diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 77a9ddc145d0b..8cc1480d316c2 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -26,8 +26,8 @@ COPY . . # Set nvcc architecture ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 +# Enable CUDA +ENV LLAMA_CUDA=1 RUN make diff --git a/.devops/llama-cpp-cublas.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec similarity index 81% rename from .devops/llama-cpp-cublas.srpm.spec rename to .devops/llama-cpp-cuda.srpm.spec index f847ebb1e8613..66bdc871e4c64 100644 --- a/.devops/llama-cpp-cublas.srpm.spec +++ b/.devops/llama-cpp-cuda.srpm.spec @@ -12,7 +12,7 @@ # 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries. # It is up to the user to install the correct vendor-specific support. -Name: llama.cpp-cublas +Name: llama.cpp-cuda Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) @@ -32,16 +32,16 @@ CPU inference for Meta's Lllama2 models using default options. %setup -n llama.cpp-master %build -make -j LLAMA_CUBLAS=1 +make -j LLAMA_CUDA=1 %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamacppcublas -cp -p server %{buildroot}%{_bindir}/llamacppcublasserver -cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple +cp -p main %{buildroot}%{_bindir}/llamacppcuda +cp -p server %{buildroot}%{_bindir}/llamacppcudaserver +cp -p simple %{buildroot}%{_bindir}/llamacppcudasimple mkdir -p %{buildroot}/usr/lib/systemd/system -%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacublas.service +%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacuda.service [Unit] Description=Llama.cpp server, CPU only (no GPU support in this build). After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target @@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS +ExecStart=/usr/bin/llamacppcudaserver $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -67,10 +67,10 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamacppcublas -%{_bindir}/llamacppcublasserver -%{_bindir}/llamacppcublassimple -/usr/lib/systemd/system/llamacublas.service +%{_bindir}/llamacppcuda +%{_bindir}/llamacppcudaserver +%{_bindir}/llamacppcudasimple +/usr/lib/systemd/system/llamacuda.service %config /etc/sysconfig/llama %pre diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile index 2b7faf7c11c0b..b937a482988b6 100644 --- a/.devops/main-cuda.Dockerfile +++ b/.devops/main-cuda.Dockerfile @@ -20,8 +20,8 @@ COPY . . # Set nvcc architecture ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 +# Enable CUDA +ENV LLAMA_CUDA=1 RUN make diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 76d96e63cd6eb..0330b79d9143a 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -192,7 +192,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) (cmakeBool "LLAMA_BLAS" useBlas) (cmakeBool "LLAMA_CLBLAST" useOpenCL) - (cmakeBool "LLAMA_CUBLAS" useCuda) + (cmakeBool "LLAMA_CUDA" useCuda) (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile index 4f83904bc9ff0..5683a364652b1 100644 --- a/.devops/server-cuda.Dockerfile +++ b/.devops/server-cuda.Dockerfile @@ -20,8 +20,8 @@ COPY . . # Set nvcc architecture ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 +# Enable CUDA +ENV LLAMA_CUDA=1 RUN make diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0e7643bbaa6a0..9329b94ee9e60 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -728,13 +728,13 @@ jobs: path: | llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip - windows-latest-cmake-cublas: + windows-latest-cmake-cuda: runs-on: windows-latest strategy: matrix: cuda: ['12.2.0', '11.7.1'] - build: ['cublas'] + build: ['cuda'] steps: - name: Clone @@ -755,7 +755,7 @@ jobs: run: | mkdir build cd build - cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON -DBUILD_SHARED_LIBS=ON + cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUDA=ON -DBUILD_SHARED_LIBS=ON cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} - name: Determine tag name @@ -911,7 +911,7 @@ jobs: - macOS-latest-make - macOS-latest-cmake - windows-latest-cmake - - windows-latest-cmake-cublas + - windows-latest-cmake-cuda - macOS-latest-cmake-arm64 - macOS-latest-cmake-x64 diff --git a/CMakeLists.txt b/CMakeLists.txt index b25cfd2fc0e17..3f23ba4d36933 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -89,8 +89,8 @@ endif() option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") -option(LLAMA_CUBLAS "llama: use CUDA" OFF) -#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF) +option(LLAMA_CUDA "llama: use CUDA" OFF) +option(LLAMA_CUBLAS "llama: use CUDA (deprecated, use LLAMA_CUDA)" OFF) option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) option(LLAMA_CUDA_FORCE_MMQ "llama: use mmq kernels instead of cuBLAS" OFF) set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") @@ -360,11 +360,16 @@ if (LLAMA_QKK_64) endif() if (LLAMA_CUBLAS) + message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead") + set(LLAMA_CUDA ON) +endif() + +if (LLAMA_CUDA) cmake_minimum_required(VERSION 3.17) find_package(CUDAToolkit) if (CUDAToolkit_FOUND) - message(STATUS "cuBLAS found") + message(STATUS "CUDA found") enable_language(CUDA) @@ -373,7 +378,7 @@ if (LLAMA_CUBLAS) file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") - add_compile_definitions(GGML_USE_CUBLAS) + add_compile_definitions(GGML_USE_CUDA) if (LLAMA_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV) endif() @@ -422,7 +427,7 @@ if (LLAMA_CUBLAS) message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") else() - message(WARNING "cuBLAS not found") + message(WARNING "CUDA not found") endif() endif() @@ -525,7 +530,7 @@ if (LLAMA_HIPBLAS) file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") - add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) + add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA) if (LLAMA_HIP_UMA) add_compile_definitions(GGML_HIP_UMA) @@ -830,7 +835,7 @@ endif() set(CUDA_CXX_FLAGS "") -if (LLAMA_CUBLAS) +if (LLAMA_CUDA) set(CUDA_FLAGS -use_fast_math) if (LLAMA_FATAL_WARNINGS) @@ -1055,7 +1060,7 @@ endif() add_compile_options("$<$:${ARCH_FLAGS}>") add_compile_options("$<$:${ARCH_FLAGS}>") -if (LLAMA_CUBLAS) +if (LLAMA_CUDA) list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS}) list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") diff --git a/Makefile b/Makefile index 08eafb1e74828..1741151eb8a99 100644 --- a/Makefile +++ b/Makefile @@ -390,12 +390,17 @@ ifdef LLAMA_BLIS endif # LLAMA_BLIS ifdef LLAMA_CUBLAS +# LLAMA_CUBLAS is deprecated and will be removed in the future + LLAMA_CUDA := 1 +endif + +ifdef LLAMA_CUDA ifneq ('', '$(wildcard /opt/cuda)') CUDA_PATH ?= /opt/cuda else CUDA_PATH ?= /usr/local/cuda endif - MK_CPPFLAGS += -DGGML_USE_CUBLAS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) @@ -462,7 +467,7 @@ endif ifdef JETSON_EOL_MODULE_DETECT define NVCC_COMPILE - $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ + $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endef # NVCC_COMPILE else define NVCC_COMPILE @@ -476,7 +481,7 @@ ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/com ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(NVCC_COMPILE) -endif # LLAMA_CUBLAS +endif # LLAMA_CUDA ifdef LLAMA_CLBLAST @@ -533,7 +538,7 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 - MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS + MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA ifdef LLAMA_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA endif # LLAMA_HIP_UMA @@ -609,7 +614,7 @@ override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS) override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) # identify CUDA host compiler -ifdef LLAMA_CUBLAS +ifdef LLAMA_CUDA GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler include scripts/get-flags.mk CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic @@ -634,7 +639,7 @@ $(info I NVCCFLAGS: $(NVCCFLAGS)) $(info I LDFLAGS: $(LDFLAGS)) $(info I CC: $(shell $(CC) --version | head -n 1)) $(info I CXX: $(shell $(CXX) --version | head -n 1)) -ifdef LLAMA_CUBLAS +ifdef LLAMA_CUDA $(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])') ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) @@ -644,9 +649,16 @@ $(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be exp endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) -endif # LLAMA_CUBLAS +endif # LLAMA_CUDA $(info ) +ifdef LLAMA_CUBLAS +$(info !!!!) +$(info LLAMA_CUBLAS is deprecated and will be removed in the future. Use LLAMA_CUDA instead.) +$(info !!!!) +$(info ) +endif + # # Build library # diff --git a/README.md b/README.md index f9cf1961629d0..ce678f0c36bd9 100644 --- a/README.md +++ b/README.md @@ -448,30 +448,27 @@ Building the program with BLAS support may lead to some performance improvements Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. -- #### cuBLAS +- #### CUDA - This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). + This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. - Using `make`: ```bash - make LLAMA_CUBLAS=1 + make LLAMA_CUDA=1 ``` - Using `CMake`: ```bash mkdir build cd build - cmake .. -DLLAMA_CUBLAS=ON + cmake .. -DLLAMA_CUDA=ON cmake --build . --config Release ``` The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: - | Option | Legal values | Default | Description | |--------------------------------|------------------------|---------|-------------| | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | diff --git a/ci/run.sh b/ci/run.sh index 51f4c74cc2cf5..85acc46d3939c 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -40,7 +40,7 @@ if [ ! -z ${GG_BUILD_METAL} ]; then fi if [ ! -z ${GG_BUILD_CUDA} ]; then - CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1" + CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUDA=1" fi if [ ! -z ${GG_BUILD_SYCL} ]; then @@ -412,8 +412,8 @@ function gg_run_open_llama_7b_v2 { set -e - (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log python3 ../convert.py ${path_models} diff --git a/common/common.cpp b/common/common.cpp index 9dec084303dc7..5fd33e2a1e097 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -48,12 +48,12 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) -#define GGML_USE_CUBLAS_SYCL +#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) +#define GGML_USE_CUDA_SYCL #endif -#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) -#define GGML_USE_CUBLAS_SYCL_VULKAN +#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) +#define GGML_USE_CUDA_SYCL_VULKAN #endif #if defined(LLAMA_USE_CURL) @@ -861,9 +861,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } params.main_gpu = std::stoi(argv[i]); -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL +#ifndef GGML_USE_CUDA_SYCL + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL return true; } if (arg == "--split-mode" || arg == "-sm") { @@ -889,9 +889,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL +#ifndef GGML_USE_CUDA_SYCL + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL return true; } if (arg == "--tensor-split" || arg == "-ts") { @@ -917,9 +917,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.tensor_split[i] = 0.0f; } } -#ifndef GGML_USE_CUBLAS_SYCL_VULKAN - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL +#ifndef GGML_USE_CUDA_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL_VULKAN return true; } if (arg == "--no-mmap") { @@ -2387,7 +2387,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "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"); diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md index d7e863dff5c01..3c43431471243 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/token_generation_performance_tips.md @@ -1,7 +1,7 @@ # Token generation performance troubleshooting -## Verifying that the model is running on the GPU with cuBLAS -Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: +## Verifying that the model is running on the GPU with CUDA +Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: ```shell ./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " ``` diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md index 578e8fc27177a..458c01b8751f1 100644 --- a/examples/imatrix/README.md +++ b/examples/imatrix/README.md @@ -22,7 +22,7 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument ## Example ```bash -LLAMA_CUBLAS=1 make -j +LLAMA_CUDA=1 make -j # generate importance matrix (imatrix.dat) ./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 82413b79d0255..27e113203f1ea 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -113,7 +113,7 @@ static std::string get_cpu_info() { static std::string get_gpu_info() { std::string id; -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA int count = ggml_backend_cuda_get_device_count(); for (int i = 0; i < count; i++) { char buf[128]; @@ -808,7 +808,7 @@ 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_cublas(); +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(); diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 4d5fef020f406..b3b66331fd9df 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -124,7 +124,7 @@ llama_print_timings: total time = 34570.79 ms ## Orin compile and run ### compile ```sh -make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 +make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 ``` ### run on Orin diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 48caafa872aed..40c9762617cfd 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -7,7 +7,7 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA #include "ggml-cuda.h" #endif @@ -968,7 +968,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA new_clip->backend = ggml_backend_cuda_init(0); printf("%s: CLIP using CUDA backend\n", __func__); #endif diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md index 6d665f28fe9bd..f599fbaec46b5 100644 --- a/examples/main-cmake-pkg/README.md +++ b/examples/main-cmake-pkg/README.md @@ -8,7 +8,7 @@ Because this example is "outside of the source tree", it is important to first b ### Considerations -When hardware acceleration libraries are used (e.g. CUBlas, 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, 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_. ### Build llama.cpp and install to C:\LlamaCPP directory diff --git a/examples/main/README.md b/examples/main/README.md index 6a8d1e1c50cbb..9c83fd3bf5b05 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -316,8 +316,8 @@ These options provide extra functionality and customization when running the LLa - `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated. - `--verbose-prompt`: Print the prompt before generating text. -- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. -- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. -- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. +- `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance. +- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. +- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. - `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. diff --git a/examples/server/README.md b/examples/server/README.md index 49121a460f8c3..aadc73b4ba81f 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -25,9 +25,9 @@ The project is under active development, and we are [looking for feedback and co - `-hff FILE, --hf-file FILE`: Hugging Face model file (default: unused). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. -- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. -- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. -- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. +- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance. +- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. +- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. - `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`. - `-ub N`, `--ubatch-size N`: physical maximum batch size. Default: `512`. - `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 338e60f28d625..c4c545c3e0ac4 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2510,15 +2510,15 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } -#ifndef GGML_USE_CUBLAS - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUBLAS +#ifndef GGML_USE_CUDA + fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUDA } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; break; } -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) std::string arg_next = argv[i]; // split string by , and / @@ -2535,17 +2535,17 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, } } #else - LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUBLAS + LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {}); +#endif // GGML_USE_CUDA } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; break; } -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) params.main_gpu = std::stoi(argv[i]); #else - LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); + LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {}); #endif } else if (arg == "--lora") { if (++i >= argc) { diff --git a/ggml-backend.c b/ggml-backend.c index 6026570ae95aa..402d86ef3ac8b 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -420,7 +420,7 @@ GGML_CALL static void ggml_backend_registry_init(void) { ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL); // add forward decls here to avoid including the backend headers -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA extern GGML_CALL void ggml_backend_cuda_reg_devices(void); ggml_backend_cuda_reg_devices(); #endif diff --git a/ggml.c b/ggml.c index 203a9e54038d7..62b8339599642 100644 --- a/ggml.c +++ b/ggml.c @@ -21674,15 +21674,15 @@ 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_CUBLAS) || 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_CLBLAST) || defined(GGML_USE_SYCL) return 1; #else return 0; #endif } -int ggml_cpu_has_cublas(void) { -#if defined(GGML_USE_CUBLAS) +int ggml_cpu_has_cuda(void) { +#if defined(GGML_USE_CUDA) return 1; #else return 0; @@ -21722,7 +21722,7 @@ int ggml_cpu_has_sycl(void) { } int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || + return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); } diff --git a/ggml.h b/ggml.h index 0a5af72058815..c670caa6a3140 100644 --- a/ggml.h +++ b/ggml.h @@ -2354,7 +2354,7 @@ extern "C" { GGML_API int ggml_cpu_has_fp16_va (void); GGML_API int ggml_cpu_has_wasm_simd (void); GGML_API int ggml_cpu_has_blas (void); - GGML_API int ggml_cpu_has_cublas (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); diff --git a/llama.cpp b/llama.cpp index 61587cb7abf5a..384021efdb474 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7,7 +7,7 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA # include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) # include "ggml-opencl.h" @@ -1505,7 +1505,7 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) { ggml_backend_buffer_type_t buft = nullptr; -#if defined(GGML_USE_CUBLAS) +#if defined(GGML_USE_CUDA) // host buffers should only be used when data is expected to be copied to/from the GPU if (host_buffer) { buft = ggml_backend_cuda_host_buffer_type(); @@ -1535,7 +1535,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { #ifdef GGML_USE_METAL buft = ggml_backend_metal_buffer_type(); -#elif defined(GGML_USE_CUBLAS) +#elif defined(GGML_USE_CUDA) buft = ggml_backend_cuda_buffer_type(gpu); #elif defined(GGML_USE_VULKAN) buft = ggml_backend_vk_buffer_type(gpu); @@ -1561,7 +1561,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) { ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA if (ggml_backend_cuda_get_device_count() > 1) { buft = ggml_backend_cuda_split_buffer_type(tensor_split); } @@ -1582,7 +1582,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g } static size_t llama_get_device_count() { -#if defined(GGML_USE_CUBLAS) +#if defined(GGML_USE_CUDA) return ggml_backend_cuda_get_device_count(); #elif defined(GGML_USE_SYCL) return ggml_backend_sycl_get_device_count(); @@ -1594,7 +1594,7 @@ static size_t llama_get_device_count() { } static size_t llama_get_device_memory(int device) { -#if defined(GGML_USE_CUBLAS) +#if defined(GGML_USE_CUDA) size_t total; size_t free; ggml_backend_cuda_get_device_memory(device, &total, &free); @@ -2080,7 +2080,7 @@ struct llama_model { ggml_free(ctx); } for (ggml_backend_buffer_t buf : bufs) { -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) { ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf)); } @@ -5269,7 +5269,7 @@ static bool llm_load_tensors( } model.bufs.push_back(buf); bufs.emplace(idx, buf); -#ifdef GGML_USE_CUBLAS +#ifdef GGML_USE_CUDA if (n_layer >= n_gpu_layers) { ggml_backend_cuda_register_host_buffer( ggml_backend_buffer_get_base(buf), @@ -13371,7 +13371,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { size_t llama_max_devices(void) { #if defined(GGML_USE_METAL) return 1; -#elif defined(GGML_USE_CUBLAS) +#elif defined(GGML_USE_CUDA) return GGML_CUDA_MAX_DEVICES; #elif defined(GGML_USE_SYCL) return GGML_SYCL_MAX_DEVICES; @@ -13391,8 +13391,8 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -13597,7 +13597,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(ctx->backend_metal); } -#elif defined(GGML_USE_CUBLAS) +#elif defined(GGML_USE_CUDA) if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu); @@ -13744,7 +13744,7 @@ struct llama_context * llama_new_context_with_model( // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER; -#ifndef GGML_USE_CUBLAS +#ifndef GGML_USE_CUDA // pipeline parallelism requires support for async compute and events // currently this is only implemented in the CUDA backend pipeline_parallel = false; diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in index 6a6d8e39ee013..f842c7137517c 100644 --- a/scripts/LlamaConfig.cmake.in +++ b/scripts/LlamaConfig.cmake.in @@ -3,7 +3,7 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) set(LLAMA_BLAS @LLAMA_BLAS@) -set(LLAMA_CUBLAS @LLAMA_CUBLAS@) +set(LLAMA_CUDA @LLAMA_CUDA@) set(LLAMA_METAL @LLAMA_METAL@) set(LLAMA_MPI @LLAMA_MPI@) set(LLAMA_CLBLAST @LLAMA_CLBLAST@) @@ -27,7 +27,7 @@ if (LLAMA_BLAS) find_package(BLAS REQUIRED) endif() -if (LLAMA_CUBLAS) +if (LLAMA_CUDA) find_package(CUDAToolkit REQUIRED) endif() diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh index 331c4b9ce9e91..d1272506cd58a 100755 --- a/scripts/compare-commits.sh +++ b/scripts/compare-commits.sh @@ -23,7 +23,7 @@ fi make_opts="" if [[ "$backend" == "cuda" ]]; then - make_opts="LLAMA_CUBLAS=1" + make_opts="LLAMA_CUDA=1" fi git checkout $1 diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh index 6cf1ab4f352a6..2058ceabf9730 100644 --- a/scripts/pod-llama.sh +++ b/scripts/pod-llama.sh @@ -42,7 +42,7 @@ git clone https://github.com/ggerganov/llama.cpp cd llama.cpp -LLAMA_CUBLAS=1 make -j +LLAMA_CUDA=1 make -j ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3 ./models/tinyllama-1b ln -sfn /workspace/CodeLlama-7b-hf ./models/codellama-7b @@ -60,7 +60,7 @@ cd /workspace/llama.cpp mkdir build-cublas cd build-cublas -cmake -DLLAMA_CUBLAS=1 ../ +cmake -DLLAMA_CUDA=1 ../ make -j if [ "$1" -eq "0" ]; then @@ -186,17 +186,17 @@ if [ "$1" -eq "1" ]; then # batched cd /workspace/llama.cpp - LLAMA_CUBLAS=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999 + LLAMA_CUDA=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999 # batched-bench cd /workspace/llama.cpp - LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32 + LLAMA_CUDA=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32 # parallel cd /workspace/llama.cpp - LLAMA_CUBLAS=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb + LLAMA_CUDA=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb fi @@ -204,10 +204,10 @@ fi #if [ "$1" -eq "7" ]; then # cd /workspace/llama.cpp # -# LLAMA_CUBLAS=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0 +# LLAMA_CUDA=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0 #fi # more benches -#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 -#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 +#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 +#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 30bbac3215f96..eb6ce458e5399 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -380,7 +380,7 @@ fi if [[ "$backend" == "cuda" ]]; then printf "[+] Building with CUDA backend\n" - LLAMA_CUBLAS=1 make -j server $log + LLAMA_CUDA=1 make -j server $log elif [[ "$backend" == "cpu" ]]; then printf "[+] Building with CPU backend\n" make -j server $log