diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index e824136a5..93669d531 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -688,8 +688,6 @@ jobs:
env:
OPENBLAS_VERSION: 0.3.23
- OPENCL_VERSION: 2023.04.17
- CLBLAST_VERSION: 1.6.0
SDE_VERSION: 9.33.0-2024-01-07
VULKAN_VERSION: 1.3.261.1
@@ -706,8 +704,6 @@ jobs:
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
- build: 'avx512-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
- - build: 'clblast-x64'
- defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
- build: 'openblas-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
- build: 'kompute-x64'
@@ -732,27 +728,6 @@ jobs:
run: |
git submodule update --init kompute
- - name: Download OpenCL SDK
- id: get_opencl
- if: ${{ matrix.build == 'clblast-x64' }}
- run: |
- curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip"
- mkdir $env:RUNNER_TEMP/opencl
- tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl
-
- - name: Download CLBlast
- id: get_clblast
- if: ${{ matrix.build == 'clblast-x64' }}
- run: |
- curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z"
- curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE"
- 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z
- rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast
- foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) {
- $txt = Get-Content -Path $f -Raw
- $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
- }
-
- name: Download OpenBLAS
id: get_openblas
if: ${{ matrix.build == 'openblas-x64' }}
@@ -786,13 +761,6 @@ jobs:
cmake -S . -B build ${{ matrix.defines }}
cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
- - name: Add clblast.dll
- id: add_clblast_dll
- if: ${{ matrix.build == 'clblast-x64' }}
- run: |
- cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release
- cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt
-
- name: Add libopenblas.dll
id: add_libopenblas_dll
if: ${{ matrix.build == 'openblas-x64' }}
@@ -816,7 +784,7 @@ jobs:
- name: Test
id: cmake_test
# not all machines have native AVX-512
- if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+ if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
run: |
cd build
ctest -L main -C Release --verbose --timeout 900
@@ -1071,7 +1039,7 @@ jobs:
# hypervisor: 'qemu'
# run: |
# sudo pkg update
-# sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas
+# sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas
# gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`
release:
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 76ea27412..cf37d5bb2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -111,7 +111,6 @@ option(LLAMA_CUDA_FA_ALL_QUANTS "llama: compile all quants for Flas
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF)
-option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_VULKAN "llama: use Vulkan" OFF)
option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF)
option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF)
@@ -502,22 +501,6 @@ if (LLAMA_RPC)
set(GGML_SOURCES_RPC ggml-rpc.cpp)
endif()
-if (LLAMA_CLBLAST)
- find_package(CLBlast)
- if (CLBlast_FOUND)
- message(STATUS "CLBlast found")
-
- set(GGML_HEADERS_OPENCL ggml-opencl.h)
- set(GGML_SOURCES_OPENCL ggml-opencl.cpp)
-
- add_compile_definitions(GGML_USE_CLBLAST)
-
- set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
- else()
- message(WARNING "CLBlast not found")
- endif()
-endif()
-
if (LLAMA_VULKAN)
find_package(Vulkan)
if (Vulkan_FOUND)
@@ -1265,7 +1248,6 @@ add_library(ggml OBJECT
ggml-quants.c
ggml-quants.h
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
- ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
@@ -1353,8 +1335,9 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)
set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h"
- "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}"
- "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}")
+ "${GGML_HEADERS_CUDA}"
+ "${GGML_HEADERS_METAL}"
+ "${GGML_HEADERS_EXTRA}")
set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
install(TARGETS ggml PUBLIC_HEADER)
diff --git a/Makefile b/Makefile
index 27eb69871..802ee6a47 100644
--- a/Makefile
+++ b/Makefile
@@ -547,23 +547,6 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h
$(NVCC_COMPILE)
endif # LLAMA_CUDA
-ifdef LLAMA_CLBLAST
- MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
- MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
- MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
-
- # Mac provides OpenCL as a framework
- ifeq ($(UNAME_S),Darwin)
- MK_LDFLAGS += -lclblast -framework OpenCL
- else
- MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL)
- endif
- OBJS += ggml-opencl.o
-
-ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
- $(CXX) $(CXXFLAGS) -c $< -o $@
-endif # LLAMA_CLBLAST
-
ifdef LLAMA_VULKAN
MK_CPPFLAGS += -DGGML_USE_VULKAN
MK_LDFLAGS += -lvulkan
diff --git a/README-sycl.md b/README-sycl.md
index 37f0306dc..62b38135c 100644
--- a/README-sycl.md
+++ b/README-sycl.md
@@ -29,7 +29,7 @@ The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based o
When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
## News
diff --git a/README.md b/README.md
index 1f2d9b1f2..9d2a59d89 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,7 @@ variety of hardware - locally and in the cloud.
- AVX, AVX2 and AVX512 support for x86 architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
-- Vulkan, SYCL, and (partial) OpenCL backend support
+- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
@@ -371,16 +371,11 @@ In order to build llama.cpp you have four different options.
3. Install compilation dependencies.
```bash
- sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
- opencl clblast openblas
+ sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
```
- **Notes:** With this packages you can build llama.cpp with OPENBLAS and
- CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
- the instructions for use and activate this options in this document below.
-
### Homebrew
On Mac and Linux, the homebrew package manager can be used via
@@ -399,7 +394,7 @@ argument.
### BLAS Build
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
- #### Accelerate Framework:
@@ -553,111 +548,6 @@ Building the program with BLAS support may lead to some performance improvements
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
-- #### CLBlast
-
- OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
-
- You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
- - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
-
- - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
-
- -
- Installing the OpenCL SDK from source
-
- ```sh
- git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
- cd OpenCL-SDK
- cmake -B build -DBUILD_DOCS=OFF \
- -DBUILD_EXAMPLES=OFF \
- -DBUILD_TESTING=OFF \
- -DOPENCL_SDK_BUILD_SAMPLES=OFF \
- -DOPENCL_SDK_TEST_SAMPLES=OFF
- cmake --build build
- cmake --install build --prefix /some/path
- ```
-
-
- ##### Installing CLBlast
-
- Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
-
- Linux packaging:
- Fedora Linux:
- ```bash
- sudo dnf install clblast
- ```
-
- Alternatively, they may be built from source.
-
- -
- Windows:
-
- ```cmd
- set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
- git clone https://github.com/CNugteren/CLBlast.git
- cd CLBlast
- cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
- cmake --build build --config Release
- cmake --install build --prefix C:/CLBlast
- ```
-
- (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
-
- -
- Unix:
-
- ```sh
- git clone https://github.com/CNugteren/CLBlast.git
- cd CLBlast
- cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
- cmake --build build --config Release
- cmake --install build --prefix /some/path
- ```
-
- Where `/some/path` is where the built library will be installed (default is `/usr/local`).
-
-
- ##### Building Llama with CLBlast
-
- - Build with make:
- ```sh
- make LLAMA_CLBLAST=1
- ```
- - CMake (Unix):
- ```sh
- cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
- cmake --build build --config Release
- ```
- - CMake (Windows):
- ```cmd
- set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
- git clone https://github.com/ggerganov/llama.cpp
- cd llama.cpp
- cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
- cmake --build build --config Release
- cmake --install build --prefix C:/LlamaCPP
- ```
-
- ##### Running Llama with CLBlast
-
- The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.
-
- To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`.
- The selection can be a number (starting from 0) or a text string to search:
-
- ```sh
- GGML_OPENCL_PLATFORM=1 ./main ...
- GGML_OPENCL_DEVICE=2 ./main ...
- GGML_OPENCL_PLATFORM=Intel ./main ...
- GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
- ```
-
- The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful.
- Using the variables it is possible to select a CPU-based driver as well, if so desired.
-
- You can get a list of platforms and devices from the `clinfo -l` command, etc.
-
- #### Vulkan
**With docker**:
diff --git a/common/common.cpp b/common/common.cpp
index 022bfe287..df583db83 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -2844,7 +2844,6 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
- fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md
index 857840564..fd95b35f4 100644
--- a/examples/llama-bench/README.md
+++ b/examples/llama-bench/README.md
@@ -162,7 +162,7 @@ $ ./llama-bench -o csv
```
```csv
-build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
+build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"
```
@@ -179,7 +179,6 @@ $ ./llama-bench -o json
"build_commit": "3469684",
"build_number": 1275,
"cuda": true,
- "opencl": false,
"metal": false,
"gpu_blas": true,
"blas": true,
@@ -210,7 +209,6 @@ $ ./llama-bench -o json
"build_commit": "3469684",
"build_number": 1275,
"cuda": true,
- "opencl": false,
"metal": false,
"gpu_blas": true,
"blas": true,
@@ -253,7 +251,6 @@ CREATE TABLE IF NOT EXISTS test (
build_commit TEXT,
build_number INTEGER,
cuda INTEGER,
- opencl INTEGER,
metal INTEGER,
gpu_blas INTEGER,
blas INTEGER,
@@ -279,6 +276,6 @@ CREATE TABLE IF NOT EXISTS test (
stddev_ts REAL
);
-INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
-INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
+INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
+INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
```
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 5d3cbd842..fa7ad1bdb 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -723,7 +723,6 @@ struct test {
static const std::string build_commit;
static const int build_number;
static const bool cuda;
- static const bool opencl;
static const bool vulkan;
static const bool kompute;
static const bool metal;
@@ -812,9 +811,6 @@ struct test {
if (cuda) {
return GGML_CUDA_NAME;
}
- if (opencl) {
- return "OpenCL";
- }
if (vulkan) {
return "Vulkan";
}
@@ -843,7 +839,7 @@ struct test {
static const std::vector & get_fields() {
static const std::vector fields = {
"build_commit", "build_number",
- "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
+ "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_ubatch",
@@ -869,7 +865,7 @@ struct test {
field == "avg_ns" || field == "stddev_ns") {
return INT;
}
- if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" ||
+ if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
return BOOL;
@@ -898,7 +894,7 @@ struct test {
}
std::vector values = {
build_commit, std::to_string(build_number),
- std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
+ std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan),
std::to_string(metal), std::to_string(sycl), std::to_string(rpc), std::to_string(gpu_blas), std::to_string(blas),
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
@@ -927,7 +923,6 @@ struct test {
const std::string test::build_commit = LLAMA_COMMIT;
const int test::build_number = LLAMA_BUILD_NUMBER;
const bool test::cuda = !!ggml_cpu_has_cuda();
-const bool test::opencl = !!ggml_cpu_has_clblast();
const bool test::vulkan = !!ggml_cpu_has_vulkan();
const bool test::kompute = !!ggml_cpu_has_kompute();
const bool test::metal = !!ggml_cpu_has_metal();
diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md
index edf20d8db..a88e92f23 100644
--- a/examples/main-cmake-pkg/README.md
+++ b/examples/main-cmake-pkg/README.md
@@ -8,16 +8,14 @@ Because this example is "outside of the source tree", it is important to first b
### Considerations
-When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
+When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package.
### Build llama.cpp and install to C:\LlamaCPP directory
-In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`.
-
```cmd
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/LlamaCPP
```
@@ -27,7 +25,7 @@ cmake --install build --prefix C:/LlamaCPP
```cmd
cd ..\examples\main-cmake-pkg
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/MyLlamaApp
```
diff --git a/flake.nix b/flake.nix
index 9cd3756e5..0a52ea52e 100644
--- a/flake.nix
+++ b/flake.nix
@@ -159,7 +159,6 @@
windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
}
// lib.optionalAttrs pkgs.stdenv.isLinux {
- opencl = config.packages.default.override { useOpenCL = true; };
cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp;
mpi-cpu = config.packages.default.override { useMpi = true; };
diff --git a/ggml-metal.h b/ggml-metal.h
index a5c542189..e7543ae79 100644
--- a/ggml-metal.h
+++ b/ggml-metal.h
@@ -1,7 +1,7 @@
// An interface allowing to compute ggml_cgraph with Metal
//
// This is a fully functional interface that extends ggml with GPU support for Apple devices.
-// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.)
//
// How it works?
//
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
deleted file mode 100644
index e28566a7b..000000000
--- a/ggml-opencl.cpp
+++ /dev/null
@@ -1,2305 +0,0 @@
-#include "ggml.h"
-#include "ggml-opencl.h"
-#include "ggml-backend-impl.h"
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-#define CL_TARGET_OPENCL_VERSION 120
-#include
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-#define CL_DMMV_LOCAL_SIZE 32
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 1
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#define MULTILINE_QUOTE(...) #__VA_ARGS__
-static std::string program_source = MULTILINE_QUOTE(
-
-typedef char int8_t;
-typedef uchar uint8_t;
-typedef short int16_t;
-typedef ushort uint16_t;
-typedef int int32_t;
-typedef uint uint32_t;
-
-struct __attribute__ ((packed)) block_q4_0
-{
- half d;
- uint8_t qs[QK4_0 / 2];
-};
-
-struct __attribute__ ((packed)) block_q4_1
-{
- half d;
- half m;
- uint8_t qs[QK4_1 / 2];
-};
-
-struct __attribute__ ((packed)) block_q5_0
-{
- half d;
- uint32_t qh;
- uint8_t qs[QK5_0 / 2];
-};
-
-struct __attribute__ ((packed)) block_q5_1
-{
- half d;
- half m;
- uint32_t qh;
- uint8_t qs[QK5_1 / 2];
-};
-
-struct __attribute__ ((packed)) block_q8_0
-{
- half d;
- int8_t qs[QK8_0];
-};
-
-struct __attribute__((packed)) block_q2_K
-{
- uint8_t scales[16];
- uint8_t qs[64];
- half d;
- half dmin;
-};
-
-struct __attribute__((packed)) block_q3_K
-{
- uint8_t hmask[32];
- uint8_t qs[64];
- uint8_t scales[12];
- half d;
-};
-
-struct __attribute__((packed)) block_q4_K
-{
- half d;
- half dmin;
- uint8_t scales[12];
- uint8_t qs[128];
-};
-
-struct __attribute__((packed)) block_q5_K
-{
- half d;
- half dmin;
- uint8_t scales[12];
- uint8_t qh[32];
- uint8_t qs[128];
-};
-
-struct __attribute__((packed)) block_q6_K
-{
- uint8_t ql[128];
- uint8_t qh[64];
- int8_t scales[16];
- half d;
-};
-
-__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
- const uint i = get_global_id(0);
-
- y[i] = vload_half(0, &x[i]);
-}
-
-void dequantize_q4_0(__global const struct block_q4_0* x, const int ib, const int iqs, float* v0, float* v1) {
- const float d = vload_half(0, &x[ib].d);
-
- const uint8_t vui = x[ib].qs[iqs];
-
- const int8_t vi0 = vui & 0xF;
- const int8_t vi1 = vui >> 4;
-
- *v0 = (vi0 - 8)*d;
- *v1 = (vi1 - 8)*d;
-}
-void dequantize_q4_1(__global const struct block_q4_1* x, const int ib, const int iqs, float* v0, float* v1) {
- const float d = vload_half(0, &x[ib].d);
- const float m = vload_half(0, &x[ib].m);
-
- const uint8_t vui = x[ib].qs[iqs];
-
- const int8_t vi0 = vui & 0xF;
- const int8_t vi1 = vui >> 4;
-
- *v0 = vi0*d + m;
- *v1 = vi1*d + m;
-}
-void dequantize_q5_0(__global const struct block_q5_0* x, const int ib, const int iqs, float* v0, float* v1) {
- const float d = vload_half(0, &x[ib].d);
-
- uint32_t qh = x[ib].qh;
-
- const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
- const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
-
- const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16;
- const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1) - 16;
-
- *v0 = x0*d;
- *v1 = x1*d;
-}
-void dequantize_q5_1(__global const struct block_q5_1* x, const int ib, const int iqs, float* v0, float* v1) {
- const float d = vload_half(0, &x[ib].d);
- const float m = vload_half(0, &x[ib].m);
-
- uint32_t qh = x[ib].qh;
-
- const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
- const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
-
- const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0);
- const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1);
-
- *v0 = x0*d + m;
- *v1 = x1*d + m;
-}
-void dequantize_q8_0(__global const struct block_q8_0* x, const int ib, const int iqs, float* v0, float* v1) {
- const float d = vload_half(0, &x[ib].d);
-
- const int8_t vi0 = x[ib].qs[iqs + 0];
- const int8_t vi1 = x[ib].qs[iqs + 1];
-
- *v0 = vi0*d;
- *v1 = vi1*d;
-}
-void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float* v1){
- *v0 = vload_half(0, &x[ib + 0]);
- *v1 = vload_half(0, &x[ib + 1]);
-}
-);
-
-static std::string k_quants_source = MULTILINE_QUOTE(
-inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
-{
- if (j < 4)
- {
- *d = q[j] & 63;
- *m = q[j + 4] & 63;
- }
- else
- {
- *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
- *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
- }
-}
-
-__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
-{
- const int i = get_group_id(0) + get_global_offset(0);
- const int tid = get_local_id(0);
- const int n = tid / 32;
- const int l = tid - 32 * n;
- const int is = 8 * n + l / 16;
-
- const uint8_t q = x[i].qs[32 * n + l];
- __global float *y = yy + get_group_id(0) * QK_K + 128 * n;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4);
- y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4);
- y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4);
- y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4);
-}
-
-__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
-{
- int r = get_local_id(0) / 4;
- int i = get_group_id(0) + get_global_offset(0);
- int tid = r / 2;
- int is0 = r % 2;
- int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
- int n = tid / 4;
- int j = tid - 4 * n;
-
- uint8_t m = 1 << (4 * n + j);
- int is = 8 * n + 2 * j + is0;
- int shift = 2 * j;
-
- int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4)
- : is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4)
- : is < 12 ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4)
- : (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4);
- float d_all = vload_half(0, &x[i].d);
- float dl = d_all * (us - 32);
-
- __global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j;
- const __global uint8_t *q = x[i].qs + 32 * n;
- const __global uint8_t *hm = x[i].hmask;
-
- for (int l = l0; l < l0 + 4; ++l)
- y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-}
-
-__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
-{
- const int i = get_group_id(0) + get_global_offset(0);
- const int tid = get_local_id(0);
- const int il = tid / 8;
- const int ir = tid % 8;
- const int is = 2 * il;
- const int n = 4;
-
- __global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- __global const uint8_t *q = x[i].qs + 32 * il + n * ir;
-
- uint8_t sc, m;
- get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
- float d1 = dall * sc;
- float m1 = dmin * m;
- get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
- float d2 = dall * sc;
- float m2 = dmin * m;
- for (int l = 0; l < n; ++l)
- {
- y[l + 0] = d1 * (q[l] & 0xF) - m1;
- y[l + 32] = d2 * (q[l] >> 4) - m2;
- }
-}
-
-__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
-{
- const int i = get_group_id(0) + get_global_offset(0);
- const int tid = get_local_id(0);
- const int il = tid / 16;
- const int ir = tid % 16;
- const int is = 2 * il;
-
- __global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- __global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir;
- __global const uint8_t *qh = x[i].qh + 2 * ir;
-
- uint8_t sc, m;
- get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
- const float d1 = dall * sc;
- const float m1 = dmin * m;
- get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
- const float d2 = dall * sc;
- const float m2 = dmin * m;
-
- uint8_t hm = 1 << (2 * il);
- y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1;
- y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1;
- hm <<= 1;
- y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2;
- y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2;
-}
-
-__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
-{
- const int i = get_group_id(0) + get_global_offset(0);
- const int tid = get_local_id(0);
- const int ip = tid / 32;
- const int il = tid - 32 * ip;
- const int is = 8 * ip + il / 16;
-
- __global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il;
-
- const float d = vload_half(0, &x[i].d);
-
- __global const uint8_t *ql = x[i].ql + 64 * ip + il;
- const uint8_t qh = x[i].qh[32 * ip + il];
- __global const int8_t *sc = x[i].scales + is;
-
- y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
- y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
- y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
- y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-}
-
-__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
- const int row = get_group_id(0);
-
- const int num_blocks_per_row = ncols / QK_K;
- const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
- __global const struct block_q2_K * x = xx + ib0;
-
- const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...15
- const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1
-
- const int step = 16/K_QUANTS_PER_ITERATION;
-
- const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
- const int in = tid - step*im; // 0...15 or 0...7
-
- const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2
- const int q_offset = 32*im + l0;
- const int s_offset = 8*im;
- const int y_offset = 128*im + l0;
-
- tmp[16 * ix + tid] = 0;
-
- uint32_t aux[4];
- const uint8_t * d = (const uint8_t *)aux;
- const uint8_t * m = (const uint8_t *)(aux + 2);
-
- for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
- __global const float * y = yy + i * QK_K + y_offset;
- __global const uint8_t * q = x[i].qs + q_offset;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset);
- aux[0] = a[0] & 0x0f0f0f0f;
- aux[1] = a[1] & 0x0f0f0f0f;
- aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
- aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
-
- float sum1 = 0, sum2 = 0;
- for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
- sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
- + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
- + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
- + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
- + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
- + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
- + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
- +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
- sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
- + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
-
- }
- tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
-
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=16; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-
-__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
- const uint16_t kmask1 = 0x0303;
- const uint16_t kmask2 = 0x0f0f;
-
- const int row = get_group_id(0);
-
- const int num_blocks_per_row = ncols / QK_K;
- const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
- __global const struct block_q3_K * x = xx + ib0;
-
- const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
- const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1
-
- const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop
- const int step = 16/K_QUANTS_PER_ITERATION;
- const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
- const int in = tid - step*im; // 0....15 or 0...7
-
- const uint8_t m = 1 << (4*im);
-
- const int l0 = n*in; // 0...15 or 0...14 in steps of 2
- const int q_offset = 32*im + l0;
- const int y_offset = 128*im + l0;
-
- uint16_t utmp[4];
- const int8_t * s = (const int8_t *)utmp;
-
- const uint16_t s_shift = 4*im;
-
- tmp[16 * ix + tid] = 0;
-
- for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
- __global const float * y = yy + i * QK_K + y_offset;
- __global const uint8_t * q = x[i].qs + q_offset;
- __global const uint8_t * h = x[i].hmask + l0;
-
- __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
- utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
- utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
- utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
- utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
-
- const float d = vload_half(0, &x[i].d);
-
- float sum = 0;
- for (int l = 0; l < n; ++l) {
- sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
- + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
- + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
- + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
- sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
- + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
- + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
- + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
- }
- tmp[16 * ix + tid] += d * sum;
-
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=16; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-
-__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
- //to rename it later, just to test now
- const uint16_t kmask1 = 0x3f3f;
- const uint16_t kmask2 = 0x0f0f;
- const uint16_t kmask3 = 0xc0c0;
-
- const int row = get_group_id(0);
- const int num_blocks_per_row = ncols / QK_K;
- const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
- const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...15
- const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION;
-
- const int step = 8/K_QUANTS_PER_ITERATION;
-
- const int il = tid/step; // 0...3
- const int ir = tid - step*il;// 0...3
- const int n = 2*K_QUANTS_PER_ITERATION;
-
- const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
- const int in = il%2;
-
- const int l0 = n*(2*ir + in);
- const int q_offset = 32*im + l0;
- const int y_offset = 64*im + l0;
-
- uint16_t aux[4];
- const uint8_t * sc = (const uint8_t *)aux;
-
- __global const struct block_q4_K * x = xx + ib0;
-
- tmp[16 * ix + tid] = 0;
-
- for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
- __global const uint8_t * q1 = x[i].qs + q_offset;
- __global const uint8_t * q2 = q1 + 64;
- __global const float * y1 = yy + i*QK_K + y_offset;
- __global const float * y2 = y1 + 128;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
- aux[0] = a[im+0] & kmask1;
- aux[1] = a[im+2] & kmask1;
- aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
- aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
- float4 s = (float4)(0.f);
- float smin = 0;
- for (int l = 0; l < n; ++l) {
- s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
- s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
- smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
- }
- tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
-
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=16; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-
-__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
- const uint16_t kmask1 = 0x3f3f;
- const uint16_t kmask2 = 0x0f0f;
- const uint16_t kmask3 = 0xc0c0;
-
- const int row = get_group_id(0);
- const int num_blocks_per_row = ncols / QK_K;
- const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
- const int tid = get_local_id(0)/2; // 0...15
- const int ix = get_local_id(0)%2;
-
- const int il = tid/4; // 0...3
- const int ir = tid - 4*il;// 0...3
- const int n = 2;
-
- const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
- const int in = il%2;
-
- const int l0 = n*(2*ir + in);
- const int q_offset = 32*im + l0;
- const int y_offset = 64*im + l0;
-
- const uint8_t hm1 = 1 << (2*im);
- const uint8_t hm2 = hm1 << 4;
-
- uint16_t aux[4];
- const uint8_t * sc = (const uint8_t *)aux;
-
- __global const struct block_q5_K * x = xx + ib0;
-
- tmp[16 * ix + tid] = 0;
-
- for (int i = ix; i < num_blocks_per_row; i += 2) {
-
- __global const uint8_t * ql1 = x[i].qs + q_offset;
- __global const uint8_t * ql2 = ql1 + 64;
- __global const uint8_t * qh = x[i].qh + l0;
- __global const float * y1 = yy + i*QK_K + y_offset;
- __global const float * y2 = y1 + 128;
-
- const float dall = vload_half(0, &x[i].d);
- const float dmin = vload_half(0, &x[i].dmin);
-
- __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
- aux[0] = a[im+0] & kmask1;
- aux[1] = a[im+2] & kmask1;
- aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
- aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
- float4 sum = (float4)(0.f);
- float smin = 0;
- for (int l = 0; l < n; ++l) {
- sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
- + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
- sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
- + y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
- sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
- + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
- sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
- + y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
- smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
- + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
- }
- tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
-
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=16; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-
-__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) {
-
- const int row = get_group_id(0);
-
- const int num_blocks_per_row = ncols / QK_K;
- const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
- __global const struct block_q6_K * x = xx + ib0;
-
- const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
- const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0, 1
-
- const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
-
- const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
- const int in = tid - step*im; // 0...15 or 0...7
-
-\n#if K_QUANTS_PER_ITERATION == 1\n
- const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
- const int is = 0;
-
-\n#else\n
-
- const int l0 = 4 * in; // 0, 4, 8, ..., 28
- const int is = in / 4;
-
-\n#endif\n
-
- const int ql_offset = 64*im + l0;
- const int qh_offset = 32*im + l0;
- const int s_offset = 8*im + is;
- const int y_offset = 128*im + l0;
-
- tmp[16 * ix + tid] = 0; // partial sum for thread in warp
-
- for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
- __global const float * y = yy + i * QK_K + y_offset;
- __global const uint8_t * ql = x[i].ql + ql_offset;
- __global const uint8_t * qh = x[i].qh + qh_offset;
- __global const int8_t * s = x[i].scales + s_offset;
-
- const float d = vload_half(0, &x[i].d);
-
-\n#if K_QUANTS_PER_ITERATION == 1\n
- float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
- + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
- + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
- + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
- + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
- + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
- + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
- +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
- tmp[16 * ix + tid] += sum;
-\n#else\n
- float sum = 0;
- for (int l = 0; l < 4; ++l) {
- sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
- + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
- + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
- + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
- }
- tmp[16 * ix + tid] += sum;
-\n#endif\n
-
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=16; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-);
-
-
-std::string dequant_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
- const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2;
-
- if (i >= get_global_size(0)) {
- return;
- }
-
- const uint qk = QUANT_K;
- const uint qr = QUANT_R;
-
- const int ib = i/qk + get_global_offset(0); // block index
- const int iqs = (i%qk)/qr; // quant index
- const int iybs = i - i%qk; // y block start index
- const int y_offset = qr == 1 ? 1 : qk/2;
-
- // dequantize
- float v0, v1;
- DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
- y[iybs + iqs + 0] = v0;
- y[iybs + iqs + y_offset] = v1;
-}
-);
-
-std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
- const int local_size = get_local_size(0);
- const int row = get_group_id(0);
- const int tid = get_local_id(0);
-
- const uint qk = QUANT_K;
- const uint qr = QUANT_R;
-
- const int col_step = local_size * 2;
- const int y_offset = qr == 1 ? 1 : qk/2;
-
- x += get_global_offset(0);
-
- tmp[tid] = 0;
-
- for (int col = tid*2; col < ncols; col += col_step) {
- const int ib = (row*ncols + col)/qk; // block index
- const int iqs = (col%qk)/qr; // quant index
- const int iybs = col - col%qk; // y block start index
-
- // dequantize
- float v0, v1;
- DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
-
- // matrix multiplication
- tmp[tid] += v0 * y[iybs + iqs + 0];
- tmp[tid] += v1 * y[iybs + iqs + y_offset];
- }
-
- // sum up partial sums and write back result
- barrier(CLK_LOCAL_MEM_FENCE);
- for (int s=local_size/2; s>0; s>>=1) {
- if (tid < s) {
- tmp[tid] += tmp[tid + s];
- }
- barrier(CLK_LOCAL_MEM_FENCE);
- }
- if (tid == 0) {
- dst[row] = tmp[0];
- }
-}
-
-);
-
-
-std::string mul_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
- const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
-
- if (i >= get_global_size(0)) {
- return;
- }
-
- dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky];
-}
-);
-
-std::string add_template = MULTILINE_QUOTE(
-__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) {
- const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
-
- if (i >= get_global_size(0)) {
- return;
- }
-
- dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky];
-}
-);
-
-#define CL_CHECK(err) \
- do { \
- cl_int err_ = (err); \
- if (err_ != CL_SUCCESS) { \
- fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
- #err, err_, __FILE__, __LINE__); \
- exit(1); \
- } \
- } while (0)
-
-#define CLBLAST_CHECK(err) \
- do { \
- CLBlastStatusCode err_ = (err); \
- if (err_ != CLBlastSuccess) { \
- fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
- #err, err_, __FILE__, __LINE__); \
- exit(1); \
- } \
- } while (0)
-
-std::array dequant_str_keys = {
- "KERNEL_NAME", "X_TYPE", "QUANT_K", "QUANT_R", "DEQUANT_FUNC"
-};
-
-std::array dequant_str_values = {
- "dequantize_row_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
- "dequantize_row_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
- "dequantize_row_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
- "dequantize_row_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
- "dequantize_row_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
- "convert_row_f16", "half", "1", "1", "convert_f16"
-};
-
-std::array dequant_mul_mat_vec_str_values = {
- "dequantize_mul_mat_vec_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
- "dequantize_mul_mat_vec_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
- "dequantize_mul_mat_vec_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
- "dequantize_mul_mat_vec_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
- "dequantize_mul_mat_vec_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
- "convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16"
-};
-
-std::array mul_str_keys = {
- "KERNEL_NAME", "TYPE"
-};
-std::array mul_str_values = {
- "mul_f32", "float"
-};
-
-static std::string& replace(std::string& s, const std::string& from, const std::string& to) {
- size_t pos = 0;
- while ((pos = s.find(from, pos)) != std::string::npos) {
- s.replace(pos, from.length(), to);
- pos += to.length();
- }
- return s;
-}
-
-static std::string generate_kernels() {
- std::stringstream src;
- src << program_source << '\n';
- src << k_quants_source << '\n';
- for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) {
- std::string dequant_kernel = dequant_template;
- std::string dmmv_kernel = dequant_mul_mat_vec_template;
- for (size_t j = 0; j < dequant_str_keys.size(); j++) {
- replace(dequant_kernel, dequant_str_keys[j], dequant_str_values[i + j]);
- replace(dmmv_kernel, dequant_str_keys[j], dequant_mul_mat_vec_str_values[i + j]);
- }
- src << dequant_kernel << '\n';
- src << dmmv_kernel << '\n';
- }
- for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) {
- std::string mul_kernel = mul_template;
- for (size_t j = 0; j < mul_str_keys.size(); j++) {
- replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]);
- }
- src << mul_kernel << '\n';
- }
- src << add_template << '\n';
-
- return src.str();
-}
-
-static cl_platform_id platform;
-static cl_device_id device;
-static cl_context context;
-static cl_command_queue queue;
-static cl_program program;
-static cl_kernel convert_row_f16_cl;
-static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
-static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
-static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
-static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
-static cl_kernel mul_f32_cl;
-static cl_kernel add_f32_cl;
-static bool fp16_support;
-
-static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
- cl_program p;
- char *program_log;
- size_t program_size;
- size_t log_size;
- int err;
-
- program_size = strlen(program_buffer);
-
- p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
- if(err < 0) {
- fprintf(stderr, "OpenCL error creating program");
- exit(1);
- }
-
- std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
- "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 "
- "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION);
-
- 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);
- fprintf(stderr, "ggml_opencl: kernel compile error:\n\n%s\n", program_log);
- free(program_log);
- exit(1);
- }
-
- return p;
-}
-
-void ggml_cl_init(void) {
- static bool initialized = false;
- if (initialized) {
- return;
- }
- initialized = true;
-
- cl_int err;
-
- struct cl_device;
- struct cl_platform {
- cl_platform_id id;
- unsigned number;
- char name[128];
- char vendor[128];
- struct cl_device * devices;
- unsigned n_devices;
- struct cl_device * default_device;
- };
-
- struct cl_device {
- struct cl_platform * platform;
- cl_device_id id;
- unsigned number;
- cl_device_type type;
- char name[128];
- };
-
- enum { NPLAT = 16, NDEV = 16 };
-
- struct cl_platform platforms[NPLAT];
- unsigned n_platforms = 0;
- struct cl_device devices[NDEV];
- unsigned n_devices = 0;
- struct cl_device * default_device = NULL;
-
- platform = NULL;
- device = NULL;
-
- cl_platform_id platform_ids[NPLAT];
- CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
-
- for (unsigned i = 0; i < n_platforms; i++) {
- struct cl_platform * p = &platforms[i];
- p->number = i;
- p->id = platform_ids[i];
- CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
- CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
-
- cl_device_id device_ids[NDEV];
- cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
- if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
- p->n_devices = 0;
- } else {
- CL_CHECK(clGetDeviceIDsError);
- }
- p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
- p->default_device = NULL;
-
- for (unsigned j = 0; j < p->n_devices; j++) {
- struct cl_device * d = &devices[n_devices];
- d->number = n_devices++;
- d->id = device_ids[j];
- d->platform = p;
- CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
- CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
-
- if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
- p->default_device = d;
- }
- }
-
- if (default_device == NULL && p->default_device != NULL) {
- default_device = p->default_device;
- }
- }
-
- if (n_devices == 0) {
- fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
- exit(1);
- }
-
- char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
- char * user_device_string = getenv("GGML_OPENCL_DEVICE");
- int user_platform_number = -1;
- int user_device_number = -1;
-
- unsigned n;
- if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
- user_platform_number = (int)n;
- }
- if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
- user_device_number = (int)n;
- }
- if (user_platform_number != -1 && user_device_number != -1) {
- cl_platform* platform = &platforms[user_platform_number];
- if ((unsigned)user_device_number >= platform->n_devices) {
- fprintf(stderr, "ggml_opencl: invalid device number %d\n", user_device_number);
- exit(1);
- }
- default_device = &platform->devices[user_device_number];
- } else {
-
- struct cl_device * selected_devices = devices;
- unsigned n_selected_devices = n_devices;
-
- if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
- for (unsigned i = 0; i < n_platforms; i++) {
- struct cl_platform * p = &platforms[i];
- if (strstr(p->name, user_platform_string) != NULL ||
- strstr(p->vendor, user_platform_string) != NULL) {
- user_platform_number = (int)i;
- break;
- }
- }
- if (user_platform_number == -1) {
- fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
- exit(1);
- }
- }
- if (user_platform_number != -1) {
- struct cl_platform * p = &platforms[user_platform_number];
- selected_devices = p->devices;
- n_selected_devices = p->n_devices;
- default_device = p->default_device;
- if (n_selected_devices == 0) {
- fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
- exit(1);
- }
- }
-
- if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
- for (unsigned i = 0; i < n_selected_devices; i++) {
- struct cl_device * d = &selected_devices[i];
- if (strstr(d->name, user_device_string) != NULL) {
- user_device_number = d->number;
- break;
- }
- }
- if (user_device_number == -1) {
- fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
- exit(1);
- }
- }
- if (user_device_number != -1) {
- selected_devices = &devices[user_device_number];
- n_selected_devices = 1;
- default_device = &selected_devices[0];
- }
-
- GGML_ASSERT(n_selected_devices > 0);
-
- if (default_device == NULL) {
- default_device = &selected_devices[0];
- }
- }
-
- fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
- fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
- if (default_device->type != CL_DEVICE_TYPE_GPU) {
- fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
- }
-
- platform = default_device->platform->id;
- device = default_device->id;
-
- size_t ext_str_size;
- clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
- char *ext_buffer = (char *)alloca(ext_str_size + 1);
- clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
- ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
- // Disabled due to faulty outputs
- // Check if ext_buffer contains cl_khr_fp16
- fp16_support = false; // strstr(ext_buffer, "cl_khr_fp16") != NULL;
- // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
-
- cl_context_properties properties[] = {
- (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
- };
-
- CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
-
- CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
- (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err :
- (queue = clCreateCommandQueue(context, device, 0, &err), err)
- )));
-
- const std::string kernel_src = generate_kernels();
-
- program = build_program_from_source(context, device, kernel_src.c_str());
-
- // FP16 to FP32 kernel
- CL_CHECK((convert_row_f16_cl = clCreateKernel(program, "convert_row_f16", &err), err));
-
- // Dequantize kernels
- CL_CHECK((dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
- CL_CHECK((dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
- CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
- CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
- CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
- CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
- CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err));
- CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err));
- CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err));
- CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err));
- CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err));
-
- // dequant mul mat kernel
- CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q4_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_1", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q5_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_0", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
- CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err));
- CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err));
-
- // mul kernel
- CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
-
- CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err));
-}
-
-static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
- switch (type) {
- case GGML_TYPE_Q4_0:
- return &dequantize_row_q4_0_cl;
- case GGML_TYPE_Q4_1:
- return &dequantize_row_q4_1_cl;
- case GGML_TYPE_Q5_0:
- return &dequantize_row_q5_0_cl;
- case GGML_TYPE_Q5_1:
- return &dequantize_row_q5_1_cl;
- case GGML_TYPE_Q8_0:
- return &dequantize_row_q8_0_cl;
- case GGML_TYPE_Q2_K:
- return &dequantize_block_q2_k_cl;
- case GGML_TYPE_Q3_K:
- return &dequantize_block_q3_k_cl;
- case GGML_TYPE_Q4_K:
- return &dequantize_block_q4_k_cl;
- case GGML_TYPE_Q5_K:
- return &dequantize_block_q5_k_cl;
- case GGML_TYPE_Q6_K:
- return &dequantize_block_q6_k_cl;
- case GGML_TYPE_F16:
- return &convert_row_f16_cl;
- default:
- return nullptr;
- }
-}
-
-static size_t ggml_cl_global_denom(ggml_type type) {
- switch (type) {
- case GGML_TYPE_Q4_0:
- case GGML_TYPE_Q4_1:
- case GGML_TYPE_Q5_0:
- case GGML_TYPE_Q5_1:
- case GGML_TYPE_Q8_0:
- return 1;
- case GGML_TYPE_Q2_K:
- case GGML_TYPE_Q3_K:
- return 4;
- case GGML_TYPE_Q4_K:
- return 8;
- case GGML_TYPE_Q5_K:
- case GGML_TYPE_Q6_K:
- return 4;
- case GGML_TYPE_F16:
- default:
- return 1;
- }
-}
-
-static size_t ggml_cl_local_size(ggml_type type) {
- switch (type) {
- case GGML_TYPE_Q4_0:
- case GGML_TYPE_Q4_1:
- case GGML_TYPE_Q5_0:
- case GGML_TYPE_Q5_1:
- case GGML_TYPE_Q8_0:
- return 0;
- case GGML_TYPE_Q2_K:
- case GGML_TYPE_Q3_K:
- return 64;
- case GGML_TYPE_Q4_K:
- return 32;
- case GGML_TYPE_Q5_K:
- case GGML_TYPE_Q6_K:
- return 64;
- case GGML_TYPE_F16:
- default:
- return 0;
- }
-}
-
-static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
- switch (type) {
- case GGML_TYPE_Q4_0:
- return &dequantize_mul_mat_vec_q4_0_cl;
- case GGML_TYPE_Q4_1:
- return &dequantize_mul_mat_vec_q4_1_cl;
- case GGML_TYPE_Q5_0:
- return &dequantize_mul_mat_vec_q5_0_cl;
- case GGML_TYPE_Q5_1:
- return &dequantize_mul_mat_vec_q5_1_cl;
- case GGML_TYPE_Q8_0:
- return &dequantize_mul_mat_vec_q8_0_cl;
- case GGML_TYPE_F16:
- return &convert_mul_mat_vec_f16_cl;
- case GGML_TYPE_Q2_K:
- return &dequantize_mul_mat_vec_q2_K_cl;
- case GGML_TYPE_Q3_K:
- return &dequantize_mul_mat_vec_q3_K_cl;
- case GGML_TYPE_Q4_K:
- return &dequantize_mul_mat_vec_q4_K_cl;
- case GGML_TYPE_Q5_K:
- return &dequantize_mul_mat_vec_q5_K_cl;
- case GGML_TYPE_Q6_K:
- return &dequantize_mul_mat_vec_q6_K_cl;
- default:
- return nullptr;
- }
-}
-
-// buffer pool for cl
-#define MAX_CL_BUFFERS 256
-
-struct scoped_spin_lock {
- std::atomic_flag& lock;
- scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
- while (lock.test_and_set(std::memory_order_acquire)) {
- ; // spin
- }
- }
- ~scoped_spin_lock() {
- lock.clear(std::memory_order_release);
- }
- scoped_spin_lock(const scoped_spin_lock&) = delete;
- scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-struct cl_buffer {
- cl_mem mem;
- size_t size = 0;
-};
-
-static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
-static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
-
-static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
- scoped_spin_lock lock(g_cl_pool_lock);
- cl_int err;
-
- int best_i = -1;
- size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs
- int worst_i = -1;
- size_t worst_size = 0; //largest unused buffer seen so far
- for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
- cl_buffer &b = g_cl_buffer_pool[i];
- if (b.size > 0 && b.size >= size && b.size < best_size)
- {
- best_i = i;
- best_size = b.size;
- }
- if (b.size > 0 && b.size > worst_size)
- {
- worst_i = i;
- worst_size = b.size;
- }
- }
- if(best_i!=-1) //found the smallest buffer that fits our needs
- {
- cl_buffer& b = g_cl_buffer_pool[best_i];
- cl_mem mem = b.mem;
- *actual_size = b.size;
- b.size = 0;
- return mem;
- }
- if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
- {
- cl_buffer& b = g_cl_buffer_pool[worst_i];
- cl_mem mem = b.mem;
- b.size = 0;
- clReleaseMemObject(mem);
- }
- cl_mem mem;
- CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
- *actual_size = size;
- return mem;
-}
-
-static void ggml_cl_pool_free(cl_mem mem, size_t size) {
- scoped_spin_lock lock(g_cl_pool_lock);
-
- for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
- cl_buffer& b = g_cl_buffer_pool[i];
- if (b.size == 0) {
- b.mem = mem;
- b.size = size;
- return;
- }
- }
- fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n");
- clReleaseMemObject(mem);
-}
-
-void ggml_cl_free_data(const struct ggml_tensor* tensor) {
- if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
- return;
- }
-
- cl_mem mem = (cl_mem)tensor->extra;
- clReleaseMemObject(mem);
-}
-
-static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) {
- cl_int err;
- const uint64_t ne0 = src->ne[0];
- const uint64_t ne1 = src->ne[1];
- const uint64_t nb0 = src->nb[0];
- const uint64_t nb1 = src->nb[1];
- const uint64_t nb2 = src->nb[2];
- const uint64_t nb3 = src->nb[3];
- const enum ggml_type type = src->type;
- const size_t ts = ggml_type_size(type);
- const size_t bs = ggml_blck_size(type);
- const uint64_t row_size = ts*ne0/bs;
-
- const char * x = (const char *) src->data + i2*nb2 + i3*nb3;
- if (nb0 == ts && nb1 == row_size) {
- return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev);
- }
- if (nb0 == ts) {
- const size_t buffer_origin[3] = { offset, 0, 0 };
- const size_t host_origin[3] = { 0, 0, 0 };
- const size_t region[3] = { row_size, ne1, 1 };
- return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev);
- }
- std::vector events;
- if (ev && ne1>1) events.reserve(ne1-1);
- for (uint64_t i1 = 0; i1 < ne1; i1++) {
- // pretend the row is a matrix with cols=1
- const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 };
- const size_t host_origin[3] = { 0, 0, 0 };
- const size_t region[3] = { ts, ne0/bs, 1 };
- // if an event is requested, make the last write wait for all previous writes to complete
- if (ev && i1) {
- events.push_back(*ev);
- }
- cl_uint nevents = i1 == ne1-1 ? events.size() : 0U;
- err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev);
- if (err != CL_SUCCESS) {
- for (auto event : events) {
- clReleaseEvent(event);
- }
- return err;
- }
- }
- for (auto event : events) {
- CL_CHECK(clReleaseEvent(event));
- }
- return CL_SUCCESS;
-}
-
-static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
- GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
- const int64_t ne00 = src0->ne[0];
- const int64_t ne01 = src0->ne[1];
- const int64_t ne02 = src0->ne[2];
- const int64_t ne03 = src0->ne[3];
- const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
- const int64_t ne12 = src1->ne[2];
- const int64_t ne13 = src1->ne[3];
- const int nb2 = dst->nb[2];
- const int nb3 = dst->nb[3];
- size_t x_size;
- size_t d_size;
-
- cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
- cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
- cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
-
-
- for (int64_t i03 = 0; i03 < ne03; i03++) {
- for (int64_t i02 = 0; i02 < ne02; i02++) {
- cl_event ev;
-
- // copy src0 to device
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
-
- const int64_t i13 = i03%ne13;
- const int64_t i12 = i02%ne12;
- const int i1 = i13*ne12*ne11 + i12*ne11;
-
- cl_int x_offset = 0;
- cl_int y_offset = i1*ne10;
- cl_int d_offset = 0;
-
- size_t global = ne00 * ne01;
- cl_int ky = ne10 * ne11;
-
- CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
- CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
- CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-
- CL_CHECK(clReleaseEvent(ev));
- CL_CHECK(clFinish(queue));
-
- // copy dst to host
- float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
- CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
- }
- }
- ggml_cl_pool_free(d_X, x_size);
- ggml_cl_pool_free(d_D, d_size);
-}
-
-void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
- GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
- ggml_cl_mul_f32(src0, src1, dst);
-}
-
-static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
- GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
- const int64_t ne00 = src0->ne[0];
- const int64_t ne01 = src0->ne[1];
- const int64_t ne02 = src0->ne[2];
- const int64_t ne03 = src0->ne[3];
- const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
- const int64_t ne12 = src1->ne[2];
- const int64_t ne13 = src1->ne[3];
- const int nb2 = dst->nb[2];
- const int nb3 = dst->nb[3];
- size_t x_size;
- size_t d_size;
-
- cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
- cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
- cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
-
-
- for (int64_t i03 = 0; i03 < ne03; i03++) {
- for (int64_t i02 = 0; i02 < ne02; i02++) {
- cl_event ev;
-
- // copy src0 to device
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
-
- const int64_t i13 = i03%ne13;
- const int64_t i12 = i02%ne12;
- const int i1 = i13*ne12*ne11 + i12*ne11;
-
- cl_int x_offset = 0;
- cl_int y_offset = i1*ne10;
- cl_int d_offset = 0;
-
- size_t global = ne00 * ne01;
- cl_int ky = ne10 * ne11;
-
- CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X));
- CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset));
- CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y));
- CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset));
- CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D));
- CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset));
- CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky));
- CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-
- CL_CHECK(clReleaseEvent(ev));
- CL_CHECK(clFinish(queue));
-
- // copy dst to host
- float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
- CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
- }
- }
- ggml_cl_pool_free(d_X, x_size);
- ggml_cl_pool_free(d_D, d_size);
-}
-
-void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
- GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
- ggml_cl_add_f32(src0, src1, dst);
-}
-
-static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
- const int64_t ne00 = src0->ne[0];
- const int64_t ne01 = src0->ne[1];
- const int64_t ne02 = src0->ne[2];
- const int64_t ne03 = src0->ne[3];
-
- const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
- const int64_t ne12 = src1->ne[2];
- const int64_t ne13 = src1->ne[3];
-
- const int nb2 = dst->nb[2];
- const int nb3 = dst->nb[3];
-
- const int64_t r2 = ne12 / ne02;
- const int64_t r3 = ne13 / ne03;
-
- const float alpha = 1.0f;
- const float beta = 0.0f;
- const int x_ne = ne01 * ne00;
- const int y_ne = ne11 * ne10;
- const int d_ne = ne11 * ne01;
-
- size_t x_size;
- size_t y_size;
- size_t d_size;
- cl_mem d_X;
- if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
- d_X = (cl_mem) src0->extra;
- } else {
- d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
- }
- cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
- cl_mem d_D = dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
-
- size_t x_offset = 0;
-
- for (int64_t i03 = 0; i03 < ne03; i03++) {
- // TODO: copy src0 here when r3>1
- for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
- for (int64_t i02 = 0; i02 < ne02; i02++) {
- if (src0->backend == GGML_BACKEND_TYPE_GPU) {
- x_offset = (i03 * ne02 + i02) * x_ne;
- } else {
- // copy src0 to device
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
- }
-
- for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
- // copy src1 to device
- if (src1->backend == GGML_BACKEND_TYPE_CPU) {
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
- }
-
- CL_CHECK(clFinish(queue));
-
- // compute
- cl_event ev_sgemm;
- clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
- clblast::Transpose::kYes, clblast::Transpose::kNo,
- ne01, ne11, ne10,
- alpha,
- d_X, x_offset, ne00,
- d_Y, 0, ne10,
- beta,
- d_D, 0, ne01,
- &queue, &ev_sgemm);
-
- if (status != clblast::StatusCode::kSuccess) {
- GGML_ASSERT(false);
- }
-
- // copy dst to host
- if (dst->backend == GGML_BACKEND_TYPE_CPU) {
- float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
- CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
- }
- }
- }
- }
- }
-
- if (src0->backend != GGML_BACKEND_TYPE_GPU) {
- ggml_cl_pool_free(d_X, x_size);
- }
- if (src1->backend != GGML_BACKEND_TYPE_GPU) {
- ggml_cl_pool_free(d_Y, y_size);
- }
- if (dst->backend != GGML_BACKEND_TYPE_GPU) {
- ggml_cl_pool_free(d_D, d_size);
- }
-}
-
-static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
- GGML_ASSERT(fp16_support);
-
- const int64_t ne00 = src0->ne[0];
- const int64_t ne01 = src0->ne[1];
- const int64_t ne02 = src0->ne[2];
- const int64_t ne03 = src0->ne[3];
-
- const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
- const int64_t ne12 = src1->ne[2];
- const int64_t ne13 = src1->ne[3];
-
- const int nb10 = src1->nb[0];
- const int nb11 = src1->nb[1];
- const int nb12 = src1->nb[2];
- const int nb13 = src1->nb[3];
-
- const int nb2 = dst->nb[2];
- const int nb3 = dst->nb[3];
-
- const int64_t r2 = ne12 / ne02;
- const int64_t r3 = ne13 / ne03;
-
- const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f);
- const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f);
- const int x_ne = ne01 * ne00;
- const int y_ne = ne11 * ne10;
- const int d_ne = ne11 * ne01;
-
- GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne);
- GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne);
- ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata;
-
- size_t x_size;
- size_t y_size;
- size_t d_size;
- cl_mem d_X;
- if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
- d_X = (cl_mem) src0->extra;
- } else {
- d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
- }
- cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
- cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
-
- bool src1_cont_rows = nb10 == sizeof(float);
- bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
-
- size_t x_offset = 0;
-
- for (int64_t i03 = 0; i03 < ne03; i03++) {
- // TODO: copy src0 here when r3>1
- for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
- for (int64_t i02 = 0; i02 < ne02; i02++) {
- if (src0->backend == GGML_BACKEND_TYPE_GPU) {
- x_offset = (i03 * ne02 + i02) * x_ne;
- } else {
- // copy src0 to device
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
- }
-
- // FIXME: convert on device
-
- for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
- // convert src1 to fp16
- // TODO: use multiple threads
- char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
- if (src1_cont_rows) {
- if (src1_cont_cols) {
- ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
- }
- else {
- for (int64_t i11 = 0; i11 < ne11; i11++) {
- ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
- }
- }
- }
- else {
- for (int64_t i11 = 0; i11 < ne11; i11++) {
- for (int64_t i10 = 0; i10 < ne10; i10++) {
- // very slow due to no inlining
- tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
- }
- }
- }
-
- // copy src1 to device
- CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL));
-
- CL_CHECK(clFinish(queue));
-
- // compute
- cl_event ev_sgemm;
- clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
- clblast::Transpose::kYes, clblast::Transpose::kNo,
- ne01, ne11, ne10,
- alpha,
- d_X, x_offset, ne00,
- d_Y, 0, ne10,
- beta,
- d_D, 0, ne01,
- &queue, &ev_sgemm);
-
- if (status != clblast::StatusCode::kSuccess) {
- GGML_ASSERT(false);
- }
-
- // copy dst to host, then convert to float
- if (dst->backend == GGML_BACKEND_TYPE_CPU) {
- CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
- float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
- ggml_fp16_to_fp32_row(tmp, d, d_ne);
- } else {
- // FIXME: convert dst to fp32 on device
- }
- }
- }
- }
- }
-
- if (src0->backend != GGML_BACKEND_TYPE_GPU) {
- ggml_cl_pool_free(d_X, x_size);
- }
- ggml_cl_pool_free(d_Y, y_size);
- ggml_cl_pool_free(d_D, d_size);
-}
-
-static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
- const int64_t ne00 = src0->ne[0];
- const int64_t ne01 = src0->ne[1];
- const int64_t ne02 = src0->ne[2];
- const int64_t ne03 = src0->ne[3];
-
- const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
- const int64_t ne12 = src1->ne[2];
- const int64_t ne13 = src1->ne[3];
-
- const int nb2 = dst->nb[2];
- const int nb3 = dst->nb[3];
- const ggml_type type = src0->type;
- const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0;
-
- const int64_t r2 = ne12 / ne02;
- const int64_t r3 = ne13 / ne03;
-
- const float alpha = 1.0f;
- const float beta = 0.0f;
- const int x_ne = ne01 * ne00;
- const int y_ne = ne11 * ne10;
- const int d_ne = ne11 * ne01;
- const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice
- const size_t q_sz = ggml_type_size(type) * x_bps;
-
- size_t x_size;
- size_t y_size;
- size_t d_size;
- size_t q_size;
- cl_mem d_X;
- if (!mul_mat_vec) {
- d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
- }
- cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
- cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
- cl_mem d_Q;
- if (src0->backend == GGML_BACKEND_TYPE_CPU) {
- d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
- }
-
- cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
- cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
- GGML_ASSERT(to_fp32_cl != nullptr);
-
- const size_t global_denom = ggml_cl_global_denom(type);
- const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type);
-
- size_t ev_idx = 0;
- std::vector events;
-
- for (int64_t i03 = 0; i03 < ne03; i03++) {
- // TODO: copy and dequantize src0 here when r3>1
- for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
- for (int64_t i02 = 0; i02 < ne02; i02++) {
- // copy src0 to device if necessary
- if (src0->backend == GGML_BACKEND_TYPE_CPU) {
- events.emplace_back();
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
- } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
- d_Q = (cl_mem) src0->extra;
- } else {
- GGML_ASSERT(false);
- }
-
- if (!mul_mat_vec) {
- // convert src0 to fp32 on device
- const size_t global = x_ne / global_denom;
- const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
- CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
- CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
- CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
- }
-
- int64_t i12 = i02 * r2;
- int64_t e12 = i12 + r2;
- events.reserve(e12 - i12);
- for (; i12 < e12; i12++) {
- if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
- // copy src1 to device
- events.emplace_back();
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
-
- // compute
- const size_t global = ne01 * local;
- const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
- const cl_int ncols = ne00;
- events.emplace_back();
- CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
- CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
- CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
- CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
- CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
- CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
- } else { // CLBlast matrix matrix multiplication
- // copy src1 to device
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
-
- // wait for conversion
- CL_CHECK(clFinish(queue));
-
- // compute
- events.emplace_back();
- clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor,
- clblast::Transpose::kYes, clblast::Transpose::kNo,
- ne01, ne11, ne10,
- alpha,
- d_X, 0, ne00,
- d_Y, 0, ne10,
- beta,
- d_D, 0, ne01,
- &queue, events.data() + ev_idx++);
-
- if (status != clblast::StatusCode::kSuccess) {
- GGML_ASSERT(false);
- }
- }
-
- // copy dst to host
- float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
- CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
- for (auto *event : events) {
- clReleaseEvent(event);
- }
-
- ev_idx = 0;
- events.clear();
- }
- }
- }
- }
-
- if (!mul_mat_vec) {
- ggml_cl_pool_free(d_X, x_size);
- }
- ggml_cl_pool_free(d_Y, y_size);
- ggml_cl_pool_free(d_D, d_size);
- if (src0->backend == GGML_BACKEND_TYPE_CPU) {
- ggml_cl_pool_free(d_Q, q_size);
- }
-}
-
-
-bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) {
- const int64_t ne10 = src1->ne[0];
-
- const int64_t ne0 = dst->ne[0];
- const int64_t ne1 = dst->ne[1];
-
- // TODO: find the optimal values for these
- if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
- src1->type == GGML_TYPE_F32 &&
- dst->type == GGML_TYPE_F32 &&
- ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
- return true;
- }
-
- return false;
-}
-
-static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
- // If device doesn't support FP16
- if (!fp16_support) {
- return false;
- }
-
- size_t src0_sz = ggml_nbytes(src0);
- size_t src1_sz = ggml_nbytes(src1);
-
- // mul_mat_q: src0 is converted to fp32 on device
- size_t mul_mat_q_transfer = src0_sz + src1_sz;
-
- // mul_mat_f16: src1 is converted to fp16 on cpu
- size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1);
-
- // choose the smaller one to transfer to the device
- // TODO: this is not always the best choice due to the overhead of converting to fp16
- return mul_mat_f16_transfer < mul_mat_q_transfer;
-}
-
-void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) {
- GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst));
-
- if (src0->type == GGML_TYPE_F32) {
- ggml_cl_mul_mat_f32(src0, src1, dst);
- }
- else if (src0->type == GGML_TYPE_F16) {
- if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
- ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize);
- }
- else {
- ggml_cl_mul_mat_q_f32(src0, src1, dst);
- }
- }
- else if (ggml_is_quantized(src0->type)) {
- ggml_cl_mul_mat_q_f32(src0, src1, dst);
- }
- else {
- GGML_ASSERT(false);
- }
-}
-
-size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
- if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
- return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]);
- }
- return 0;
-}
-
-void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
- const int64_t ne0 = tensor->ne[0];
- const int64_t ne1 = tensor->ne[1];
- const int64_t ne2 = tensor->ne[2];
- const int64_t ne3 = tensor->ne[3];
-
- const ggml_type type = tensor->type;
- const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type));
- const size_t q_sz = s_sz * (size_t) (ne2 * ne3);
-
- size_t q_size;
- cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
-
- tensor->data = data;
- // copy tensor to device
- size_t offset = 0;
- for (int64_t i3 = 0; i3 < ne3; i3++) {
- for (int64_t i2 = 0; i2 < ne2; i2++) {
- CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL));
- offset += s_sz;
- }
- }
-
- CL_CHECK(clFinish(queue));
-
- tensor->extra = dst;
- GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-}
-
-// ggml-backend
-
-// buffer
-
-struct ggml_backend_opencl_buffer_context {
- ~ggml_backend_opencl_buffer_context() {
- if (buffer) {
- clReleaseMemObject(buffer);
- }
- for (auto * sub_buffer : sub_buffers) {
- clReleaseMemObject(sub_buffer);
- }
- }
-
- cl_mem buffer;
- std::vector sub_buffers;
-};
-
-static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;
-
-static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) {
- return "OpenCL";
-
- GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
- ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
- delete ctx;
-}
-
-static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
- return cl_ptr_base;
-
- GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
- if (tensor->view_src != NULL && tensor->view_offs == 0) {
- tensor->extra = tensor->view_src->extra;
- } else {
- ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
- cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)};
- cl_int err;
- cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err);
- CL_CHECK(err);
- ctx->sub_buffers.push_back(sub_buffer);
- tensor->extra = sub_buffer;
- }
- tensor->backend = GGML_BACKEND_TYPE_GPU;
-}
-
-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) {
- cl_mem tensor_buffer = (cl_mem) tensor->extra;
- CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
- CL_CHECK(clFinish(queue));
-
- GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
- cl_mem tensor_buffer = (cl_mem) tensor->extra;
- CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
- CL_CHECK(clFinish(queue));
-
- GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
- ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
- CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL));
- CL_CHECK(clFinish(queue));
-}
-
-static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) {
- ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
- for (auto * sub_buffer : ctx->sub_buffers) {
- clReleaseMemObject(sub_buffer);
- }
- ctx->sub_buffers.clear();
-}
-
-static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = {
- /* .get_name = */ ggml_backend_opencl_buffer_get_name,
- /* .free_buffer = */ ggml_backend_opencl_buffer_free_buffer,
- /* .get_base = */ ggml_backend_opencl_buffer_get_base,
- /* .init_tensor = */ ggml_backend_opencl_buffer_init_tensor,
- /* .set_tensor = */ ggml_backend_opencl_buffer_set_tensor,
- /* .get_tensor = */ ggml_backend_opencl_buffer_get_tensor,
- /* .cpy_tensor = */ NULL,
- /* .clear = */ ggml_backend_opencl_buffer_clear,
- /* .reset = */ ggml_backend_opencl_buffer_reset,
-};
-
-// buffer type
-
-static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) {
- return "OpenCL";
-
- GGML_UNUSED(buffer_type);
-}
-
-static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) {
- ggml_cl_init();
-
- cl_int err;
- cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err);
- if (err != CL_SUCCESS) {
- fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
- return nullptr;
- }
-
- ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}};
-
- return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size);
-}
-
-static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
- // FIXME: not thread safe, device may not be initialized yet
- static cl_uint alignment = -1;
- if (alignment == (cl_uint)-1) {
- ggml_cl_init();
- clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL);
- alignment /= 8; // bits to bytes
- }
- return alignment;
-
- GGML_UNUSED(buffer_type);
-}
-
-static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
- static size_t max_size = -1;
- if (max_size == (size_t)-1) {
- ggml_cl_init();
- clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL);
- }
- return max_size;
-}
-
-static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) {
- //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend
- return ggml_backend_is_cpu(backend);
-
- GGML_UNUSED(buffer_type);
-}
-
-static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = {
- /* .get_name = */ ggml_backend_opencl_buffer_type_name,
- /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer,
- /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment,
- /* .get_max_size = */ ggml_backend_opencl_buffer_type_get_max_size,
- /* .get_alloc_size = */ NULL,
- /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend,
- /* .is_host = */ NULL,
-};
-
-
-ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() {
- static ggml_backend_buffer_type buffer_type = {
- /* .iface = */ ggml_backend_opencl_buffer_type_interface,
- /* .context = */ nullptr,
- };
-
- return &buffer_type;
-}
-
-#if 0
-// host buffer type
-
-static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
- return "CL_Host";
-
- GGML_UNUSED(buft);
-}
-
-static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) {
- return "CL_Host";
-
- GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
- ggml_cl_host_free(buffer->context);
-}
-
-static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
- void * ptr = ggml_cl_host_malloc(size);
-
- if (ptr == nullptr) {
- // fallback to cpu buffer
- return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
- }
-
- ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
- buffer->buft = buft;
- buffer->iface.get_name = ggml_backend_opencl_host_buffer_name;
- buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer;
-
- return buffer;
-}
-
-ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() {
- static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = {
- /* .iface = */ {
- /* .get_name = */ ggml_backend_opencl_host_buffer_type_name,
- /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer,
- /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
- /* .get_max_size = */ NULL, // defaults to SIZE_MAX
- /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
- /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
- /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host,
- },
- /* .context = */ nullptr,
- };
-
- return &ggml_backend_opencl_buffer_type_host;
-}
-
-// backend
-
-static const char * ggml_backend_opencl_name(ggml_backend_t backend) {
- return "OpenCL";
-
- GGML_UNUSED(backend);
-}
-
-static void ggml_backend_opencl_free(ggml_backend_t backend) {
- GGML_UNUSED(backend);
-}
-
-static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) {
- return ggml_backend_opencl_buffer_type();
-
- GGML_UNUSED(backend);
-}
-
-static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
- for (int i = 0; i < graph->n_nodes; ++i) {
- ggml_tensor * node = graph->nodes[i];
-
- if (ggml_is_empty(node)) {
- continue;
- }
-
- switch (node->op) {
- case GGML_OP_MUL_MAT:
- ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0);
- break;
- case GGML_OP_MUL:
- ggml_cl_mul(node->src[0], node->src[1], node);
- break;
- default:
- GGML_ASSERT(false);
- }
- }
-
- return GGML_STATUS_SUCCESS;
-
- GGML_UNUSED(backend);
-}
-
-static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
- switch (op->op) {
- case GGML_OP_MUL_MAT:
- return ggml_cl_can_mul_mat(op->src[0], op->src[1], op);
- case GGML_OP_MUL:
- // return ggml_can_repeat_rows(op->src[1], op->src[0]);
- return true;
- default:
- return false;
- }
-
- GGML_UNUSED(backend);
-}
-
-static ggml_backend_i opencl_backend_i = {
- /* .get_name = */ ggml_backend_opencl_name,
- /* .free = */ ggml_backend_opencl_free,
- /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type,
- /* .set_tensor_async = */ NULL,
- /* .get_tensor_async = */ NULL,
- /* .cpy_tensor_from_async = */ NULL,
- /* .cpy_tensor_to_async = */ NULL,
- /* .synchronize = */ NULL,
- /* .graph_plan_create = */ NULL,
- /* .graph_plan_free = */ NULL,
- /* .graph_plan_compute = */ NULL,
- /* .graph_compute = */ ggml_backend_opencl_graph_compute,
- /* .supports_op = */ ggml_backend_opencl_supports_op,
-};
-
-ggml_backend_t ggml_backend_opencl_init() {
- ggml_backend_t backend = new ggml_backend {
- /* .interface = */ opencl_backend_i,
- /* .context = */ nullptr
- };
-
- return backend;
-}
-
-bool ggml_backend_is_opencl(ggml_backend_t backend) {
- return backend && backend->iface.get_name == ggml_backend_opencl_name;
-}
-#endif
diff --git a/ggml-opencl.h b/ggml-opencl.h
deleted file mode 100644
index 257a6be6a..000000000
--- a/ggml-opencl.h
+++ /dev/null
@@ -1,36 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-#include "ggml-backend.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-GGML_API void ggml_cl_init(void);
-
-GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
-GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
-
-// GGML_API void * ggml_cl_host_malloc(size_t size);
-// GGML_API void ggml_cl_host_free(void * ptr);
-
-GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor);
-
-GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
-
-// backend API
-
-// GGML_API ggml_backend_t ggml_backend_opencl_init(void);
-
-// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend);
-
-GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
-// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
-
-#ifdef __cplusplus
-}
-#endif
diff --git a/ggml.c b/ggml.c
index 8869e146a..11e5c34ab 100644
--- a/ggml.c
+++ b/ggml.c
@@ -297,17 +297,12 @@ inline static void * ggml_calloc(size_t num, size_t size) {
#if defined(GGML_USE_ACCELERATE)
#include
-#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
-#include "ggml-opencl.h"
-#endif
#elif defined(GGML_USE_OPENBLAS)
#if defined(GGML_BLAS_USE_MKL)
#include
#else
#include
#endif
-#elif defined(GGML_USE_CLBLAST)
-#include "ggml-opencl.h"
#endif
// floating point type used to accumulate sums
@@ -3380,10 +3375,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
}
-#if defined(GGML_USE_CLBLAST)
- ggml_cl_init();
-#endif
-
ggml_setup_op_has_task_pass();
is_first_call = false;
@@ -9053,17 +9044,6 @@ static void ggml_compute_forward_add_f32(
const int ith = params->ith;
const int nth = params->nth;
-#ifdef GGML_USE_CLBLAST
- if (src1->backend == GGML_BACKEND_TYPE_GPU) {
- // TODO: OpenCL kernel support full broadcast
- GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
- if (ith == 0) {
- ggml_cl_add(src0, src1, dst);
- }
- return;
- }
-#endif
-
const int nr = ggml_nrows(src0);
GGML_TENSOR_BINARY_OP_LOCALS
@@ -10171,17 +10151,6 @@ static void ggml_compute_forward_mul_f32(
const int ith = params->ith;
const int nth = params->nth;
-#if defined(GGML_USE_CLBLAST)
- if (src1->backend == GGML_BACKEND_TYPE_GPU) {
- // TODO: OpenCL kernel support full broadcast
- GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
- if (ith == 0) {
- ggml_cl_mul(src0, src1, dst);
- }
- return;
- }
-#endif
-
const int64_t nr = ggml_nrows(src0);
GGML_TENSOR_BINARY_OP_LOCALS
@@ -12417,15 +12386,6 @@ static void ggml_compute_forward_mul_mat(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
-#if defined(GGML_USE_CLBLAST)
- if (ggml_cl_can_mul_mat(src0, src1, dst)) {
- if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) {
- ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
- }
- return;
- }
-#endif
-
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(dst)) {
const int64_t ne_plane = ne01*ne00;
@@ -12873,8 +12833,6 @@ static void ggml_compute_forward_out_prod_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
- // TODO: #if defined(GGML_USE_CLBLAST)
-
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
bool use_blas = ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
@@ -13072,7 +13030,7 @@ static void ggml_compute_forward_out_prod_q_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
- // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
+ // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (params->type == GGML_TASK_TYPE_INIT) {
if (ith != 0) {
@@ -19546,11 +19504,6 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
{
const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type;
-#if defined(GGML_USE_CLBLAST)
- if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
- cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node);
- } else
-#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node)) {
if (node->src[0]->type != GGML_TYPE_F32) {
@@ -22859,7 +22812,7 @@ int ggml_cpu_has_wasm_simd(void) {
}
int ggml_cpu_has_blas(void) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL)
return 1;
#else
return 0;
@@ -22874,14 +22827,6 @@ int ggml_cpu_has_cuda(void) {
#endif
}
-int ggml_cpu_has_clblast(void) {
-#if defined(GGML_USE_CLBLAST)
- return 1;
-#else
- return 0;
-#endif
-}
-
int ggml_cpu_has_vulkan(void) {
#if defined(GGML_USE_VULKAN)
return 1;
@@ -22915,8 +22860,7 @@ int ggml_cpu_has_rpc(void) {
}
int ggml_cpu_has_gpublas(void) {
- return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
- ggml_cpu_has_sycl();
+ return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl();
}
int ggml_cpu_has_sse3(void) {
diff --git a/ggml.h b/ggml.h
index f38699698..addcf1bfe 100644
--- a/ggml.h
+++ b/ggml.h
@@ -2425,7 +2425,6 @@ extern "C" {
GGML_API int ggml_cpu_has_wasm_simd (void);
GGML_API int ggml_cpu_has_blas (void);
GGML_API int ggml_cpu_has_cuda (void);
- GGML_API int ggml_cpu_has_clblast (void);
GGML_API int ggml_cpu_has_vulkan (void);
GGML_API int ggml_cpu_has_kompute (void);
GGML_API int ggml_cpu_has_gpublas (void);
diff --git a/llama.cpp b/llama.cpp
index 92c33f53e..c05e2bdb7 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -13,8 +13,6 @@
#ifdef GGML_USE_CUDA
# include "ggml-cuda.h"
-#elif defined(GGML_USE_CLBLAST)
-# include "ggml-opencl.h"
#elif defined(GGML_USE_VULKAN)
# include "ggml-vulkan.h"
#elif defined(GGML_USE_SYCL)
@@ -2406,8 +2404,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
-#elif defined(GGML_USE_CLBLAST)
- buft = ggml_backend_opencl_buffer_type();
#elif defined(GGML_USE_KOMPUTE)
buft = ggml_backend_kompute_buffer_type(gpu);
if (buft == nullptr) {
@@ -2530,10 +2526,6 @@ static bool llama_kv_cache_init(
}
}
-#ifdef GGML_USE_CLBLAST
- offload = false;
-#endif
-
// count used buffer types
std::map buft_layer_count;
if (offload) {
@@ -15921,7 +15913,7 @@ bool llama_supports_mlock(void) {
}
bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
return true;
diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in
index 92e39708b..9311055d9 100644
--- a/scripts/LlamaConfig.cmake.in
+++ b/scripts/LlamaConfig.cmake.in
@@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
set(LLAMA_BLAS @LLAMA_BLAS@)
set(LLAMA_CUDA @LLAMA_CUDA@)
set(LLAMA_METAL @LLAMA_METAL@)
-set(LLAMA_CLBLAST @LLAMA_CLBLAST@)
set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@)
set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@)
@@ -36,10 +35,6 @@ if (LLAMA_METAL)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
-if (LLAMA_CLBLAST)
- find_package(CLBlast REQUIRED)
-endif()
-
if (LLAMA_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 6016eb2c0..513dde5e1 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -19,17 +19,17 @@ logger = logging.getLogger("compare-llama-bench")
# Properties by which to differentiate results per commit:
KEY_PROPERTIES = [
- "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
+ "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
"blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads",
"type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen"
]
# Properties that are boolean and are converted to Yes/No for the table:
-BOOL_PROPERTIES = ["cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"]
+BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"]
# Header names for the table:
PRETTY_NAMES = {
- "cuda": "CUDA", "opencl": "OpenCL", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC",
+ "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC",
"gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
"model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size",
"n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode",
diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
index eb6ce458e..b3715e204 100644
--- a/scripts/server-llm.sh
+++ b/scripts/server-llm.sh
@@ -3,7 +3,7 @@
# Helper script for deploying llama.cpp server with a single Bash command
#
# - Works on Linux and macOS
-# - Supports: CPU, CUDA, Metal, OpenCL
+# - Supports: CPU, CUDA, Metal
# - Can run all GGUF models from HuggingFace
# - Can serve requests in parallel
# - Always builds latest llama.cpp from GitHub
@@ -19,7 +19,7 @@
# --port: port number, default is 8888
# --repo: path to a repo containing GGUF model files
# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input
-# --backend: cpu, cuda, metal, opencl, depends on the OS
+# --backend: cpu, cuda, metal, depends on the OS
# --gpu-id: gpu id, default is 0
# --n-parallel: number of parallel requests, default is 8
# --n-kv: KV cache size, default is 4096
@@ -72,7 +72,7 @@ function print_usage {
printf " --port: port number, default is 8888\n"
printf " --repo: path to a repo containing GGUF model files\n"
printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
- printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n"
+ printf " --backend: cpu, cuda, metal, depends on the OS\n"
printf " --gpu-id: gpu id, default is 0\n"
printf " --n-parallel: number of parallel requests, default is 8\n"
printf " --n-kv: KV cache size, default is 4096\n"
@@ -387,9 +387,6 @@ elif [[ "$backend" == "cpu" ]]; then
elif [[ "$backend" == "metal" ]]; then
printf "[+] Building with Metal backend\n"
make -j server $log
-elif [[ "$backend" == "opencl" ]]; then
- printf "[+] Building with OpenCL backend\n"
- LLAMA_CLBLAST=1 make -j server $log
else
printf "[-] Unknown backend: %s\n" "$backend"
exit 1
@@ -407,8 +404,6 @@ elif [[ "$backend" == "cpu" ]]; then
args="-ngl 0"
elif [[ "$backend" == "metal" ]]; then
args="-ngl 999"
-elif [[ "$backend" == "opencl" ]]; then
- args="-ngl 999"
else
printf "[-] Unknown backend: %s\n" "$backend"
exit 1
diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 3f8ddf37b..9e34dc8b9 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -106,8 +106,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
# src/ggml-kompute.h -> ggml-kompute.h
# src/ggml-metal.h -> ggml-metal.h
# src/ggml-metal.m -> ggml-metal.m
- # src/ggml-opencl.cpp -> ggml-opencl.cpp
- # src/ggml-opencl.h -> ggml-opencl.h
# src/ggml-quants.c -> ggml-quants.c
# src/ggml-quants.h -> ggml-quants.h
# src/ggml-rpc.cpp -> ggml-rpc.cpp
@@ -143,8 +141,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
-e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \
-e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
-e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
- -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \
- -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \
-e 's/src\/ggml-quants\.c/ggml-quants.c/g' \
-e 's/src\/ggml-quants\.h/ggml-quants.h/g' \
-e 's/src\/ggml-rpc\.cpp/ggml-rpc.cpp/g' \
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index fbae6b7f8..4843f8a4a 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -14,8 +14,6 @@ cp -rpv ../ggml/src/ggml-kompute.h ./ggml-kompute.h
cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h
cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
-cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp
-cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c
cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h
cp -rpv ../ggml/src/ggml-rpc.cpp ./ggml-rpc.cpp