adreno_bin_kernel_func) { + GGML_LOG_INFO("ggml_opencl: loaded bin kernel library %s\n", KERNEL_LIB_NAME); + backend_ctx->adreno_use_bin_kernels = true; + } else { + GGML_LOG_INFO("ggml_opencl: bin kernel library %s is invalid, will use builtin kernels\n", KERNEL_LIB_NAME); + } + } else { + GGML_LOG_INFO("ggml_opencl: failed to load %s, will use builtin kernels\n", KERNEL_LIB_NAME); + } + } +#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS + cl_int err; // A local ref of cl_context for convenience @@ -14972,6 +15152,99 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clReleaseMemObject(b_img)); CL_CHECK(clReleaseMemObject(b_sub_buf)); } else { + // use bin kernel if available + if (backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin) { + int K_pad = K; + + cl_mem b_sub_buf = nullptr; + cl_mem d_sub_buf = nullptr; + + cl_mem a_img = nullptr; + cl_mem s_img = nullptr; + cl_mem b_img = nullptr; + cl_mem d_img = nullptr; + + // subbuffer for activations + region.origin = offset1; + region.size = K_pad * N * sizeof(float); + CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + // Create subbuffer and image1d_buffer for dst + region.origin = (extrad->offset); // + dst->view_offs; + region.size = M * N * sizeof(float); + CL_CHECK((d_sub_buf = clCreateSubBuffer((extrad->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + // create an image for A + img_fmt = { CL_R, CL_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = M * K / 4; // Divide by 4 for char -> float + img_desc.buffer = extra0_q8_0->q; + CL_CHECK((a_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // create an image for Scale + img_fmt = { CL_R, CL_HALF_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = M * K / 32; // Block size is 32 + img_desc.buffer = extra0_q8_0->d; + CL_CHECK((s_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // create an image for B from sub_buffer + img_fmt = {CL_R, CL_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = K_pad * N; + img_desc.buffer = b_sub_buf; + CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // img for d + img_fmt = {CL_R, CL_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = M * N; + img_desc.buffer = d_sub_buf; + CL_CHECK((d_img = clCreateImage(context, CL_MEM_WRITE_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // gemm + kernel = backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin; + + bool layoutA_Mfirst = true; + bool layoutS_Mfirst = true; + bool layoutB_Nfirst = false; + bool layoutC_Mfirst = true; + + cl_uint lineStrideMatrixAinBytes = layoutA_Mfirst ? M * 4 : K; // int8 + cl_uint lineStrideMatrixSinBytes = layoutS_Mfirst ? M * 2 : (K / 32) * 2; // fp16 + cl_uint lineStrideMatrixBinBytes = layoutB_Nfirst ? N * 4 : K_pad * 4; // fp32 + cl_uint lineStrideMatrixCinBytes = layoutC_Mfirst ? M * 4 : N * 4; // fp32 + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &a_img)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &s_img)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &b_img)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &extra1->offset)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &d_img)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &extrad->offset)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &K)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &lineStrideMatrixAinBytes)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &lineStrideMatrixSinBytes)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &lineStrideMatrixBinBytes)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &lineStrideMatrixCinBytes)); + + size_t global_work_size[] = { 64, (size_t)CEIL_DIV(M, 64), (size_t)CEIL_DIV(N, 64)}; + size_t local_work_size[] = { 64, 2, 2 }; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + + CL_CHECK(clReleaseMemObject(b_sub_buf)); + CL_CHECK(clReleaseMemObject(d_sub_buf)); + CL_CHECK(clReleaseMemObject(a_img)); + CL_CHECK(clReleaseMemObject(s_img)); + CL_CHECK(clReleaseMemObject(b_img)); + CL_CHECK(clReleaseMemObject(d_img)); + return; + } + cl_mem b_sub_buf = nullptr; cl_mem b_sub_buf_trans = nullptr; cl_mem b_img = nullptr; @@ -17825,6 +18098,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns; + if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -17870,6 +18146,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, cl_image_desc image_desc_buf_src1; image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); @@ -18042,6 +18323,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_q4_1_f32_ns; + if (backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -18087,6 +18371,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, cl_image_desc image_desc_buf_src1; image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); @@ -18648,6 +18937,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_q4_k_f32_ns; + if (backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -18689,6 +18981,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, CL_CHECK(status); cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); @@ -19172,6 +19469,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32_ns; + if (backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -19218,6 +19518,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, cl_image_desc image_desc_buf_src1; image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); diff --git a/ggml/src/ggml-opencl/libdl.h b/ggml/src/ggml-opencl/libdl.h new file mode 100644 index 000000000..8ca5016f0 --- /dev/null +++ b/ggml/src/ggml-opencl/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